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https://github.com/huggingface/datasets/issues/3310 | Fatal error condition occurred in aws-c-io | I have also had this issue since a few days, when running scripts using PyCharm in particular, but it does not seem to affect the script from running, only reporting this error at the end of the run. | ## Describe the bug
Fatal error when using the library
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
```
## Expected results
No fatal errors
## Actual results
```
Fatal error condition occurred in D:\bld\aws-c-io_1633633258269\work\source\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
```
## Environment info
- `datasets` version: 1.15.2.dev0
- Platform: Windows-10-10.0.22504-SP0
- Python version: 3.8.12
- PyArrow version: 6.0.0
| 38 | Fatal error condition occurred in aws-c-io
## Describe the bug
Fatal error when using the library
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
```
## Expected results
No fatal errors
## Actual results
```
Fatal error condition occurred in D:\bld\aws-c-io_1633633258269\work\source\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
```
## Environment info
- `datasets` version: 1.15.2.dev0
- Platform: Windows-10-10.0.22504-SP0
- Python version: 3.8.12
- PyArrow version: 6.0.0
I have also had this issue since a few days, when running scripts using PyCharm in particular, but it does not seem to affect the script from running, only reporting this error at the end of the run. | [
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https://github.com/huggingface/datasets/issues/3310 | Fatal error condition occurred in aws-c-io | I also get this issue, It appears after my script has finished running. I get the following error message
```
Fatal error condition occurred in /home/conda/feedstock_root/build_artifacts/aws-c-io_1637179816120/work/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
################################################################################
Stack trace:
################################################################################
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_backtrace_print+0x59) [0x2aabe0479579]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_fatal_assert+0x48) [0x2aabe04696c8]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x13ad3) [0x2aabe0624ad3]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x113ca) [0x2aabe06223ca]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-crt-cpp.so(_ZN3Aws3Crt2Io15ClientBootstrapD1Ev+0x3a) [0x2aabe041cf5a]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././libaws-cpp-sdk-core.so(+0x5f570) [0x2aabe00eb570]
/lib64/libc.so.6(+0x39ce9) [0x2aaaab835ce9]
/lib64/libc.so.6(+0x39d37) [0x2aaaab835d37]
/lib64/libc.so.6(__libc_start_main+0xfc) [0x2aaaab81e55c]
python(+0x1c721d) [0x55555571b21d]
Aborted
```
I don't get this issue when running my code in a container, and it seems more relevant to PyArrow but thought a more complete stack trace might be helpful to someone
| ## Describe the bug
Fatal error when using the library
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
```
## Expected results
No fatal errors
## Actual results
```
Fatal error condition occurred in D:\bld\aws-c-io_1633633258269\work\source\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
```
## Environment info
- `datasets` version: 1.15.2.dev0
- Platform: Windows-10-10.0.22504-SP0
- Python version: 3.8.12
- PyArrow version: 6.0.0
| 95 | Fatal error condition occurred in aws-c-io
## Describe the bug
Fatal error when using the library
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
```
## Expected results
No fatal errors
## Actual results
```
Fatal error condition occurred in D:\bld\aws-c-io_1633633258269\work\source\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
```
## Environment info
- `datasets` version: 1.15.2.dev0
- Platform: Windows-10-10.0.22504-SP0
- Python version: 3.8.12
- PyArrow version: 6.0.0
I also get this issue, It appears after my script has finished running. I get the following error message
```
Fatal error condition occurred in /home/conda/feedstock_root/build_artifacts/aws-c-io_1637179816120/work/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
################################################################################
Stack trace:
################################################################################
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_backtrace_print+0x59) [0x2aabe0479579]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_fatal_assert+0x48) [0x2aabe04696c8]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x13ad3) [0x2aabe0624ad3]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x113ca) [0x2aabe06223ca]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-crt-cpp.so(_ZN3Aws3Crt2Io15ClientBootstrapD1Ev+0x3a) [0x2aabe041cf5a]
/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././libaws-cpp-sdk-core.so(+0x5f570) [0x2aabe00eb570]
/lib64/libc.so.6(+0x39ce9) [0x2aaaab835ce9]
/lib64/libc.so.6(+0x39d37) [0x2aaaab835d37]
/lib64/libc.so.6(__libc_start_main+0xfc) [0x2aaaab81e55c]
python(+0x1c721d) [0x55555571b21d]
Aborted
```
I don't get this issue when running my code in a container, and it seems more relevant to PyArrow but thought a more complete stack trace might be helpful to someone
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https://github.com/huggingface/datasets/issues/3310 | Fatal error condition occurred in aws-c-io | @CallumMcMahon Do you have a small reproducer for this problem on Linux? I can reproduce this on Windows but sadly not with linux. | ## Describe the bug
Fatal error when using the library
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
```
## Expected results
No fatal errors
## Actual results
```
Fatal error condition occurred in D:\bld\aws-c-io_1633633258269\work\source\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
```
## Environment info
- `datasets` version: 1.15.2.dev0
- Platform: Windows-10-10.0.22504-SP0
- Python version: 3.8.12
- PyArrow version: 6.0.0
| 23 | Fatal error condition occurred in aws-c-io
## Describe the bug
Fatal error when using the library
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
```
## Expected results
No fatal errors
## Actual results
```
Fatal error condition occurred in D:\bld\aws-c-io_1633633258269\work\source\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
```
## Environment info
- `datasets` version: 1.15.2.dev0
- Platform: Windows-10-10.0.22504-SP0
- Python version: 3.8.12
- PyArrow version: 6.0.0
@CallumMcMahon Do you have a small reproducer for this problem on Linux? I can reproduce this on Windows but sadly not with linux. | [
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https://github.com/huggingface/datasets/issues/3308 | "dataset_infos.json" missing for chr_en and mc4 | Hi ! Thanks for reporting :)
We can easily add the metadata for `chr_en` IMO, but for mC4 it will take more time, since it requires to count the number of examples in each language | ## Describe the bug
In the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.
## Steps to reproduce the bug
Check [chr_en](https://github.com/huggingface/datasets/tree/master/datasets/chr_en) and [mc4](https://github.com/huggingface/datasets/tree/master/datasets/mc4) | 35 | "dataset_infos.json" missing for chr_en and mc4
## Describe the bug
In the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.
## Steps to reproduce the bug
Check [chr_en](https://github.com/huggingface/datasets/tree/master/datasets/chr_en) and [mc4](https://github.com/huggingface/datasets/tree/master/datasets/mc4)
Hi ! Thanks for reporting :)
We can easily add the metadata for `chr_en` IMO, but for mC4 it will take more time, since it requires to count the number of examples in each language | [
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https://github.com/huggingface/datasets/issues/3308 | "dataset_infos.json" missing for chr_en and mc4 | No problem. I am trying to do some analysis on the metadata of all available datasets. Is reading `metadata_infos.json` for each dataset the correct way to go?
I noticed that the same information is also available as special variables inside .py file of each dataset. So, I was wondering if `metadata_infos.json` has been deprecated?
| ## Describe the bug
In the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.
## Steps to reproduce the bug
Check [chr_en](https://github.com/huggingface/datasets/tree/master/datasets/chr_en) and [mc4](https://github.com/huggingface/datasets/tree/master/datasets/mc4) | 55 | "dataset_infos.json" missing for chr_en and mc4
## Describe the bug
In the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.
## Steps to reproduce the bug
Check [chr_en](https://github.com/huggingface/datasets/tree/master/datasets/chr_en) and [mc4](https://github.com/huggingface/datasets/tree/master/datasets/mc4)
No problem. I am trying to do some analysis on the metadata of all available datasets. Is reading `metadata_infos.json` for each dataset the correct way to go?
I noticed that the same information is also available as special variables inside .py file of each dataset. So, I was wondering if `metadata_infos.json` has been deprecated?
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https://github.com/huggingface/datasets/issues/3308 | "dataset_infos.json" missing for chr_en and mc4 | The `dataset_infos.json` files have more information and are made to be used to analyze the datasets without having to run/parse the python scripts. Moreover some datasets on the Hugging face don't even have a python script, and for those ones we'll make tools to generate the JSON file automatically :) | ## Describe the bug
In the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.
## Steps to reproduce the bug
Check [chr_en](https://github.com/huggingface/datasets/tree/master/datasets/chr_en) and [mc4](https://github.com/huggingface/datasets/tree/master/datasets/mc4) | 50 | "dataset_infos.json" missing for chr_en and mc4
## Describe the bug
In the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.
## Steps to reproduce the bug
Check [chr_en](https://github.com/huggingface/datasets/tree/master/datasets/chr_en) and [mc4](https://github.com/huggingface/datasets/tree/master/datasets/mc4)
The `dataset_infos.json` files have more information and are made to be used to analyze the datasets without having to run/parse the python scripts. Moreover some datasets on the Hugging face don't even have a python script, and for those ones we'll make tools to generate the JSON file automatically :) | [
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https://github.com/huggingface/datasets/issues/3304 | Dataset object has no attribute `to_tf_dataset` | The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.
```
# upgrade transformers and datasets to latest versions
!pip install --upgrade transformers
!pip install --upgrade datasets
```
Regards! | I am following HuggingFace Course. I am at Fine-tuning a model.
Link: https://huggingface.co/course/chapter3/2?fw=tf
I use tokenize_function and `map` as mentioned in the course to process data.
`# define a tokenize function`
`def Tokenize_function(example):`
` return tokenizer(example['sentence'], truncation=True)`
`# tokenize entire data`
`tokenized_data = raw_data.map(Tokenize_function, batched=True)`
I get Dataset object at this point. When I try converting this to a TF dataset object as mentioned in the course, it throws the following error.
`# convert to TF dataset`
`train_data = tokenized_data["train"].to_tf_dataset( `
` columns = ['attention_mask', 'input_ids', 'token_type_ids'], `
` label_cols = ['label'], `
` shuffle = True, `
` collate_fn = data_collator, `
` batch_size = 8 `
`)`
Output:
`---------------------------------------------------------------------------`
`AttributeError Traceback (most recent call last)`
`/tmp/ipykernel_42/103099799.py in <module>`
` 1 # convert to TF dataset`
`----> 2 train_data = tokenized_data["train"].to_tf_dataset( \`
` 3 columns = ['attention_mask', 'input_ids', 'token_type_ids'], \`
` 4 label_cols = ['label'], \`
` 5 shuffle = True, \`
`AttributeError: 'Dataset' object has no attribute 'to_tf_dataset'`
When I look for `dir(tokenized_data["train"])`, there is no method or attribute in the name of `to_tf_dataset`.
