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https://github.com/huggingface/datasets/issues/1785
I was able to patch the builder code in my notebook before the load data call and it works. ``` import datasets datasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True ```
Not enough disk space (Needed: Unknown size) when caching on a cluster
I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk. The exact error thrown: ```bash >>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path") OSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size) ``` [`utils.has_sufficient_disk_space`](https://github.com/huggingface/datasets/blob/8a03ab7d123a76ee744304f21ce868c75f411214/src/datasets/utils/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(".").free` can't compute on the cluster's shared disk). This is exactly where the error gets thrown: https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L502 ```python if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root): raise IOError( "Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})".format( utils.size_str(self.info.size_in_bytes or 0), utils.size_str(self.info.download_size or 0), utils.size_str(self.info.dataset_size or 0), utils.size_str(self.info.post_processing_size or 0), ) ) ``` What would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. Would it be possible to pass a flag to skip this check on disk space?
29
Not enough disk space (Needed: Unknown size) when caching on a cluster I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk. The exact error thrown: ```bash >>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path") OSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size) ``` [`utils.has_sufficient_disk_space`](https://github.com/huggingface/datasets/blob/8a03ab7d123a76ee744304f21ce868c75f411214/src/datasets/utils/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(".").free` can't compute on the cluster's shared disk). This is exactly where the error gets thrown: https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L502 ```python if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root): raise IOError( "Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})".format( utils.size_str(self.info.size_in_bytes or 0), utils.size_str(self.info.download_size or 0), utils.size_str(self.info.dataset_size or 0), utils.size_str(self.info.post_processing_size or 0), ) ) ``` What would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. Would it be possible to pass a flag to skip this check on disk space? I was able to patch the builder code in my notebook before the load data call and it works. ``` import datasets datasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True ```
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https://github.com/huggingface/datasets/issues/1784
Hi ! The `json` dataset script does support this format. For example loading a dataset with this format works on my side: ```json {"key1":11, "key2":12, "key3":13} {"key1":21, "key2":22, "key3":23} ``` Can you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?
JSONDecodeError on JSON with multiple lines
Hello :), I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported: ```json {"key1":11, "key2":12, "key3":13} {"key1":21, "key2":22, "key3":23} ``` But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported. When I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either. Please let me know :) Thanks, Gunjan
49
JSONDecodeError on JSON with multiple lines Hello :), I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported: ```json {"key1":11, "key2":12, "key3":13} {"key1":21, "key2":22, "key3":23} ``` But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported. When I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either. Please let me know :) Thanks, Gunjan Hi ! The `json` dataset script does support this format. For example loading a dataset with this format works on my side: ```json {"key1":11, "key2":12, "key3":13} {"key1":21, "key2":22, "key3":23} ``` Can you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?
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https://github.com/huggingface/datasets/issues/1784
Hi Quentin! I apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it. I repeated it with `datasets 1.2.1` and `pyarrow 2.0.0` and it worked. Closing this issue. Again, sorry for the bother. Thanks, Gunjan
JSONDecodeError on JSON with multiple lines
Hello :), I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported: ```json {"key1":11, "key2":12, "key3":13} {"key1":21, "key2":22, "key3":23} ``` But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported. When I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either. Please let me know :) Thanks, Gunjan
56
JSONDecodeError on JSON with multiple lines Hello :), I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported: ```json {"key1":11, "key2":12, "key3":13} {"key1":21, "key2":22, "key3":23} ``` But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported. When I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either. Please let me know :) Thanks, Gunjan Hi Quentin! I apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it. I repeated it with `datasets 1.2.1` and `pyarrow 2.0.0` and it worked. Closing this issue. Again, sorry for the bother. Thanks, Gunjan
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https://github.com/huggingface/datasets/issues/1783
Hi @ChewKokWah, We're working on it! In the meantime, you can still find the dataset explorer at the following URL: https://huggingface.co/datasets/viewer/
Dataset Examples Explorer
In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version. Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation.
21
Dataset Examples Explorer In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version. Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation. Hi @ChewKokWah, We're working on it! In the meantime, you can still find the dataset explorer at the following URL: https://huggingface.co/datasets/viewer/
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https://github.com/huggingface/datasets/issues/1783
Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. Hope similar feature can be retain in the future version.
Dataset Examples Explorer
In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version. Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation.
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Dataset Examples Explorer In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version. Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation. Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. Hope similar feature can be retain in the future version.
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https://github.com/huggingface/datasets/issues/1781
Hi ! I'm not able to reproduce the issue. Can you try restarting your runtime ? The PyExtensionType is available in pyarrow starting 0.17.1 iirc. If restarting your runtime doesn't fix this, can you try updating pyarrow ? ``` pip install pyarrow --upgrade ```
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)
44
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png) Hi ! I'm not able to reproduce the issue. Can you try restarting your runtime ? The PyExtensionType is available in pyarrow starting 0.17.1 iirc. If restarting your runtime doesn't fix this, can you try updating pyarrow ? ``` pip install pyarrow --upgrade ```
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https://github.com/huggingface/datasets/issues/1781
Yes indeed. Also it looks like Pyarrow 3.0.0 got released on pypi 10 hours ago. This might be related to the bug, I'll investigate EDIT: looks like the 3.0.0 release doesn't have unexpected breaking changes for us, so I don't think the issue comes from that
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)
46
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png) Yes indeed. Also it looks like Pyarrow 3.0.0 got released on pypi 10 hours ago. This might be related to the bug, I'll investigate EDIT: looks like the 3.0.0 release doesn't have unexpected breaking changes for us, so I don't think the issue comes from that
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https://github.com/huggingface/datasets/issues/1781
Installing datasets installs pyarrow>=0.17.1 so in theory it doesn't matter which version of pyarrow colab has by default (which is currently pyarrow 0.14.1). Also now the colab runtime refresh the pyarrow version automatically after the update from pip (previously you needed to restart your runtime). I guess what happened is that Colab didn't refresh pyarrow for some reason, and the AttributeError was raised *before* the pyarrow version check from `datasets` at https://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)
72
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png) Installing datasets installs pyarrow>=0.17.1 so in theory it doesn't matter which version of pyarrow colab has by default (which is currently pyarrow 0.14.1). Also now the colab runtime refresh the pyarrow version automatically after the update from pip (previously you needed to restart your runtime). I guess what happened is that Colab didn't refresh pyarrow for some reason, and the AttributeError was raised *before* the pyarrow version check from `datasets` at https://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60
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https://github.com/huggingface/datasets/issues/1781
Yes colab doesn’t reload preloaded library unless you restart the instance. Maybe we should move the check on top of the init
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)
22
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png) Yes colab doesn’t reload preloaded library unless you restart the instance. Maybe we should move the check on top of the init
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https://github.com/huggingface/datasets/issues/1776
We are very actively working on this. How does your dataset look like in practice (number/size/type of files)?
[Question & Bug Report] Can we preprocess a dataset on the fly?
I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532
18
[Question & Bug Report] Can we preprocess a dataset on the fly? I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532 We are very actively working on this. How does your dataset look like in practice (number/size/type of files)?
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https://github.com/huggingface/datasets/issues/1776
It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
[Question & Bug Report] Can we preprocess a dataset on the fly?
I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532
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[Question & Bug Report] Can we preprocess a dataset on the fly? I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532 It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
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https://github.com/huggingface/datasets/issues/1776
Indeed I will submit a PR in a fez days to enable processing on-the-fly :) This can be useful in language modeling for tokenization, padding etc.
[Question & Bug Report] Can we preprocess a dataset on the fly?
I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532
26
[Question & Bug Report] Can we preprocess a dataset on the fly? I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532 Indeed I will submit a PR in a fez days to enable processing on-the-fly :) This can be useful in language modeling for tokenization, padding etc.
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https://github.com/huggingface/datasets/issues/1776
Hi @acul3, Please look at the discussion on a related Issue #1825. I think using `set_transform` after building from source should do.
[Question & Bug Report] Can we preprocess a dataset on the fly?
I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532
22
[Question & Bug Report] Can we preprocess a dataset on the fly? I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532 Hi @acul3, Please look at the discussion on a related Issue #1825. I think using `set_transform` after building from source should do.
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https://github.com/huggingface/datasets/issues/1775
It seems that selecting a subset of colums directly from the dataset, i.e., dataset["column"], is slow.
Efficient ways to iterate the dataset
For a large dataset that does not fits the memory, how can I select only a subset of features from each example? If I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this? Thanks
16
Efficient ways to iterate the dataset For a large dataset that does not fits the memory, how can I select only a subset of features from each example? If I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this? Thanks It seems that selecting a subset of colums directly from the dataset, i.e., dataset["column"], is slow.
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https://github.com/huggingface/datasets/issues/1774
Hi ! Thanks for reporting. I am working on changes in the way data are sliced from arrow. I can probably fix your issue with the changes I'm doing. If you have some code to reproduce the issue it would be nice so I can make sure that this case will be supported :) I'll make a PR in a few days
is it possible to make slice to be more compatible like python list and numpy?
Hi, see below error: ``` AssertionError: Requested slice [:10000000000000000] incompatible with 20 examples. ```
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is it possible to make slice to be more compatible like python list and numpy? Hi, see below error: ``` AssertionError: Requested slice [:10000000000000000] incompatible with 20 examples. ``` Hi ! Thanks for reporting. I am working on changes in the way data are sliced from arrow. I can probably fix your issue with the changes I'm doing. If you have some code to reproduce the issue it would be nice so I can make sure that this case will be supported :) I'll make a PR in a few days
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https://github.com/huggingface/datasets/issues/1773
Looks like an issue with your csv file. Did you use the right delimiter ? Apparently at line 37 the CSV reader from pandas reads 2 fields instead of 1.
bug in loading datasets
Hi, I need to load a dataset, I use these commands: ``` from datasets import load_dataset dataset = load_dataset('csv', data_files={'train': 'sick/train.csv', 'test': 'sick/test.csv', 'validation': 'sick/validation.csv'}) print(dataset['validation']) ``` the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq ``` Using custom data configuration default Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Traceback (most recent call last): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir yield tmp_dir File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables for batch_idx, df in enumerate(csv_file_reader): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__ return self.get_chunk() File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk return self.read(nrows=size) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read index, columns, col_dict = self._engine.read(nrows) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read data = self._reader.read(nrows) File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "write_sick.py", line 19, in <module> 'validation': 'sick/validation.csv'}) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare self._save_info() File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__ self.gen.throw(type, value, traceback) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir shutil.rmtree(tmp_dir) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree onerror(os.rmdir, path, sys.exc_info()) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree os.rmdir(path) OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete' ```
30
bug in loading datasets Hi, I need to load a dataset, I use these commands: ``` from datasets import load_dataset dataset = load_dataset('csv', data_files={'train': 'sick/train.csv', 'test': 'sick/test.csv', 'validation': 'sick/validation.csv'}) print(dataset['validation']) ``` the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq ``` Using custom data configuration default Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Traceback (most recent call last): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir yield tmp_dir File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables for batch_idx, df in enumerate(csv_file_reader): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__ return self.get_chunk() File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk return self.read(nrows=size) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read index, columns, col_dict = self._engine.read(nrows) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read data = self._reader.read(nrows) File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "write_sick.py", line 19, in <module> 'validation': 'sick/validation.csv'}) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare self._save_info() File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__ self.gen.throw(type, value, traceback) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir shutil.rmtree(tmp_dir) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree onerror(os.rmdir, path, sys.exc_info()) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree os.rmdir(path) OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete' ``` Looks like an issue with your csv file. Did you use the right delimiter ? Apparently at line 37 the CSV reader from pandas reads 2 fields instead of 1.
