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https://github.com/huggingface/datasets/issues/2128
Dialogue action slot name and value are reversed in MultiWoZ 2.2
Hi Good catch ! Thanks for reporting If you are interested in contributing, feel free to open a PR to fix this :)
Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial! I spot an error that the order of Dialogue action slot names and values are reversed. https://github.com/huggingface/datasets/blob/649b2c469779bc4221e1b6969aa2496d63eb5953/datasets/multi_woz_v22/multi_woz_v22.py#L251-L262
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Dialogue action slot name and value are reversed in MultiWoZ 2.2 Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial! I spot an error that the order of Dialogue action slot names and values are reversed. https://github.com/huggingface/datasets/blob/649b2c469779bc4221e1b6969aa2496d63eb5953/datasets/multi_woz_v22/multi_woz_v22.py#L251-L262 Hi Good catch ! Thanks for reporting If you are interested in contributing, feel free to open a PR to fix this :)
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https://github.com/huggingface/datasets/issues/2123
Problem downloading GEM wiki_auto_asset_turk dataset
Hi, sadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with: ```bash pip install git+https://github.com/huggingface/datasets ```
@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family
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Problem downloading GEM wiki_auto_asset_turk dataset @yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family Hi, sadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with: ```bash pip install git+https://github.com/huggingface/datasets ```
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https://github.com/huggingface/datasets/issues/2123
Problem downloading GEM wiki_auto_asset_turk dataset
Is there an error message ? What stacktrace do you get if you interrupt the execution of the program while downloading ?
@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family
22
Problem downloading GEM wiki_auto_asset_turk dataset @yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family Is there an error message ? What stacktrace do you get if you interrupt the execution of the program while downloading ?
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https://github.com/huggingface/datasets/issues/2123
Problem downloading GEM wiki_auto_asset_turk dataset
Sorry for the long time since my last comment, I tried again and don't seem to have the problem anymore, thanks for your support!
@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family
24
Problem downloading GEM wiki_auto_asset_turk dataset @yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family Sorry for the long time since my last comment, I tried again and don't seem to have the problem anymore, thanks for your support!
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https://github.com/huggingface/datasets/issues/2123
Problem downloading GEM wiki_auto_asset_turk dataset
Great ! I'm closing the issue then. Feel free to re-open if you experience this issue again
@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family
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Problem downloading GEM wiki_auto_asset_turk dataset @yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family Great ! I'm closing the issue then. Feel free to re-open if you experience this issue again
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https://github.com/huggingface/datasets/issues/2116
Creating custom dataset results in error while calling the map() function
Hi, the `_data` attribute is missing due to `MyDataset.__init__` not calling the parent `__init__`. However, I don't think it's a good idea to subclass the `datasets.Dataset` class (e.g. it's kind of dangerous to override `datasets.Dataset.__getitem__`). Instead, it's better to follow the "association over inheritance" approach with a simple wrapper class that delegates calls to a wrapped `Dataset` (map, etc.). Btw, the library offers the `datasets.Dataset.from_pandas` class method to directly create a `datasets.Dataset` from the dataframe.
calling `map()` of `datasets` library results into an error while defining a Custom dataset. Reproducible example: ``` import datasets class MyDataset(datasets.Dataset): def __init__(self, sentences): "Initialization" self.samples = sentences def __len__(self): "Denotes the total number of samples" return len(self.samples) def __getitem__(self, index): "Generates one sample of data" # Select sample # Load data and get label samples = self.samples[index] return samples def preprocess_function_train(examples): inputs = examples labels = [example+tokenizer.eos_token for example in examples ] inputs = tokenizer(inputs, max_length=30, padding=True, truncation=True) labels = tokenizer(labels, max_length=30, padding=True, truncation=True) model_inputs = inputs model_inputs["labels"] = labels["input_ids"] print("about to return") return model_inputs ##train["sentence"] is dataframe column train_dataset = MyDataset(train['sentence'].values.tolist()) train_dataset = train_dataset.map( preprocess_function, batched = True, batch_size=32 ) ``` Stack trace of error: ``` Traceback (most recent call last): File "dir/train_generate.py", line 362, in <module> main() File "dir/train_generate.py", line 245, in main train_dataset = train_dataset.map( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1244, in map return self._map_single( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 149, in wrapper unformatted_columns = set(self.column_names) - set(self._format_columns or []) File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 526, in column_names return self._data.column_names AttributeError: 'MyDataset' object has no attribute '_data' ```
75
Creating custom dataset results in error while calling the map() function calling `map()` of `datasets` library results into an error while defining a Custom dataset. Reproducible example: ``` import datasets class MyDataset(datasets.Dataset): def __init__(self, sentences): "Initialization" self.samples = sentences def __len__(self): "Denotes the total number of samples" return len(self.samples) def __getitem__(self, index): "Generates one sample of data" # Select sample # Load data and get label samples = self.samples[index] return samples def preprocess_function_train(examples): inputs = examples labels = [example+tokenizer.eos_token for example in examples ] inputs = tokenizer(inputs, max_length=30, padding=True, truncation=True) labels = tokenizer(labels, max_length=30, padding=True, truncation=True) model_inputs = inputs model_inputs["labels"] = labels["input_ids"] print("about to return") return model_inputs ##train["sentence"] is dataframe column train_dataset = MyDataset(train['sentence'].values.tolist()) train_dataset = train_dataset.map( preprocess_function, batched = True, batch_size=32 ) ``` Stack trace of error: ``` Traceback (most recent call last): File "dir/train_generate.py", line 362, in <module> main() File "dir/train_generate.py", line 245, in main train_dataset = train_dataset.map( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1244, in map return self._map_single( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 149, in wrapper unformatted_columns = set(self.column_names) - set(self._format_columns or []) File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 526, in column_names return self._data.column_names AttributeError: 'MyDataset' object has no attribute '_data' ``` Hi, the `_data` attribute is missing due to `MyDataset.__init__` not calling the parent `__init__`. However, I don't think it's a good idea to subclass the `datasets.Dataset` class (e.g. it's kind of dangerous to override `datasets.Dataset.__getitem__`). Instead, it's better to follow the "association over inheritance" approach with a simple wrapper class that delegates calls to a wrapped `Dataset` (map, etc.). Btw, the library offers the `datasets.Dataset.from_pandas` class method to directly create a `datasets.Dataset` from the dataframe.
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https://github.com/huggingface/datasets/issues/2106
WMT19 Dataset for Kazakh-English is not formatted correctly
Hi ! Thanks for reporting By looking at the raw `news-commentary-v14.en-kk.tsv` file, it looks like there are at least 17 lines with this issue. Moreover these issues are not always the same: - L97 is only `kk` text and must be appended at the end of the `kk` text of the **next** line - L2897 is only `kk` text and must be appended at the end of the `kk` text of the **previous** line - L1247 and L1248 are only `kk` texts and must be inserted at the **beginning** of the `kk` text of the next line - (and there are many others) It would be nice to have a corrected version of this file ! The file is available in the `wmt/news-commentary` repository on the Datasets Hub here: https://huggingface.co/datasets/wmt/news-commentary/tree/main/v14/training Then maybe we can notify the WMT authors and host the corrected version somewhere
In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error. The News Commentary v14 parallel data set for kk-en from http://www.statmt.org/wmt19/translation-task.html has a bug here: > Line 94. The Swiss National Bank, for its part, has been battling with the deflationary effects of the franc’s dramatic appreciation over the past few years. Швейцарияның Ұлттық банкі өз тарапынан, соңғы бірнеше жыл ішінде франк құнының қатты өсуінің дефляциялық әсерімен күресіп келеді. > > Line 95. Дефляциялық күштер 2008 жылы терең және ұзаққа созылған жаһандық дағдарысқа байланысты орын алған ірі экономикалық және қаржылық орын алмасулардың арқасында босатылды. Жеке қарыз қаражаты үлесінің қысқаруы орталық банктің рефляцияға жұмсалған күш-жігеріне тұрақты соққан қарсы желдей болды. > > Line 96. The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. As you can see, line 95 has only the Kazakh translation which should be part of line 96. This causes all of the following English-Kazakh translation pairs to be one off rendering ALL of those translations incorrect. This issue was not fixed when the dataset was imported to Huggingface. By running this code ``` import datasets from datasets import load_dataset dataset = load_dataset('wmt19', 'kk-en') for key in dataset['train']['translation']: if 'The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008.' in key['kk']: print(key['en']) print(key['kk']) break ``` we get: > 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. > The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. which shows that the issue still persists in the Huggingface dataset. The Kazakh sentence matches up to the next English sentence in the dataset instead of the current one. Please let me know if there's you have any ideas to fix this one-off error from the dataset or if this can be fixed by Huggingface.
144
WMT19 Dataset for Kazakh-English is not formatted correctly In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error. The News Commentary v14 parallel data set for kk-en from http://www.statmt.org/wmt19/translation-task.html has a bug here: > Line 94. The Swiss National Bank, for its part, has been battling with the deflationary effects of the franc’s dramatic appreciation over the past few years. Швейцарияның Ұлттық банкі өз тарапынан, соңғы бірнеше жыл ішінде франк құнының қатты өсуінің дефляциялық әсерімен күресіп келеді. > > Line 95. Дефляциялық күштер 2008 жылы терең және ұзаққа созылған жаһандық дағдарысқа байланысты орын алған ірі экономикалық және қаржылық орын алмасулардың арқасында босатылды. Жеке қарыз қаражаты үлесінің қысқаруы орталық банктің рефляцияға жұмсалған күш-жігеріне тұрақты соққан қарсы желдей болды. > > Line 96. The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. As you can see, line 95 has only the Kazakh translation which should be part of line 96. This causes all of the following English-Kazakh translation pairs to be one off rendering ALL of those translations incorrect. This issue was not fixed when the dataset was imported to Huggingface. By running this code ``` import datasets from datasets import load_dataset dataset = load_dataset('wmt19', 'kk-en') for key in dataset['train']['translation']: if 'The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008.' in key['kk']: print(key['en']) print(key['kk']) break ``` we get: > 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. > The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. which shows that the issue still persists in the Huggingface dataset. The Kazakh sentence matches up to the next English sentence in the dataset instead of the current one. Please let me know if there's you have any ideas to fix this one-off error from the dataset or if this can be fixed by Huggingface. Hi ! Thanks for reporting By looking at the raw `news-commentary-v14.en-kk.tsv` file, it looks like there are at least 17 lines with this issue. Moreover these issues are not always the same: - L97 is only `kk` text and must be appended at the end of the `kk` text of the **next** line - L2897 is only `kk` text and must be appended at the end of the `kk` text of the **previous** line - L1247 and L1248 are only `kk` texts and must be inserted at the **beginning** of the `kk` text of the next line - (and there are many others) It would be nice to have a corrected version of this file ! The file is available in the `wmt/news-commentary` repository on the Datasets Hub here: https://huggingface.co/datasets/wmt/news-commentary/tree/main/v14/training Then maybe we can notify the WMT authors and host the corrected version somewhere
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https://github.com/huggingface/datasets/issues/2105
Request to remove S2ORC dataset
Hello @kyleclo! Currently, we are getting the data from your bucket, so if you remove it the HF script won't work anymore :) Until you solve things on your end, @lhoestq suggested we just return a warning message when people try to load that dataset from HF. What would you like it to say?
Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks!
54
Request to remove S2ORC dataset Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks! Hello @kyleclo! Currently, we are getting the data from your bucket, so if you remove it the HF script won't work anymore :) Until you solve things on your end, @lhoestq suggested we just return a warning message when people try to load that dataset from HF. What would you like it to say?
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https://github.com/huggingface/datasets/issues/2105
Request to remove S2ORC dataset
Hi @kyleclo, as of today, you have not removed your bucket data yet, and therefore HuggingFace can download it from there. Is it OK? Are you planning to eventually delete it? Thank you.
Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks!
33
Request to remove S2ORC dataset Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks! Hi @kyleclo, as of today, you have not removed your bucket data yet, and therefore HuggingFace can download it from there. Is it OK? Are you planning to eventually delete it? Thank you.
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https://github.com/huggingface/datasets/issues/2105
Request to remove S2ORC dataset
Hi! Sorry I missed @yjernite 's previous message, thanks for responding! Is there an option where we can keep our data in our bucket, but the HF script no longer pulls data from it?
Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks!
34
Request to remove S2ORC dataset Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks! Hi! Sorry I missed @yjernite 's previous message, thanks for responding! Is there an option where we can keep our data in our bucket, but the HF script no longer pulls data from it?
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https://github.com/huggingface/datasets/issues/2104
Trouble loading wiki_movies
Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`. To use `wiki_movies`, please update `datasets` with ``` pip install --upgrade datasets ```
Hello, I am trying to load_dataset("wiki_movies") and it gives me this error - `FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py` Trying to do `python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name wiki_movies \` also gives the same error. Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. Thank you!
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Trouble loading wiki_movies Hello, I am trying to load_dataset("wiki_movies") and it gives me this error - `FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py` Trying to do `python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name wiki_movies \` also gives the same error. Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. Thank you! Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`. To use `wiki_movies`, please update `datasets` with ``` pip install --upgrade datasets ```
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https://github.com/huggingface/datasets/issues/2104
Trouble loading wiki_movies
Thanks a lot! That solved it and I was able to upload a model trained on it as well :)
Hello, I am trying to load_dataset("wiki_movies") and it gives me this error - `FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py` Trying to do `python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name wiki_movies \` also gives the same error. Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. Thank you!