Why do I get this error? And how to clear this?
Please help me. | 39 | Dataset object has no attribute `to_tf_dataset`
I am following HuggingFace Course. I am at Fine-tuning a model.
Link: https://huggingface.co/course/chapter3/2?fw=tf
I use tokenize_function and `map` as mentioned in the course to process data.
`# define a tokenize function`
`def Tokenize_function(example):`
` return tokenizer(example['sentence'], truncation=True)`
`# tokenize entire data`
`tokenized_data = raw_data.map(Tokenize_function, batched=True)`
I get Dataset object at this point. When I try converting this to a TF dataset object as mentioned in the course, it throws the following error.
`# convert to TF dataset`
`train_data = tokenized_data["train"].to_tf_dataset( `
` columns = ['attention_mask', 'input_ids', 'token_type_ids'], `
` label_cols = ['label'], `
` shuffle = True, `
` collate_fn = data_collator, `
` batch_size = 8 `
`)`
Output:
`---------------------------------------------------------------------------`
`AttributeError Traceback (most recent call last)`
`/tmp/ipykernel_42/103099799.py in <module>`
` 1 # convert to TF dataset`
`----> 2 train_data = tokenized_data["train"].to_tf_dataset( \`
` 3 columns = ['attention_mask', 'input_ids', 'token_type_ids'], \`
` 4 label_cols = ['label'], \`
` 5 shuffle = True, \`
`AttributeError: 'Dataset' object has no attribute 'to_tf_dataset'`
When I look for `dir(tokenized_data["train"])`, there is no method or attribute in the name of `to_tf_dataset`.
Why do I get this error? And how to clear this?
Please help me.
The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.
```
# upgrade transformers and datasets to latest versions
!pip install --upgrade transformers
!pip install --upgrade datasets
```
Regards! | [
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] |
https://github.com/huggingface/datasets/issues/3303 | DataCollatorWithPadding: TypeError |
>
> Input:
>
> ```
> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
> ```
>
> Output:
>
> ```
> TypeError Traceback (most recent call last)
> /tmp/ipykernel_42/1563280798.py in <module>
> 1 checkpoint = 'bert-base-uncased'
> 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt")
> TypeError: __init__() got an unexpected keyword argument 'return_tensors'
> ```
>
The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.
`# upgrade transformers and datasets to latest versions`
`!pip install --upgrade transformers`
`!pip install --upgrade datasets`
Cheers! | Hi,
I am following the HuggingFace course. I am now at Fine-tuning [https://huggingface.co/course/chapter3/3?fw=tf](https://huggingface.co/course/chapter3/3?fw=tf). When I set up `DataCollatorWithPadding` as following I got an error while trying to reproduce the course code in Kaggle. This error occurs with either a CPU-only-device or a GPU-device.
Input:
```checkpoint = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
```
Output:
```---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_42/1563280798.py in <module>
1 checkpoint = 'bert-base-uncased'
2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt")
TypeError: __init__() got an unexpected keyword argument 'return_tensors'
```
When I call `help` method, it too confirms that there is no argument `return_tensors`.
Input:
```
help(DataCollatorWithPadding.__init__)
```
Output:
```
Help on function __init__ in module transformers.data.data_collator:
__init__(self, tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = True, max_length: Union[int, NoneType] = None, pad_to_multiple_of: Union[int, NoneType] = None) -> None
```
But, the source file *[Data Collator - docs](https://huggingface.co/transformers/main_classes/data_collator.html#datacollatorwithpadding)* says that there is such an argument. By default, it returns Pytorch tensors while I need TF tensors.
Where do I miss?
Please help me. | 100 | DataCollatorWithPadding: TypeError
Hi,
I am following the HuggingFace course. I am now at Fine-tuning [https://huggingface.co/course/chapter3/3?fw=tf](https://huggingface.co/course/chapter3/3?fw=tf). When I set up `DataCollatorWithPadding` as following I got an error while trying to reproduce the course code in Kaggle. This error occurs with either a CPU-only-device or a GPU-device.
Input:
```checkpoint = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
```
Output:
```---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_42/1563280798.py in <module>
1 checkpoint = 'bert-base-uncased'
2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt")
TypeError: __init__() got an unexpected keyword argument 'return_tensors'
```
When I call `help` method, it too confirms that there is no argument `return_tensors`.
Input:
```
help(DataCollatorWithPadding.__init__)
```
Output:
```
Help on function __init__ in module transformers.data.data_collator:
__init__(self, tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = True, max_length: Union[int, NoneType] = None, pad_to_multiple_of: Union[int, NoneType] = None) -> None
```
But, the source file *[Data Collator - docs](https://huggingface.co/transformers/main_classes/data_collator.html#datacollatorwithpadding)* says that there is such an argument. By default, it returns Pytorch tensors while I need TF tensors.
Where do I miss?
Please help me.
>
> Input:
>
> ```
> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
> ```
>
> Output:
>
> ```
> TypeError Traceback (most recent call last)
> /tmp/ipykernel_42/1563280798.py in <module>
> 1 checkpoint = 'bert-base-uncased'
> 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt")
> TypeError: __init__() got an unexpected keyword argument 'return_tensors'
> ```
>
The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.
`# upgrade transformers and datasets to latest versions`
`!pip install --upgrade transformers`
`!pip install --upgrade datasets`
Cheers! | [
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https://github.com/huggingface/datasets/issues/3300 | ❓ Dataset loading script from Hugging Face Hub | Hi ! In the next version of `datasets`, your train and test splits will be correctly separated (changes from #3027) if you create a dataset repository with only your CSV files.
Also it seems that you overwrite the `data_files` and `data_dir` arguments in your code, when you instantiate the AGNewsConfig objects. Those parameters are not necessary since you already know which files you want to load.
You can find an example on how to specify which file the dataset has to download in this [example script](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107):
```python
_URLS = {
"train": "train-v1.1.json", # you can use a URL or a relative path from the python script to your file in the repository
"dev": "dev-v1.1.json",
}
```
```python
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
]
``` | Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :) | 133 | ❓ Dataset loading script from Hugging Face Hub
Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :)
Hi ! In the next version of `datasets`, your train and test splits will be correctly separated (changes from #3027) if you create a dataset repository with only your CSV files.
Also it seems that you overwrite the `data_files` and `data_dir` arguments in your code, when you instantiate the AGNewsConfig objects. Those parameters are not necessary since you already know which files you want to load.
You can find an example on how to specify which file the dataset has to download in this [example script](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107):
```python
_URLS = {
"train": "train-v1.1.json", # you can use a URL or a relative path from the python script to your file in the repository
"dev": "dev-v1.1.json",
}
```
```python
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
]
``` | [
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https://github.com/huggingface/datasets/issues/3300 | ❓ Dataset loading script from Hugging Face Hub | Also I think the viewer will be updated when you fix the dataset script, let me know if it doesn't | Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :) | 20 | ❓ Dataset loading script from Hugging Face Hub
Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :)
Also I think the viewer will be updated when you fix the dataset script, let me know if it doesn't | [
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https://github.com/huggingface/datasets/issues/3300 | ❓ Dataset loading script from Hugging Face Hub | Hi @lhoestq,
Thanks a lot for the super quick answer!
Your suggestion solves my issue. I am now able to load the dataset properly 🚀
However, the dataviewer is not working yet.
Really, thanks a lot for your help and consideration!
Best,
Pietro | Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :) | 43 | ❓ Dataset loading script from Hugging Face Hub
Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :)
Hi @lhoestq,
Thanks a lot for the super quick answer!
Your suggestion solves my issue. I am now able to load the dataset properly 🚀
However, the dataviewer is not working yet.
Really, thanks a lot for your help and consideration!
Best,
Pietro | [
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https://github.com/huggingface/datasets/issues/3300 | ❓ Dataset loading script from Hugging Face Hub | @lhoestq I think I am having a related problem.
My call to load_dataset() looks like this:
```
datasets = load_dataset(
os.path.abspath(layoutlmft.data.datasets.xfun.__file__),
f"xfun.{data_args.lang}",
additional_langs=data_args.additional_langs,
keep_in_memory=True,
)
```
My _split_generation code is:
```
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract("https://guillaumejaume.github.io/FUNSD/dataset.zip")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training_data/"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset/testing_data/"}
),
]
```
However I get the error "TypeError: _generate_examples() got an unexpected keyword argument 'filepath'"
The path looks right and I see the data in the path so I think the only problem I have is that it doesn't like the key "filepath". However, the documentation (example [here](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107)) seems to show that this is the correct parameter.
Here is the full stack trace:
```
Downloading and preparing dataset xfun/xfun.en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/caseygre/.cache/huggingface/datasets/xfun/xfun.en/0.0.0/96b8cb7c57f6f822f0ab37ae3be7b82d84ac57062e774c9361ccf0a4b9ef61cc...
Traceback (most recent call last):
File "/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py", line 574, in download_and_prepare
self._download_and_prepare(
File "/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py", line 975, in _prepare_split
generator = self._generate_examples(**split_generator.gen_kwargs)
TypeError: _generate_examples() got an unexpected keyword argument 'filepath'
python-BaseException
``` | Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :) | 173 | ❓ Dataset loading script from Hugging Face Hub
Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :)
@lhoestq I think I am having a related problem.
My call to load_dataset() looks like this:
```
datasets = load_dataset(
os.path.abspath(layoutlmft.data.datasets.xfun.__file__),
f"xfun.{data_args.lang}",
additional_langs=data_args.additional_langs,
keep_in_memory=True,
)
```
My _split_generation code is:
```
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract("https://guillaumejaume.github.io/FUNSD/dataset.zip")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training_data/"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset/testing_data/"}
),
]
```
However I get the error "TypeError: _generate_examples() got an unexpected keyword argument 'filepath'"
The path looks right and I see the data in the path so I think the only problem I have is that it doesn't like the key "filepath". However, the documentation (example [here](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107)) seems to show that this is the correct parameter.
Here is the full stack trace:
```
Downloading and preparing dataset xfun/xfun.en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/caseygre/.cache/huggingface/datasets/xfun/xfun.en/0.0.0/96b8cb7c57f6f822f0ab37ae3be7b82d84ac57062e774c9361ccf0a4b9ef61cc...