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https://github.com/huggingface/datasets/issues/1773
Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do ```python from datasets import load_dataset dataset = load_dataset('csv', data_files=data_files, sep="\t") ``` for example to use a tab separator. You can see the full list of arguments here: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/csv/csv.py (I've not found the list in the documentation though, we definitely must add them !)
bug in loading datasets
Hi, I need to load a dataset, I use these commands: ``` from datasets import load_dataset dataset = load_dataset('csv', data_files={'train': 'sick/train.csv', 'test': 'sick/test.csv', 'validation': 'sick/validation.csv'}) print(dataset['validation']) ``` the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq ``` Using custom data configuration default Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Traceback (most recent call last): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir yield tmp_dir File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables for batch_idx, df in enumerate(csv_file_reader): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__ return self.get_chunk() File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk return self.read(nrows=size) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read index, columns, col_dict = self._engine.read(nrows) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read data = self._reader.read(nrows) File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "write_sick.py", line 19, in <module> 'validation': 'sick/validation.csv'}) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare self._save_info() File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__ self.gen.throw(type, value, traceback) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir shutil.rmtree(tmp_dir) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree onerror(os.rmdir, path, sys.exc_info()) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree os.rmdir(path) OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete' ```
64
bug in loading datasets Hi, I need to load a dataset, I use these commands: ``` from datasets import load_dataset dataset = load_dataset('csv', data_files={'train': 'sick/train.csv', 'test': 'sick/test.csv', 'validation': 'sick/validation.csv'}) print(dataset['validation']) ``` the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq ``` Using custom data configuration default Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Traceback (most recent call last): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir yield tmp_dir File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables for batch_idx, df in enumerate(csv_file_reader): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__ return self.get_chunk() File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk return self.read(nrows=size) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read index, columns, col_dict = self._engine.read(nrows) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read data = self._reader.read(nrows) File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "write_sick.py", line 19, in <module> 'validation': 'sick/validation.csv'}) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare self._save_info() File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__ self.gen.throw(type, value, traceback) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir shutil.rmtree(tmp_dir) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree onerror(os.rmdir, path, sys.exc_info()) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree os.rmdir(path) OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete' ``` Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do ```python from datasets import load_dataset dataset = load_dataset('csv', data_files=data_files, sep="\t") ``` for example to use a tab separator. You can see the full list of arguments here: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/csv/csv.py (I've not found the list in the documentation though, we definitely must add them !)
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https://github.com/huggingface/datasets/issues/1771
Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library. See PR #1726 We'll do a new release soon :)
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py
Hi, When I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset? ``` Traceback (most recent call last): File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 343, in cached_path max_retries=download_config.max_retries, File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py ```
36
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py Hi, When I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset? ``` Traceback (most recent call last): File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 343, in cached_path max_retries=download_config.max_retries, File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py ``` Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library. See PR #1726 We'll do a new release soon :)
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https://github.com/huggingface/datasets/issues/1770
Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :) For now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188
how can I combine 2 dataset with different/same features?
to combine 2 dataset by one-one map like ds = zip(ds1, ds2): ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} or different feature: ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}
45
how can I combine 2 dataset with different/same features? to combine 2 dataset by one-one map like ds = zip(ds1, ds2): ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} or different feature: ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :) For now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188
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https://github.com/huggingface/datasets/issues/1770
Good to hear. Currently I did not use map , just fetch src and tgt from the 2 dataset and merge them. It will be a release if you can deal with it at the backend. Thanks.
how can I combine 2 dataset with different/same features?
to combine 2 dataset by one-one map like ds = zip(ds1, ds2): ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} or different feature: ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}
37
how can I combine 2 dataset with different/same features? to combine 2 dataset by one-one map like ds = zip(ds1, ds2): ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} or different feature: ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} Good to hear. Currently I did not use map , just fetch src and tgt from the 2 dataset and merge them. It will be a release if you can deal with it at the backend. Thanks.
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https://github.com/huggingface/datasets/issues/1770
Hi! You can rename the columns and concatenate the datasets along `axis=1` to get the desired result as follows: ```python ds1 = ds1.rename_column("text", "src") ds2 = ds2.rename_column("text", "tgt") ds = datasets.concatenate_datasets(["ds1", "ds2"], axis=1) ```
how can I combine 2 dataset with different/same features?
to combine 2 dataset by one-one map like ds = zip(ds1, ds2): ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} or different feature: ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}
34
how can I combine 2 dataset with different/same features? to combine 2 dataset by one-one map like ds = zip(ds1, ds2): ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} or different feature: ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} Hi! You can rename the columns and concatenate the datasets along `axis=1` to get the desired result as follows: ```python ds1 = ds1.rename_column("text", "src") ds2 = ds2.rename_column("text", "tgt") ds = datasets.concatenate_datasets(["ds1", "ds2"], axis=1) ```
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https://github.com/huggingface/datasets/issues/1769
Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine. The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py Script args: ``` --model_name_or_path ../../../model/chinese-roberta-wwm-ext --train_file /nfs/volume-377-2/bert/data/test/train.txt --output_dir test --do_train --per_device_train_batch_size 2 --gradient_accumulation_steps 2 --learning_rate 1e-4 --max_steps 1000 --warmup_steps 10 --save_steps 1000 --save_total_limit 1 --seed 23333 --max_seq_length 512 --preprocessing_num_workers 2 --cache_dir /nfs/volume-377-2/bert/data/test/cache ``` Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily. Full Traceback: ``` Traceback (most recent call last): File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module> main() File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks put(task) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ```
21
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine. The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py Script args: ``` --model_name_or_path ../../../model/chinese-roberta-wwm-ext --train_file /nfs/volume-377-2/bert/data/test/train.txt --output_dir test --do_train --per_device_train_batch_size 2 --gradient_accumulation_steps 2 --learning_rate 1e-4 --max_steps 1000 --warmup_steps 10 --save_steps 1000 --save_total_limit 1 --seed 23333 --max_seq_length 512 --preprocessing_num_workers 2 --cache_dir /nfs/volume-377-2/bert/data/test/cache ``` Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily. Full Traceback: ``` Traceback (most recent call last): File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module> main() File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks put(task) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ``` Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?
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https://github.com/huggingface/datasets/issues/1769
> Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ? python==3.6.10 datasets==1.2.1 dill==0.3.2 pickle.format_version==4.0
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine. The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py Script args: ``` --model_name_or_path ../../../model/chinese-roberta-wwm-ext --train_file /nfs/volume-377-2/bert/data/test/train.txt --output_dir test --do_train --per_device_train_batch_size 2 --gradient_accumulation_steps 2 --learning_rate 1e-4 --max_steps 1000 --warmup_steps 10 --save_steps 1000 --save_total_limit 1 --seed 23333 --max_seq_length 512 --preprocessing_num_workers 2 --cache_dir /nfs/volume-377-2/bert/data/test/cache ``` Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily. Full Traceback: ``` Traceback (most recent call last): File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module> main() File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks put(task) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ```
26
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine. The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py Script args: ``` --model_name_or_path ../../../model/chinese-roberta-wwm-ext --train_file /nfs/volume-377-2/bert/data/test/train.txt --output_dir test --do_train --per_device_train_batch_size 2 --gradient_accumulation_steps 2 --learning_rate 1e-4 --max_steps 1000 --warmup_steps 10 --save_steps 1000 --save_total_limit 1 --seed 23333 --max_seq_length 512 --preprocessing_num_workers 2 --cache_dir /nfs/volume-377-2/bert/data/test/cache ``` Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily. Full Traceback: ``` Traceback (most recent call last): File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module> main() File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks put(task) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ``` > Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ? python==3.6.10 datasets==1.2.1 dill==0.3.2 pickle.format_version==4.0
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https://github.com/huggingface/datasets/issues/1769
Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`. However objects like `typing.Union[str, NoneType]` are not picklable in python <3.7. Can you try to update your python version to python>=3.7 ?
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine. The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py Script args: ``` --model_name_or_path ../../../model/chinese-roberta-wwm-ext --train_file /nfs/volume-377-2/bert/data/test/train.txt --output_dir test --do_train --per_device_train_batch_size 2 --gradient_accumulation_steps 2 --learning_rate 1e-4 --max_steps 1000 --warmup_steps 10 --save_steps 1000 --save_total_limit 1 --seed 23333 --max_seq_length 512 --preprocessing_num_workers 2 --cache_dir /nfs/volume-377-2/bert/data/test/cache ``` Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily. Full Traceback: ``` Traceback (most recent call last): File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module> main() File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks put(task) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ```
41
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine. The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py Script args: ``` --model_name_or_path ../../../model/chinese-roberta-wwm-ext --train_file /nfs/volume-377-2/bert/data/test/train.txt --output_dir test --do_train --per_device_train_batch_size 2 --gradient_accumulation_steps 2 --learning_rate 1e-4 --max_steps 1000 --warmup_steps 10 --save_steps 1000 --save_total_limit 1 --seed 23333 --max_seq_length 512 --preprocessing_num_workers 2 --cache_dir /nfs/volume-377-2/bert/data/test/cache ``` Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily. Full Traceback: ``` Traceback (most recent call last): File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module> main() File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks put(task) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ``` Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`. However objects like `typing.Union[str, NoneType]` are not picklable in python <3.7. Can you try to update your python version to python>=3.7 ?
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https://github.com/huggingface/datasets/issues/1766
Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace "default_experiment" with the experiment id that you provide in the arrow filename. Also when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate.
Issues when run two programs compute the same metrics
I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches: ``` File "train_matching_min.py", line 160, in <module>ch_9_label avg_loss = valid(epoch, args.batch, args.validation, args.with_label) File "train_matching_min.py", line 93, in valid bleu += eval.compute() File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute return self.metric.compute()['score'] File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute self._finalize() File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths])) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files pa_table = self._read_files(files) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename pa_table = f.read_all() File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396 ```
69
Issues when run two programs compute the same metrics I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches: ``` File "train_matching_min.py", line 160, in <module>ch_9_label avg_loss = valid(epoch, args.batch, args.validation, args.with_label) File "train_matching_min.py", line 93, in valid bleu += eval.compute() File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute return self.metric.compute()['score'] File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute self._finalize() File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths])) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files pa_table = self._read_files(files) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename pa_table = f.read_all() File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396 ``` Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace "default_experiment" with the experiment id that you provide in the arrow filename. Also when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate.