20
Trouble loading wiki_movies Hello, I am trying to load_dataset("wiki_movies") and it gives me this error - `FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py` Trying to do `python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name wiki_movies \` also gives the same error. Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. Thank you! Thanks a lot! That solved it and I was able to upload a model trained on it as well :)
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https://github.com/huggingface/datasets/issues/2103
citation, homepage, and license fields of `dataset_info.json` are duplicated many times
Thanks for reporting :) Maybe we can concatenate fields only if they are different. Currently this is done here: https://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/info.py#L180-L196 This can be a good first contribution to the library. Please comment if you'd like to improve this and open a PR :)
This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation. Example result: ``` "citation": "@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n ``` @lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times.
43
citation, homepage, and license fields of `dataset_info.json` are duplicated many times This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation. Example result: ``` "citation": "@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n ``` @lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times. Thanks for reporting :) Maybe we can concatenate fields only if they are different. Currently this is done here: https://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/info.py#L180-L196 This can be a good first contribution to the library. Please comment if you'd like to improve this and open a PR :)
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Hi ! Can you share more information about the features of your dataset ? You can get them by printing `my_dataset.features` Can you also share the code of your `map` function ?
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
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load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Hi ! Can you share more information about the features of your dataset ? You can get them by printing `my_dataset.features` Can you also share the code of your `map` function ?
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a list of integers. The `text` column is removed during tokenization. ``` def add_len_and_seq(example): end_idx = example['input_ids'].index(SEP) example['actual_len'] = end_idx-1 seq_len = len(example['input_ids']) example['seq'] = [PAD_ID] + [np.uint8(example['some_integer'])]*(end_idx-1) + [PAD_ID]*(seq_len-end_idx) return example ```
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
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load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a list of integers. The `text` column is removed during tokenization. ``` def add_len_and_seq(example): end_idx = example['input_ids'].index(SEP) example['actual_len'] = end_idx-1 seq_len = len(example['input_ids']) example['seq'] = [PAD_ID] + [np.uint8(example['some_integer'])]*(end_idx-1) + [PAD_ID]*(seq_len-end_idx) return example ```
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Is `PAD_ID` a python integer ? You need all the integers in `example['seq']` to have the same type. Does this work if you remove the `np.uint8` and use python integers instead ?
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
32
load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Is `PAD_ID` a python integer ? You need all the integers in `example['seq']` to have the same type. Does this work if you remove the `np.uint8` and use python integers instead ?
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
yup I casted it to `np.uint8` outside the function where it was defined. It was originally using python integers.
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
19
load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) yup I casted it to `np.uint8` outside the function where it was defined. It was originally using python integers.
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Strangely, even when I manually created `np.arrays` of specific `dtypes`, the types in the final `dataset_info.json` that gets written are still `int64`. Update: I tried creating lists of `int8`s and got the same result.
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
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load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Strangely, even when I manually created `np.arrays` of specific `dtypes`, the types in the final `dataset_info.json` that gets written are still `int64`. Update: I tried creating lists of `int8`s and got the same result.
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Yes this is a known issue: #625 We're working on making the precision kept for numpy :) To specify the precision of the integers, currently one needs to specify the output features with `.map(..., features=output_features)`
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
35
load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Yes this is a known issue: #625 We're working on making the precision kept for numpy :) To specify the precision of the integers, currently one needs to specify the output features with `.map(..., features=output_features)`
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Do you know what step is taking forever in the code ? What happens if you interrupt the execution of the dataset loading ?
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
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load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Do you know what step is taking forever in the code ? What happens if you interrupt the execution of the dataset loading ?
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
After a synchronous discussion, we found that the cache file sizes have an enormous effect on the loading speed: smaller cache files result in faster load times. `num_proc` controls the number of cache files that are being written and is inversely proportional to the individual file size. In other words, increase `num_proc` for smaller cache files :) Maybe this can be highlighted somewhere in the docs.
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
66
load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) After a synchronous discussion, we found that the cache file sizes have an enormous effect on the loading speed: smaller cache files result in faster load times. `num_proc` controls the number of cache files that are being written and is inversely proportional to the individual file size. In other words, increase `num_proc` for smaller cache files :) Maybe this can be highlighted somewhere in the docs.
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
Hi ! We plan to add streaming features in the future. This should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead. What do you think about this ? If you have ideas or suggestions of what you expect from such features as a user, feel free to share them, this is really valuable to us !
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
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How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? Hi ! We plan to add streaming features in the future. This should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead. What do you think about this ? If you have ideas or suggestions of what you expect from such features as a user, feel free to share them, this is really valuable to us !
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
People mainly want this feature either because it takes too much time too make arrow tables, or they occupy too much memory on the disk. I think both the problem can be solved if we provide arrow tables themselves on datasets hub. Can we do this currently @lhoestq ?
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
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How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? People mainly want this feature either because it takes too much time too make arrow tables, or they occupy too much memory on the disk. I think both the problem can be solved if we provide arrow tables themselves on datasets hub. Can we do this currently @lhoestq ?
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
@lhoestq I think the ```try_from_hf_gcs``` provide the same functionality. What all datasets are available on HF GCS? Are all the datasets on huggingFace datasets hub are made available on GCS, automatically?
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
31
How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? @lhoestq I think the ```try_from_hf_gcs``` provide the same functionality. What all datasets are available on HF GCS? Are all the datasets on huggingFace datasets hub are made available on GCS, automatically?
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
Only datasets like wikipedia, wiki40b, wiki_dpr and natural questions are available already processed on the HF google storage. This is used to download directly the arrow file instead of building it from the original data files.
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
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How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? Only datasets like wikipedia, wiki40b, wiki_dpr and natural questions are available already processed on the HF google storage. This is used to download directly the arrow file instead of building it from the original data files.
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
@lhoestq How can we make sure that the data we upload on HuggingFace hub is available in form of preprocessed arrow files ?
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
23
How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? @lhoestq How can we make sure that the data we upload on HuggingFace hub is available in form of preprocessed arrow files ?
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
We're still working on this :) This will be available soon Users will be able to put their processed arrow files on the Hub
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
24
How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? We're still working on this :) This will be available soon Users will be able to put their processed arrow files on the Hub
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https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
Hi ! We are using the [datasets-tagging app](https://github.com/huggingface/datasets-tagging) to select the tags to add. We are also adding the full list of tags in #2107 This covers multilinguality, language_creators, licenses, size_categories and task_categories. In general if you want to add a tag that doesn't exist (for example for a custom license) you must make it start with `other-` and then a custom tag name. edit (@theo-m) if you ever find yourself resorting to adding an `other-*` tag, please do ping us somewhere so we can think about adding it to the "official" list :)
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
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Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip Hi ! We are using the [datasets-tagging app](https://github.com/huggingface/datasets-tagging) to select the tags to add. We are also adding the full list of tags in #2107 This covers multilinguality, language_creators, licenses, size_categories and task_categories. In general if you want to add a tag that doesn't exist (for example for a custom license) you must make it start with `other-` and then a custom tag name. edit (@theo-m) if you ever find yourself resorting to adding an `other-*` tag, please do ping us somewhere so we can think about adding it to the "official" list :)
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https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
@lhoestq hmm - ok thanks for the answer. To be honest I am not sure if this issue can be closed now. I just wanted to point out that this should either be documented or linked in the documentation. If you feel like it is (will be) please just close this.
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
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Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip @lhoestq hmm - ok thanks for the answer. To be honest I am not sure if this issue can be closed now. I just wanted to point out that this should either be documented or linked in the documentation. If you feel like it is (will be) please just close this.
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https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
We're still working on the validation+documentation in this. Feel free to keep this issue open till we've added them
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
19
Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip We're still working on the validation+documentation in this. Feel free to keep this issue open till we've added them
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https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
Hi ! There's the tagging app at https://huggingface.co/datasets/tagging/ that you can use. It shows the list of all the tags you can use. It is based on all the tag sets defined in this folder: https://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
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Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip Hi ! There's the tagging app at https://huggingface.co/datasets/tagging/ that you can use. It shows the list of all the tags you can use. It is based on all the tag sets defined in this folder: https://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources
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https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
I don't think so. Feel free to take a look at the tags of other models (example [here](https://huggingface.co/bert-base-uncased/blob/main/README.md)). But we should definitely have some docs or an app to write the tags. Feel free to open an issue in the `transformers` repo or in the `huggingface_hub` repo so we can discuss this
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
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Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip I don't think so. Feel free to take a look at the tags of other models (example [here](https://huggingface.co/bert-base-uncased/blob/main/README.md)). But we should definitely have some docs or an app to write the tags. Feel free to open an issue in the `transformers` repo or in the `huggingface_hub` repo so we can discuss this
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https://github.com/huggingface/datasets/issues/2083
`concatenate_datasets` throws error when changing the order of datasets to concatenate
Hi, this bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit: ```python common_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment']) ``` The order is important because the resulting dataset inherits the schema metadata of the first dataset passed to the `concatenate_datasets(...)` function (`pa.concat_tables` [docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html)). I'll try to fix this ASAP.
Hey, I played around with the `concatenate_datasets(...)` function: https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets and noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO. Here is a google colab to reproduce the error: https://colab.research.google.com/drive/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing
70
`concatenate_datasets` throws error when changing the order of datasets to concatenate Hey, I played around with the `concatenate_datasets(...)` function: https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets and noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO. Here is a google colab to reproduce the error: https://colab.research.google.com/drive/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing Hi, this bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit: ```python common_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment']) ``` The order is important because the resulting dataset inherits the schema metadata of the first dataset passed to the `concatenate_datasets(...)` function (`pa.concat_tables` [docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html)). I'll try to fix this ASAP.
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https://github.com/huggingface/datasets/issues/2080
Multidimensional arrays in a Dataset
Hi ! This is actually supported ! but not yet in `from_pandas`. You can use `from_dict` for now instead: ```python from datasets import Dataset, Array2D, Features, Value import pandas as pd import numpy as np dataset = { 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] } dataset = Dataset.from_dict(dataset) ``` This will work but to use it with the torch formatter you must specify the `Array2D` feature type in order to tell the shape: ```python from datasets import Dataset, Array2D, Features, Value import pandas as pd import numpy as np dataset = { 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] } dataset = Dataset.from_dict(dataset, features=Features({ "bbox": Array2D(shape=(3, 4), dtype="int64"), "input_ids": Value("int64") })) dataset.set_format("torch") print(dataset[0]['bbox']) # tensor([[1, 2, 3, 4], # [1, 2, 3, 4], # [1, 2, 3, 4]]) ``` If you don't specify the `Array2D` feature type, then the inferred type will be Sequence(Sequence(Value("int64"))) and therefore the torch formatter will return list of tensors
Hi, I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row. The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`) ``` from datasets import Dataset import pandas as pd import numpy as np dataset = pd.DataFrame({ 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) ``` Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists. ``` import torch from datasets import Dataset import pandas as pd dataset = pd.DataFrame({ 'bbox': [ [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]] ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) def test(examples): return {'bbbox': torch.Tensor(examples['bbox'])} dataset = dataset.map(test) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) def test2(examples): return {'bbbox': torch.stack(examples['bbox'])} dataset = dataset.map(test2) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) ``` Is is possible to support n-D arrays/tensors in datasets? It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263).
165
Multidimensional arrays in a Dataset Hi, I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row. The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`) ``` from datasets import Dataset import pandas as pd import numpy as np dataset = pd.DataFrame({ 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) ``` Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists. ``` import torch from datasets import Dataset import pandas as pd dataset = pd.DataFrame({ 'bbox': [ [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]] ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) def test(examples): return {'bbbox': torch.Tensor(examples['bbox'])} dataset = dataset.map(test) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) def test2(examples): return {'bbbox': torch.stack(examples['bbox'])} dataset = dataset.map(test2) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) ``` Is is possible to support n-D arrays/tensors in datasets? It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263). Hi ! This is actually supported ! but not yet in `from_pandas`. You can use `from_dict` for now instead: ```python from datasets import Dataset, Array2D, Features, Value import pandas as pd import numpy as np dataset = { 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] } dataset = Dataset.from_dict(dataset) ``` This will work but to use it with the torch formatter you must specify the `Array2D` feature type in order to tell the shape: ```python from datasets import Dataset, Array2D, Features, Value import pandas as pd import numpy as np dataset = { 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] } dataset = Dataset.from_dict(dataset, features=Features({ "bbox": Array2D(shape=(3, 4), dtype="int64"), "input_ids": Value("int64") })) dataset.set_format("torch") print(dataset[0]['bbox']) # tensor([[1, 2, 3, 4], # [1, 2, 3, 4], # [1, 2, 3, 4]]) ``` If you don't specify the `Array2D` feature type, then the inferred type will be Sequence(Sequence(Value("int64"))) and therefore the torch formatter will return list of tensors
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https://github.com/huggingface/datasets/issues/2080
Multidimensional arrays in a Dataset
Thanks for the explanation. With my original DataFrame, I did ``` dataset = dataset.to_dict("list") ``` and then the rest of the transformation from dictionary works just fine.
Hi, I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row. The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`) ``` from datasets import Dataset import pandas as pd import numpy as np dataset = pd.DataFrame({ 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) ``` Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists. ``` import torch from datasets import Dataset import pandas as pd dataset = pd.DataFrame({ 'bbox': [ [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]] ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) def test(examples): return {'bbbox': torch.Tensor(examples['bbox'])} dataset = dataset.map(test) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) def test2(examples): return {'bbbox': torch.stack(examples['bbox'])} dataset = dataset.map(test2) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) ``` Is is possible to support n-D arrays/tensors in datasets? It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263).