Traceback (most recent call last):
File "/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py", line 574, in download_and_prepare
self._download_and_prepare(
File "/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py", line 975, in _prepare_split
generator = self._generate_examples(**split_generator.gen_kwargs)
TypeError: _generate_examples() got an unexpected keyword argument 'filepath'
python-BaseException
``` | [
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https://github.com/huggingface/datasets/issues/3300 | ❓ Dataset loading script from Hugging Face Hub | Hi ! The `gen_kwargs` dictionary is passed to `_generate_examples`, so in your case it must be defined this way:
```python
def _generate_examples(self, filepath):
...
```
And here is an additional tip: you can use `os.path.join(downloaded_file, "dataset/testing_data")` instead of `f"downloaded_file}/dataset/testing_data/"` to get compatibility with Windows and streaming.
Indeed Windows uses a backslash separator, not a slash, and streaming uses chained URLs (like `zip://dataset/testing_data::https://https://guillaumejaume.github.io/FUNSD/dataset.zip` for example) | Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :) | 64 | ❓ Dataset loading script from Hugging Face Hub
Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :)
Hi ! The `gen_kwargs` dictionary is passed to `_generate_examples`, so in your case it must be defined this way:
```python
def _generate_examples(self, filepath):
...
```
And here is an additional tip: you can use `os.path.join(downloaded_file, "dataset/testing_data")` instead of `f"downloaded_file}/dataset/testing_data/"` to get compatibility with Windows and streaming.
Indeed Windows uses a backslash separator, not a slash, and streaming uses chained URLs (like `zip://dataset/testing_data::https://https://guillaumejaume.github.io/FUNSD/dataset.zip` for example) | [
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] |
https://github.com/huggingface/datasets/issues/3300 | ❓ Dataset loading script from Hugging Face Hub | Thanks for you quick reply @lhoestq and so sorry for my very delayed response.
We have gotten around the error another way but I will try to duplicate this when I can. We may have had "filepaths" instead of "filepath" in our def of _generate_examples() and not noticed the difference. If I find a more useful answer for others I will add to this ticket so they know what the issue was.
Note: we do have our own _generate_examples() defined with the same def as Quentin has. (But one version does have "filepaths".)
| Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :) | 93 | ❓ Dataset loading script from Hugging Face Hub
Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :)
Thanks for you quick reply @lhoestq and so sorry for my very delayed response.
We have gotten around the error another way but I will try to duplicate this when I can. We may have had "filepaths" instead of "filepath" in our def of _generate_examples() and not noticed the difference. If I find a more useful answer for others I will add to this ticket so they know what the issue was.
Note: we do have our own _generate_examples() defined with the same def as Quentin has. (But one version does have "filepaths".)
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https://github.com/huggingface/datasets/issues/3298 | Agnews dataset viewer is not working | Hi ! Thanks for reporting
We've already fixed the code that generates the preview for this dataset, we'll release the fix soon :) | ## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/ag_news
Hi there, the `ag_news` dataset viewer is not working.
Am I the one who added this dataset? No
| 23 | Agnews dataset viewer is not working
## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/ag_news
Hi there, the `ag_news` dataset viewer is not working.
Am I the one who added this dataset? No
Hi ! Thanks for reporting
We've already fixed the code that generates the preview for this dataset, we'll release the fix soon :) | [
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https://github.com/huggingface/datasets/issues/3297 | .map() cache is wrongfully reused - only happens when the mapping function is imported | Hi ! Thanks for reporting. Indeed this is a current limitation of the usage we have of `dill` in `datasets`. I'd suggest you use your workaround for now until we find a way to fix this. Maybe functions that are not coming from a module not installed with pip should be dumped completely, rather than only taking their locations into account | ## Describe the bug
When `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified.
The reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https://stackoverflow.com/a/67851411).
I guess it is not a widespread case, but it can still lead to unwanted results unnoticeably.
## Steps to reproduce the bug
Create files `a.py` and `b.py`:
```python
# a.py
from datasets import load_dataset
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
if __name__ == "__main__":
main()
```
```python
# b.py
from datasets import load_dataset
from a import mapping_func
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
if __name__ == "__main__":
main()
```
Run `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function.
## Expected results
Run `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result.
## Workaround
Put the mapping function inside a dummy class as a static method:
```python
# a.py
class MappingFuncClass:
@staticmethod
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
```
```python
# b.py
from datasets import load_dataset
from a import MappingFuncClass
def main():
squad = load_dataset("squad")
squad.map(MappingFuncClass.mapping_func, batched=True)
if __name__ == "__main__":
main()
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 61 | .map() cache is wrongfully reused - only happens when the mapping function is imported
## Describe the bug
When `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified.
The reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https://stackoverflow.com/a/67851411).
I guess it is not a widespread case, but it can still lead to unwanted results unnoticeably.
## Steps to reproduce the bug
Create files `a.py` and `b.py`:
```python
# a.py
from datasets import load_dataset
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
if __name__ == "__main__":
main()
```
```python
# b.py
from datasets import load_dataset
from a import mapping_func
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
if __name__ == "__main__":
main()
```
Run `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function.
## Expected results
Run `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result.
## Workaround
Put the mapping function inside a dummy class as a static method:
```python
# a.py
class MappingFuncClass:
@staticmethod
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
```
```python
# b.py
from datasets import load_dataset
from a import MappingFuncClass
def main():
squad = load_dataset("squad")
squad.map(MappingFuncClass.mapping_func, batched=True)
if __name__ == "__main__":
main()
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
Hi ! Thanks for reporting. Indeed this is a current limitation of the usage we have of `dill` in `datasets`. I'd suggest you use your workaround for now until we find a way to fix this. Maybe functions that are not coming from a module not installed with pip should be dumped completely, rather than only taking their locations into account | [
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https://github.com/huggingface/datasets/issues/3297 | .map() cache is wrongfully reused - only happens when the mapping function is imported | I agree. Sounds like a solution for it would be pretty dirty, even [cloudpickle](https://stackoverflow.com/a/16891169) doesn't help in this case.
In the meanwhile I think that adding a warning and the workaround somewhere in the documentation can be helpful. | ## Describe the bug
When `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified.
The reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https://stackoverflow.com/a/67851411).
I guess it is not a widespread case, but it can still lead to unwanted results unnoticeably.
## Steps to reproduce the bug
Create files `a.py` and `b.py`:
```python
# a.py
from datasets import load_dataset
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
if __name__ == "__main__":
main()
```
```python
# b.py
from datasets import load_dataset
from a import mapping_func
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
if __name__ == "__main__":
main()
```
Run `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function.
## Expected results
Run `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result.
## Workaround
Put the mapping function inside a dummy class as a static method:
```python
# a.py
class MappingFuncClass:
@staticmethod
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
```
```python
# b.py
from datasets import load_dataset
from a import MappingFuncClass
def main():
squad = load_dataset("squad")
squad.map(MappingFuncClass.mapping_func, batched=True)
if __name__ == "__main__":
main()
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 38 | .map() cache is wrongfully reused - only happens when the mapping function is imported
## Describe the bug
When `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified.
The reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https://stackoverflow.com/a/67851411).
I guess it is not a widespread case, but it can still lead to unwanted results unnoticeably.
## Steps to reproduce the bug
Create files `a.py` and `b.py`:
```python
# a.py
from datasets import load_dataset
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
if __name__ == "__main__":
main()
```
```python
# b.py
from datasets import load_dataset
from a import mapping_func
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
if __name__ == "__main__":
main()
```
Run `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function.
## Expected results
Run `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result.
## Workaround
Put the mapping function inside a dummy class as a static method:
```python
# a.py
class MappingFuncClass:
@staticmethod
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
```
```python
# b.py
from datasets import load_dataset
from a import MappingFuncClass
def main():
squad = load_dataset("squad")
squad.map(MappingFuncClass.mapping_func, batched=True)
if __name__ == "__main__":
main()
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
I agree. Sounds like a solution for it would be pretty dirty, even [cloudpickle](https://stackoverflow.com/a/16891169) doesn't help in this case.
In the meanwhile I think that adding a warning and the workaround somewhere in the documentation can be helpful. | [
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https://github.com/huggingface/datasets/issues/3295 | Temporary dataset_path for remote fs URIs not built properly in arrow_dataset.py::load_from_disk | Hi ! Good catch and thanks for opening a PR :)
I just responded in your PR | ## Describe the bug
When trying to build a temporary dataset path from a remote URI in this block of code:
https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042
the result is not the expected when passing an absolute path in an URI like `hdfs:///absolute/path`.
## Steps to reproduce the bug
```python
dataset_path = "hdfs:///absolute/path"
src_dataset_path = extract_path_from_uri(dataset_path)
tmp_dir = get_temporary_cache_files_directory()
dataset_path = Path(tmp_dir, src_dataset_path)
print(dataset_path)
```
## Expected results
With the code above, we would expect a value in `dataset_path` similar to:
`/tmp/tmpnwxyvao5/absolute/path`
## Actual results
However, we get a `dataset_path` value like:
`/absolute/path`
This is because this line here: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1041
returns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments.
## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
| 17 | Temporary dataset_path for remote fs URIs not built properly in arrow_dataset.py::load_from_disk
## Describe the bug
When trying to build a temporary dataset path from a remote URI in this block of code:
https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042
the result is not the expected when passing an absolute path in an URI like `hdfs:///absolute/path`.
## Steps to reproduce the bug
```python
dataset_path = "hdfs:///absolute/path"
src_dataset_path = extract_path_from_uri(dataset_path)
tmp_dir = get_temporary_cache_files_directory()
dataset_path = Path(tmp_dir, src_dataset_path)
print(dataset_path)
```
## Expected results
With the code above, we would expect a value in `dataset_path` similar to:
`/tmp/tmpnwxyvao5/absolute/path`
## Actual results
However, we get a `dataset_path` value like:
`/absolute/path`
This is because this line here: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1041
returns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments.
## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
Hi ! Good catch and thanks for opening a PR :)
I just responded in your PR | [
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https://github.com/huggingface/datasets/issues/3292 | Not able to load 'wikipedia' dataset | Hi ! Indeed it looks like the code snippet on the Hugging face Hub doesn't show the second parameter
Thanks for reporting, I'm taking a look
| ## Describe the bug
I am following the instruction for loading the wikipedia dataset using datasets. However getting the below error.
## Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("wikipedia")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
339 "Config name is missing."
340 "\nPlease pick one among the available configs: %s" % list(self.builder_configs.keys())
--> 341 + "\nExample of usage:\n\t`{}`".format(example_of_usage)
342 )
343 builder_config = self.BUILDER_CONFIGS[0]
ValueError: Config name is missing.
Please pick one among the available configs: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']
Example of usage:
`load_dataset('wikipedia', '20200501.aa')`
I think the other parameter is missing in the load_dataset function that is not shown in the instruction. | 27 | Not able to load 'wikipedia' dataset
## Describe the bug
I am following the instruction for loading the wikipedia dataset using datasets. However getting the below error.
## Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("wikipedia")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
339 "Config name is missing."
340 "\nPlease pick one among the available configs: %s" % list(self.builder_configs.keys())
--> 341 + "\nExample of usage:\n\t`{}`".format(example_of_usage)
342 )
343 builder_config = self.BUILDER_CONFIGS[0]
ValueError: Config name is missing.
Please pick one among the available configs: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']
Example of usage:
`load_dataset('wikipedia', '20200501.aa')`
I think the other parameter is missing in the load_dataset function that is not shown in the instruction.
Hi ! Indeed it looks like the code snippet on the Hugging face Hub doesn't show the second parameter
Thanks for reporting, I'm taking a look
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https://github.com/huggingface/datasets/issues/3285 | Add IEMOCAP dataset | The IEMOCAP dataset is private and available only on request.
```
To obtain the IEMOCAP data you just need to fill out an electronic release form below.
```
- [Request form](https://sail.usc.edu/iemocap/release_form.php)
- [License ](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf)
> We do not share the dataset for commercial purposes due to privacy concerns surrounding the participants of the research. The login details will only be emailed to the given academic email address.
I think it won't be possible to add this dataset to 🤗 datasets. | ## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| 80 | Add IEMOCAP dataset
## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
The IEMOCAP dataset is private and available only on request.
```
To obtain the IEMOCAP data you just need to fill out an electronic release form below.
```
- [Request form](https://sail.usc.edu/iemocap/release_form.php)
- [License ](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf)
> We do not share the dataset for commercial purposes due to privacy concerns surrounding the participants of the research. The login details will only be emailed to the given academic email address.
I think it won't be possible to add this dataset to 🤗 datasets. | [
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https://github.com/huggingface/datasets/issues/3285 | Add IEMOCAP dataset | Hi @dnaveenr ! We can contact the authors to see if they are interested in hosting the dataset on the Hub. In the meantime, feel free to work on a script with manual download. | ## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| 34 | Add IEMOCAP dataset
## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Hi @dnaveenr ! We can contact the authors to see if they are interested in hosting the dataset on the Hub. In the meantime, feel free to work on a script with manual download. | [
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https://github.com/huggingface/datasets/issues/3285 | Add IEMOCAP dataset | Hi @mariosasko . Thanks for your response. Sure, I will mail them and find out if they're open to this.
Work on a script with manual download ? This is new to me, any guidelines would be helpful here.
| ## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| 39 | Add IEMOCAP dataset
## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Hi @mariosasko . Thanks for your response. Sure, I will mail them and find out if they're open to this.
Work on a script with manual download ? This is new to me, any guidelines would be helpful here.
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https://github.com/huggingface/datasets/issues/3285 | Add IEMOCAP dataset | > Thanks for your response. Sure, I will mail them and find out if they're open to this.
It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.
> Work on a script with manual download ? This is new to me, any guidelines would be helpful here.
For instance, this is one of the scripts with manual download: https://huggingface.co/datasets/arxiv_dataset. Compared to the standard dataset, it has the `manual_download_instructions` attribute and uses `dl_manager.manual_dir` (derived from `load_dataset(..., data_dir="path/to/data")`) to access the dataset's data files. | ## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| 100 | Add IEMOCAP dataset
## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
> Thanks for your response. Sure, I will mail them and find out if they're open to this.
It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.
> Work on a script with manual download ? This is new to me, any guidelines would be helpful here.
For instance, this is one of the scripts with manual download: https://huggingface.co/datasets/arxiv_dataset. Compared to the standard dataset, it has the `manual_download_instructions` attribute and uses `dl_manager.manual_dir` (derived from `load_dataset(..., data_dir="path/to/data")`) to access the dataset's data files. | [
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https://github.com/huggingface/datasets/issues/3285 | Add IEMOCAP dataset | > It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.
Yes. That would be perfect. Thanks.
----
Okay. Thanks for giving a reference. This is helpful. I will go through it.
| ## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| 49 | Add IEMOCAP dataset
## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
> It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.
Yes. That would be perfect. Thanks.
----
Okay. Thanks for giving a reference. This is helpful. I will go through it.
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https://github.com/huggingface/datasets/issues/3282 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py | Hi ! Thanks for reporting :)
I think this is because the dataset is behind an access page. We can fix the dataset viewer
If you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset. | ## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library. | 55 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.
Hi ! Thanks for reporting :)
I think this is because the dataset is behind an access page. We can fix the dataset viewer
If you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset. | [
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https://github.com/huggingface/datasets/issues/3282 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py | Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.
Regards! | ## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library. | 19 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.
Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.
Regards! | [
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https://github.com/huggingface/datasets/issues/3282 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py | Hi,
Using `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.
Should I leave the issue open until you fix the Dataset viewer issue? | ## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library. | 29 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.
Hi,
Using `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.
Should I leave the issue open until you fix the Dataset viewer issue? | [
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https://github.com/huggingface/datasets/issues/3282 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py | Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks | ## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library. | 22 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.
Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks | [
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https://github.com/huggingface/datasets/issues/3282 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py | The error I get when trying to load OSCAR 21.09 is this
```
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
The URL I get in the browser is this
```
https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py
```
Maybe URL is the issue? (resolve vs blob) | ## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library. | 38 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.
The error I get when trying to load OSCAR 21.09 is this
```
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
The URL I get in the browser is this
```
https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py
```
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https://github.com/huggingface/datasets/issues/3282 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py | > The error I get when trying to load OSCAR 21.09 is this
>
> ```
> ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
> ```
>
> The URL I get in the browser is this
>
> ```
> https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py
> ```
>
> Maybe URL is the issue? (resolve vs blob)
You need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:
`
load_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)` | ## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library. | 75 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.
> The error I get when trying to load OSCAR 21.09 is this
>
> ```
> ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
> ```
>
> The URL I get in the browser is this
>
> ```
> https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py
> ```
>
> Maybe URL is the issue? (resolve vs blob)
You need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:
`
load_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)` | [
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https://github.com/huggingface/datasets/issues/3272 | Make iter_archive work with ZIP files | Hi ! Sure this is open for any contributor. If you're interested feel free to self-assign this issue to you by commenting `#self-assign`. Then if you have any question or if I can help, feel free to ping me.
To begin with, feel free to take a look at both implementations of `iter_archive` for local downloads and for data streaming:
In the `DownloadManager` for local dowloads:
https://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/download_manager.py#L218-L242
In the `StreamingDownloadManager` to stream the content of the archive directly from the remote file:
https://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/streaming_download_manager.py#L502-L526
Notice the call to `xopen` that opens and streams a file given either an URL or a local path :) | Currently users can use `dl_manager.iter_archive` in their dataset script to iterate over all the files of a TAR archive.
It would be nice if it could work with ZIP files too ! | 103 | Make iter_archive work with ZIP files
Currently users can use `dl_manager.iter_archive` in their dataset script to iterate over all the files of a TAR archive.
It would be nice if it could work with ZIP files too !
Hi ! Sure this is open for any contributor. If you're interested feel free to self-assign this issue to you by commenting `#self-assign`. Then if you have any question or if I can help, feel free to ping me.
To begin with, feel free to take a look at both implementations of `iter_archive` for local downloads and for data streaming:
In the `DownloadManager` for local dowloads:
https://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/download_manager.py#L218-L242
In the `StreamingDownloadManager` to stream the content of the archive directly from the remote file:
https://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/streaming_download_manager.py#L502-L526
Notice the call to `xopen` that opens and streams a file given either an URL or a local path :) | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | Hi @ZhaofengWu, thanks for reporting.
Unfortunately, I'm not able to reproduce your bug:
```python
In [1]: from datasets import load_dataset
In [2]: ds = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.91MB/s]
Downloading: 1.79kB [00:00, 1.79MB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to .cache\coqa\default\1.0.0\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 49.0M/49.0M [00:06<00:00, 7.17MB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9.09M/9.09M [00:01<00:00, 6.08MB/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:12<00:00, 6.48s/it]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 333.26it/s]
Dataset coqa downloaded and prepared to .cache\coqa\default\1.0.0\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 285.49it/s]
In [3]: ds
Out[3]:
DatasetDict({
train: Dataset({
features: ['source', 'story', 'questions', 'answers'],
num_rows: 7199
})
validation: Dataset({
features: ['source', 'story', 'questions', 'answers'],
num_rows: 500
})
})
```
Could you please give more details about your development environment? You can run the command `datasets-cli env` and copy-and-paste its output:
```
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
```
It might be because you are using an old version of `datasets`. Could you please update it (`pip install -U datasets`) and confirm if the problem parsists? | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 180 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
Hi @ZhaofengWu, thanks for reporting.
Unfortunately, I'm not able to reproduce your bug:
```python
In [1]: from datasets import load_dataset
In [2]: ds = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.91MB/s]
Downloading: 1.79kB [00:00, 1.79MB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to .cache\coqa\default\1.0.0\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 49.0M/49.0M [00:06<00:00, 7.17MB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9.09M/9.09M [00:01<00:00, 6.08MB/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:12<00:00, 6.48s/it]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 333.26it/s]
Dataset coqa downloaded and prepared to .cache\coqa\default\1.0.0\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 285.49it/s]
In [3]: ds
Out[3]:
DatasetDict({
train: Dataset({
features: ['source', 'story', 'questions', 'answers'],
num_rows: 7199
})
validation: Dataset({
features: ['source', 'story', 'questions', 'answers'],
num_rows: 500
})
})
```
Could you please give more details about your development environment? You can run the command `datasets-cli env` and copy-and-paste its output:
```
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
```
It might be because you are using an old version of `datasets`. Could you please update it (`pip install -U datasets`) and confirm if the problem parsists? | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | I'm getting the same error in two separate environments:
```
- `datasets` version: 1.15.1
- Platform: Linux-5.4.0-84-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.11
- PyArrow version: 6.0.0
```
```
- `datasets` version: 1.15.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 6.0.0
``` | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 43 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I'm getting the same error in two separate environments:
```
- `datasets` version: 1.15.1
- Platform: Linux-5.4.0-84-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.11
- PyArrow version: 6.0.0
```
```
- `datasets` version: 1.15.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 6.0.0
``` | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | I'm sorry, but don't get to reproduce the error in the Linux environment.