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https://github.com/huggingface/datasets/issues/1766
Thank you for your response. I fixed the issue by set "keep_in_memory=True" when load_metric. I cannot share the entire source code but below is the wrapper I wrote: ```python class Evaluation: def __init__(self, metric='sacrebleu'): # self.metric = load_metric(metric, keep_in_memory=True) self.metric = load_metric(metric) def add(self, predictions, references): self.metric.add_batch(predictions=predictions, references=references) def compute(self): return self.metric.compute()['score'] ``` Then call the given wrapper as follows: ```python eval = Evaluation(metric='sacrebleu') for query, candidates, labels in tqdm(dataset): predictions = net.generate(query) references = [[s] for s in labels] eval.add(predictions, references) if n % 100 == 0: bleu += eval.compute() eval = Evaluation(metric='sacrebleu')
Issues when run two programs compute the same metrics
I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches: ``` File "train_matching_min.py", line 160, in <module>ch_9_label avg_loss = valid(epoch, args.batch, args.validation, args.with_label) File "train_matching_min.py", line 93, in valid bleu += eval.compute() File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute return self.metric.compute()['score'] File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute self._finalize() File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths])) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files pa_table = self._read_files(files) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename pa_table = f.read_all() File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396 ```
94
Issues when run two programs compute the same metrics I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches: ``` File "train_matching_min.py", line 160, in <module>ch_9_label avg_loss = valid(epoch, args.batch, args.validation, args.with_label) File "train_matching_min.py", line 93, in valid bleu += eval.compute() File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute return self.metric.compute()['score'] File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute self._finalize() File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths])) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files pa_table = self._read_files(files) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename pa_table = f.read_all() File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396 ``` Thank you for your response. I fixed the issue by set "keep_in_memory=True" when load_metric. I cannot share the entire source code but below is the wrapper I wrote: ```python class Evaluation: def __init__(self, metric='sacrebleu'): # self.metric = load_metric(metric, keep_in_memory=True) self.metric = load_metric(metric) def add(self, predictions, references): self.metric.add_batch(predictions=predictions, references=references) def compute(self): return self.metric.compute()['score'] ``` Then call the given wrapper as follows: ```python eval = Evaluation(metric='sacrebleu') for query, candidates, labels in tqdm(dataset): predictions = net.generate(query) references = [[s] for s in labels] eval.add(predictions, references) if n % 100 == 0: bleu += eval.compute() eval = Evaluation(metric='sacrebleu')
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https://github.com/huggingface/datasets/issues/1765
Instead of: ```python dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ``` It should be: ```python dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32) ``` `batch_sampler` accepts a Sampler object or an Iterable, so you get an error.
Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ```
30
Error iterating over Dataset with DataLoader I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ``` Instead of: ```python dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ``` It should be: ```python dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32) ``` `batch_sampler` accepts a Sampler object or an Iterable, so you get an error.
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https://github.com/huggingface/datasets/issues/1765
@mariosasko I thought that would fix it, but now I'm getting a different error: ``` /usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.) return torch.tensor(x, **format_kwargs) --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-20-3af1d82bf93a> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32) ----> 2 next(iter(dataloader)) 5 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch) 53 storage = elem.storage()._new_shared(numel) 54 out = elem.new(storage) ---> 55 return torch.stack(batch, 0, out=out) 56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ 57 and elem_type.__name__ != 'string_': RuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1 ``` Any thoughts what this means?I Do I need padding?
Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ```
169
Error iterating over Dataset with DataLoader I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ``` @mariosasko I thought that would fix it, but now I'm getting a different error: ``` /usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.) return torch.tensor(x, **format_kwargs) --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-20-3af1d82bf93a> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32) ----> 2 next(iter(dataloader)) 5 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch) 53 storage = elem.storage()._new_shared(numel) 54 out = elem.new(storage) ---> 55 return torch.stack(batch, 0, out=out) 56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ 57 and elem_type.__name__ != 'string_': RuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1 ``` Any thoughts what this means?I Do I need padding?
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https://github.com/huggingface/datasets/issues/1765
Yes, padding is an answer. This can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding.
Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ```
23
Error iterating over Dataset with DataLoader I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ``` Yes, padding is an answer. This can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding.
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https://github.com/huggingface/datasets/issues/1765
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4) batch = next(iter(dataloader)) getting ValueError: cannot reshape array of size 8192 into shape (1,512,4) I had put padding as 2048 for encoded_dataset kindly help
Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ```
28
Error iterating over Dataset with DataLoader I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4) batch = next(iter(dataloader)) getting ValueError: cannot reshape array of size 8192 into shape (1,512,4) I had put padding as 2048 for encoded_dataset kindly help
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https://github.com/huggingface/datasets/issues/1765
data_loader_val = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=True, drop_last=False, num_workers=0) dataiter = iter(data_loader_val) images, _ = next(dataiter) getting -> TypeError: 'list' object is not callable Cannot iterate through the data. Kindly suggest.
Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ```
29
Error iterating over Dataset with DataLoader I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ``` data_loader_val = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=True, drop_last=False, num_workers=0) dataiter = iter(data_loader_val) images, _ = next(dataiter) getting -> TypeError: 'list' object is not callable Cannot iterate through the data. Kindly suggest.
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https://github.com/huggingface/datasets/issues/1762
Hi ! You can get CUDA tensors with ```python dataset.set_format("torch", columns=columns, device="cuda") ``` Indeed `set_format` passes the `**kwargs` to `torch.tensor`
Unable to format dataset to CUDA Tensors
Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan
20
Unable to format dataset to CUDA Tensors Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan Hi ! You can get CUDA tensors with ```python dataset.set_format("torch", columns=columns, device="cuda") ``` Indeed `set_format` passes the `**kwargs` to `torch.tensor`
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https://github.com/huggingface/datasets/issues/1762
Hi @lhoestq, Thanks a lot. Is this true for all format types? As in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'?
Unable to format dataset to CUDA Tensors
Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan
38
Unable to format dataset to CUDA Tensors Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan Hi @lhoestq, Thanks a lot. Is this true for all format types? As in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'?
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https://github.com/huggingface/datasets/issues/1762
Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`. We don't support the kwargs for pandas on the other hand.
Unable to format dataset to CUDA Tensors
Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan
26
Unable to format dataset to CUDA Tensors Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`. We don't support the kwargs for pandas on the other hand.
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https://github.com/huggingface/datasets/issues/1762
Thanks @lhoestq, Would it be okay if I added this to the docs and made a PR?
Unable to format dataset to CUDA Tensors
Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan
17
Unable to format dataset to CUDA Tensors Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan Thanks @lhoestq, Would it be okay if I added this to the docs and made a PR?
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https://github.com/huggingface/datasets/issues/1759
Hi ! From what pickle file fo you get this ? I guess you mean the dataset loaded using `load_dataset` ?
wikipedia dataset incomplete
Hey guys, I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset. Unfortunately, I found out that there is an incompleteness for the German dataset. For reasons unknown to me, the number of inhabitants has been removed from many pages: Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche). The pickle file however shows: französische Gemeinde mit Einwohnern (Stand). Is it possible to fix this? Best regards Chris
21
wikipedia dataset incomplete Hey guys, I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset. Unfortunately, I found out that there is an incompleteness for the German dataset. For reasons unknown to me, the number of inhabitants has been removed from many pages: Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche). The pickle file however shows: französische Gemeinde mit Einwohnern (Stand). Is it possible to fix this? Best regards Chris Hi ! From what pickle file fo you get this ? I guess you mean the dataset loaded using `load_dataset` ?
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https://github.com/huggingface/datasets/issues/1759
yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline.
wikipedia dataset incomplete
Hey guys, I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset. Unfortunately, I found out that there is an incompleteness for the German dataset. For reasons unknown to me, the number of inhabitants has been removed from many pages: Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche). The pickle file however shows: französische Gemeinde mit Einwohnern (Stand). Is it possible to fix this? Best regards Chris
28
wikipedia dataset incomplete Hey guys, I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset. Unfortunately, I found out that there is an incompleteness for the German dataset. For reasons unknown to me, the number of inhabitants has been removed from many pages: Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche). The pickle file however shows: französische Gemeinde mit Einwohnern (Stand). Is it possible to fix this? Best regards Chris yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline.
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https://github.com/huggingface/datasets/issues/1759
The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell There also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.
wikipedia dataset incomplete
Hey guys, I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset. Unfortunately, I found out that there is an incompleteness for the German dataset. For reasons unknown to me, the number of inhabitants has been removed from many pages: Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche). The pickle file however shows: französische Gemeinde mit Einwohnern (Stand). Is it possible to fix this? Best regards Chris
48
wikipedia dataset incomplete Hey guys, I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset. Unfortunately, I found out that there is an incompleteness for the German dataset. For reasons unknown to me, the number of inhabitants has been removed from many pages: Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche). The pickle file however shows: französische Gemeinde mit Einwohnern (Stand). Is it possible to fix this? Best regards Chris The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell There also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.
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https://github.com/huggingface/datasets/issues/1758
Hi ! I tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index. Maybe this is because the index is not updated yet on the ElasticSearch side ?
dataset.search() (elastic) cannot reliably retrieve search results
I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices. The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer. I am indexing data that looks like the following from the HF SQuAD 2.0 data set: ``` ['57318658e6313a140071d02b', '56f7165e3d8e2e1400e3733a', '570e2f6e0b85d914000d7d21', '5727e58aff5b5019007d97d0', '5a3b5a503ff257001ab8441f', '57262fab271a42140099d725'] ``` To reproduce the issue, try: ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. 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", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # build base examples, features set of training data shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list() examples = load_dataset("squad_v2").shuffle(seed=1)['train'] features = load_dataset("squad_v2").shuffle(seed=1)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # reorder features by the training process features = features.select(indices=shuffled_idx) # get the example ids to match with the "example" data; get unique entries id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions in the examples data set; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # retrieve the example index example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist] example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list] example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist] len(example_idx_k1) # should be 130319 len(example_idx_k2) # should be 130319 #trial 1 lengths: # k=1: 130314 # k=3: 130319 # trial 2: # just run k=3 first: 130310 # try k=1 after k=3: 130319 ```
41
dataset.search() (elastic) cannot reliably retrieve search results I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices. The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer. I am indexing data that looks like the following from the HF SQuAD 2.0 data set: ``` ['57318658e6313a140071d02b', '56f7165e3d8e2e1400e3733a', '570e2f6e0b85d914000d7d21', '5727e58aff5b5019007d97d0', '5a3b5a503ff257001ab8441f', '57262fab271a42140099d725'] ``` To reproduce the issue, try: ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. 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", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # build base examples, features set of training data shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list() examples = load_dataset("squad_v2").shuffle(seed=1)['train'] features = load_dataset("squad_v2").shuffle(seed=1)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # reorder features by the training process features = features.select(indices=shuffled_idx) # get the example ids to match with the "example" data; get unique entries id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions in the examples data set; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # retrieve the example index example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist] example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list] example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist] len(example_idx_k1) # should be 130319 len(example_idx_k2) # should be 130319 #trial 1 lengths: # k=1: 130314 # k=3: 130319 # trial 2: # just run k=3 first: 130310 # try k=1 after k=3: 130319 ``` Hi ! I tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index. Maybe this is because the index is not updated yet on the ElasticSearch side ?