27
Multidimensional arrays in a Dataset Hi, I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row. The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`) ``` from datasets import Dataset import pandas as pd import numpy as np dataset = pd.DataFrame({ 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) ``` Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists. ``` import torch from datasets import Dataset import pandas as pd dataset = pd.DataFrame({ 'bbox': [ [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]] ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) def test(examples): return {'bbbox': torch.Tensor(examples['bbox'])} dataset = dataset.map(test) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) def test2(examples): return {'bbbox': torch.stack(examples['bbox'])} dataset = dataset.map(test2) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) ``` Is is possible to support n-D arrays/tensors in datasets? It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263). Thanks for the explanation. With my original DataFrame, I did ``` dataset = dataset.to_dict("list") ``` and then the rest of the transformation from dictionary works just fine.
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Hi ! Thanks for reporting. We're indeed using `jiwer` to compute the WER. Maybe instead of calling `jiwer.wer` once for all the preditions/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible. Currently the code to compute the WER is defined here: https://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/metrics/wer/wer.py#L93-L94
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
56
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Hi ! Thanks for reporting. We're indeed using `jiwer` to compute the WER. Maybe instead of calling `jiwer.wer` once for all the preditions/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible. Currently the code to compute the WER is defined here: https://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/metrics/wer/wer.py#L93-L94
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Hi, I've just pushed a pull request that is related to this issue https://github.com/huggingface/datasets/pull/2169. It's not iterative, but it should avoid memory errors. It's based on the editdistance python library. An iterative implementation should be as easy as storing scores and words stepwise and dividing at the end.
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
48
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Hi, I've just pushed a pull request that is related to this issue https://github.com/huggingface/datasets/pull/2169. It's not iterative, but it should avoid memory errors. It's based on the editdistance python library. An iterative implementation should be as easy as storing scores and words stepwise and dividing at the end.
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
I see, this was solved by other thread. Ok, let me know if you want to switch the implementation for any reason :)
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
23
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. I see, this was solved by other thread. Ok, let me know if you want to switch the implementation for any reason :)
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Thanks for diving into this anyway ^^' As you said this actually got solved a few days ago
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
18
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Thanks for diving into this anyway ^^' As you said this actually got solved a few days ago
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Someone created an issue https://github.com/jitsi/jiwer/issues/40 at jiwer which shows that this is still a problem in the current version. Would be curious to figure out how this can be fixed by jiwer... :) I assume that it runs of out memory because it's trying to compute the WER over (too many) test samples?
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
53
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Someone created an issue https://github.com/jitsi/jiwer/issues/40 at jiwer which shows that this is still a problem in the current version. Would be curious to figure out how this can be fixed by jiwer... :) I assume that it runs of out memory because it's trying to compute the WER over (too many) test samples?
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Hi ! It's computed iteratively so not sure what could go wrong https://github.com/huggingface/datasets/blob/8afd0ba8c27800a55ea69d9fcd702dc97d9c16d8/metrics/wer/wer.py#L100-L106 @NiklasHoltmeyer what version of `datasets` are you running ?
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
22
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Hi ! It's computed iteratively so not sure what could go wrong https://github.com/huggingface/datasets/blob/8afd0ba8c27800a55ea69d9fcd702dc97d9c16d8/metrics/wer/wer.py#L100-L106 @NiklasHoltmeyer what version of `datasets` are you running ?
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
One possible explanation might be that it is the user who is passing all the sentences in a single element to `wer.compute`? As current implementation iterates over the elements of `predictions` and `references`, this can be problematic if `predictions` and `references` contain a single huge element each. This could be the case, for example, with a single string with all sentences: ```python result["predicted"] = "One sentence. Other sentence." ``` or with a __double__ nested list of sentence lists ```python result["predicted"] = [[ ["One sentence."], ["Other sentence"] ]] ``` The user should check the dimensions of the data structure passed to `predictions` and `references`.
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
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MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. One possible explanation might be that it is the user who is passing all the sentences in a single element to `wer.compute`? As current implementation iterates over the elements of `predictions` and `references`, this can be problematic if `predictions` and `references` contain a single huge element each. This could be the case, for example, with a single string with all sentences: ```python result["predicted"] = "One sentence. Other sentence." ``` or with a __double__ nested list of sentence lists ```python result["predicted"] = [[ ["One sentence."], ["Other sentence"] ]] ``` The user should check the dimensions of the data structure passed to `predictions` and `references`.
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Hi all, in my case I was using and older version of datasets and, as @albertvillanova points out, passing the full list of sentences for the metric calculation. The problem was in the way jiwer implements WER, as it tries to compute WER for the full list at once instead of doing it element-wise. I think that with the latest implementation of datasets, or by using the alternative WER function that I've contributed on this [pull request](https://github.com/huggingface/datasets/pull/2169) there shouldn't be memory errors.
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
82
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Hi all, in my case I was using and older version of datasets and, as @albertvillanova points out, passing the full list of sentences for the metric calculation. The problem was in the way jiwer implements WER, as it tries to compute WER for the full list at once instead of doing it element-wise. I think that with the latest implementation of datasets, or by using the alternative WER function that I've contributed on this [pull request](https://github.com/huggingface/datasets/pull/2169) there shouldn't be memory errors.
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
@lhoestq i was using Datasets==1.5.0 with 1.6.1 it worked (atleast the first run) but 1.5.0 is not compatible with my preprocessing. i cant save my dataset to a parquet file while using the latest datasets version -> ``` File "../preprocess_dataset.py", line 132, in <module> pq.write_table(train_dataset.data, f'{resampled_data_dir}/{data_args.dataset_config_name}.train.parquet') File "/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py", line 1674, in write_table writer.write_table(table, row_group_size=row_group_size) File "/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py", line 588, in write_table self.writer.write_table(table, row_group_size=row_group_size) TypeError: Argument 'table' has incorrect type (expected pyarrow.lib.Table, got ConcatenationTable) ``` if i do ``` import pyarrow.parquet as pq ... ... pq.write_table(train_dataset.data, 'train.parquet') pq.write_table(eval_dataset.data, 'eval.parquet') ``` while using 1.6.1. and its working with 1.5.0
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
96
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. @lhoestq i was using Datasets==1.5.0 with 1.6.1 it worked (atleast the first run) but 1.5.0 is not compatible with my preprocessing. i cant save my dataset to a parquet file while using the latest datasets version -> ``` File "../preprocess_dataset.py", line 132, in <module> pq.write_table(train_dataset.data, f'{resampled_data_dir}/{data_args.dataset_config_name}.train.parquet') File "/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py", line 1674, in write_table writer.write_table(table, row_group_size=row_group_size) File "/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py", line 588, in write_table self.writer.write_table(table, row_group_size=row_group_size) TypeError: Argument 'table' has incorrect type (expected pyarrow.lib.Table, got ConcatenationTable) ``` if i do ``` import pyarrow.parquet as pq ... ... pq.write_table(train_dataset.data, 'train.parquet') pq.write_table(eval_dataset.data, 'eval.parquet') ``` while using 1.6.1. and its working with 1.5.0
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https://github.com/huggingface/datasets/issues/2076
Issue: Dataset download error
Hi @XuhuiZhou, thanks for reporting this issue. Indeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.
The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?
32
Issue: Dataset download error The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link? Hi @XuhuiZhou, thanks for reporting this issue. Indeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.
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https://github.com/huggingface/datasets/issues/2076
Issue: Dataset download error
It would be nice to update the urls indeed ! To do this, you just need to replace the urls in `iwslt2017.py` and then update the dataset_infos.json file with ``` datasets-cli test ./datasets/iwslt2017 --all_configs --save_infos --ignore_verifications ```
The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?
37
Issue: Dataset download error The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link? It would be nice to update the urls indeed ! To do this, you just need to replace the urls in `iwslt2017.py` and then update the dataset_infos.json file with ``` datasets-cli test ./datasets/iwslt2017 --all_configs --save_infos --ignore_verifications ```
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https://github.com/huggingface/datasets/issues/2076
Issue: Dataset download error
Is this a command to update my local files or fix the file Github repo in general? (I am not so familiar with the datasets-cli command here) I also took a brief look at the **Sharing your dataset** section, looks like I could fix that locally and push it to the repo? I guess we are "canonical" category?
The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?
58
Issue: Dataset download error The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link? Is this a command to update my local files or fix the file Github repo in general? (I am not so familiar with the datasets-cli command here) I also took a brief look at the **Sharing your dataset** section, looks like I could fix that locally and push it to the repo? I guess we are "canonical" category?
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https://github.com/huggingface/datasets/issues/2076
Issue: Dataset download error
This command will update your local file. Then you can open a Pull Request to push your fix to the github repo :) And yes you are right, it is a "canonical" dataset, i.e. a dataset script defined in this github repo (as opposed to dataset repositories of users on the huggingface hub)
The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?
53
Issue: Dataset download error The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link? This command will update your local file. Then you can open a Pull Request to push your fix to the github repo :) And yes you are right, it is a "canonical" dataset, i.e. a dataset script defined in this github repo (as opposed to dataset repositories of users on the huggingface hub)
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https://github.com/huggingface/datasets/issues/2076
Issue: Dataset download error
Hi, thanks for the answer. I gave a try to the problem today. But I encountered an upload error: ``` git push -u origin fix_link_iwslt Enter passphrase for key '/home2/xuhuizh/.ssh/id_rsa': ERROR: Permission to huggingface/datasets.git denied to XuhuiZhou. fatal: Could not read from remote repository. Please make sure you have the correct access rights and the repository exists. ``` Any insight here? By the way, when I run the datasets-cli command, it shows the following error, but does not seem to be the error coming from `iwslt.py` ``` Traceback (most recent call last): File "/home2/xuhuizh/anaconda3/envs/UMT/bin/datasets-cli", line 33, in <module> sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')()) File "/home2/xuhuizh/projects/datasets/src/datasets/commands/datasets_cli.py", line 35, in main service.run() File "/home2/xuhuizh/projects/datasets/src/datasets/commands/test.py", line 141, in run try_from_hf_gcs=False, File "/home2/xuhuizh/projects/datasets/src/datasets/builder.py", line 579, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home2/xuhuizh/projects/datasets/src/datasets/builder.py", line 639, in _download_and_prepare self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" File "/home2/xuhuizh/projects/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz'} ```
The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?
148
Issue: Dataset download error The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link? Hi, thanks for the answer. I gave a try to the problem today. But I encountered an upload error: ``` git push -u origin fix_link_iwslt Enter passphrase for key '/home2/xuhuizh/.ssh/id_rsa': ERROR: Permission to huggingface/datasets.git denied to XuhuiZhou. fatal: Could not read from remote repository. Please make sure you have the correct access rights and the repository exists. ``` Any insight here? By the way, when I run the datasets-cli command, it shows the following error, but does not seem to be the error coming from `iwslt.py` ``` Traceback (most recent call last): File "/home2/xuhuizh/anaconda3/envs/UMT/bin/datasets-cli", line 33, in <module> sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')()) File "/home2/xuhuizh/projects/datasets/src/datasets/commands/datasets_cli.py", line 35, in main service.run() File "/home2/xuhuizh/projects/datasets/src/datasets/commands/test.py", line 141, in run try_from_hf_gcs=False, File "/home2/xuhuizh/projects/datasets/src/datasets/builder.py", line 579, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home2/xuhuizh/projects/datasets/src/datasets/builder.py", line 639, in _download_and_prepare self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" File "/home2/xuhuizh/projects/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz'} ```
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https://github.com/huggingface/datasets/issues/2076
Issue: Dataset download error
Hi ! To create a PR on this repo your must fork it and create a branch on your fork. See how to fork the repo [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#start-by-preparing-your-environment). And to make the command work without the `ExpectedMoreDownloadedFiles` error, you just need to use the `--ignore_verifications` flag.
The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?
45
Issue: Dataset download error The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link? Hi ! To create a PR on this repo your must fork it and create a branch on your fork. See how to fork the repo [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#start-by-preparing-your-environment). And to make the command work without the `ExpectedMoreDownloadedFiles` error, you just need to use the `--ignore_verifications` flag.
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https://github.com/huggingface/datasets/issues/2076
Issue: Dataset download error
Hi @XuhuiZhou, As @lhoestq has well explained, you need to fork HF's repository, create a feature branch in your fork, push your changes to it and then open a Pull Request to HF's upstream repository. This is so because at HuggingFace Datasets we follow a development model called "Fork and Pull Model". You can find more information here: - [Understanding the GitHub flow](https://guides.github.com/introduction/flow/) - [Forking Projects](https://guides.github.com/activities/forking/) Alternatively, if you find all these steps too complicated, you can use the GitHub official command line tool: [GitHub CLI](https://cli.github.com/). Once installed, in order to create a Pull Request, you only need to use this command: ```shell gh pr create --web ``` This utility will automatically create the fork, push your changes and open a Pull Request, under the hood.
The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?