@mariosasko @lhoestq can you reproduce it? | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 19 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I'm sorry, but don't get to reproduce the error in the Linux environment.
@mariosasko @lhoestq can you reproduce it? | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | I also can't reproduce the error on Windows/Linux (tested both the master and the `1.15.1` version). | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 16 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I also can't reproduce the error on Windows/Linux (tested both the master and the `1.15.1` version). | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | Maybe the file had issues during the download ? Could you try to delete your cache and try again ?
By default the downloads cache is at `~/.cache/huggingface/datasets/downloads`
Also can you check if you have a proxy that could prevent the download to succeed ? Are you able to download those files via your browser ? | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 56 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
Maybe the file had issues during the download ? Could you try to delete your cache and try again ?
By default the downloads cache is at `~/.cache/huggingface/datasets/downloads`
Also can you check if you have a proxy that could prevent the download to succeed ? Are you able to download those files via your browser ? | [
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] |
https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | I got the same error in a third environment (google cloud) as well. The internet for these three environments are all different so I don't think that's the reason.
```
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-1022-gcp-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 6.0.0
```
I deleted the entire `~/.cache/huggingface/datasets` on my local mac, and got a different first time error.
```
Python 3.9.5 (default, May 18 2021, 12:31:01)
[Clang 10.0.0 ] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.19MB/s]
Downloading: 1.79kB [00:00, 712kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.36MB/s]
50%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 1/2 [00:00<00:00, 2.47it/s]Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/zhaofengw/.cache/huggingface/modules/datasets_modules/datasets/coqa/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0/coqa.py", line 70, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json
>>> dataset = load_dataset("coqa")
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 6.26it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1087.45it/s]
50%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 1/2 [00:45<00:45, 45.60s/it]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I can access the URL using my browser, though I did notice a redirection -- could that have something to do with it? | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 380 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I got the same error in a third environment (google cloud) as well. The internet for these three environments are all different so I don't think that's the reason.
```
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-1022-gcp-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 6.0.0
```
I deleted the entire `~/.cache/huggingface/datasets` on my local mac, and got a different first time error.
```
Python 3.9.5 (default, May 18 2021, 12:31:01)
[Clang 10.0.0 ] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.19MB/s]
Downloading: 1.79kB [00:00, 712kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.36MB/s]
50%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 1/2 [00:00<00:00, 2.47it/s]Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/zhaofengw/.cache/huggingface/modules/datasets_modules/datasets/coqa/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0/coqa.py", line 70, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json
>>> dataset = load_dataset("coqa")
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 6.26it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1087.45it/s]
50%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 1/2 [00:45<00:45, 45.60s/it]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I can access the URL using my browser, though I did notice a redirection -- could that have something to do with it? | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | I can run your notebook fine, but if I create one myself, it has that error: https://colab.research.google.com/drive/107GIdhrauPO6ZiFDY7G9S74in4qqI2Kx?usp=sharing.
It's so funny -- it's like whenever you guys run it it's fine but whenever I run it it fails, whatever the environment is. | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 41 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I can run your notebook fine, but if I create one myself, it has that error: https://colab.research.google.com/drive/107GIdhrauPO6ZiFDY7G9S74in4qqI2Kx?usp=sharing.
It's so funny -- it's like whenever you guys run it it's fine but whenever I run it it fails, whatever the environment is. | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | I guess it must be some connection issue: the data owner may be blocking requests coming from your country or IP range... | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 22 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I guess it must be some connection issue: the data owner may be blocking requests coming from your country or IP range... | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | I mean, I don't think google colab sends the connection from my IP. Same applies to google cloud. | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 18 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I mean, I don't think google colab sends the connection from my IP. Same applies to google cloud. | [
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] |
https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | Hello, I am having the same error with @ZhaofengWu first with "social bias frames" dataset. As I found this report, I tried also "coqa" and it fails as well.
I test this on Google Colab.
```
- `datasets` version: 1.15.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
```
Then another environment
```
- `datasets` version: 1.15.1
- Platform: macOS-12.0.1-arm64-arm-64bit
- Python version: 3.9.7
- PyArrow version: 6.0.1
```
I tried the notebook @albertvillanova provided earlier, and it fails...
| ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 82 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
Hello, I am having the same error with @ZhaofengWu first with "social bias frames" dataset. As I found this report, I tried also "coqa" and it fails as well.
I test this on Google Colab.
```
- `datasets` version: 1.15.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
```
Then another environment
```
- `datasets` version: 1.15.1
- Platform: macOS-12.0.1-arm64-arm-64bit
- Python version: 3.9.7
- PyArrow version: 6.0.1
```
I tried the notebook @albertvillanova provided earlier, and it fails...
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | Hi, still not able to reproduce the issue with `coqa`. If you still have this issue, could you please run these additional commands ?
```python
>>> import os
>>> from hashlib import md5
>>> from datasets.utils import DownloadManager, DownloadConfig
>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download("https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json") # it returns the cached file
>>> os.path.getsize(path)
9090845
>>> m = md5()
>>> m.update(open(path, "rb").read())
>>> m.hexdigest()
`95d427588e3733e4ebec55f6938dbba6`
>>> open(path).read(500)
'{\n "version": "1.0",\n "data": [\n {\n "source": "mctest",\n "id": "3dr23u6we5exclen4th8uq9rb42tel",\n "filename": "mc160.test.41",\n "story": "Once upon a time, in a barn near a farm house, there lived a little white kitten named Cotton. Cotton lived high up in a nice warm place above the barn where all of the farmer\'s horses slept. But Cotton wasn\'t alone in her little home above the barn, oh no. She shared her hay bed with her mommy and 5 other sisters. All of her sisters w'
```
This way we can know whether you downloaded a corrupted file or an error file that could cause the `NonMatchingChecksumError` error to happen | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 169 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
Hi, still not able to reproduce the issue with `coqa`. If you still have this issue, could you please run these additional commands ?
```python
>>> import os
>>> from hashlib import md5
>>> from datasets.utils import DownloadManager, DownloadConfig
>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download("https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json") # it returns the cached file
>>> os.path.getsize(path)
9090845
>>> m = md5()
>>> m.update(open(path, "rb").read())
>>> m.hexdigest()
`95d427588e3733e4ebec55f6938dbba6`
>>> open(path).read(500)
'{\n "version": "1.0",\n "data": [\n {\n "source": "mctest",\n "id": "3dr23u6we5exclen4th8uq9rb42tel",\n "filename": "mc160.test.41",\n "story": "Once upon a time, in a barn near a farm house, there lived a little white kitten named Cotton. Cotton lived high up in a nice warm place above the barn where all of the farmer\'s horses slept. But Cotton wasn\'t alone in her little home above the barn, oh no. She shared her hay bed with her mommy and 5 other sisters. All of her sisters w'
```
This way we can know whether you downloaded a corrupted file or an error file that could cause the `NonMatchingChecksumError` error to happen | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | ```
>>> import os
>>> from hashlib import md5
>>> from datasets.utils import DownloadManager, DownloadConfig
>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download("https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json") # it returns the cached file
>>> os.path.getsize(path)
222
>>> m = md5()
>>> m.update(open(path, "rb").read())
>>> m.hexdigest()
'1195812a37c01a4481a4748c85d0c6a9'
>>> open(path).read(500)
'<html>\n<head><title>503 Service Temporarily Unavailable</title></head>\n<body bgcolor="white">\n<center><h1>503 Service Temporarily Unavailable</h1></center>\n<hr><center>nginx/1.10.3 (Ubuntu)</center>\n</body>\n</html>\n'
```
Looks like there was a server-side error when downloading the dataset? But I don't believe this is a transient error given (a) deleting the cache and re-downloading gives the same error; (b) it happens on multiple platforms with different network configurations; (c) other people are getting this error too, see above. So I'm not sure why it works for some people but not others. | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 114 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
```
>>> import os
>>> from hashlib import md5
>>> from datasets.utils import DownloadManager, DownloadConfig
>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download("https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json") # it returns the cached file
>>> os.path.getsize(path)
222
>>> m = md5()
>>> m.update(open(path, "rb").read())
>>> m.hexdigest()
'1195812a37c01a4481a4748c85d0c6a9'
>>> open(path).read(500)
'<html>\n<head><title>503 Service Temporarily Unavailable</title></head>\n<body bgcolor="white">\n<center><h1>503 Service Temporarily Unavailable</h1></center>\n<hr><center>nginx/1.10.3 (Ubuntu)</center>\n</body>\n</html>\n'
```
Looks like there was a server-side error when downloading the dataset? But I don't believe this is a transient error given (a) deleting the cache and re-downloading gives the same error; (b) it happens on multiple platforms with different network configurations; (c) other people are getting this error too, see above. So I'm not sure why it works for some people but not others. | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | `wget https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json` does work. So I suspect there might be some problem in `datasets`' networking code? Can you give me some snippet that simulates how `datasets` requests the resource which I can run on my end? | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 36 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
`wget https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json` does work. So I suspect there might be some problem in `datasets`' networking code? Can you give me some snippet that simulates how `datasets` requests the resource which I can run on my end? | [
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] |
https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | Ok This is an issue with the server that hosts the data at `https://nlp.stanford.edu/nlp/data` that randomly returns 503 (by trying several times it also happens on my side), hopefully it can be fixed soon. I'll try to reach the people in charge of hosting the data | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 46 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
Ok This is an issue with the server that hosts the data at `https://nlp.stanford.edu/nlp/data` that randomly returns 503 (by trying several times it also happens on my side), hopefully it can be fixed soon. I'll try to reach the people in charge of hosting the data | [
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https://github.com/huggingface/datasets/issues/3269 | coqa NonMatchingChecksumError | You're right. I just opened a PR that would show this error if it happens again:
```python
ConnectionError: Couldn't reach https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json (error 503)
``` | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
``` | 24 | coqa NonMatchingChecksumError
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
You're right. I just opened a PR that would show this error if it happens again:
```python
ConnectionError: Couldn't reach https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json (error 503)
``` | [
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https://github.com/huggingface/datasets/issues/3268 | Dataset viewer issue for 'liweili/c4_200m' | Hi ! I think the issue comes from this [line](https://huggingface.co/datasets/liweili/c4_200m/blob/main/c4_200m.py#L87):
```python
path = filepath + "/*.tsv*"
```
You can fix this by doing this instead:
```python
path = os.path.join(filepath, "/*.tsv*")
```
Here is why:
Locally you can append `"/*.tsv*"` to your local path, however it doesn't work in streaming mode, and the dataset viewer does use the streaming mode.