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https://github.com/huggingface/datasets/issues/1758
Thanks for the feedback! I added a 30 second "sleep" and that seemed to work well!
dataset.search() (elastic) cannot reliably retrieve search results
I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices. The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer. I am indexing data that looks like the following from the HF SQuAD 2.0 data set: ``` ['57318658e6313a140071d02b', '56f7165e3d8e2e1400e3733a', '570e2f6e0b85d914000d7d21', '5727e58aff5b5019007d97d0', '5a3b5a503ff257001ab8441f', '57262fab271a42140099d725'] ``` To reproduce the issue, try: ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. 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", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # build base examples, features set of training data shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list() examples = load_dataset("squad_v2").shuffle(seed=1)['train'] features = load_dataset("squad_v2").shuffle(seed=1)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # reorder features by the training process features = features.select(indices=shuffled_idx) # get the example ids to match with the "example" data; get unique entries id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions in the examples data set; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # retrieve the example index example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist] example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list] example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist] len(example_idx_k1) # should be 130319 len(example_idx_k2) # should be 130319 #trial 1 lengths: # k=1: 130314 # k=3: 130319 # trial 2: # just run k=3 first: 130310 # try k=1 after k=3: 130319 ```
16
dataset.search() (elastic) cannot reliably retrieve search results I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices. The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer. I am indexing data that looks like the following from the HF SQuAD 2.0 data set: ``` ['57318658e6313a140071d02b', '56f7165e3d8e2e1400e3733a', '570e2f6e0b85d914000d7d21', '5727e58aff5b5019007d97d0', '5a3b5a503ff257001ab8441f', '57262fab271a42140099d725'] ``` To reproduce the issue, try: ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. 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", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # build base examples, features set of training data shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list() examples = load_dataset("squad_v2").shuffle(seed=1)['train'] features = load_dataset("squad_v2").shuffle(seed=1)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # reorder features by the training process features = features.select(indices=shuffled_idx) # get the example ids to match with the "example" data; get unique entries id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions in the examples data set; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # retrieve the example index example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist] example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list] example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist] len(example_idx_k1) # should be 130319 len(example_idx_k2) # should be 130319 #trial 1 lengths: # k=1: 130314 # k=3: 130319 # trial 2: # just run k=3 first: 130310 # try k=1 after k=3: 130319 ``` Thanks for the feedback! I added a 30 second "sleep" and that seemed to work well!
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https://github.com/huggingface/datasets/issues/1757
@dspoka Please check the following link : https://github.com/thunlp/FewRel This link mentions two versions of the datasets. Also, this one seems to be the official link. I am assuming this is the correct link and implementing based on the same.
FewRel
## Adding a Dataset - **Name:** FewRel - **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset - **Paper:** @inproceedings{han2018fewrel, title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation}, author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong}, booktitle={EMNLP}, year={2018}} - **Data:** https://github.com/ProKil/FewRel - **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
39
FewRel ## Adding a Dataset - **Name:** FewRel - **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset - **Paper:** @inproceedings{han2018fewrel, title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation}, author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong}, booktitle={EMNLP}, year={2018}} - **Data:** https://github.com/ProKil/FewRel - **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). @dspoka Please check the following link : https://github.com/thunlp/FewRel This link mentions two versions of the datasets. Also, this one seems to be the official link. I am assuming this is the correct link and implementing based on the same.
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https://github.com/huggingface/datasets/issues/1755
Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad` ``` examples = examples.flatten_indices() features = features.flatten_indices() ``` That helped quite a bit!
Using select/reordering datasets slows operations down immensely
I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour. The below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score. The problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes. ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. 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", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30): all_start_logits, all_end_logits = starting_logits, ending_logits # Build a map example to its corresponding features. example_id_to_index = {k: i for i, k in enumerate(examples["id"])} features_per_example = collections.defaultdict(list) for i, feature in enumerate(features): features_per_example[example_id_to_index[feature["example_id"]]].append(i) # The dictionaries we have to fill. predictions = collections.OrderedDict() # Logging. print(f"Post-processing {len(examples)} example predictions split into {len(features)} features.") # Let's loop over all the examples! for example_index, example in enumerate(tqdm(examples)): # Those are the indices of the features associated to the current example. feature_indices = features_per_example[example_index] min_null_score = None # Only used if squad_v2 is True. valid_answers = [] context = example["context"] # Looping through all the features associated to the current example. for feature_index in feature_indices: # We grab the predictions of the model for this feature. start_logits = all_start_logits[feature_index] end_logits = all_end_logits[feature_index] # This is what will allow us to map some the positions in our logits to span of texts in the original # context. offset_mapping = features[feature_index]["offset_mapping"] # Update minimum null prediction. cls_index = features[feature_index]["input_ids"].index(tokenizer.cls_token_id) feature_null_score = start_logits[cls_index] + end_logits[cls_index] if min_null_score is None or min_null_score < feature_null_score: min_null_score = feature_null_score # Go through all possibilities for the `n_best_size` greater start and end logits. start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist() end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist() for start_index in start_indexes: for end_index in end_indexes: # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond # to part of the input_ids that are not in the context. if ( start_index >= len(offset_mapping) or end_index >= len(offset_mapping) or offset_mapping[start_index] is None or offset_mapping[end_index] is None ): continue # Don't consider answers with a length that is either < 0 or > max_answer_length. if end_index < start_index or end_index - start_index + 1 > max_answer_length: continue start_char = offset_mapping[start_index][0] end_char = offset_mapping[end_index][1] valid_answers.append( { "score": start_logits[start_index] + end_logits[end_index], "text": context[start_char: end_char] } ) if len(valid_answers) > 0: best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0] else: # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid # failure. best_answer = {"text": "", "score": 0.0} # Let's pick our final answer: the best one or the null answer (only for squad_v2) if not squad_v2: predictions[example["id"]] = best_answer["text"] else: answer = best_answer["text"] if best_answer["score"] > min_null_score else "" predictions[example["id"]] = answer return predictions # build base examples, features from training data examples = load_dataset("squad_v2").shuffle(seed=5)['train'] features = load_dataset("squad_v2").shuffle(seed=5)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # sim some shuffled training indices that we want to use to re-order the data to compare how we did shuffle_idx = np.arange(0, 131754) np.random.shuffle(shuffle_idx) # create a new dataset with rows selected following the training shuffle features = features.select(indices=shuffle_idx) # get unique example ids to match with the "example" data id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # search the examples for their index position example_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] # drop the elastic search examples.drop_index(index_name='id') # put examples in the right order examples = examples.select(indices=example_idx) # generate some fake data logits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)} def score_squad(logits, n_best_size, max_answer): # proceed with QA calculation final_predictions = postprocess_qa_predictions(examples=examples, features=features, starting_logits=logits['starting_logits'], ending_logits=logits['ending_logits'], n_best_size=20, max_answer_length=30) metric = load_metric("squad_v2") formatted_predictions = [{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in final_predictions.items()] references = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples] metrics = metric.compute(predictions=formatted_predictions, references=references) return metrics metrics = score_squad(logits, n_best_size=20, max_answer=30) ```
39
Using select/reordering datasets slows operations down immensely I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour. The below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score. The problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes. ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. 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", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30): all_start_logits, all_end_logits = starting_logits, ending_logits # Build a map example to its corresponding features. example_id_to_index = {k: i for i, k in enumerate(examples["id"])} features_per_example = collections.defaultdict(list) for i, feature in enumerate(features): features_per_example[example_id_to_index[feature["example_id"]]].append(i) # The dictionaries we have to fill. predictions = collections.OrderedDict() # Logging. print(f"Post-processing {len(examples)} example predictions split into {len(features)} features.") # Let's loop over all the examples! for example_index, example in enumerate(tqdm(examples)): # Those are the indices of the features associated to the current example. feature_indices = features_per_example[example_index] min_null_score = None # Only used if squad_v2 is True. valid_answers = [] context = example["context"] # Looping through all the features associated to the current example. for feature_index in feature_indices: # We grab the predictions of the model for this feature. start_logits = all_start_logits[feature_index] end_logits = all_end_logits[feature_index] # This is what will allow us to map some the positions in our logits to span of texts in the original # context. offset_mapping = features[feature_index]["offset_mapping"] # Update minimum null prediction. cls_index = features[feature_index]["input_ids"].index(tokenizer.cls_token_id) feature_null_score = start_logits[cls_index] + end_logits[cls_index] if min_null_score is None or min_null_score < feature_null_score: min_null_score = feature_null_score # Go through all possibilities for the `n_best_size` greater start and end logits. start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist() end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist() for start_index in start_indexes: for end_index in end_indexes: # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond # to part of the input_ids that are not in the context. if ( start_index >= len(offset_mapping) or end_index >= len(offset_mapping) or offset_mapping[start_index] is None or offset_mapping[end_index] is None ): continue # Don't consider answers with a length that is either < 0 or > max_answer_length. if end_index < start_index or end_index - start_index + 1 > max_answer_length: continue start_char = offset_mapping[start_index][0] end_char = offset_mapping[end_index][1] valid_answers.append( { "score": start_logits[start_index] + end_logits[end_index], "text": context[start_char: end_char] } ) if len(valid_answers) > 0: best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0] else: # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid # failure. best_answer = {"text": "", "score": 0.0} # Let's pick our final answer: the best one or the null answer (only for squad_v2) if not squad_v2: predictions[example["id"]] = best_answer["text"] else: answer = best_answer["text"] if best_answer["score"] > min_null_score else "" predictions[example["id"]] = answer return predictions # build base examples, features from training data examples = load_dataset("squad_v2").shuffle(seed=5)['train'] features = load_dataset("squad_v2").shuffle(seed=5)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # sim some shuffled training indices that we want to use to re-order the data to compare how we did shuffle_idx = np.arange(0, 131754) np.random.shuffle(shuffle_idx) # create a new dataset with rows selected following the training shuffle features = features.select(indices=shuffle_idx) # get unique example ids to match with the "example" data id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # search the examples for their index position example_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] # drop the elastic search examples.drop_index(index_name='id') # put examples in the right order examples = examples.select(indices=example_idx) # generate some fake data logits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)} def score_squad(logits, n_best_size, max_answer): # proceed with QA calculation final_predictions = postprocess_qa_predictions(examples=examples, features=features, starting_logits=logits['starting_logits'], ending_logits=logits['ending_logits'], n_best_size=20, max_answer_length=30) metric = load_metric("squad_v2") formatted_predictions = [{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in final_predictions.items()] references = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples] metrics = metric.compute(predictions=formatted_predictions, references=references) return metrics metrics = score_squad(logits, n_best_size=20, max_answer=30) ``` Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad` ``` examples = examples.flatten_indices() features = features.flatten_indices() ``` That helped quite a bit!