126
Issue: Dataset download error The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link? Hi @XuhuiZhou, As @lhoestq has well explained, you need to fork HF's repository, create a feature branch in your fork, push your changes to it and then open a Pull Request to HF's upstream repository. This is so because at HuggingFace Datasets we follow a development model called "Fork and Pull Model". You can find more information here: - [Understanding the GitHub flow](https://guides.github.com/introduction/flow/) - [Forking Projects](https://guides.github.com/activities/forking/) Alternatively, if you find all these steps too complicated, you can use the GitHub official command line tool: [GitHub CLI](https://cli.github.com/). Once installed, in order to create a Pull Request, you only need to use this command: ```shell gh pr create --web ``` This utility will automatically create the fork, push your changes and open a Pull Request, under the hood.
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https://github.com/huggingface/datasets/issues/2075
ConnectionError: Couldn't reach common_voice.py
Hi @LifaSun, thanks for reporting this issue. Sometimes, GitHub has some connectivity problems. Could you confirm that the problem persists?
When I run: from datasets import load_dataset, load_metric common_voice_train = load_dataset("common_voice", "zh-CN", split="train+validation") common_voice_test = load_dataset("common_voice", "zh-CN", split="test") Got: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/master/datasets/common_voice/common_voice.py Version: 1.4.1 Thanks! @lhoestq @LysandreJik @thomwolf
20
ConnectionError: Couldn't reach common_voice.py When I run: from datasets import load_dataset, load_metric common_voice_train = load_dataset("common_voice", "zh-CN", split="train+validation") common_voice_test = load_dataset("common_voice", "zh-CN", split="test") Got: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/master/datasets/common_voice/common_voice.py Version: 1.4.1 Thanks! @lhoestq @LysandreJik @thomwolf Hi @LifaSun, thanks for reporting this issue. Sometimes, GitHub has some connectivity problems. Could you confirm that the problem persists?
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https://github.com/huggingface/datasets/issues/2070
ArrowInvalid issue for squad v2 dataset
Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column. Indeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a batch. However it seems like `tokenized_examples` doesn't have the same number of elements in each field. One field seems to have `1180` elements while `candidate_attention_mask` only has `1178`.
Hello, I am using the huggingface official question answering example notebook (https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb). In the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error: `ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178` My code is as follows: ``` def generate_candidate_questions(examples): val_questions = examples["question"] candididate_questions = random.sample(datasets["train"]["question"], len(val_questions)) candididate_questions = [x[:max_length] for x in candididate_questions] return candididate_questions def prepare_validation_features(examples, use_mixing=False): pad_on_right = tokenizer.padding_side == "right" tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) if use_mixing: candidate_questions = generate_candidate_questions(examples) tokenized_candidates = tokenizer( candidate_questions if pad_on_right else examples["context"], examples["context"] if pad_on_right else candidate_questions, truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") tokenized_examples["example_id"] = [] if use_mixing: tokenized_examples["candidate_input_ids"] = tokenized_candidates["input_ids"] tokenized_examples["candidate_attention_mask"] = tokenized_candidates["attention_mask"] tokenized_examples["candidate_token_type_ids"] = tokenized_candidates["token_type_ids"] for i in range(len(tokenized_examples["input_ids"])): sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) tokenized_examples["offset_mapping"][i] = [ (o if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples validation_features = datasets["validation"].map( lambda xs: prepare_validation_features(xs, True), batched=True, remove_columns=datasets["validation"].column_names ) ``` I guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help!
80
ArrowInvalid issue for squad v2 dataset Hello, I am using the huggingface official question answering example notebook (https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb). In the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error: `ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178` My code is as follows: ``` def generate_candidate_questions(examples): val_questions = examples["question"] candididate_questions = random.sample(datasets["train"]["question"], len(val_questions)) candididate_questions = [x[:max_length] for x in candididate_questions] return candididate_questions def prepare_validation_features(examples, use_mixing=False): pad_on_right = tokenizer.padding_side == "right" tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) if use_mixing: candidate_questions = generate_candidate_questions(examples) tokenized_candidates = tokenizer( candidate_questions if pad_on_right else examples["context"], examples["context"] if pad_on_right else candidate_questions, truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") tokenized_examples["example_id"] = [] if use_mixing: tokenized_examples["candidate_input_ids"] = tokenized_candidates["input_ids"] tokenized_examples["candidate_attention_mask"] = tokenized_candidates["attention_mask"] tokenized_examples["candidate_token_type_ids"] = tokenized_candidates["token_type_ids"] for i in range(len(tokenized_examples["input_ids"])): sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) tokenized_examples["offset_mapping"][i] = [ (o if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples validation_features = datasets["validation"].map( lambda xs: prepare_validation_features(xs, True), batched=True, remove_columns=datasets["validation"].column_names ) ``` I guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help! Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column. Indeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a batch. However it seems like `tokenized_examples` doesn't have the same number of elements in each field. One field seems to have `1180` elements while `candidate_attention_mask` only has `1178`.
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https://github.com/huggingface/datasets/issues/2068
PyTorch not available error on SageMaker GPU docker though it is installed
Hey @sivakhno, how does your `requirements.txt` look like to install the `datasets` library and which version of it are you running? Can you try to install `datasets>=1.4.0`
I get en error when running data loading using SageMaker SDK ``` File "main.py", line 34, in <module> run_training() File "main.py", line 25, in run_training dm.setup('fit') File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn return fn(*args, **kwargs) File "/opt/ml/code/data_module.py", line 103, in setup self.dataset[split].set_format(type="torch", columns=self.columns) File "/opt/conda/lib/python3.6/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 995, in set_format _ = get_formatter(type, **format_kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/formatting/__init__.py", line 114, in get_formatter raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type] ValueError: PyTorch needs to be installed to be able to return PyTorch tensors. ``` when trying to execute dataset loading using this notebook https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb, specifically lines ``` self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] self.dataset[split].set_format(type="torch", columns=self.columns) ``` The SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.4.0-gpu-py3 . By running container interactively I have checked that torch loading completes successfully by executing `https://github.com/huggingface/datasets/blob/master/src/datasets/config.py#L39`. Also as a first line in the data loading module I have ``` import os os.environ["USE_TF"] = "0" os.environ["USE_TORCH"] = "1" ```` But unfortunately the error stills persists. Any suggestions would be appreciated as I am stack. Many Thanks!
27
PyTorch not available error on SageMaker GPU docker though it is installed I get en error when running data loading using SageMaker SDK ``` File "main.py", line 34, in <module> run_training() File "main.py", line 25, in run_training dm.setup('fit') File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn return fn(*args, **kwargs) File "/opt/ml/code/data_module.py", line 103, in setup self.dataset[split].set_format(type="torch", columns=self.columns) File "/opt/conda/lib/python3.6/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 995, in set_format _ = get_formatter(type, **format_kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/formatting/__init__.py", line 114, in get_formatter raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type] ValueError: PyTorch needs to be installed to be able to return PyTorch tensors. ``` when trying to execute dataset loading using this notebook https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb, specifically lines ``` self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] self.dataset[split].set_format(type="torch", columns=self.columns) ``` The SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.4.0-gpu-py3 . By running container interactively I have checked that torch loading completes successfully by executing `https://github.com/huggingface/datasets/blob/master/src/datasets/config.py#L39`. Also as a first line in the data loading module I have ``` import os os.environ["USE_TF"] = "0" os.environ["USE_TORCH"] = "1" ```` But unfortunately the error stills persists. Any suggestions would be appreciated as I am stack. Many Thanks! Hey @sivakhno, how does your `requirements.txt` look like to install the `datasets` library and which version of it are you running? Can you try to install `datasets>=1.4.0`
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https://github.com/huggingface/datasets/issues/2068
PyTorch not available error on SageMaker GPU docker though it is installed
Hi @philschmid - thanks for suggestion. I am using `datasets==1.4.1`. I have also tried using `torch=1.6.0` (docker `763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.6.0-gpu-py3 `), but the error is the same.
I get en error when running data loading using SageMaker SDK ``` File "main.py", line 34, in <module> run_training() File "main.py", line 25, in run_training dm.setup('fit') File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn return fn(*args, **kwargs) File "/opt/ml/code/data_module.py", line 103, in setup self.dataset[split].set_format(type="torch", columns=self.columns) File "/opt/conda/lib/python3.6/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 995, in set_format _ = get_formatter(type, **format_kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/formatting/__init__.py", line 114, in get_formatter raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type] ValueError: PyTorch needs to be installed to be able to return PyTorch tensors. ``` when trying to execute dataset loading using this notebook https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb, specifically lines ``` self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] self.dataset[split].set_format(type="torch", columns=self.columns) ``` The SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.4.0-gpu-py3 . By running container interactively I have checked that torch loading completes successfully by executing `https://github.com/huggingface/datasets/blob/master/src/datasets/config.py#L39`. Also as a first line in the data loading module I have ``` import os os.environ["USE_TF"] = "0" os.environ["USE_TORCH"] = "1" ```` But unfortunately the error stills persists. Any suggestions would be appreciated as I am stack. Many Thanks!
25
PyTorch not available error on SageMaker GPU docker though it is installed I get en error when running data loading using SageMaker SDK ``` File "main.py", line 34, in <module> run_training() File "main.py", line 25, in run_training dm.setup('fit') File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn return fn(*args, **kwargs) File "/opt/ml/code/data_module.py", line 103, in setup self.dataset[split].set_format(type="torch", columns=self.columns) File "/opt/conda/lib/python3.6/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 995, in set_format _ = get_formatter(type, **format_kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/formatting/__init__.py", line 114, in get_formatter raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type] ValueError: PyTorch needs to be installed to be able to return PyTorch tensors. ``` when trying to execute dataset loading using this notebook https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb, specifically lines ``` self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] self.dataset[split].set_format(type="torch", columns=self.columns) ``` The SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.4.0-gpu-py3 . By running container interactively I have checked that torch loading completes successfully by executing `https://github.com/huggingface/datasets/blob/master/src/datasets/config.py#L39`. Also as a first line in the data loading module I have ``` import os os.environ["USE_TF"] = "0" os.environ["USE_TORCH"] = "1" ```` But unfortunately the error stills persists. Any suggestions would be appreciated as I am stack. Many Thanks! Hi @philschmid - thanks for suggestion. I am using `datasets==1.4.1`. I have also tried using `torch=1.6.0` (docker `763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.6.0-gpu-py3 `), but the error is the same.
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https://github.com/huggingface/datasets/issues/2068
PyTorch not available error on SageMaker GPU docker though it is installed
Could paste the code you use the start your training job and the fine-tuning script you run?
I get en error when running data loading using SageMaker SDK ``` File "main.py", line 34, in <module> run_training() File "main.py", line 25, in run_training dm.setup('fit') File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn return fn(*args, **kwargs) File "/opt/ml/code/data_module.py", line 103, in setup self.dataset[split].set_format(type="torch", columns=self.columns) File "/opt/conda/lib/python3.6/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 995, in set_format _ = get_formatter(type, **format_kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/formatting/__init__.py", line 114, in get_formatter raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type] ValueError: PyTorch needs to be installed to be able to return PyTorch tensors. ``` when trying to execute dataset loading using this notebook https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb, specifically lines ``` self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] self.dataset[split].set_format(type="torch", columns=self.columns) ``` The SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.4.0-gpu-py3 . By running container interactively I have checked that torch loading completes successfully by executing `https://github.com/huggingface/datasets/blob/master/src/datasets/config.py#L39`. Also as a first line in the data loading module I have ``` import os os.environ["USE_TF"] = "0" os.environ["USE_TORCH"] = "1" ```` But unfortunately the error stills persists. Any suggestions would be appreciated as I am stack. Many Thanks!
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PyTorch not available error on SageMaker GPU docker though it is installed I get en error when running data loading using SageMaker SDK ``` File "main.py", line 34, in <module> run_training() File "main.py", line 25, in run_training dm.setup('fit') File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn return fn(*args, **kwargs) File "/opt/ml/code/data_module.py", line 103, in setup self.dataset[split].set_format(type="torch", columns=self.columns) File "/opt/conda/lib/python3.6/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 995, in set_format _ = get_formatter(type, **format_kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/formatting/__init__.py", line 114, in get_formatter raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type] ValueError: PyTorch needs to be installed to be able to return PyTorch tensors. ``` when trying to execute dataset loading using this notebook https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb, specifically lines ``` self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] self.dataset[split].set_format(type="torch", columns=self.columns) ``` The SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.4.0-gpu-py3 . By running container interactively I have checked that torch loading completes successfully by executing `https://github.com/huggingface/datasets/blob/master/src/datasets/config.py#L39`. Also as a first line in the data loading module I have ``` import os os.environ["USE_TF"] = "0" os.environ["USE_TORCH"] = "1" ```` But unfortunately the error stills persists. Any suggestions would be appreciated as I am stack. Many Thanks! Could paste the code you use the start your training job and the fine-tuning script you run?
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https://github.com/huggingface/datasets/issues/2067
Multiprocessing windows error
Hi ! Thanks for reporting. This looks like a bug, could you try to provide a minimal code example that reproduces the issue ? This would be very helpful ! Otherwise I can try to run the wav2vec2 code above on my side but probably not this week..
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
48
Multiprocessing windows error As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop Hi ! Thanks for reporting. This looks like a bug, could you try to provide a minimal code example that reproduces the issue ? This would be very helpful ! Otherwise I can try to run the wav2vec2 code above on my side but probably not this week..