In streaming mode, the download and extract part is done lazily. It means that instead of using local paths, it's still passing around URLs and [chained URLs](https://filesystem-spec.readthedocs.io/en/latest/features.html#url-chaining)
Therefore in streaming mode, `filepath` is not a local path, but instead is equal to
```python
zip://::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip
```
The `zip://` part means that we navigate inside the remote ZIP file.
You must use `os.path.join` to navigate inside it and get your TSV files:
```python
>>> os.path.join(filepath, "/*.tsv*")
zip://*.tsv*::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip
```
`datasets` extends `os.path.join`, `glob.glob`, etc. in your dataset scripts to work with remote files. | ## Dataset viewer issue for '*liweili/c4_200m*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*
*Server Error*
```
Status code: 404
Exception: Status404Error
Message: Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```
Am I the one who added this dataset ? Yes
| 149 | Dataset viewer issue for 'liweili/c4_200m'
## Dataset viewer issue for '*liweili/c4_200m*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*
*Server Error*
```
Status code: 404
Exception: Status404Error
Message: Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```
Am I the one who added this dataset ? Yes
Hi ! I think the issue comes from this [line](https://huggingface.co/datasets/liweili/c4_200m/blob/main/c4_200m.py#L87):
```python
path = filepath + "/*.tsv*"
```
You can fix this by doing this instead:
```python
path = os.path.join(filepath, "/*.tsv*")
```
Here is why:
Locally you can append `"/*.tsv*"` to your local path, however it doesn't work in streaming mode, and the dataset viewer does use the streaming mode.
In streaming mode, the download and extract part is done lazily. It means that instead of using local paths, it's still passing around URLs and [chained URLs](https://filesystem-spec.readthedocs.io/en/latest/features.html#url-chaining)
Therefore in streaming mode, `filepath` is not a local path, but instead is equal to
```python
zip://::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip
```
The `zip://` part means that we navigate inside the remote ZIP file.
You must use `os.path.join` to navigate inside it and get your TSV files:
```python
>>> os.path.join(filepath, "/*.tsv*")
zip://*.tsv*::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip
```
`datasets` extends `os.path.join`, `glob.glob`, etc. in your dataset scripts to work with remote files. | [
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https://github.com/huggingface/datasets/issues/3268 | Dataset viewer issue for 'liweili/c4_200m' | hi @lhoestq ! thanks for the tip! i've updated the line of code but it's still not working. am i doing something else wrong? thank you! | ## Dataset viewer issue for '*liweili/c4_200m*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*
*Server Error*
```
Status code: 404
Exception: Status404Error
Message: Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```
Am I the one who added this dataset ? Yes
| 26 | Dataset viewer issue for 'liweili/c4_200m'
## Dataset viewer issue for '*liweili/c4_200m*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*
*Server Error*
```
Status code: 404
Exception: Status404Error
Message: Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```
Am I the one who added this dataset ? Yes
hi @lhoestq ! thanks for the tip! i've updated the line of code but it's still not working. am i doing something else wrong? thank you! | [
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https://github.com/huggingface/datasets/issues/3268 | Dataset viewer issue for 'liweili/c4_200m' | Hi ! Your dataset code is all good now :)
```python
In [1]: from datasets import load_dataset
In [2]: d = load_dataset("liweili/c4_200m", streaming=True)
Downloading: 100%|█████████████████████████████████████████████| 2.79k/2.79k [00:00<00:00, 4.83MB/s]
Using custom data configuration default
In [3]: next(iter(d["train"]))
Out[3]:
{'input': 'Bitcoin is for $7,094 this morning, which CoinDesk says.',
'output': 'Bitcoin goes for $7,094 this morning, according to CoinDesk.'}
```
Though the viewer doesn't seem to be updated, I'll take a look at what's wrong | ## Dataset viewer issue for '*liweili/c4_200m*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*
*Server Error*
```
Status code: 404
Exception: Status404Error
Message: Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```
Am I the one who added this dataset ? Yes
| 73 | Dataset viewer issue for 'liweili/c4_200m'
## Dataset viewer issue for '*liweili/c4_200m*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*
*Server Error*
```
Status code: 404
Exception: Status404Error
Message: Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```
Am I the one who added this dataset ? Yes
Hi ! Your dataset code is all good now :)
```python
In [1]: from datasets import load_dataset
In [2]: d = load_dataset("liweili/c4_200m", streaming=True)
Downloading: 100%|█████████████████████████████████████████████| 2.79k/2.79k [00:00<00:00, 4.83MB/s]
Using custom data configuration default
In [3]: next(iter(d["train"]))
Out[3]:
{'input': 'Bitcoin is for $7,094 this morning, which CoinDesk says.',
'output': 'Bitcoin goes for $7,094 this morning, according to CoinDesk.'}
```
Though the viewer doesn't seem to be updated, I'll take a look at what's wrong | [
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https://github.com/huggingface/datasets/issues/3265 | Checksum error for kilt_task_wow | Using `dataset = load_dataset("kilt_tasks", "wow", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution. | ## Describe the bug
Checksum failed when downloads kilt_tasks_wow. See error output for details.
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('kilt_tasks','wow')
```
## Expected results
Download successful
## Actual results
```
Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5121.25it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1527.42it/s]
Traceback (most recent call last):
File "kilt_wow.py", line 30, in <module>
main()
File "kilt_wow.py", line 27, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "kilt_wow.py", line 21, in load_dataset
return datasets.load_dataset('kilt_tasks','wow')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
| 19 | Checksum error for kilt_task_wow
## Describe the bug
Checksum failed when downloads kilt_tasks_wow. See error output for details.
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('kilt_tasks','wow')
```
## Expected results
Download successful
## Actual results
```
Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5121.25it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1527.42it/s]
Traceback (most recent call last):
File "kilt_wow.py", line 30, in <module>
main()
File "kilt_wow.py", line 27, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "kilt_wow.py", line 21, in load_dataset
return datasets.load_dataset('kilt_tasks','wow')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
Using `dataset = load_dataset("kilt_tasks", "wow", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution. | [
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https://github.com/huggingface/datasets/issues/3265 | Checksum error for kilt_task_wow | Hi @slyviacassell, thanks for reporting.
Yes, there is an issue with the checksum verification. I'm fixing it.
And as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`. | ## Describe the bug
Checksum failed when downloads kilt_tasks_wow. See error output for details.
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('kilt_tasks','wow')
```
## Expected results
Download successful
## Actual results
```
Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5121.25it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1527.42it/s]
Traceback (most recent call last):
File "kilt_wow.py", line 30, in <module>
main()
File "kilt_wow.py", line 27, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "kilt_wow.py", line 21, in load_dataset
return datasets.load_dataset('kilt_tasks','wow')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
| 33 | Checksum error for kilt_task_wow
## Describe the bug
Checksum failed when downloads kilt_tasks_wow. See error output for details.
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('kilt_tasks','wow')
```
## Expected results
Download successful
## Actual results
```
Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5121.25it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1527.42it/s]
Traceback (most recent call last):
File "kilt_wow.py", line 30, in <module>
main()
File "kilt_wow.py", line 27, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "kilt_wow.py", line 21, in load_dataset
return datasets.load_dataset('kilt_tasks','wow')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
Hi @slyviacassell, thanks for reporting.
Yes, there is an issue with the checksum verification. I'm fixing it.
And as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`. | [
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https://github.com/huggingface/datasets/issues/3264 | Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution | #take
I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy with new ones provided by authors.
As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. I can include them in the dataset folder as the files are <1MB in size total. | ## Describe the bug
- WikiAuto Manual
The original manual datasets with the following downloading URL in this [repository](https://github.com/chaojiang06/wiki-auto) was [deleted](https://github.com/chaojiang06/wiki-auto/commit/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.
```
https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
The downloading URL for jeopardy may move from
```
http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
to
```
https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg
```
- definite_pronoun_resolution
The following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.
```
http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('wiki_auto','manual')
datasets.load_datasets('jeopardy')
datasets.load_datasets('definite_pronoun_resolution')
```
## Expected results
Download successfully
## Actual results
- WikiAuto Manual
```
Downloading and preparing dataset wiki_auto/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to /root/.cache/huggingface/datasets/wiki_auto/manual/1.0.0/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...
0%| | 0/3 [00:00<?, ?it/s]Traceback (most recent call last):
File "wiki_auto.py", line 43, in <module>
main()
File "wiki_auto.py", line 40, in main
train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 24, in generate_k_shot_data
dataset = self.load_dataset()
File "wiki_auto.py", line 34, in load_dataset
return datasets.load_dataset('wiki_auto', 'manual')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wiki_auto/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8/wiki_auto.py", line 193, in _split_generators
data_dir = dl_manager.download_and_extract(my_urls)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 592, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
```
Using custom data configuration default
Downloading and preparing dataset jeopardy/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to /root/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...
Traceback (most recent call last):
File "jeopardy.py", line 45, in <module>
main()
File "jeopardy.py", line 42, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "jeopardy.py", line 36, in load_dataset
return datasets.load_dataset("jeopardy")
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators
filepath = dl_manager.download_and_extract(_DATA_URL)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
- definite_pronoun_resolution
```
Downloading and preparing dataset definite_pronoun_resolution/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to /root/.cache/huggingface/datasets/definite_pronoun_resolution/plain_text/1.0.0/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...