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https://github.com/huggingface/datasets/issues/1747
Hi :) The slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data. You can shuffle and then take a subset of your dataset with ```python # shuffle and take the first 100 examples dataset = dataset.shuffle(seed=42).select(range(100)) ``` You can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows
datasets slicing with seed
Hi I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html I could not find a seed option, could you assist me please how I can get a slice for different seeds? thank you. @lhoestq
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datasets slicing with seed Hi I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html I could not find a seed option, could you assist me please how I can get a slice for different seeds? thank you. @lhoestq Hi :) The slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data. You can shuffle and then take a subset of your dataset with ```python # shuffle and take the first 100 examples dataset = dataset.shuffle(seed=42).select(range(100)) ``` You can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows
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https://github.com/huggingface/datasets/issues/1747
thank you so much On Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <notifications@github.com> wrote: > Hi :) > The slicing API doesn't shuffle the data. > You can shuffle and then take a subset of your dataset with > > # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100)) > > You can find more information about shuffling and selecting rows in the > documentation: > https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/1747#issuecomment-762278134>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ> > . >
datasets slicing with seed
Hi I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html I could not find a seed option, could you assist me please how I can get a slice for different seeds? thank you. @lhoestq
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datasets slicing with seed Hi I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html I could not find a seed option, could you assist me please how I can get a slice for different seeds? thank you. @lhoestq thank you so much On Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <notifications@github.com> wrote: > Hi :) > The slicing API doesn't shuffle the data. > You can shuffle and then take a subset of your dataset with > > # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100)) > > You can find more information about shuffling and selecting rows in the > documentation: > https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/1747#issuecomment-762278134>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ> > . >
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https://github.com/huggingface/datasets/issues/1745
From the description given in the dataset script for `wsc.fixed`: ``` This version fixes issues where the spans are not actually substrings of the text. ```
difference between wsc and wsc.fixed for superglue
Hi I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq
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difference between wsc and wsc.fixed for superglue Hi I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq From the description given in the dataset script for `wsc.fixed`: ``` This version fixes issues where the spans are not actually substrings of the text. ```
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https://github.com/huggingface/datasets/issues/1743
Currently it's only possible to define the features for the two columns `references` and `predictions`. The data for these columns can then be passed to `metric.add_batch` and `metric.compute`. Instead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions. For example ```python features = datasets.Features({ 'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence({ "references_ids": datasets.Value("int32"), "offset_mapping": datasets.Value("int32"), 'text': datasets.Value('string'), "ground": datasets.Value("int32") }), }) ``` Another option would be to simply have the two features like ```python features = datasets.Features({ 'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")), }) ``` and keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`). What is the metric you would like to implement ? I'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users. What do you think ?
Issue while Creating Custom Metric
Hi Team, I am trying to create a custom metric for my training as follows, where f1 is my own metric: ```python def _info(self): # TODO: Specifies the datasets.MetricInfo object return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}), # Homepage of the metric for documentation homepage="http://metric.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_metric"], reference_urls=["http://path.to.reference.url/new_metric"] ) def _compute(self,predictions,references,text,offset_mapping,spans): pred_spans = [] for i,preds in enumerate(predictions): current_preds = [] for j,token_preds in enumerate(preds): if (preds>0.5): current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1])) pred_spans.append(current_spans) return { "Token Wise F1": f1_score(references,predictions,labels=[0,1]), "Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)]) } ``` I believe this is not correct. But that's not the issue I am facing right now. I get this error : ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-144-ed7349b50821> in <module>() ----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] ) 2 frames /usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch) 802 encoded_batch = {} 803 if set(batch) != set(self): --> 804 print(batch) 805 print(self) 806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self))) ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'} ``` On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references. How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work? Thanks, Gunjan
151
Issue while Creating Custom Metric Hi Team, I am trying to create a custom metric for my training as follows, where f1 is my own metric: ```python def _info(self): # TODO: Specifies the datasets.MetricInfo object return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}), # Homepage of the metric for documentation homepage="http://metric.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_metric"], reference_urls=["http://path.to.reference.url/new_metric"] ) def _compute(self,predictions,references,text,offset_mapping,spans): pred_spans = [] for i,preds in enumerate(predictions): current_preds = [] for j,token_preds in enumerate(preds): if (preds>0.5): current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1])) pred_spans.append(current_spans) return { "Token Wise F1": f1_score(references,predictions,labels=[0,1]), "Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)]) } ``` I believe this is not correct. But that's not the issue I am facing right now. I get this error : ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-144-ed7349b50821> in <module>() ----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] ) 2 frames /usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch) 802 encoded_batch = {} 803 if set(batch) != set(self): --> 804 print(batch) 805 print(self) 806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self))) ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'} ``` On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references. How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work? Thanks, Gunjan Currently it's only possible to define the features for the two columns `references` and `predictions`. The data for these columns can then be passed to `metric.add_batch` and `metric.compute`. Instead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions. For example ```python features = datasets.Features({ 'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence({ "references_ids": datasets.Value("int32"), "offset_mapping": datasets.Value("int32"), 'text': datasets.Value('string'), "ground": datasets.Value("int32") }), }) ``` Another option would be to simply have the two features like ```python features = datasets.Features({ 'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")), }) ``` and keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`). What is the metric you would like to implement ? I'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users. What do you think ?
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https://github.com/huggingface/datasets/issues/1743
Hi @lhoestq, I am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping. Including them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know.
Issue while Creating Custom Metric
Hi Team, I am trying to create a custom metric for my training as follows, where f1 is my own metric: ```python def _info(self): # TODO: Specifies the datasets.MetricInfo object return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}), # Homepage of the metric for documentation homepage="http://metric.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_metric"], reference_urls=["http://path.to.reference.url/new_metric"] ) def _compute(self,predictions,references,text,offset_mapping,spans): pred_spans = [] for i,preds in enumerate(predictions): current_preds = [] for j,token_preds in enumerate(preds): if (preds>0.5): current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1])) pred_spans.append(current_spans) return { "Token Wise F1": f1_score(references,predictions,labels=[0,1]), "Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)]) } ``` I believe this is not correct. But that's not the issue I am facing right now. I get this error : ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-144-ed7349b50821> in <module>() ----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] ) 2 frames /usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch) 802 encoded_batch = {} 803 if set(batch) != set(self): --> 804 print(batch) 805 print(self) 806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self))) ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'} ``` On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references. How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work? Thanks, Gunjan
75
Issue while Creating Custom Metric Hi Team, I am trying to create a custom metric for my training as follows, where f1 is my own metric: ```python def _info(self): # TODO: Specifies the datasets.MetricInfo object return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}), # Homepage of the metric for documentation homepage="http://metric.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_metric"], reference_urls=["http://path.to.reference.url/new_metric"] ) def _compute(self,predictions,references,text,offset_mapping,spans): pred_spans = [] for i,preds in enumerate(predictions): current_preds = [] for j,token_preds in enumerate(preds): if (preds>0.5): current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1])) pred_spans.append(current_spans) return { "Token Wise F1": f1_score(references,predictions,labels=[0,1]), "Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)]) } ``` I believe this is not correct. But that's not the issue I am facing right now. I get this error : ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-144-ed7349b50821> in <module>() ----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] ) 2 frames /usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch) 802 encoded_batch = {} 803 if set(batch) != set(self): --> 804 print(batch) 805 print(self) 806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self))) ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'} ``` On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references. How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work? Thanks, Gunjan Hi @lhoestq, I am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping. Including them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know.
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https://github.com/huggingface/datasets/issues/1733
Hello @juliahane, which config of GLUE causes you trouble? The URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py
connection issue with glue, what is the data url for glue?
Hi my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not thanks
20
connection issue with glue, what is the data url for glue? Hi my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not thanks Hello @juliahane, which config of GLUE causes you trouble? The URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py
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https://github.com/huggingface/datasets/issues/1731
Hi @yangp725, The SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`. You can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471). Let me know if this helps !
Couldn't reach swda.py
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py
54
Couldn't reach swda.py ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py Hi @yangp725, The SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`. You can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471). Let me know if this helps !
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https://github.com/huggingface/datasets/issues/1729
Hi @ZurMaD! Thanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617 MNIST is also on the way: #1730 If you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated 🚀
Is there support for Deep learning datasets?
I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets
55
Is there support for Deep learning datasets? I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets Hi @ZurMaD! Thanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617 MNIST is also on the way: #1730 If you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated 🚀
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https://github.com/huggingface/datasets/issues/1728
Hi @ameet-1997, I think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets For your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset. Let me know If this helps!
Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
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Add an entry to an arrow dataset Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences? Hi @ameet-1997, I think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets For your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset. Let me know If this helps!
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https://github.com/huggingface/datasets/issues/1728
That's a great idea! Thank you so much! When I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something? Code: ``` python combined_dataset = concatenate_datasets([datasets, modified_dataset]) ``` Error: ``` AttributeError: 'DatasetDict' object has no attribute 'features' ``` Output: ``` (Pdb) datasets DatasetDict({ train: Dataset({ features: ['attention_mask', 'input_ids', 'special_tokens_mask'], num_rows: 493 }) }) (Pdb) modified_dataset DatasetDict({ train: Dataset({ features: ['attention_mask', 'input_ids', 'special_tokens_mask'], num_rows: 493 }) }) ``` The error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution?
Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
123
Add an entry to an arrow dataset Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences? That's a great idea! Thank you so much! When I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something? Code: ``` python combined_dataset = concatenate_datasets([datasets, modified_dataset]) ``` Error: ``` AttributeError: 'DatasetDict' object has no attribute 'features' ``` Output: ``` (Pdb) datasets DatasetDict({ train: Dataset({ features: ['attention_mask', 'input_ids', 'special_tokens_mask'], num_rows: 493 }) }) (Pdb) modified_dataset DatasetDict({ train: Dataset({ features: ['attention_mask', 'input_ids', 'special_tokens_mask'], num_rows: 493 }) }) ``` The error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution?
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https://github.com/huggingface/datasets/issues/1728
You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])` Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.
Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
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Add an entry to an arrow dataset Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences? You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])` Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.
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https://github.com/huggingface/datasets/issues/1728
> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases. My opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors. For instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split="train"` is given for example it's always returning a Dataset.
Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
96
Add an entry to an arrow dataset Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences? > Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases. My opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors. For instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split="train"` is given for example it's always returning a Dataset.
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https://github.com/huggingface/datasets/issues/1727
And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!
BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ```
18
BLEURT score calculation raises UnrecognizedFlagError Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ``` And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!
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https://github.com/huggingface/datasets/issues/1727
I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb: ``` !pip install git+https://github.com/google-research/bleurt.git references = ["foo bar baz", "one two three"] bleurt_metric = load_metric('bleurt') predictions = ["foo bar", "four five six"] bleurt_metric.compute(predictions=predictions, references=references) ```
BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ```
39
BLEURT score calculation raises UnrecognizedFlagError Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ``` I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb: ``` !pip install git+https://github.com/google-research/bleurt.git references = ["foo bar baz", "one two three"] bleurt_metric = load_metric('bleurt') predictions = ["foo bar", "four five six"] bleurt_metric.compute(predictions=predictions, references=references) ```
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https://github.com/huggingface/datasets/issues/1727
@aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ? I tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error. However, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment. This made me suspect that the problem is somehow related to the Jupyter notebook. More environment details: ``` OS: Ubuntu Linux 18.04 conda==4.8.3 python==3.8.5 datasets==1.3.0 tensorflow==2.4.0 BLEURT==0.0.1 notebook==6.2.0 ```
BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ```
112
BLEURT score calculation raises UnrecognizedFlagError Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ``` @aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ? I tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error. However, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment. This made me suspect that the problem is somehow related to the Jupyter notebook. More environment details: ``` OS: Ubuntu Linux 18.04 conda==4.8.3 python==3.8.5 datasets==1.3.0 tensorflow==2.4.0 BLEURT==0.0.1 notebook==6.2.0 ```
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https://github.com/huggingface/datasets/issues/1727
This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt. Running this before calling bleurt fixes it: ``` import sys sys.argv = sys.argv[:1] ``` Not the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface? This is the output of `print(sys.argv)` when running on colab: ``` ['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json'] ```
BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ```
71
BLEURT score calculation raises UnrecognizedFlagError Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ``` This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt. Running this before calling bleurt fixes it: ``` import sys sys.argv = sys.argv[:1] ``` Not the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface? This is the output of `print(sys.argv)` when running on colab: ``` ['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json'] ```
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https://github.com/huggingface/datasets/issues/1727
I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase.
BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ```
25
BLEURT score calculation raises UnrecognizedFlagError Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ``` I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase.
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https://github.com/huggingface/datasets/issues/1725
You should rephrase your question or give more examples and details on what you want to do. it’s not possible to understand it and help you with only this information.
load the local dataset
your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot!
30
load the local dataset your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot! You should rephrase your question or give more examples and details on what you want to do. it’s not possible to understand it and help you with only this information.
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https://github.com/huggingface/datasets/issues/1725
sorry for that. i want to know how could i load the train set and the test set from the local ,which api or function should i use .
load the local dataset
your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot!
29
load the local dataset your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot! sorry for that. i want to know how could i load the train set and the test set from the local ,which api or function should i use .
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https://github.com/huggingface/datasets/issues/1725
thanks a lot i find that the problem is i dont use vpn... so i have to keep my net work even if i want to load the local data ?
load the local dataset
your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot!
31
load the local dataset your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot! thanks a lot i find that the problem is i dont use vpn... so i have to keep my net work even if i want to load the local data ?
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https://github.com/huggingface/datasets/issues/1725
Hi! `json` is a packaged dataset now, which means its script comes with the library and doesn't require an internet connection.
load the local dataset
your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot!
21
load the local dataset your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot! Hi! `json` is a packaged dataset now, which means its script comes with the library and doesn't require an internet connection.
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https://github.com/huggingface/datasets/issues/1724
Hi @lkcao ! Your issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py. Then you can change the line 221 of `run_mlm_new.py` into: ```python datasets = load_dataset('/path/to/text.py', data_files=data_files) ``` Where `/path/to/text.py` is the path on the server where you saved the `text.py` script.
could not run models on a offline server successfully
Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
64
could not run models on a offline server successfully Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... Hi @lkcao ! Your issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py. Then you can change the line 221 of `run_mlm_new.py` into: ```python datasets = load_dataset('/path/to/text.py', data_files=data_files) ``` Where `/path/to/text.py` is the path on the server where you saved the `text.py` script.
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https://github.com/huggingface/datasets/issues/1724
We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline
could not run models on a offline server successfully
Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
24
could not run models on a offline server successfully Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline
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https://github.com/huggingface/datasets/issues/1724
The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :) You can now use them offline ```python datasets = load_dataset('text', data_files=data_files) ``` We'll do a new release soon
could not run models on a offline server successfully
Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
38
could not run models on a offline server successfully Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :) You can now use them offline ```python datasets = load_dataset('text', data_files=data_files) ``` We'll do a new release soon
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https://github.com/huggingface/datasets/issues/1724
> The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :) > You can now use them offline > > ```python > datasets = load_dataset('text', data_files=data_files) > ``` > > We'll do a new release soon so the new version release now?
could not run models on a offline server successfully
Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
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could not run models on a offline server successfully Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... > The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :) > You can now use them offline > > ```python > datasets = load_dataset('text', data_files=data_files) > ``` > > We'll do a new release soon so the new version release now?
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https://github.com/huggingface/datasets/issues/1718
Thanks for reporting ! I was able to reproduce thanks to your code and find the origin of the bug. The cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result. I'm opening a PR to fix this. Also we plan to do a new release in the coming days so you can expect the fix to be available soon. Note that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to.
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
129
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` Thanks for reporting ! I was able to reproduce thanks to your code and find the origin of the bug. The cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result. I'm opening a PR to fix this. Also we plan to do a new release in the coming days so you can expect the fix to be available soon. Note that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to.
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https://github.com/huggingface/datasets/issues/1718
Thanks for the fast reply, waiting for the fix :) I tried to use `cache_file_names` and wasn't sure how, I tried to give it the following: ``` tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets} ) ``` and got an error: ``` multiprocess.pool.RemoteTraceback: """ Traceback (most recent call last): File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker result = (True, func(*args, **kwds)) File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 157, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/venv/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper out = func(self, *args, **kwargs) File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1491, in _map_single tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False) File "/usr/lib/python3.6/tempfile.py", line 690, in NamedTemporaryFile (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type) File "/usr/lib/python3.6/tempfile.py", line 401, in _mkstemp_inner fd = _os.open(file, flags, 0o600) FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop' """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "test.py", line 48, in <module> cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets} File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in map transformed_shards = [r.get() for r in results] File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in <listcomp> transformed_shards = [r.get() for r in results] File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop' ```
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
229
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` Thanks for the fast reply, waiting for the fix :) I tried to use `cache_file_names` and wasn't sure how, I tried to give it the following: ``` tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets} ) ``` and got an error: ``` multiprocess.pool.RemoteTraceback: """ Traceback (most recent call last): File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker result = (True, func(*args, **kwds)) File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 157, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/venv/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper out = func(self, *args, **kwargs) File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1491, in _map_single tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False) File "/usr/lib/python3.6/tempfile.py", line 690, in NamedTemporaryFile (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type) File "/usr/lib/python3.6/tempfile.py", line 401, in _mkstemp_inner fd = _os.open(file, flags, 0o600) FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop' """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "test.py", line 48, in <module> cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets} File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in map transformed_shards = [r.get() for r in results] File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in <listcomp> transformed_shards = [r.get() for r in results] File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop' ```
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https://github.com/huggingface/datasets/issues/1718
The documentation says ``` cache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the results of the computation instead of the automatically generated cache file name. You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary. ``` What is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like ```python cache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets} ```
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
90
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` The documentation says ``` cache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the results of the computation instead of the automatically generated cache file name. You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary. ``` What is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like ```python cache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets} ```
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https://github.com/huggingface/datasets/issues/1718
Managed to get `cache_file_names` working and caching works well with it Had to make a small modification for it to work: ``` cache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets} ```
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
31
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` Managed to get `cache_file_names` working and caching works well with it Had to make a small modification for it to work: ``` cache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets} ```
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https://github.com/huggingface/datasets/issues/1718
Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work. I can confirm that this is how it works in `datasets==1.1.3`
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
41
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work. I can confirm that this is how it works in `datasets==1.1.3`
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https://github.com/huggingface/datasets/issues/1718
Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
18
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path
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https://github.com/huggingface/datasets/issues/1718
I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug: ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) # CHANGE print('hello') # CHANGE tokenized_datasets = datasets.map( tokenize_function, batched=True, ... ``` I am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird Edit: accidently clicked the close issue button
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
136
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug: ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) # CHANGE print('hello') # CHANGE tokenized_datasets = datasets.map( tokenize_function, batched=True, ... ``` I am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird Edit: accidently clicked the close issue button
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https://github.com/huggingface/datasets/issues/1718
This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different. Not sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that. Moreover we're already ignoring the line definition for lambda functions.
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
86
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different. Not sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that. Moreover we're already ignoring the line definition for lambda functions.
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https://github.com/huggingface/datasets/issues/1718
Sounds great, thank you for your quick responses and help! Looking forward for the next release.
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
16
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` Sounds great, thank you for your quick responses and help! Looking forward for the next release.
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https://github.com/huggingface/datasets/issues/1718
I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help! ```python # Datasets train = sorted(glob(args.data_dir + '*.{}'.format(args.ext))) if args.dev_split >= len(train): raise ValueError("Not enough dev files") dev = [] state = random.Random(1001) for _ in range(args.dev_split): dev.append(train.pop(state.randint(0, len(train) - 1))) max_seq_length = min(args.max_seq_length, tokenizer.model_max_length) def tokenize_function(examples): return tokenizer(examples['text'], return_special_tokens_mask=True) def group_texts(examples): # Concatenate all texts from our dataset and generate chunks of max_seq_length concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # Truncate (not implementing padding) total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_seq_length result = { k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result datasets = load_dataset( 'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]}, cache_dir=args.data_cache_dir) datasets = datasets.map(tokenize_function, batched=True, remove_columns=['text'], cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets}, load_from_cache_file=not args.overwrite_cache) datasets = datasets.map(group_texts, batched=True, cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets}, load_from_cache_file=not args.overwrite_cache) ``` And this is the log ``` 04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1 04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5) 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s] 04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped 04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped ```
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
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Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help! ```python # Datasets train = sorted(glob(args.data_dir + '*.{}'.format(args.ext))) if args.dev_split >= len(train): raise ValueError("Not enough dev files") dev = [] state = random.Random(1001) for _ in range(args.dev_split): dev.append(train.pop(state.randint(0, len(train) - 1))) max_seq_length = min(args.max_seq_length, tokenizer.model_max_length) def tokenize_function(examples): return tokenizer(examples['text'], return_special_tokens_mask=True) def group_texts(examples): # Concatenate all texts from our dataset and generate chunks of max_seq_length concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # Truncate (not implementing padding) total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_seq_length result = { k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result datasets = load_dataset( 'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]}, cache_dir=args.data_cache_dir) datasets = datasets.map(tokenize_function, batched=True, remove_columns=['text'], cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets}, load_from_cache_file=not args.overwrite_cache) datasets = datasets.map(group_texts, batched=True, cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets}, load_from_cache_file=not args.overwrite_cache) ``` And this is the log ``` 04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1 04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5) 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s] 04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped 04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped ```
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https://github.com/huggingface/datasets/issues/1718
This error happened to me too, when I tried to supply my own fingerprint to `map()` via the `new_fingerprint` arg. Edit: realized it was because my path was weird and had colons and brackets and slashes in it, since one of the variable values I included in the fingerprint was a dataset split like "train[:10%]". I fixed it with [this solution](https://stackoverflow.com/a/13593932/2287177) from StackOverflow to just remove those invalid characters from the fingerprint.
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
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Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` This error happened to me too, when I tried to supply my own fingerprint to `map()` via the `new_fingerprint` arg. Edit: realized it was because my path was weird and had colons and brackets and slashes in it, since one of the variable values I included in the fingerprint was a dataset split like "train[:10%]". I fixed it with [this solution](https://stackoverflow.com/a/13593932/2287177) from StackOverflow to just remove those invalid characters from the fingerprint.
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https://github.com/huggingface/datasets/issues/1718
Good catch @jxmorris12, maybe we should do additional checks on the valid characters for fingerprints ! Would you like to contribute this ? I think this can be added here, when we set the fingerprint(s) that are passed `map`: https://github.com/huggingface/datasets/blob/25bb7c9cbf519fbbf9abf3898083b529e7762705/src/datasets/fingerprint.py#L449-L454 maybe something like ```python if kwargs.get(fingerprint_name) is None: ... else: # In this case, it's the user who specified the fingerprint manually: # we need to make sure it's a valid hash validate_fingerprint(kwargs[fingerprint_name]) ``` Otherwise I can open a PR later
Possible cache miss in datasets
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
81
Possible cache miss in datasets Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] 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=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ``` Good catch @jxmorris12, maybe we should do additional checks on the valid characters for fingerprints ! Would you like to contribute this ? I think this can be added here, when we set the fingerprint(s) that are passed `map`: https://github.com/huggingface/datasets/blob/25bb7c9cbf519fbbf9abf3898083b529e7762705/src/datasets/fingerprint.py#L449-L454 maybe something like ```python if kwargs.get(fingerprint_name) is None: ... else: # In this case, it's the user who specified the fingerprint manually: # we need to make sure it's a valid hash validate_fingerprint(kwargs[fingerprint_name]) ``` Otherwise I can open a PR later
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https://github.com/huggingface/datasets/issues/1717
Hi Dave, You are more than welcome to open a PR to make these changes! 🤗 You will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Pinging also @lhoestq for the Google cloud matter.