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https://github.com/huggingface/datasets/issues/2067
Multiprocessing windows error
``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4) ```
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
22
Multiprocessing windows error As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4) ```
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https://github.com/huggingface/datasets/issues/2067
Multiprocessing windows error
I was able to copy some of the shell This is repeating every half second Win 10, Anaconda with python 3.8, datasets installed from main branche ``` File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path _check_not_importing_main() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 116, in spawn_main File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 134, in _check_not_importing_main main_content = runpy.run_path(main_path, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 265, in run_path exitcode = _main(fd, parent_sentinel) raise RuntimeError(''' File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 125, in _main RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. return _run_module_code(code, init_globals, run_name, prepare(preparation_data) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 97, in _run_module_code File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 236, in prepare _run_code(code, mod_globals, init_globals, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 87, in _run_code _fixup_main_from_path(data['init_main_from_path']) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path exec(code, run_globals) File "F:\Codes\Python Apps\asr\test.py", line 6, in <module> updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4) main_content = runpy.run_path(main_path, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 1370, in map File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 265, in run_path with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool: File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\context.py", line 119, in Pool return _run_module_code(code, init_globals, run_name, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 97, in _run_module_code _run_code(code, mod_globals, init_globals, return Pool(processes, initializer, initargs, maxtasksperchild, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 87, in _run_code File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 212, in __init__ exec(code, run_globals) File "F:\Codes\Python Apps\asr\test.py", line 6, in <module> self._repopulate_pool() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 303, in _repopulate_pool updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 1370, in map return self._repopulate_pool_static(self._ctx, self.Process, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 326, in _repopulate_pool_static with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool: File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\context.py", line 119, in Pool w.start() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\process.py", line 121, in start return Pool(processes, initializer, initargs, maxtasksperchild, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 212, in __init__ self._popen = self._Popen(self) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\context.py", line 327, in _Popen self._repopulate_pool() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 303, in _repopulate_pool return Popen(process_obj) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\popen_spawn_win32.py", line 45, in __init__ return self._repopulate_pool_static(self._ctx, self.Process, prep_data = spawn.get_preparation_data(process_obj._name) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 326, in _repopulate_pool_static File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 134, in _check_not_importing_main w.start() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\process.py", line 121, in start raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... ```
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
433
Multiprocessing windows error As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop I was able to copy some of the shell This is repeating every half second Win 10, Anaconda with python 3.8, datasets installed from main branche ``` File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path _check_not_importing_main() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 116, in spawn_main File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 134, in _check_not_importing_main main_content = runpy.run_path(main_path, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 265, in run_path exitcode = _main(fd, parent_sentinel) raise RuntimeError(''' File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 125, in _main RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. return _run_module_code(code, init_globals, run_name, prepare(preparation_data) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 97, in _run_module_code File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 236, in prepare _run_code(code, mod_globals, init_globals, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 87, in _run_code _fixup_main_from_path(data['init_main_from_path']) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path exec(code, run_globals) File "F:\Codes\Python Apps\asr\test.py", line 6, in <module> updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4) main_content = runpy.run_path(main_path, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 1370, in map File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 265, in run_path with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool: File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\context.py", line 119, in Pool return _run_module_code(code, init_globals, run_name, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 97, in _run_module_code _run_code(code, mod_globals, init_globals, return Pool(processes, initializer, initargs, maxtasksperchild, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 87, in _run_code File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 212, in __init__ exec(code, run_globals) File "F:\Codes\Python Apps\asr\test.py", line 6, in <module> self._repopulate_pool() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 303, in _repopulate_pool updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 1370, in map return self._repopulate_pool_static(self._ctx, self.Process, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 326, in _repopulate_pool_static with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool: File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\context.py", line 119, in Pool w.start() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\process.py", line 121, in start return Pool(processes, initializer, initargs, maxtasksperchild, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 212, in __init__ self._popen = self._Popen(self) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\context.py", line 327, in _Popen self._repopulate_pool() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 303, in _repopulate_pool return Popen(process_obj) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\popen_spawn_win32.py", line 45, in __init__ return self._repopulate_pool_static(self._ctx, self.Process, prep_data = spawn.get_preparation_data(process_obj._name) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\pool.py", line 326, in _repopulate_pool_static File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 134, in _check_not_importing_main w.start() File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\process.py", line 121, in start raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... ```
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https://github.com/huggingface/datasets/issues/2067
Multiprocessing windows error
Thanks this is really helpful ! I'll try to reproduce on my side and come back to you
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
18
Multiprocessing windows error As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop Thanks this is really helpful ! I'll try to reproduce on my side and come back to you
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https://github.com/huggingface/datasets/issues/2067
Multiprocessing windows error
if __name__ == '__main__': This line before calling the map function stops the error but the script still repeats endless
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
20
Multiprocessing windows error As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop if __name__ == '__main__': This line before calling the map function stops the error but the script still repeats endless
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https://github.com/huggingface/datasets/issues/2067
Multiprocessing windows error
Indeed you needed `if __name__ == '__main__'` since accoding to [this stackoverflow post](https://stackoverflow.com/a/18205006): > On Windows the subprocesses will import (i.e. execute) the main module at start. You need to insert an if __name__ == '__main__': guard in the main module to avoid creating subprocesses recursively. Regarding the hanging issue, can you try to update `dill` and `multiprocess` ?
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
59
Multiprocessing windows error As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop Indeed you needed `if __name__ == '__main__'` since accoding to [this stackoverflow post](https://stackoverflow.com/a/18205006): > On Windows the subprocesses will import (i.e. execute) the main module at start. You need to insert an if __name__ == '__main__': guard in the main module to avoid creating subprocesses recursively. Regarding the hanging issue, can you try to update `dill` and `multiprocess` ?
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https://github.com/huggingface/datasets/issues/2067
Multiprocessing windows error
``` Traceback (most recent call last): File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\shutil.py", line 791, in move os.rename(src, real_dst) FileExistsError: [WinError 183] Eine Datei kann nicht erstellt werden, wenn sie bereits vorhanden ist: 'D:\\huggingfacecache\\common_voice\\de\\6.1.0\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\tmpx9fl_jg8' -> 'D:\\huggingfacecache\\common_voice\\de\\6.1.0\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\cache-9b4f203a63742dfc.arrow' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<string>", line 1, in <module> File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 125, in _main prepare(preparation_data) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 265, in run_path return _run_module_code(code, init_globals, run_name, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 97, in _run_module_code _run_code(code, mod_globals, init_globals, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "F:\Codes\Python Apps\asr\cvtrain.py", line 243, in <module> common_voice_train = common_voice_train.map(remove_special_characters, remove_columns=["sentence"]) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 1339, in map return self._map_single( File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 203, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 1646, in _map_single shutil.move(tmp_file.name, cache_file_name) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\shutil.py", line 805, in move copy_function(src, real_dst) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\shutil.py", line 435, in copy2 copyfile(src, dst, follow_symlinks=follow_symlinks) 0%| | 0/27771 [00:00<?, ?ex/s] File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\shutil.py", line 264, in copyfile with open(src, 'rb') as fsrc, open(dst, 'wb') as fdst: OSError: [Errno 22] Invalid argument: 'D:\\huggingfacecache\\common_voice\\de\\6.1.0\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\cache-9b4f203a63742dfc.arrow' ``` I was adding freeze support before calling the mapping function like this if __name__ == '__main__': freeze_support() dataset.map(....)
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
224
Multiprocessing windows error As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ``` Traceback (most recent call last): File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\shutil.py", line 791, in move os.rename(src, real_dst) FileExistsError: [WinError 183] Eine Datei kann nicht erstellt werden, wenn sie bereits vorhanden ist: 'D:\\huggingfacecache\\common_voice\\de\\6.1.0\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\tmpx9fl_jg8' -> 'D:\\huggingfacecache\\common_voice\\de\\6.1.0\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\cache-9b4f203a63742dfc.arrow' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<string>", line 1, in <module> File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 125, in _main prepare(preparation_data) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 265, in run_path return _run_module_code(code, init_globals, run_name, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 97, in _run_module_code _run_code(code, mod_globals, init_globals, File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "F:\Codes\Python Apps\asr\cvtrain.py", line 243, in <module> common_voice_train = common_voice_train.map(remove_special_characters, remove_columns=["sentence"]) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 1339, in map return self._map_single( File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 203, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\site-packages\datasets\arrow_dataset.py", line 1646, in _map_single shutil.move(tmp_file.name, cache_file_name) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\shutil.py", line 805, in move copy_function(src, real_dst) File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\shutil.py", line 435, in copy2 copyfile(src, dst, follow_symlinks=follow_symlinks) 0%| | 0/27771 [00:00<?, ?ex/s] File "C:\Users\flozi\anaconda3\envs\wav2vec\lib\shutil.py", line 264, in copyfile with open(src, 'rb') as fsrc, open(dst, 'wb') as fdst: OSError: [Errno 22] Invalid argument: 'D:\\huggingfacecache\\common_voice\\de\\6.1.0\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\cache-9b4f203a63742dfc.arrow' ``` I was adding freeze support before calling the mapping function like this if __name__ == '__main__': freeze_support() dataset.map(....)
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https://github.com/huggingface/datasets/issues/2067
Multiprocessing windows error
Usually OSError of an arrow file on windows means that the file is currently opened as a dataset object, so you can't overwrite it until the dataset object falls out of scope. Can you make sure that there's no dataset object that loaded the `cache-9b4f203a63742dfc.arrow` file ?
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
47
Multiprocessing windows error As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop Usually OSError of an arrow file on windows means that the file is currently opened as a dataset object, so you can't overwrite it until the dataset object falls out of scope. Can you make sure that there's no dataset object that loaded the `cache-9b4f203a63742dfc.arrow` file ?
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https://github.com/huggingface/datasets/issues/2067
Multiprocessing windows error
Now I understand The error occures because the script got restarted in another thread, so the object is already loaded. Still don't have an idea why a new thread starts the whole script again
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
34
Multiprocessing windows error As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 When using the num_proc argument on windows the whole Python environment crashes and hanging in loop. For example at the map_to_array part. An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop Now I understand The error occures because the script got restarted in another thread, so the object is already loaded. Still don't have an idea why a new thread starts the whole script again
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Hi ! Thanks for reporting. Currently there's no way to specify this. When loading/processing a dataset, the arrow file is written using a temporary file. Then once writing is finished, it's moved to the cache directory (using `shutil.move` [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1646)) That means it keeps the permissions specified by the `tempfile.NamedTemporaryFile` object, i.e. `-rw-------` instead of `-rw-r--r--`. Improving this could be a nice first contribution to the library :)
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Hi ! Thanks for reporting. Currently there's no way to specify this. When loading/processing a dataset, the arrow file is written using a temporary file. Then once writing is finished, it's moved to the cache directory (using `shutil.move` [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1646)) That means it keeps the permissions specified by the `tempfile.NamedTemporaryFile` object, i.e. `-rw-------` instead of `-rw-r--r--`. Improving this could be a nice first contribution to the library :)
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Hi @lhoestq, I looked into this and yes you're right. The `NamedTemporaryFile` is always created with mode 0600, which prevents group from reading the file. Should we change the permissions of `tmp_file.name` [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1871) and [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1590), post creation to 0644 inorder for group and others to read it?
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
47
Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Hi @lhoestq, I looked into this and yes you're right. The `NamedTemporaryFile` is always created with mode 0600, which prevents group from reading the file. Should we change the permissions of `tmp_file.name` [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1871) and [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1590), post creation to 0644 inorder for group and others to read it?
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Good idea :) we could even update the permissions after the file has been moved by shutil.move [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1899) and [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1646) actually. Apparently they set the default 0600 for temporary files for security reasons, so let's update the umask only after the file has been moved
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Good idea :) we could even update the permissions after the file has been moved by shutil.move [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1899) and [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1646) actually. Apparently they set the default 0600 for temporary files for security reasons, so let's update the umask only after the file has been moved
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Would it be possible to actually set the umask based on a user provided argument? For example, a popular usecase my team has is using a shared file-system for processing datasets. This may involve writing/deleting other files, or changing filenames, which a -rw-r--r-- wouldn't fix.
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Would it be possible to actually set the umask based on a user provided argument? For example, a popular usecase my team has is using a shared file-system for processing datasets. This may involve writing/deleting other files, or changing filenames, which a -rw-r--r-- wouldn't fix.
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Note that you can get the cache files of a dataset with the `cache_files` attributes. Then you can `chmod` those files and all the other cache files in the same directory. Moreover we can probably keep the same permissions after each transform. This way you just need to set the permissions once after doing `load_dataset` for example, and then all the new transformed cached files will have the same permissions. What do you think ?
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Note that you can get the cache files of a dataset with the `cache_files` attributes. Then you can `chmod` those files and all the other cache files in the same directory. Moreover we can probably keep the same permissions after each transform. This way you just need to set the permissions once after doing `load_dataset` for example, and then all the new transformed cached files will have the same permissions. What do you think ?
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
This means we'll check the permissions of other `cache_files` already created for a dataset before setting permissions for new `cache_files`?
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
20
Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? This means we'll check the permissions of other `cache_files` already created for a dataset before setting permissions for new `cache_files`?
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
> This way you just need to set the permissions once after doing load_dataset for example, and then all the new transformed cached files will have the same permissions. I was referring to this. Ensuring that newly generated `cache_files` have the same permissions
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? > This way you just need to set the permissions once after doing load_dataset for example, and then all the new transformed cached files will have the same permissions. I was referring to this. Ensuring that newly generated `cache_files` have the same permissions
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Yes exactly I imagine users can first do `load_dataset`, then chmod on the arrow files. After that all the new cache files could have the same permissions as the first arrow files. Opinions on this ?