0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):
File "definite_pronoun_resolution.py", line 37, in <module>
main()
File "definite_pronoun_resolution.py", line 34, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "definite_pronoun_resolution.py", line 28, in load_dataset
return datasets.load_dataset('definite_pronoun_resolution')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/definite_pronoun_resolution/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff/definite_pronoun_resolution.py", line 76, in _split_generators
files = dl_manager.download_and_extract(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
| 60 | Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution
## Describe the bug
- WikiAuto Manual
The original manual datasets with the following downloading URL in this [repository](https://github.com/chaojiang06/wiki-auto) was [deleted](https://github.com/chaojiang06/wiki-auto/commit/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.
```
https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
The downloading URL for jeopardy may move from
```
http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
to
```
https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg
```
- definite_pronoun_resolution
The following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.
```
http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('wiki_auto','manual')
datasets.load_datasets('jeopardy')
datasets.load_datasets('definite_pronoun_resolution')
```
## Expected results
Download successfully
## Actual results
- WikiAuto Manual
```
Downloading and preparing dataset wiki_auto/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to /root/.cache/huggingface/datasets/wiki_auto/manual/1.0.0/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...
0%| | 0/3 [00:00<?, ?it/s]Traceback (most recent call last):
File "wiki_auto.py", line 43, in <module>
main()
File "wiki_auto.py", line 40, in main
train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 24, in generate_k_shot_data
dataset = self.load_dataset()
File "wiki_auto.py", line 34, in load_dataset
return datasets.load_dataset('wiki_auto', 'manual')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wiki_auto/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8/wiki_auto.py", line 193, in _split_generators
data_dir = dl_manager.download_and_extract(my_urls)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 592, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
```
Using custom data configuration default
Downloading and preparing dataset jeopardy/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to /root/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...
Traceback (most recent call last):
File "jeopardy.py", line 45, in <module>
main()
File "jeopardy.py", line 42, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "jeopardy.py", line 36, in load_dataset
return datasets.load_dataset("jeopardy")
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators
filepath = dl_manager.download_and_extract(_DATA_URL)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
- definite_pronoun_resolution
```
Downloading and preparing dataset definite_pronoun_resolution/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to /root/.cache/huggingface/datasets/definite_pronoun_resolution/plain_text/1.0.0/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...
0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):
File "definite_pronoun_resolution.py", line 37, in <module>
main()
File "definite_pronoun_resolution.py", line 34, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "definite_pronoun_resolution.py", line 28, in load_dataset
return datasets.load_dataset('definite_pronoun_resolution')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/definite_pronoun_resolution/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff/definite_pronoun_resolution.py", line 76, in _split_generators
files = dl_manager.download_and_extract(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
#take
I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy with new ones provided by authors.
As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. I can include them in the dataset folder as the files are <1MB in size total. | [
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https://github.com/huggingface/datasets/issues/3264 | Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution | > #take I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy.
>
> As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. Anyone has opinions on whether it is preferable for me to host them somewhere (e.g. personal GDrive account) or upload them to the dataset folder directly and use github raw URLs? The files are <1MB in size.
I am planning to fix it next few days. But my to-do list is full and I do not have the cache of definite_pronoun_resolution. I am glad that you can take this. Thanks a lot! | ## Describe the bug
- WikiAuto Manual
The original manual datasets with the following downloading URL in this [repository](https://github.com/chaojiang06/wiki-auto) was [deleted](https://github.com/chaojiang06/wiki-auto/commit/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.
```
https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
The downloading URL for jeopardy may move from
```
http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
to
```
https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg
```
- definite_pronoun_resolution
The following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.
```
http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('wiki_auto','manual')
datasets.load_datasets('jeopardy')
datasets.load_datasets('definite_pronoun_resolution')
```
## Expected results
Download successfully
## Actual results
- WikiAuto Manual
```
Downloading and preparing dataset wiki_auto/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to /root/.cache/huggingface/datasets/wiki_auto/manual/1.0.0/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...
0%| | 0/3 [00:00<?, ?it/s]Traceback (most recent call last):
File "wiki_auto.py", line 43, in <module>
main()
File "wiki_auto.py", line 40, in main
train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 24, in generate_k_shot_data
dataset = self.load_dataset()
File "wiki_auto.py", line 34, in load_dataset
return datasets.load_dataset('wiki_auto', 'manual')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wiki_auto/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8/wiki_auto.py", line 193, in _split_generators
data_dir = dl_manager.download_and_extract(my_urls)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 592, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
```
Using custom data configuration default
Downloading and preparing dataset jeopardy/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to /root/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...
Traceback (most recent call last):
File "jeopardy.py", line 45, in <module>
main()
File "jeopardy.py", line 42, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "jeopardy.py", line 36, in load_dataset
return datasets.load_dataset("jeopardy")
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators
filepath = dl_manager.download_and_extract(_DATA_URL)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
- definite_pronoun_resolution
```
Downloading and preparing dataset definite_pronoun_resolution/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to /root/.cache/huggingface/datasets/definite_pronoun_resolution/plain_text/1.0.0/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...
0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):
File "definite_pronoun_resolution.py", line 37, in <module>
main()
File "definite_pronoun_resolution.py", line 34, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "definite_pronoun_resolution.py", line 28, in load_dataset
return datasets.load_dataset('definite_pronoun_resolution')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/definite_pronoun_resolution/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff/definite_pronoun_resolution.py", line 76, in _split_generators
files = dl_manager.download_and_extract(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
| 113 | Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution
## Describe the bug
- WikiAuto Manual
The original manual datasets with the following downloading URL in this [repository](https://github.com/chaojiang06/wiki-auto) was [deleted](https://github.com/chaojiang06/wiki-auto/commit/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.
```
https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
The downloading URL for jeopardy may move from
```
http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
to
```
https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg
```
- definite_pronoun_resolution
The following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.
```
http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('wiki_auto','manual')
datasets.load_datasets('jeopardy')
datasets.load_datasets('definite_pronoun_resolution')
```
## Expected results
Download successfully
## Actual results
- WikiAuto Manual
```
Downloading and preparing dataset wiki_auto/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to /root/.cache/huggingface/datasets/wiki_auto/manual/1.0.0/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...
0%| | 0/3 [00:00<?, ?it/s]Traceback (most recent call last):
File "wiki_auto.py", line 43, in <module>
main()
File "wiki_auto.py", line 40, in main
train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 24, in generate_k_shot_data
dataset = self.load_dataset()
File "wiki_auto.py", line 34, in load_dataset
return datasets.load_dataset('wiki_auto', 'manual')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wiki_auto/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8/wiki_auto.py", line 193, in _split_generators
data_dir = dl_manager.download_and_extract(my_urls)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 592, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
```
Using custom data configuration default
Downloading and preparing dataset jeopardy/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to /root/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...
Traceback (most recent call last):
File "jeopardy.py", line 45, in <module>
main()
File "jeopardy.py", line 42, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "jeopardy.py", line 36, in load_dataset
return datasets.load_dataset("jeopardy")
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators
filepath = dl_manager.download_and_extract(_DATA_URL)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
- definite_pronoun_resolution
```
Downloading and preparing dataset definite_pronoun_resolution/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to /root/.cache/huggingface/datasets/definite_pronoun_resolution/plain_text/1.0.0/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...
0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):
File "definite_pronoun_resolution.py", line 37, in <module>
main()
File "definite_pronoun_resolution.py", line 34, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "definite_pronoun_resolution.py", line 28, in load_dataset
return datasets.load_dataset('definite_pronoun_resolution')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/definite_pronoun_resolution/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff/definite_pronoun_resolution.py", line 76, in _split_generators
files = dl_manager.download_and_extract(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
> #take I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy.
>
> As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. Anyone has opinions on whether it is preferable for me to host them somewhere (e.g. personal GDrive account) or upload them to the dataset folder directly and use github raw URLs? The files are <1MB in size.
I am planning to fix it next few days. But my to-do list is full and I do not have the cache of definite_pronoun_resolution. I am glad that you can take this. Thanks a lot! | [
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https://github.com/huggingface/datasets/issues/3261 | Scifi_TV_Shows: Having trouble getting viewer to find appropriate files | Hi ! I think this is because `iter_archive` doesn't support ZIP files yet. See https://github.com/huggingface/datasets/issues/3272
You can navigate into the archive this way instead:
```python
# in split_generators
data_dir = dl_manager.download_and_extract(url)
train_filepath = os.path.join(data_dir, "all-sci-fi-data-train.txt")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_filepath,
},
),
...
])
# in generate_examples
with open(filepath, encoding="utf-8") as f:
...
``` | ## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus (Scifi_TV_Shows)*'
**Link:** [link](https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows)
I tried adding both a script (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/Scifi_TV_Shows.py) and some dummy examples (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/dummy), but the viewer still has a 404 error ("Not found. Maybe the cache is missing, or maybe the ressource does not exist."). I'm not sure what to try next. Thanks in advance!
Am I the one who added this dataset? Yes
| 56 | Scifi_TV_Shows: Having trouble getting viewer to find appropriate files
## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus (Scifi_TV_Shows)*'
**Link:** [link](https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows)
I tried adding both a script (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/Scifi_TV_Shows.py) and some dummy examples (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/dummy), but the viewer still has a 404 error ("Not found. Maybe the cache is missing, or maybe the ressource does not exist."). I'm not sure what to try next. Thanks in advance!
Am I the one who added this dataset? Yes
Hi ! I think this is because `iter_archive` doesn't support ZIP files yet. See https://github.com/huggingface/datasets/issues/3272
You can navigate into the archive this way instead:
```python
# in split_generators
data_dir = dl_manager.download_and_extract(url)
train_filepath = os.path.join(data_dir, "all-sci-fi-data-train.txt")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_filepath,
},
),
...
])
# in generate_examples
with open(filepath, encoding="utf-8") as f:
...
``` | [
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https://github.com/huggingface/datasets/issues/3257 | Use f-strings for string formatting | Hi, I would be glad to help with this. Is there anyone else working on it? | f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.
> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`. | 16 | Use f-strings for string formatting
f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.
> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`.
Hi, I would be glad to help with this. Is there anyone else working on it? | [
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https://github.com/huggingface/datasets/issues/3257 | Use f-strings for string formatting | Hi @Carlosbogo,
would you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has opened a PR that does that for all the other directories? | f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.
> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`. | 36 | Use f-strings for string formatting
f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.
> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`.