SciFact dataset - minor changes
Hi, SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated! I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? Thanks, Dave
44
SciFact dataset - minor changes Hi, SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated! I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? Thanks, Dave Hi Dave, You are more than welcome to open a PR to make these changes! 🤗 You will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Pinging also @lhoestq for the Google cloud matter.
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https://github.com/huggingface/datasets/issues/1717
> I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? Sure ! Also feel free to ping us for reviews or if we can help :) > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? What makes you think that ? Afaik there's no scifact on our google storage
SciFact dataset - minor changes
Hi, SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated! I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? Thanks, Dave
91
SciFact dataset - minor changes Hi, SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated! I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? Thanks, Dave > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? Sure ! Also feel free to ping us for reviews or if we can help :) > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? What makes you think that ? Afaik there's no scifact on our google storage
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https://github.com/huggingface/datasets/issues/1717
> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? > > Sure ! Also feel free to ping us for reviews or if we can help :) > OK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR. > > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? > > What makes you think that ? > Afaik there's no scifact on our google storage You're right, I had the data cached on my machine somewhere.
SciFact dataset - minor changes
Hi, SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated! I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? Thanks, Dave
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SciFact dataset - minor changes Hi, SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated! I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? Thanks, Dave > > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? > > Sure ! Also feel free to ping us for reviews or if we can help :) > OK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR. > > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? > > What makes you think that ? > Afaik there's no scifact on our google storage You're right, I had the data cached on my machine somewhere.
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https://github.com/huggingface/datasets/issues/1713
Great! Did you guys have a timeframe in mind for the next release? Thank you for all the great work in developing this library.
Installation using conda
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
24
Installation using conda Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. Great! Did you guys have a timeframe in mind for the next release? Thank you for all the great work in developing this library.
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https://github.com/huggingface/datasets/issues/1713
I think we can have `datasets` on conda by next week. Will see what I can do!
Installation using conda
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
17
Installation using conda Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. I think we can have `datasets` on conda by next week. Will see what I can do!
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https://github.com/huggingface/datasets/issues/1713
`datasets` has been added to the huggingface channel thanks to @LysandreJik :) It depends on conda-forge though ``` conda install -c huggingface -c conda-forge datasets ```
Installation using conda
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
26
Installation using conda Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. `datasets` has been added to the huggingface channel thanks to @LysandreJik :) It depends on conda-forge though ``` conda install -c huggingface -c conda-forge datasets ```
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https://github.com/huggingface/datasets/issues/1710
I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make. Here it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid. Valid files are paths to local files or URLs to remote files.
IsADirectoryError when trying to download C4
**TLDR**: I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure. How can the problem be fixed? **VERBOSE**: I use Python version 3.7 and have the following dependencies listed in my project: ``` datasets==1.2.0 apache-beam==2.26.0 ``` When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4: ``` from datasets import load_dataset load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner') ``` I get the following stacktrace: ``` /Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283... Traceback (most recent call last): File "/Users/fredriko/source/misc/main.py", line 3, in <module> load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner') File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare pipeline=pipeline, File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators file_paths = dl_manager.download_and_extract(files_to_download) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict with open(path, "rb") as f: IsADirectoryError: [Errno 21] Is a directory: '/' Process finished with exit code 1 ```
50
IsADirectoryError when trying to download C4 **TLDR**: I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure. How can the problem be fixed? **VERBOSE**: I use Python version 3.7 and have the following dependencies listed in my project: ``` datasets==1.2.0 apache-beam==2.26.0 ``` When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4: ``` from datasets import load_dataset load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner') ``` I get the following stacktrace: ``` /Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283... Traceback (most recent call last): File "/Users/fredriko/source/misc/main.py", line 3, in <module> load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner') File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare pipeline=pipeline, File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators file_paths = dl_manager.download_and_extract(files_to_download) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict with open(path, "rb") as f: IsADirectoryError: [Errno 21] Is a directory: '/' Process finished with exit code 1 ``` I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make. Here it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid. Valid files are paths to local files or URLs to remote files.
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https://github.com/huggingface/datasets/issues/1706
Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ? It looks like the downloaded file is corrupted and can't be extracted using `tarfile`. Could you try loading it again with ```python import datasets datasets.load_dataset("openwebtext", download_mode="force_redownload") ```
Error when downloading a large dataset on slow connection.
I receive the following error after about an hour trying to download the `openwebtext` dataset. The code used is: ```python import datasets datasets.load_dataset("openwebtext") ``` > Traceback (most recent call last): [4/28] > File "<stdin>", line 1, in <module> > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset > ignore_verifications=ignore_verifications, > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare > dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 570, in _download_and_prepare > split_generators = self._split_generators(dl_manager, **split_generators_kwargs) > File "/home/lucadiliello/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02/openwebtext.py", line 62, in _split_generators > dl_dir = dl_manager.download_and_extract(_URL) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract > return self.extract(self.download(url_or_urls)) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 235, in extract > num_proc=num_proc, > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested > return function(data_struct) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path > tar_file.extractall(output_path_extracted) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2000, in extractall > numeric_owner=numeric_owner) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2042, in extract > numeric_owner=numeric_owner) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2112, in _extract_member > self.makefile(tarinfo, targetpath) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2161, in makefile > copyfileobj(source, target, tarinfo.size, ReadError, bufsize) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 253, in copyfileobj > buf = src.read(remainder) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/lzma.py", line 200, in read > return self._buffer.read(size) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 68, in readinto > data = self.read(len(byte_view)) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 99, in read > raise EOFError("Compressed file ended before the " > EOFError: Compressed file ended before the end-of-stream marker was reached
44
Error when downloading a large dataset on slow connection. I receive the following error after about an hour trying to download the `openwebtext` dataset. The code used is: ```python import datasets datasets.load_dataset("openwebtext") ``` > Traceback (most recent call last): [4/28] > File "<stdin>", line 1, in <module> > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset > ignore_verifications=ignore_verifications, > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare > dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 570, in _download_and_prepare > split_generators = self._split_generators(dl_manager, **split_generators_kwargs) > File "/home/lucadiliello/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02/openwebtext.py", line 62, in _split_generators > dl_dir = dl_manager.download_and_extract(_URL) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract > return self.extract(self.download(url_or_urls)) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 235, in extract > num_proc=num_proc, > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested > return function(data_struct) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path > tar_file.extractall(output_path_extracted) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2000, in extractall > numeric_owner=numeric_owner) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2042, in extract > numeric_owner=numeric_owner) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2112, in _extract_member > self.makefile(tarinfo, targetpath) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2161, in makefile > copyfileobj(source, target, tarinfo.size, ReadError, bufsize) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 253, in copyfileobj > buf = src.read(remainder) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/lzma.py", line 200, in read > return self._buffer.read(size) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 68, in readinto > data = self.read(len(byte_view)) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 99, in read > raise EOFError("Compressed file ended before the " > EOFError: Compressed file ended before the end-of-stream marker was reached Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ? It looks like the downloaded file is corrupted and can't be extracted using `tarfile`. Could you try loading it again with ```python import datasets datasets.load_dataset("openwebtext", download_mode="force_redownload") ```
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https://github.com/huggingface/datasets/issues/1701
Thanks for reporting. We should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow. For c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.
Some datasets miss dataset_infos.json or dummy_data.zip
While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json : ``` c4 lm1b reclor wikihow ``` And some does not have a dummy_data.zip : ``` kor_nli math_dataset mlqa ms_marco newsgroup qa4mre qangaroo reddit_tifu super_glue trivia_qa web_of_science wmt14 wmt15 wmt16 wmt17 wmt18 wmt19 xtreme ``` But it seems that some of those last do have a "dummy" directory .
42
Some datasets miss dataset_infos.json or dummy_data.zip While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json : ``` c4 lm1b reclor wikihow ``` And some does not have a dummy_data.zip : ``` kor_nli math_dataset mlqa ms_marco newsgroup qa4mre qangaroo reddit_tifu super_glue trivia_qa web_of_science wmt14 wmt15 wmt16 wmt17 wmt18 wmt19 xtreme ``` But it seems that some of those last do have a "dummy" directory . Thanks for reporting. We should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow. For c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.
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https://github.com/huggingface/datasets/issues/1701
Closing since the dummy data generation is deprecated now (and the issue with missing metadata seems to be addressed).
Some datasets miss dataset_infos.json or dummy_data.zip
While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json : ``` c4 lm1b reclor wikihow ``` And some does not have a dummy_data.zip : ``` kor_nli math_dataset mlqa ms_marco newsgroup qa4mre qangaroo reddit_tifu super_glue trivia_qa web_of_science wmt14 wmt15 wmt16 wmt17 wmt18 wmt19 xtreme ``` But it seems that some of those last do have a "dummy" directory .
19
Some datasets miss dataset_infos.json or dummy_data.zip While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json : ``` c4 lm1b reclor wikihow ``` And some does not have a dummy_data.zip : ``` kor_nli math_dataset mlqa ms_marco newsgroup qa4mre qangaroo reddit_tifu super_glue trivia_qa web_of_science wmt14 wmt15 wmt16 wmt17 wmt18 wmt19 xtreme ``` But it seems that some of those last do have a "dummy" directory . Closing since the dummy data generation is deprecated now (and the issue with missing metadata seems to be addressed).
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https://github.com/huggingface/datasets/issues/1687
We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work.
Question: Shouldn't .info be a part of DatasetDict?
Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. For instance: ``` >>> ds = datasets.load_dataset("conll2002", "es") >>> ds.info Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'DatasetDict' object has no attribute 'info' ``` I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset. It is entirely possible that I am missing another perspective
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Question: Shouldn't .info be a part of DatasetDict? Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. For instance: ``` >>> ds = datasets.load_dataset("conll2002", "es") >>> ds.info Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'DatasetDict' object has no attribute 'info' ``` I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset. It is entirely possible that I am missing another perspective We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work.
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https://github.com/huggingface/datasets/issues/1687
Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ).
Question: Shouldn't .info be a part of DatasetDict?
Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. For instance: ``` >>> ds = datasets.load_dataset("conll2002", "es") >>> ds.info Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'DatasetDict' object has no attribute 'info' ``` I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset. It is entirely possible that I am missing another perspective
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Question: Shouldn't .info be a part of DatasetDict? Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. For instance: ``` >>> ds = datasets.load_dataset("conll2002", "es") >>> ds.info Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'DatasetDict' object has no attribute 'info' ``` I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset. It is entirely possible that I am missing another perspective Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ).