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
36
Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Yes exactly I imagine users can first do `load_dataset`, then chmod on the arrow files. After that all the new cache files could have the same permissions as the first arrow files. Opinions on this ?
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Sounds nice but I feel this is a sub-part of the approach mentioned by @siddk. Instead of letting the user set new permissions by itself first and then making sure newly generated files have same permissions why don't we ask the user initially only what they want? What are your thoughts?
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
51
Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Sounds nice but I feel this is a sub-part of the approach mentioned by @siddk. Instead of letting the user set new permissions by itself first and then making sure newly generated files have same permissions why don't we ask the user initially only what they want? What are your thoughts?
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Yes sounds good. Should this be a parameter in `load_dataset` ? Or an env variable ? Or use the value of `os.umask` ?
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Yes sounds good. Should this be a parameter in `load_dataset` ? Or an env variable ? Or use the value of `os.umask` ?
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Ideally it should be a parameter in `load_dataset` but I'm not sure how important it is for the users (considering only important things should go into `load_dataset` parameters)
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Ideally it should be a parameter in `load_dataset` but I'm not sure how important it is for the users (considering only important things should go into `load_dataset` parameters)
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
I think it's fairly important; for context, our team uses a shared file-system where many folks run experiments based on datasets that are cached by other users. For example, I might start a training run, downloading a dataset. Then, a couple of days later, a collaborator using the same repository might want to use the same dataset on the same shared filesystem, but won't be able to under the default permissions. Being able to specify directly in the top-level `load_dataset()` call seems important, but an equally valid option would be to just inherit from the running user's `umask` (this should probably be the default anyway). So basically, argument that takes a custom set of permissions, and by default, use the running user's umask!
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? I think it's fairly important; for context, our team uses a shared file-system where many folks run experiments based on datasets that are cached by other users. For example, I might start a training run, downloading a dataset. Then, a couple of days later, a collaborator using the same repository might want to use the same dataset on the same shared filesystem, but won't be able to under the default permissions. Being able to specify directly in the top-level `load_dataset()` call seems important, but an equally valid option would be to just inherit from the running user's `umask` (this should probably be the default anyway). So basically, argument that takes a custom set of permissions, and by default, use the running user's umask!
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Maybe let's start by defaulting to the user's umask ! Do you want to give it a try @bhavitvyamalik ?
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
20
Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Maybe let's start by defaulting to the user's umask ! Do you want to give it a try @bhavitvyamalik ?
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Yeah sure! Instead of using default `0o644` should I first extract umask of current user and then use `os.umask` on it? We can do it inside `Dataset` class so that all folders/files created during the call use running user's umask
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Yeah sure! Instead of using default `0o644` should I first extract umask of current user and then use `os.umask` on it? We can do it inside `Dataset` class so that all folders/files created during the call use running user's umask
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
You can get the umask using `os.umask` and then I guess you can just use `os.chmod` as in your previous PR, but with the right permissions depending on the umask.
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? You can get the umask using `os.umask` and then I guess you can just use `os.chmod` as in your previous PR, but with the right permissions depending on the umask.
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
FWIW, we have this issue with other caches - e.g. `transformers` model files. So probably will need to backport this into `transformers` as well. thanks @thomwolf for the pointer.
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? FWIW, we have this issue with other caches - e.g. `transformers` model files. So probably will need to backport this into `transformers` as well. thanks @thomwolf for the pointer.
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https://github.com/huggingface/datasets/issues/2065
Only user permission of saved cache files, not group
Hi @stas00, For this should we use the same umask code in the respective model directory inside `TRANSFORMERS_CACHE`?
Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
18
Only user permission of saved cache files, not group Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? Hi @stas00, For this should we use the same umask code in the respective model directory inside `TRANSFORMERS_CACHE`?
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https://github.com/huggingface/datasets/issues/2061
Cannot load udpos subsets from xtreme dataset using load_dataset()
@lhoestq Adding "_" to the class labels in the dataset script will fix the issue. The bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.
Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_'
35
Cannot load udpos subsets from xtreme dataset using load_dataset() Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_' @lhoestq Adding "_" to the class labels in the dataset script will fix the issue. The bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.
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https://github.com/huggingface/datasets/issues/2061
Cannot load udpos subsets from xtreme dataset using load_dataset()
Hi ! Thanks for reporting @adzcodez > @lhoestq Adding "_" to the class labels in the dataset script will fix the issue. > > The bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences. You're right: "_" should be added to the list of labels, and the examples must be sequences of tokens, not singles tokens.
Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_'
66
Cannot load udpos subsets from xtreme dataset using load_dataset() Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_' Hi ! Thanks for reporting @adzcodez > @lhoestq Adding "_" to the class labels in the dataset script will fix the issue. > > The bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences. You're right: "_" should be added to the list of labels, and the examples must be sequences of tokens, not singles tokens.
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https://github.com/huggingface/datasets/issues/2061
Cannot load udpos subsets from xtreme dataset using load_dataset()
@lhoestq Can you please label this issue with the "good first issue" label? I'm not sure I'll find time to fix this. To resolve it, the user should: 1. add `"_"` to the list of labels 2. transform the udpos subset to the conll format (I think the preprocessing logic can be borrowed from [the original repo](https://github.com/google-research/xtreme/blob/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de/utils_preprocess.py#L187-L204)) 3. update the dummy data 4. update the dataset info 5. [optional] add info about the data fields structure of the udpos subset to the dataset readme
Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_'
84
Cannot load udpos subsets from xtreme dataset using load_dataset() Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_' @lhoestq Can you please label this issue with the "good first issue" label? I'm not sure I'll find time to fix this. To resolve it, the user should: 1. add `"_"` to the list of labels 2. transform the udpos subset to the conll format (I think the preprocessing logic can be borrowed from [the original repo](https://github.com/google-research/xtreme/blob/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de/utils_preprocess.py#L187-L204)) 3. update the dummy data 4. update the dataset info 5. [optional] add info about the data fields structure of the udpos subset to the dataset readme
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https://github.com/huggingface/datasets/issues/2061
Cannot load udpos subsets from xtreme dataset using load_dataset()
I tried fixing this issue, but its working fine in the dev version : "1.6.2.dev0" I think somebody already fixed it.
Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_'
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Cannot load udpos subsets from xtreme dataset using load_dataset() Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_' I tried fixing this issue, but its working fine in the dev version : "1.6.2.dev0" I think somebody already fixed it.
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https://github.com/huggingface/datasets/issues/2061
Cannot load udpos subsets from xtreme dataset using load_dataset()
Hi, after #2326, the lines with pos tags equal to `"_"` are filtered out when generating the dataset, so this fixes the KeyError described above. However, the udpos subset should be in the conll format i.e. it should yield sequences of tokens and not single tokens, so it would be great to see this fixed (feel free to borrow the logic from [here](https://github.com/google-research/xtreme/blob/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de/utils_preprocess.py#L187-L204) if you decide to work on this).
Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_'
70
Cannot load udpos subsets from xtreme dataset using load_dataset() Hello, I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. Reprex is: `from datasets import load_dataset ` `dataset = load_dataset('xtreme', 'udpos.English')` The error is: `KeyError: '_'` The full traceback is: KeyError Traceback (most recent call last) <ipython-input-5-7181359ea09d> in <module> 1 from datasets import load_dataset ----> 2 dataset = load_dataset('xtreme', 'udpos.English') ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 738 739 # Download and prepare data --> 740 builder_instance.download_and_prepare( 741 download_config=download_config, 742 download_mode=download_mode, ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 576 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 577 if not downloaded_from_gcs: --> 578 self._download_and_prepare( 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 654 try: 655 # Prepare split will record examples associated to the split --> 656 self._prepare_split(split_generator, **prepare_split_kwargs) 657 except OSError as e: 658 raise OSError( ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator) 977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 978 ): --> 979 example = self.info.features.encode_example(record) 980 writer.write(example) 981 finally: ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example) 946 def encode_example(self, example): 947 example = cast_to_python_objects(example) --> 948 return encode_nested_example(self, example) 949 950 def encode_batch(self, batch): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 840 # Nested structures: we allow dict, list/tuples, sequences 841 if isinstance(schema, dict): --> 842 return { 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0) 841 if isinstance(schema, dict): 842 return { --> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 844 } 845 elif isinstance(schema, (list, tuple)): ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj) 868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 870 return schema.encode_example(obj) 871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 872 return obj ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data) 647 # If a string is given, convert to associated integer 648 if isinstance(example_data, str): --> 649 example_data = self.str2int(example_data) 650 651 # Allowing -1 to mean no label. ~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values) 605 if value not in self._str2int: 606 value = value.strip() --> 607 output.append(self._str2int[str(value)]) 608 else: 609 # No names provided, try to integerize KeyError: '_' Hi, after #2326, the lines with pos tags equal to `"_"` are filtered out when generating the dataset, so this fixes the KeyError described above. However, the udpos subset should be in the conll format i.e. it should yield sequences of tokens and not single tokens, so it would be great to see this fixed (feel free to borrow the logic from [here](https://github.com/google-research/xtreme/blob/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de/utils_preprocess.py#L187-L204) if you decide to work on this).
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https://github.com/huggingface/datasets/issues/2059
Error while following docs to load the `ted_talks_iwslt` dataset
This has been fixed in #2064 by @mariosasko (thanks again !) The fix is available on the master branch and we'll do a new release very soon :)
I am currently trying to load the `ted_talks_iwslt` dataset into google colab. The [docs](https://huggingface.co/datasets/ted_talks_iwslt) mention the following way of doing so. ```python dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014") ``` Executing it results in the error attached below. ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-6-7dcc67154ef9> in <module>() ----> 1 dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014") 4 frames /usr/local/lib/python3.7/dist-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, script_version, use_auth_token, **config_kwargs) 730 hash=hash, 731 features=features, --> 732 **config_kwargs, 733 ) 734 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, writer_batch_size, *args, **kwargs) 927 928 def __init__(self, *args, writer_batch_size=None, **kwargs): --> 929 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs) 930 # Batch size used by the ArrowWriter 931 # It defines the number of samples that are kept in memory before writing them /usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs) 241 name, 242 custom_features=features, --> 243 **config_kwargs, 244 ) 245 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs) 337 if "version" not in config_kwargs and hasattr(self, "VERSION") and self.VERSION: 338 config_kwargs["version"] = self.VERSION --> 339 builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs) 340 341 # otherwise use the config_kwargs to overwrite the attributes /root/.cache/huggingface/modules/datasets_modules/datasets/ted_talks_iwslt/024d06b1376b361e59245c5878ab8acf9a7576d765f2d0077f61751158e60914/ted_talks_iwslt.py in __init__(self, language_pair, year, **kwargs) 219 description=description, 220 version=datasets.Version("1.1.0", ""), --> 221 **kwargs, 222 ) 223 TypeError: __init__() got multiple values for keyword argument 'version' ``` How to resolve this? PS: Thanks a lot @huggingface team for creating this great library!
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Error while following docs to load the `ted_talks_iwslt` dataset I am currently trying to load the `ted_talks_iwslt` dataset into google colab. The [docs](https://huggingface.co/datasets/ted_talks_iwslt) mention the following way of doing so. ```python dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014") ``` Executing it results in the error attached below. ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-6-7dcc67154ef9> in <module>() ----> 1 dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014") 4 frames /usr/local/lib/python3.7/dist-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, script_version, use_auth_token, **config_kwargs) 730 hash=hash, 731 features=features, --> 732 **config_kwargs, 733 ) 734 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, writer_batch_size, *args, **kwargs) 927 928 def __init__(self, *args, writer_batch_size=None, **kwargs): --> 929 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs) 930 # Batch size used by the ArrowWriter 931 # It defines the number of samples that are kept in memory before writing them /usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs) 241 name, 242 custom_features=features, --> 243 **config_kwargs, 244 ) 245 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs) 337 if "version" not in config_kwargs and hasattr(self, "VERSION") and self.VERSION: 338 config_kwargs["version"] = self.VERSION --> 339 builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs) 340 341 # otherwise use the config_kwargs to overwrite the attributes /root/.cache/huggingface/modules/datasets_modules/datasets/ted_talks_iwslt/024d06b1376b361e59245c5878ab8acf9a7576d765f2d0077f61751158e60914/ted_talks_iwslt.py in __init__(self, language_pair, year, **kwargs) 219 description=description, 220 version=datasets.Version("1.1.0", ""), --> 221 **kwargs, 222 ) 223 TypeError: __init__() got multiple values for keyword argument 'version' ``` How to resolve this? PS: Thanks a lot @huggingface team for creating this great library! This has been fixed in #2064 by @mariosasko (thanks again !) The fix is available on the master branch and we'll do a new release very soon :)
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https://github.com/huggingface/datasets/issues/2056
issue with opus100/en-fr dataset
@lhoestq I also deleted the cache and redownload the file and still the same issue, I appreciate any help on this. thanks
Hi I am running run_mlm.py code of huggingface repo with opus100/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? Thanks a lot @lhoestq for your help in advance. ` thread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/normalizer.rs:382:21 note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace 63%|██████████████████████████████████████████████████████████▊ | 626/1000 [00:27<00:16, 22.69ba/s] Traceback (most recent call last): File "run_mlm.py", line 550, in <module> main() File "run_mlm.py", line 412, in main in zip(data_args.dataset_name, data_args.dataset_config_name)] File "run_mlm.py", line 411, in <listcomp> logger) for dataset_name, dataset_config_name\ File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 96, in get_tokenized_dataset load_from_cache_file=not data_args.overwrite_cache, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in map for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in <dictcomp> for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1309, in map update_data=update_data, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 204, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1574, in _map_single batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1490, in apply_function_on_filtered_inputs function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 89, in tokenize_function return tokenizer(examples[text_column_name], return_special_tokens_mask=True) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2347, in __call__ **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2532, in batch_encode_plus **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 384, in _batch_encode_plus is_pretokenized=is_split_into_words, pyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617 `
22
issue with opus100/en-fr dataset Hi I am running run_mlm.py code of huggingface repo with opus100/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? Thanks a lot @lhoestq for your help in advance. ` thread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/normalizer.rs:382:21 note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace 63%|██████████████████████████████████████████████████████████▊ | 626/1000 [00:27<00:16, 22.69ba/s] Traceback (most recent call last): File "run_mlm.py", line 550, in <module> main() File "run_mlm.py", line 412, in main in zip(data_args.dataset_name, data_args.dataset_config_name)] File "run_mlm.py", line 411, in <listcomp> logger) for dataset_name, dataset_config_name\ File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 96, in get_tokenized_dataset load_from_cache_file=not data_args.overwrite_cache, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in map for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in <dictcomp> for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1309, in map update_data=update_data, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 204, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1574, in _map_single batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1490, in apply_function_on_filtered_inputs function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 89, in tokenize_function return tokenizer(examples[text_column_name], return_special_tokens_mask=True) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2347, in __call__ **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2532, in batch_encode_plus **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 384, in _batch_encode_plus is_pretokenized=is_split_into_words, pyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617 ` @lhoestq I also deleted the cache and redownload the file and still the same issue, I appreciate any help on this. thanks
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https://github.com/huggingface/datasets/issues/2056
issue with opus100/en-fr dataset
Here please find the minimal code to reproduce the issue @lhoestq note this only happens with MT5TokenizerFast ``` from datasets import load_dataset from transformers import MT5TokenizerFast def get_tokenized_dataset(dataset_name, dataset_config_name, tokenizer): datasets = load_dataset(dataset_name, dataset_config_name, script_version="master") column_names = datasets["train"].column_names text_column_name = "translation" def process_dataset(datasets): def process_function(examples): lang = "fr" return {"src_texts": [example[lang] for example in examples[text_column_name]]} datasets = datasets.map( process_function, batched=True, num_proc=None, remove_columns=column_names, load_from_cache_file=True, ) return datasets datasets = process_dataset(datasets) text_column_name = "src_texts" column_names = ["src_texts"] def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=None, remove_columns=column_names, load_from_cache_file=True ) if __name__ == "__main__": tokenizer_kwargs = { "cache_dir": None, "use_fast": True, "revision": "main", "use_auth_token": None } tokenizer = MT5TokenizerFast.from_pretrained("google/mt5-small", **tokenizer_kwargs) get_tokenized_dataset(dataset_name="opus100", dataset_config_name="en-fr", tokenizer=tokenizer) ~ ```
Hi I am running run_mlm.py code of huggingface repo with opus100/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? Thanks a lot @lhoestq for your help in advance. ` thread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/normalizer.rs:382:21 note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace 63%|██████████████████████████████████████████████████████████▊ | 626/1000 [00:27<00:16, 22.69ba/s] Traceback (most recent call last): File "run_mlm.py", line 550, in <module> main() File "run_mlm.py", line 412, in main in zip(data_args.dataset_name, data_args.dataset_config_name)] File "run_mlm.py", line 411, in <listcomp> logger) for dataset_name, dataset_config_name\ File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 96, in get_tokenized_dataset load_from_cache_file=not data_args.overwrite_cache, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in map for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in <dictcomp> for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1309, in map update_data=update_data, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 204, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1574, in _map_single batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1490, in apply_function_on_filtered_inputs function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 89, in tokenize_function return tokenizer(examples[text_column_name], return_special_tokens_mask=True) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2347, in __call__ **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2532, in batch_encode_plus **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 384, in _batch_encode_plus is_pretokenized=is_split_into_words, pyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617 `
114
issue with opus100/en-fr dataset Hi I am running run_mlm.py code of huggingface repo with opus100/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? Thanks a lot @lhoestq for your help in advance. ` thread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/normalizer.rs:382:21 note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace 63%|██████████████████████████████████████████████████████████▊ | 626/1000 [00:27<00:16, 22.69ba/s] Traceback (most recent call last): File "run_mlm.py", line 550, in <module> main() File "run_mlm.py", line 412, in main in zip(data_args.dataset_name, data_args.dataset_config_name)] File "run_mlm.py", line 411, in <listcomp> logger) for dataset_name, dataset_config_name\ File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 96, in get_tokenized_dataset load_from_cache_file=not data_args.overwrite_cache, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in map for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in <dictcomp> for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1309, in map update_data=update_data, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 204, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1574, in _map_single batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1490, in apply_function_on_filtered_inputs function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 89, in tokenize_function return tokenizer(examples[text_column_name], return_special_tokens_mask=True) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2347, in __call__ **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2532, in batch_encode_plus **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 384, in _batch_encode_plus is_pretokenized=is_split_into_words, pyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617 ` Here please find the minimal code to reproduce the issue @lhoestq note this only happens with MT5TokenizerFast ``` from datasets import load_dataset from transformers import MT5TokenizerFast def get_tokenized_dataset(dataset_name, dataset_config_name, tokenizer): datasets = load_dataset(dataset_name, dataset_config_name, script_version="master") column_names = datasets["train"].column_names text_column_name = "translation" def process_dataset(datasets): def process_function(examples): lang = "fr" return {"src_texts": [example[lang] for example in examples[text_column_name]]} datasets = datasets.map( process_function, batched=True, num_proc=None, remove_columns=column_names, load_from_cache_file=True, ) return datasets datasets = process_dataset(datasets) text_column_name = "src_texts" column_names = ["src_texts"] def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=None, remove_columns=column_names, load_from_cache_file=True ) if __name__ == "__main__": tokenizer_kwargs = { "cache_dir": None, "use_fast": True, "revision": "main", "use_auth_token": None } tokenizer = MT5TokenizerFast.from_pretrained("google/mt5-small", **tokenizer_kwargs) get_tokenized_dataset(dataset_name="opus100", dataset_config_name="en-fr", tokenizer=tokenizer) ~ ```
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https://github.com/huggingface/datasets/issues/2056
issue with opus100/en-fr dataset
as per https://github.com/huggingface/tokenizers/issues/626 this looks like to be the tokenizer bug, I therefore, reported it there https://github.com/huggingface/tokenizers/issues/626 and I am closing this one.
Hi I am running run_mlm.py code of huggingface repo with opus100/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? Thanks a lot @lhoestq for your help in advance. ` thread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/normalizer.rs:382:21 note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace 63%|██████████████████████████████████████████████████████████▊ | 626/1000 [00:27<00:16, 22.69ba/s] Traceback (most recent call last): File "run_mlm.py", line 550, in <module> main() File "run_mlm.py", line 412, in main in zip(data_args.dataset_name, data_args.dataset_config_name)] File "run_mlm.py", line 411, in <listcomp> logger) for dataset_name, dataset_config_name\ File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 96, in get_tokenized_dataset load_from_cache_file=not data_args.overwrite_cache, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in map for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in <dictcomp> for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1309, in map update_data=update_data, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 204, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1574, in _map_single batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1490, in apply_function_on_filtered_inputs function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 89, in tokenize_function return tokenizer(examples[text_column_name], return_special_tokens_mask=True) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2347, in __call__ **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2532, in batch_encode_plus **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 384, in _batch_encode_plus is_pretokenized=is_split_into_words, pyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617 `
23
issue with opus100/en-fr dataset Hi I am running run_mlm.py code of huggingface repo with opus100/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? Thanks a lot @lhoestq for your help in advance. ` thread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/normalizer.rs:382:21 note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace 63%|██████████████████████████████████████████████████████████▊ | 626/1000 [00:27<00:16, 22.69ba/s] Traceback (most recent call last): File "run_mlm.py", line 550, in <module> main() File "run_mlm.py", line 412, in main in zip(data_args.dataset_name, data_args.dataset_config_name)] File "run_mlm.py", line 411, in <listcomp> logger) for dataset_name, dataset_config_name\ File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 96, in get_tokenized_dataset load_from_cache_file=not data_args.overwrite_cache, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in map for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in <dictcomp> for k, dataset in self.items() File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1309, in map update_data=update_data, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 204, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1574, in _map_single batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1490, in apply_function_on_filtered_inputs function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 89, in tokenize_function return tokenizer(examples[text_column_name], return_special_tokens_mask=True) File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2347, in __call__ **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2532, in batch_encode_plus **kwargs, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 384, in _batch_encode_plus is_pretokenized=is_split_into_words, pyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617 ` as per https://github.com/huggingface/tokenizers/issues/626 this looks like to be the tokenizer bug, I therefore, reported it there https://github.com/huggingface/tokenizers/issues/626 and I am closing this one.
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https://github.com/huggingface/datasets/issues/2055
is there a way to override a dataset object saved with save_to_disk?
I tried this way, but when there is a mapping process to the dataset, it again uses a random cache name. atm, I am trying to use the following method by setting an exact cache file, ``` dataset_with_embedding =csv_dataset.map( partial(self.embed, ctx_encoder=ctx_encoder, ctx_tokenizer=self.context_tokenizer), batched=True, batch_size=1, features=new_features, cache_file_name=cache_arrow_path, load_from_cache_file=False ) ``` So here we set a cache_file_name , after this it uses the same file name when saving again and again.
At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file. Is there a way to override such an object?
69
is there a way to override a dataset object saved with save_to_disk? At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file. Is there a way to override such an object? I tried this way, but when there is a mapping process to the dataset, it again uses a random cache name. atm, I am trying to use the following method by setting an exact cache file, ``` dataset_with_embedding =csv_dataset.map( partial(self.embed, ctx_encoder=ctx_encoder, ctx_tokenizer=self.context_tokenizer), batched=True, batch_size=1, features=new_features, cache_file_name=cache_arrow_path, load_from_cache_file=False ) ``` So here we set a cache_file_name , after this it uses the same file name when saving again and again.
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https://github.com/huggingface/datasets/issues/2055
is there a way to override a dataset object saved with save_to_disk?
I'm not sure I understand your issue, can you elaborate ? `cache_file_name` is indeed an argument you can set to specify the cache file that will be used for the processed dataset. By default the file is named with something like `cache-<fingerprint>.arrow` where the fingerprint is a hash.
At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file. Is there a way to override such an object?
48
is there a way to override a dataset object saved with save_to_disk? At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file. Is there a way to override such an object? I'm not sure I understand your issue, can you elaborate ? `cache_file_name` is indeed an argument you can set to specify the cache file that will be used for the processed dataset. By default the file is named with something like `cache-<fingerprint>.arrow` where the fingerprint is a hash.
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https://github.com/huggingface/datasets/issues/2055
is there a way to override a dataset object saved with save_to_disk?
Let's say I am updating a set of embedding in a dataset that is around 40GB inside a training loop every 500 steps (Ex: calculating the embeddings in updated ctx_encoder in RAG and saving it to the passage path). So when we use **dataset_object.save_to_disk('passage_path_directory')** it will save the new dataset object every time with a random file name, especially when we do some transformations to dataset objects such as map or shards. This way, we keep collecting unwanted files that will eventually eat up all the disk space. But if we can save the dataset object every time by a single name like **data_shard_1.arrow**, it will automatically remove the previous file and save the new one in the same directory. I found the above-mentioned code snippet useful to complete this task. Is this clear?
At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file. Is there a way to override such an object?
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is there a way to override a dataset object saved with save_to_disk? At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file. Is there a way to override such an object? Let's say I am updating a set of embedding in a dataset that is around 40GB inside a training loop every 500 steps (Ex: calculating the embeddings in updated ctx_encoder in RAG and saving it to the passage path). So when we use **dataset_object.save_to_disk('passage_path_directory')** it will save the new dataset object every time with a random file name, especially when we do some transformations to dataset objects such as map or shards. This way, we keep collecting unwanted files that will eventually eat up all the disk space. But if we can save the dataset object every time by a single name like **data_shard_1.arrow**, it will automatically remove the previous file and save the new one in the same directory. I found the above-mentioned code snippet useful to complete this task. Is this clear?