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https://github.com/huggingface/datasets/issues/1686
One the PR is merged the fix will be available in the next release of `datasets`. If you don't want to wait the next release you can still load the script from the master branch with ```python load_dataset("dane", script_version="master") ```
Dataset Error: DaNE contains empty samples at the end
The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors. ```python >>> import datasets [...] >>> dataset = datasets.load_dataset("dane") [...] >>> dataset["test"][-1] {'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []} >>> dataset["train"][-1] {'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []} ``` Best, Kenneth
40
Dataset Error: DaNE contains empty samples at the end The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors. ```python >>> import datasets [...] >>> dataset = datasets.load_dataset("dane") [...] >>> dataset["test"][-1] {'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []} >>> dataset["train"][-1] {'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []} ``` Best, Kenneth One the PR is merged the fix will be available in the next release of `datasets`. If you don't want to wait the next release you can still load the script from the master branch with ```python load_dataset("dane", script_version="master") ```
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https://github.com/huggingface/datasets/issues/1683
Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch. To fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two.
`ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext
It seems to fail the final batch ): steps to reproduce: ``` from datasets import load_dataset from elasticsearch import Elasticsearch import torch from transformers import file_utils, set_seed from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast MAX_SEQ_LENGTH = 256 ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", cache_dir="../datasets/") ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained( "facebook/dpr-ctx_encoder-single-nq-base", cache_dir="..datasets/" ) dataset = load_dataset('text', data_files='data/raw/ARC_Corpus.txt', cache_dir='../datasets') torch.set_grad_enabled(False) ds_with_embeddings = dataset.map( lambda example: { 'embeddings': ctx_encoder( **ctx_tokenizer( example["text"], padding='max_length', truncation=True, max_length=MAX_SEQ_LENGTH, return_tensors="pt" ) )[0][0].numpy(), }, batched=True, load_from_cache_file=False, batch_size=1000 ) ``` ARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip) And then the error: ``` --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) <ipython-input-13-67d139bb2ed3> in <module> 14 batched=True, 15 load_from_cache_file=False, ---> 16 batch_size=1000 17 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint) 1257 fn_kwargs=fn_kwargs, 1258 new_fingerprint=new_fingerprint, -> 1259 update_data=update_data, 1260 ) 1261 else: ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 155 } 156 # apply actual function --> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 159 # re-apply format to the output ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 161 # Call actual function 162 --> 163 out = func(self, *args, **kwargs) 164 165 # Update fingerprint of in-place transforms + update in-place history of transforms ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data) 1526 if update_data: 1527 batch = cast_to_python_objects(batch) -> 1528 writer.write_batch(batch) 1529 if update_data: 1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type) 277 typed_sequence_examples[col] = typed_sequence --> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples) 279 self.write_table(pa_table) 280 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict() ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays() ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate() ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Column 1 named text expected length 768 but got length 1000 ```
68
`ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext It seems to fail the final batch ): steps to reproduce: ``` from datasets import load_dataset from elasticsearch import Elasticsearch import torch from transformers import file_utils, set_seed from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast MAX_SEQ_LENGTH = 256 ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", cache_dir="../datasets/") ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained( "facebook/dpr-ctx_encoder-single-nq-base", cache_dir="..datasets/" ) dataset = load_dataset('text', data_files='data/raw/ARC_Corpus.txt', cache_dir='../datasets') torch.set_grad_enabled(False) ds_with_embeddings = dataset.map( lambda example: { 'embeddings': ctx_encoder( **ctx_tokenizer( example["text"], padding='max_length', truncation=True, max_length=MAX_SEQ_LENGTH, return_tensors="pt" ) )[0][0].numpy(), }, batched=True, load_from_cache_file=False, batch_size=1000 ) ``` ARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip) And then the error: ``` --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) <ipython-input-13-67d139bb2ed3> in <module> 14 batched=True, 15 load_from_cache_file=False, ---> 16 batch_size=1000 17 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint) 1257 fn_kwargs=fn_kwargs, 1258 new_fingerprint=new_fingerprint, -> 1259 update_data=update_data, 1260 ) 1261 else: ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 155 } 156 # apply actual function --> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 159 # re-apply format to the output ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 161 # Call actual function 162 --> 163 out = func(self, *args, **kwargs) 164 165 # Update fingerprint of in-place transforms + update in-place history of transforms ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data) 1526 if update_data: 1527 batch = cast_to_python_objects(batch) -> 1528 writer.write_batch(batch) 1529 if update_data: 1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type) 277 typed_sequence_examples[col] = typed_sequence --> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples) 279 self.write_table(pa_table) 280 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict() ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays() ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate() ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Column 1 named text expected length 768 but got length 1000 ``` Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch. To fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two.
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https://github.com/huggingface/datasets/issues/1681
Hi @KennethEnevoldsen , I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets. For now, you should be able to load the datasets after installing the latest (master) version of datasets using pip: pip install git+https://github.com/huggingface/datasets.git@master
Dataset "dane" missing
the `dane` dataset appear to be missing in the latest version (1.1.3). ```python >>> import datasets >>> datasets.__version__ '1.1.3' >>> "dane" in datasets.list_datasets() True ``` As we can see it should be present, but doesn't seem to be findable when using `load_dataset`. ```python >>> datasets.load_dataset("dane") Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset module_path, hash = prepare_module( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module raise FileNotFoundError( FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py ``` This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.
56
Dataset "dane" missing the `dane` dataset appear to be missing in the latest version (1.1.3). ```python >>> import datasets >>> datasets.__version__ '1.1.3' >>> "dane" in datasets.list_datasets() True ``` As we can see it should be present, but doesn't seem to be findable when using `load_dataset`. ```python >>> datasets.load_dataset("dane") Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset module_path, hash = prepare_module( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module raise FileNotFoundError( FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py ``` This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data. Hi @KennethEnevoldsen , I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets. For now, you should be able to load the datasets after installing the latest (master) version of datasets using pip: pip install git+https://github.com/huggingface/datasets.git@master
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https://github.com/huggingface/datasets/issues/1681
The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0. To load it you can just update `datasets` ``` pip install --upgrade datasets ``` and then you can load `dane` with ```python from datasets import load_dataset dataset = load_dataset("dane") ```
Dataset "dane" missing
the `dane` dataset appear to be missing in the latest version (1.1.3). ```python >>> import datasets >>> datasets.__version__ '1.1.3' >>> "dane" in datasets.list_datasets() True ``` As we can see it should be present, but doesn't seem to be findable when using `load_dataset`. ```python >>> datasets.load_dataset("dane") Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset module_path, hash = prepare_module( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module raise FileNotFoundError( FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py ``` This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.
52
Dataset "dane" missing the `dane` dataset appear to be missing in the latest version (1.1.3). ```python >>> import datasets >>> datasets.__version__ '1.1.3' >>> "dane" in datasets.list_datasets() True ``` As we can see it should be present, but doesn't seem to be findable when using `load_dataset`. ```python >>> datasets.load_dataset("dane") Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset module_path, hash = prepare_module( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module raise FileNotFoundError( FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py ``` This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data. The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0. To load it you can just update `datasets` ``` pip install --upgrade datasets ``` and then you can load `dane` with ```python from datasets import load_dataset dataset = load_dataset("dane") ```
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https://github.com/huggingface/datasets/issues/1679
cc100 was added recently, that's why it wasn't available yet. To load it you can just update `datasets` ``` pip install --upgrade datasets ``` and then you can load `cc100` with ```python from datasets import load_dataset lang = "en" dataset = load_dataset("cc100", lang=lang, split="train") ```
Can't import cc100 dataset
There is some issue to import cc100 dataset. ``` from datasets import load_dataset dataset = load_dataset("cc100") ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 280 raise FileNotFoundError( 281 "Couldn't find file locally at {}, or remotely at {} or {}".format( --> 282 combined_path, github_file_path, file_path 283 ) 284 ) FileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
45
Can't import cc100 dataset There is some issue to import cc100 dataset. ``` from datasets import load_dataset dataset = load_dataset("cc100") ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 280 raise FileNotFoundError( 281 "Couldn't find file locally at {}, or remotely at {} or {}".format( --> 282 combined_path, github_file_path, file_path 283 ) 284 ) FileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py cc100 was added recently, that's why it wasn't available yet. To load it you can just update `datasets` ``` pip install --upgrade datasets ``` and then you can load `cc100` with ```python from datasets import load_dataset lang = "en" dataset = load_dataset("cc100", lang=lang, split="train") ```
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https://github.com/huggingface/datasets/issues/1675
The pile dataset would be very nice. Benchmarks show that pile trained models achieve better results than most of actually trained models
Add the 800GB Pile dataset?
## Adding a Dataset - **Name:** The Pile - **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement - **Paper:** https://pile.eleuther.ai/paper.pdf - **Data:** https://pile.eleuther.ai/ - **Motivation:** Enables hardcore (GPT-3 scale!) language modelling ## Remarks Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
22
Add the 800GB Pile dataset? ## Adding a Dataset - **Name:** The Pile - **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement - **Paper:** https://pile.eleuther.ai/paper.pdf - **Data:** https://pile.eleuther.ai/ - **Motivation:** Enables hardcore (GPT-3 scale!) language modelling ## Remarks Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it. The pile dataset would be very nice. Benchmarks show that pile trained models achieve better results than most of actually trained models
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https://github.com/huggingface/datasets/issues/1675
The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo. However, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage.
Add the 800GB Pile dataset?
## Adding a Dataset - **Name:** The Pile - **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement - **Paper:** https://pile.eleuther.ai/paper.pdf - **Data:** https://pile.eleuther.ai/ - **Motivation:** Enables hardcore (GPT-3 scale!) language modelling ## Remarks Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
45
Add the 800GB Pile dataset? ## Adding a Dataset - **Name:** The Pile - **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement - **Paper:** https://pile.eleuther.ai/paper.pdf - **Data:** https://pile.eleuther.ai/ - **Motivation:** Enables hardcore (GPT-3 scale!) language modelling ## Remarks Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it. The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo. However, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage.
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https://github.com/huggingface/datasets/issues/1675
With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :) I'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards
Add the 800GB Pile dataset?
## Adding a Dataset - **Name:** The Pile - **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement - **Paper:** https://pile.eleuther.ai/paper.pdf - **Data:** https://pile.eleuther.ai/ - **Motivation:** Enables hardcore (GPT-3 scale!) language modelling ## Remarks Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
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Add the 800GB Pile dataset? ## Adding a Dataset - **Name:** The Pile - **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement - **Paper:** https://pile.eleuther.ai/paper.pdf - **Data:** https://pile.eleuther.ai/ - **Motivation:** Enables hardcore (GPT-3 scale!) language modelling ## Remarks Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it. With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :) I'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards
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https://github.com/huggingface/datasets/issues/1675
Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!
Add the 800GB Pile dataset?
## Adding a Dataset - **Name:** The Pile - **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement - **Paper:** https://pile.eleuther.ai/paper.pdf - **Data:** https://pile.eleuther.ai/ - **Motivation:** Enables hardcore (GPT-3 scale!) language modelling ## Remarks Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
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Add the 800GB Pile dataset? ## Adding a Dataset - **Name:** The Pile - **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement - **Paper:** https://pile.eleuther.ai/paper.pdf - **Data:** https://pile.eleuther.ai/ - **Motivation:** Enables hardcore (GPT-3 scale!) language modelling ## Remarks Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it. Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!
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