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https://github.com/huggingface/datasets/issues/2054
Could not find file for ZEST dataset
This has been fixed in #2057 by @matt-peters (thanks again !) The fix is available on the master branch and we'll do a new release very soon :)
I am trying to use zest dataset from Allen AI using below code in colab, ``` !pip install -q datasets from datasets import load_dataset dataset = load_dataset("zest") ``` I am getting the following error, ``` Using custom data configuration default Downloading and preparing dataset zest/default (download: 5.53 MiB, generated: 19.96 MiB, post-processed: Unknown size, total: 25.48 MiB) to /root/.cache/huggingface/datasets/zest/default/0.0.0/1f7a230fbfc964d979bbca0f0130fbab3259fce547ee758ad8aa4f9c9bec6cca... --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-6-18dbbc1a4b8a> in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("zest") 9 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) 612 ) 613 elif response is not None and response.status_code == 404: --> 614 raise FileNotFoundError("Couldn't find file at {}".format(url)) 615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 616 raise ConnectionError("Couldn't reach {}".format(url)) FileNotFoundError: Couldn't find file at https://ai2-datasets.s3-us-west-2.amazonaws.com/zest/zest.zip ```
28
Could not find file for ZEST dataset I am trying to use zest dataset from Allen AI using below code in colab, ``` !pip install -q datasets from datasets import load_dataset dataset = load_dataset("zest") ``` I am getting the following error, ``` Using custom data configuration default Downloading and preparing dataset zest/default (download: 5.53 MiB, generated: 19.96 MiB, post-processed: Unknown size, total: 25.48 MiB) to /root/.cache/huggingface/datasets/zest/default/0.0.0/1f7a230fbfc964d979bbca0f0130fbab3259fce547ee758ad8aa4f9c9bec6cca... --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-6-18dbbc1a4b8a> in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("zest") 9 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) 612 ) 613 elif response is not None and response.status_code == 404: --> 614 raise FileNotFoundError("Couldn't find file at {}".format(url)) 615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 616 raise ConnectionError("Couldn't reach {}".format(url)) FileNotFoundError: Couldn't find file at https://ai2-datasets.s3-us-west-2.amazonaws.com/zest/zest.zip ``` This has been fixed in #2057 by @matt-peters (thanks again !) The fix is available on the master branch and we'll do a new release very soon :)
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https://github.com/huggingface/datasets/issues/2052
Timit_asr dataset repeats examples
Hi, this was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: ```bash pip install git+https://github.com/huggingface/datasets ```
Summary When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same Steps to reproduce As an example, on this code there is the text from the training part: Code snippet: ``` from datasets import load_dataset, load_metric timit = load_dataset("timit_asr") timit['train']['text'] #['Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', ``` The same behavior happens for other columns Expected behavior: Different info on the actual timit_asr dataset Actual behavior: When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same. I've checked datasets 1.3 and the rows are different Debug info Streamlit version: (get it with $ streamlit version) Python version: Python 3.6.12 Using Conda? PipEnv? PyEnv? Pex? Using pip OS version: Centos-release-7-9.2009.1.el7.centos.x86_64 Additional information You can check the same behavior on https://huggingface.co/datasets/viewer/?dataset=timit_asr
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Timit_asr dataset repeats examples Summary When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same Steps to reproduce As an example, on this code there is the text from the training part: Code snippet: ``` from datasets import load_dataset, load_metric timit = load_dataset("timit_asr") timit['train']['text'] #['Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', # 'Would such an act of refusal be useful?', ``` The same behavior happens for other columns Expected behavior: Different info on the actual timit_asr dataset Actual behavior: When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same. I've checked datasets 1.3 and the rows are different Debug info Streamlit version: (get it with $ streamlit version) Python version: Python 3.6.12 Using Conda? PipEnv? PyEnv? Pex? Using pip OS version: Centos-release-7-9.2009.1.el7.centos.x86_64 Additional information You can check the same behavior on https://huggingface.co/datasets/viewer/?dataset=timit_asr Hi, this was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: ```bash pip install git+https://github.com/huggingface/datasets ```
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https://github.com/huggingface/datasets/issues/2050
Build custom dataset to fine-tune Wav2Vec2
Sure you can use the json loader ```python data_files = {"train": "path/to/your/train_data.json", "test": "path/to/your/test_data.json"} train_dataset = load_dataset("json", data_files=data_files, split="train") test_dataset = load_dataset("json", data_files=data_files, split="test") ``` You just need to make sure that the data contain the paths to the audio files. If not, feel free to use `.map()` to add them.
Thank you for your recent tutorial on how to finetune Wav2Vec2 on a custom dataset. The example you gave here (https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) was on the CommonVoice dataset. However, what if I want to load my own dataset? I have a manifest (transcript and their audio files) in a JSON file.
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Build custom dataset to fine-tune Wav2Vec2 Thank you for your recent tutorial on how to finetune Wav2Vec2 on a custom dataset. The example you gave here (https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) was on the CommonVoice dataset. However, what if I want to load my own dataset? I have a manifest (transcript and their audio files) in a JSON file. Sure you can use the json loader ```python data_files = {"train": "path/to/your/train_data.json", "test": "path/to/your/test_data.json"} train_dataset = load_dataset("json", data_files=data_files, split="train") test_dataset = load_dataset("json", data_files=data_files, split="test") ``` You just need to make sure that the data contain the paths to the audio files. If not, feel free to use `.map()` to add them.
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https://github.com/huggingface/datasets/issues/2046
add_faisis_index gets very slow when doing it interatively
I think faiss automatically sets the number of threads to use to build the index. Can you check how many CPU cores are being used when you build the index in `use_own_knowleldge_dataset` as compared to this script ? Are there other programs running (maybe for rank>0) ?
As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster? @lhoestq ``` def training_step(self, batch, batch_idx) -> Dict: if (not batch_idx==0) and (batch_idx%5==0): print("******************************************************") ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff list_of_gpus = ['cuda:2','cuda:3'] c_dir='/custom/cache/dir' kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir) print(kb_dataset) n=len(list_of_gpus) #nunber of dedicated GPUs kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)] #kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir') print(self.trainer.global_rank) dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank]) output = [None for _ in list_of_gpus] #self.trainer.accelerator_connector.accelerator.barrier("embedding_process") dist.all_gather_object(output, dataset_shards) #This creation and re-initlaization of the new index if (self.trainer.global_rank==0): #saving will be done in the main process combined_dataset = concatenate_datasets(output) passages_path =self.config.passages_path logger.info("saving the dataset with ") #combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage') combined_dataset.save_to_disk(passages_path) logger.info("Add faiss index to the dataset that consist of embeddings") embedding_dataset=combined_dataset index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT) embedding_dataset.add_faiss_index("embeddings", custom_index=index) embedding_dataset.get_index("embeddings").save(self.config.index_path)
47
add_faisis_index gets very slow when doing it interatively As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster? @lhoestq ``` def training_step(self, batch, batch_idx) -> Dict: if (not batch_idx==0) and (batch_idx%5==0): print("******************************************************") ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff list_of_gpus = ['cuda:2','cuda:3'] c_dir='/custom/cache/dir' kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir) print(kb_dataset) n=len(list_of_gpus) #nunber of dedicated GPUs kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)] #kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir') print(self.trainer.global_rank) dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank]) output = [None for _ in list_of_gpus] #self.trainer.accelerator_connector.accelerator.barrier("embedding_process") dist.all_gather_object(output, dataset_shards) #This creation and re-initlaization of the new index if (self.trainer.global_rank==0): #saving will be done in the main process combined_dataset = concatenate_datasets(output) passages_path =self.config.passages_path logger.info("saving the dataset with ") #combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage') combined_dataset.save_to_disk(passages_path) logger.info("Add faiss index to the dataset that consist of embeddings") embedding_dataset=combined_dataset index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT) embedding_dataset.add_faiss_index("embeddings", custom_index=index) embedding_dataset.get_index("embeddings").save(self.config.index_path) I think faiss automatically sets the number of threads to use to build the index. Can you check how many CPU cores are being used when you build the index in `use_own_knowleldge_dataset` as compared to this script ? Are there other programs running (maybe for rank>0) ?
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https://github.com/huggingface/datasets/issues/2046
add_faisis_index gets very slow when doing it interatively
Hi, I am running the add_faiss_index during the training process of the RAG from the master process (rank 0). But at the exact moment, I do not run any other process since I do it in every 5000 training steps. I think what you say is correct. It depends on the number of CPU cores. I did an experiment to compare the time taken to finish the add_faiss_index process on use_own_knowleldge_dataset.py vs the training loop thing. The training loop thing takes 40 mins more. It might be natural right? at the moment it uses around 40 cores of a 96 core machine (I am fine-tuning the entire process).
As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster? @lhoestq ``` def training_step(self, batch, batch_idx) -> Dict: if (not batch_idx==0) and (batch_idx%5==0): print("******************************************************") ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff list_of_gpus = ['cuda:2','cuda:3'] c_dir='/custom/cache/dir' kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir) print(kb_dataset) n=len(list_of_gpus) #nunber of dedicated GPUs kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)] #kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir') print(self.trainer.global_rank) dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank]) output = [None for _ in list_of_gpus] #self.trainer.accelerator_connector.accelerator.barrier("embedding_process") dist.all_gather_object(output, dataset_shards) #This creation and re-initlaization of the new index if (self.trainer.global_rank==0): #saving will be done in the main process combined_dataset = concatenate_datasets(output) passages_path =self.config.passages_path logger.info("saving the dataset with ") #combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage') combined_dataset.save_to_disk(passages_path) logger.info("Add faiss index to the dataset that consist of embeddings") embedding_dataset=combined_dataset index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT) embedding_dataset.add_faiss_index("embeddings", custom_index=index) embedding_dataset.get_index("embeddings").save(self.config.index_path)
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add_faisis_index gets very slow when doing it interatively As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster? @lhoestq ``` def training_step(self, batch, batch_idx) -> Dict: if (not batch_idx==0) and (batch_idx%5==0): print("******************************************************") ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff list_of_gpus = ['cuda:2','cuda:3'] c_dir='/custom/cache/dir' kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir) print(kb_dataset) n=len(list_of_gpus) #nunber of dedicated GPUs kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)] #kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir') print(self.trainer.global_rank) dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank]) output = [None for _ in list_of_gpus] #self.trainer.accelerator_connector.accelerator.barrier("embedding_process") dist.all_gather_object(output, dataset_shards) #This creation and re-initlaization of the new index if (self.trainer.global_rank==0): #saving will be done in the main process combined_dataset = concatenate_datasets(output) passages_path =self.config.passages_path logger.info("saving the dataset with ") #combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage') combined_dataset.save_to_disk(passages_path) logger.info("Add faiss index to the dataset that consist of embeddings") embedding_dataset=combined_dataset index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT) embedding_dataset.add_faiss_index("embeddings", custom_index=index) embedding_dataset.get_index("embeddings").save(self.config.index_path) Hi, I am running the add_faiss_index during the training process of the RAG from the master process (rank 0). But at the exact moment, I do not run any other process since I do it in every 5000 training steps. I think what you say is correct. It depends on the number of CPU cores. I did an experiment to compare the time taken to finish the add_faiss_index process on use_own_knowleldge_dataset.py vs the training loop thing. The training loop thing takes 40 mins more. It might be natural right? at the moment it uses around 40 cores of a 96 core machine (I am fine-tuning the entire process).
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https://github.com/huggingface/datasets/issues/2046
add_faisis_index gets very slow when doing it interatively
Can you try to set the number of threads manually ? If you set the same number of threads for both the `use_own_knowledge_dataset.py` and RAG training, it should take the same amount of time. You can see how to set the number of thread in the faiss wiki: https://github.com/facebookresearch/faiss/wiki/Threads-and-asynchronous-calls
As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster? @lhoestq ``` def training_step(self, batch, batch_idx) -> Dict: if (not batch_idx==0) and (batch_idx%5==0): print("******************************************************") ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff list_of_gpus = ['cuda:2','cuda:3'] c_dir='/custom/cache/dir' kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir) print(kb_dataset) n=len(list_of_gpus) #nunber of dedicated GPUs kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)] #kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir') print(self.trainer.global_rank) dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank]) output = [None for _ in list_of_gpus] #self.trainer.accelerator_connector.accelerator.barrier("embedding_process") dist.all_gather_object(output, dataset_shards) #This creation and re-initlaization of the new index if (self.trainer.global_rank==0): #saving will be done in the main process combined_dataset = concatenate_datasets(output) passages_path =self.config.passages_path logger.info("saving the dataset with ") #combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage') combined_dataset.save_to_disk(passages_path) logger.info("Add faiss index to the dataset that consist of embeddings") embedding_dataset=combined_dataset index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT) embedding_dataset.add_faiss_index("embeddings", custom_index=index) embedding_dataset.get_index("embeddings").save(self.config.index_path)
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add_faisis_index gets very slow when doing it interatively As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster? @lhoestq ``` def training_step(self, batch, batch_idx) -> Dict: if (not batch_idx==0) and (batch_idx%5==0): print("******************************************************") ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff list_of_gpus = ['cuda:2','cuda:3'] c_dir='/custom/cache/dir' kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir) print(kb_dataset) n=len(list_of_gpus) #nunber of dedicated GPUs kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)] #kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir') print(self.trainer.global_rank) dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank]) output = [None for _ in list_of_gpus] #self.trainer.accelerator_connector.accelerator.barrier("embedding_process") dist.all_gather_object(output, dataset_shards) #This creation and re-initlaization of the new index if (self.trainer.global_rank==0): #saving will be done in the main process combined_dataset = concatenate_datasets(output) passages_path =self.config.passages_path logger.info("saving the dataset with ") #combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage') combined_dataset.save_to_disk(passages_path) logger.info("Add faiss index to the dataset that consist of embeddings") embedding_dataset=combined_dataset index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT) embedding_dataset.add_faiss_index("embeddings", custom_index=index) embedding_dataset.get_index("embeddings").save(self.config.index_path) Can you try to set the number of threads manually ? If you set the same number of threads for both the `use_own_knowledge_dataset.py` and RAG training, it should take the same amount of time. You can see how to set the number of thread in the faiss wiki: https://github.com/facebookresearch/faiss/wiki/Threads-and-asynchronous-calls
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