Hi @Carlosbogo,
would you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has opened a PR that does that for all the other directories? | [
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https://github.com/huggingface/datasets/issues/3253 | `GeneratorBasedBuilder` does not support `None` values | Hi,
thanks for reporting and providing a minimal reproducible example.
This line of the PR I've linked in our discussion on the Forum will add support for `None` values:
https://github.com/huggingface/datasets/blob/a53de01842aac65c66a49b2439e18fa93ff73ceb/src/datasets/features/features.py#L835
I expect that PR to be merged soon. | ## Describe the bug
`GeneratorBasedBuilder` does not support `None` values.
## Steps to reproduce the bug
See [this repository](https://github.com/pavel-lexyr/huggingface-datasets-bug-reproduction) for minimal reproduction.
## Expected results
Dataset is initialized with a `None` value in the `value` column.
## Actual results
```
Traceback (most recent call last):
File "main.py", line 3, in <module>
datasets.load_dataset("./bad-data")
File ".../datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File ".../datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File ".../datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File ".../datasets/builder.py", line 1103, in _prepare_split
example = self.info.features.encode_example(record)
File ".../datasets/features/features.py", line 1033, in encode_example
return encode_nested_example(self, example)
File ".../datasets/features/features.py", line 808, in encode_nested_example
return {
File ".../datasets/features/features.py", line 809, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File ".../datasets/features/features.py", line 855, in encode_nested_example
return schema.encode_example(obj)
File ".../datasets/features/features.py", line 299, in encode_example
return float(value)
TypeError: float() argument must be a string or a number, not 'NoneType'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 6.0.0 | 38 | `GeneratorBasedBuilder` does not support `None` values
## Describe the bug
`GeneratorBasedBuilder` does not support `None` values.
## Steps to reproduce the bug
See [this repository](https://github.com/pavel-lexyr/huggingface-datasets-bug-reproduction) for minimal reproduction.
## Expected results
Dataset is initialized with a `None` value in the `value` column.
## Actual results
```
Traceback (most recent call last):
File "main.py", line 3, in <module>
datasets.load_dataset("./bad-data")
File ".../datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File ".../datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File ".../datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File ".../datasets/builder.py", line 1103, in _prepare_split
example = self.info.features.encode_example(record)
File ".../datasets/features/features.py", line 1033, in encode_example
return encode_nested_example(self, example)
File ".../datasets/features/features.py", line 808, in encode_nested_example
return {
File ".../datasets/features/features.py", line 809, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File ".../datasets/features/features.py", line 855, in encode_nested_example
return schema.encode_example(obj)
File ".../datasets/features/features.py", line 299, in encode_example
return float(value)
TypeError: float() argument must be a string or a number, not 'NoneType'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 6.0.0
Hi,
thanks for reporting and providing a minimal reproducible example.
This line of the PR I've linked in our discussion on the Forum will add support for `None` values:
https://github.com/huggingface/datasets/blob/a53de01842aac65c66a49b2439e18fa93ff73ceb/src/datasets/features/features.py#L835
I expect that PR to be merged soon. | [
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https://github.com/huggingface/datasets/issues/3247 | Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError | Hi,
this issue is similar to https://github.com/huggingface/datasets/issues/3093, so you can either use the solution provided there or try to load the data in one chunk (you can control the chunk size by specifying the `chunksize` parameter (`int`) in `load_dataset`).
@lhoestq Is this worth opening an issue on Jira? Basically, PyArrow doesn't allow casts that change the order of the struct fields because they treat `pa.struct` as an ordered sequence. Reordering fields manually in Python is probably too slow, so I think this needs to be fixed by them to be usable on our side. | ## Describe the bug
When trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
Splitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.
Creating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works
## Steps to reproduce the bug
```python
load_dataset("json", data_files="test.json")
```
test.json ~25MB
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
...
```
working.json ~160bytes
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
```
## Expected results
It should load the dataset from the json file without error.
## Actual results
It raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
```
Traceback (most recent call last):
File "/Users/m/workspace/xxx/project/main.py", line 60, in <module>
dataset = load_dataset("json", data_files="result.json")
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/load.py", line 1627, in load_dataset
builder_instance.download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1685, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 630, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 338, in pyarrow.lib.asarray
File "pyarrow/table.pxi", line 304, in pyarrow.lib.ChunkedArray.cast
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/pyarrow/compute.py", line 309, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 528, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 327, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct
```
## Environment info
- `datasets` version: 1.14.0
- Platform: macOS-12.0.1-arm64-arm-64bit
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 94 | Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError
## Describe the bug
When trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
Splitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.
Creating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works
## Steps to reproduce the bug
```python
load_dataset("json", data_files="test.json")
```
test.json ~25MB
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
...
```
working.json ~160bytes
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
```
## Expected results
It should load the dataset from the json file without error.
## Actual results
It raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
```
Traceback (most recent call last):
File "/Users/m/workspace/xxx/project/main.py", line 60, in <module>
dataset = load_dataset("json", data_files="result.json")
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/load.py", line 1627, in load_dataset
builder_instance.download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1685, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 630, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 338, in pyarrow.lib.asarray
File "pyarrow/table.pxi", line 304, in pyarrow.lib.ChunkedArray.cast
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/pyarrow/compute.py", line 309, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 528, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 327, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct
```
## Environment info
- `datasets` version: 1.14.0
- Platform: macOS-12.0.1-arm64-arm-64bit
- Python version: 3.9.7
- PyArrow version: 6.0.0
Hi,
this issue is similar to https://github.com/huggingface/datasets/issues/3093, so you can either use the solution provided there or try to load the data in one chunk (you can control the chunk size by specifying the `chunksize` parameter (`int`) in `load_dataset`).
@lhoestq Is this worth opening an issue on Jira? Basically, PyArrow doesn't allow casts that change the order of the struct fields because they treat `pa.struct` as an ordered sequence. Reordering fields manually in Python is probably too slow, so I think this needs to be fixed by them to be usable on our side. | [
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https://github.com/huggingface/datasets/issues/3247 | Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError | I agree I would expect PyArrow to be able to handle this, do you want to open the issue @mariosasko ?
Although maybe it's possible to fix struct casting on our side without hurting performance too much, if it's simply a matter of reordering the arrays in the StructArray | ## Describe the bug
When trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
Splitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.
Creating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works
## Steps to reproduce the bug
```python
load_dataset("json", data_files="test.json")
```
test.json ~25MB
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
...
```
working.json ~160bytes
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
```
## Expected results
It should load the dataset from the json file without error.
## Actual results
It raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
```
Traceback (most recent call last):
File "/Users/m/workspace/xxx/project/main.py", line 60, in <module>
dataset = load_dataset("json", data_files="result.json")
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/load.py", line 1627, in load_dataset
builder_instance.download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1685, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 630, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 338, in pyarrow.lib.asarray
File "pyarrow/table.pxi", line 304, in pyarrow.lib.ChunkedArray.cast
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/pyarrow/compute.py", line 309, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 528, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 327, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct
```
## Environment info
- `datasets` version: 1.14.0
- Platform: macOS-12.0.1-arm64-arm-64bit
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 49 | Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError
## Describe the bug
When trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
Splitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.
Creating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works
## Steps to reproduce the bug
```python
load_dataset("json", data_files="test.json")
```
test.json ~25MB
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
...
```
working.json ~160bytes
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
```
## Expected results
It should load the dataset from the json file without error.
## Actual results
It raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
```
Traceback (most recent call last):
File "/Users/m/workspace/xxx/project/main.py", line 60, in <module>
dataset = load_dataset("json", data_files="result.json")
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/load.py", line 1627, in load_dataset
builder_instance.download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1685, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 630, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 338, in pyarrow.lib.asarray
File "pyarrow/table.pxi", line 304, in pyarrow.lib.ChunkedArray.cast
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/pyarrow/compute.py", line 309, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 528, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 327, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct
```
## Environment info
- `datasets` version: 1.14.0
- Platform: macOS-12.0.1-arm64-arm-64bit
- Python version: 3.9.7
- PyArrow version: 6.0.0
I agree I would expect PyArrow to be able to handle this, do you want to open the issue @mariosasko ?
Although maybe it's possible to fix struct casting on our side without hurting performance too much, if it's simply a matter of reordering the arrays in the StructArray | [
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https://github.com/huggingface/datasets/issues/3242 | Adding ANERcorp-CAMeLLab dataset | Adding ANERcorp dataset
## Adding a Dataset
- **Name:** *ANERcorp-CAMeLLab*
- **Description:** *Since its creation in 2008, the ANERcorp dataset (Benajiba & Rosso, 2008) has been a standard reference used by Arabic named entity recognition researchers around the world. However, over time, this dataset was copied over from user to user, modified slightly here and there, and split in many different configurations that made it hard to compare fairly across papers and systems.
In 2020, a group of researchers from CAMeL Lab (Habash, Alhafni and Oudah), and Mind Lab (Antoun and Baly) met with the creator of the corpus, Yassine Benajiba, to consult with him and collectively agree on an exact split, and accepted minor corrections from the original dataset. Bashar Alhafni from CAMeL Lab working with Nizar Habash implemented the decisions provided in this release.*
- **Paper:** *(a) Benajiba, Yassine, Paolo Rosso, and José Miguel Benedí Ruiz. "Anersys: An Arabic named entity recognition system based on maximum entropy." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 143-153. Springer, Berlin, Heidelberg, 2007.
(b)Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. "CAMeL Tools: An Open Source Python Toolkit, for Arabic Natural Language Processing." In Proceedings of the Conference on Language Resources and Evaluation (LREC 2020), Marseille, 2020.*
- **Data:** *https://camel.abudhabi.nyu.edu/anercorp/*
- **Motivation:** This is the standard dataset for evaluating NER performance in Arabic*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | null | 248 | Adding ANERcorp-CAMeLLab dataset
Adding ANERcorp dataset
## Adding a Dataset
- **Name:** *ANERcorp-CAMeLLab*
- **Description:** *Since its creation in 2008, the ANERcorp dataset (Benajiba & Rosso, 2008) has been a standard reference used by Arabic named entity recognition researchers around the world. However, over time, this dataset was copied over from user to user, modified slightly here and there, and split in many different configurations that made it hard to compare fairly across papers and systems.
In 2020, a group of researchers from CAMeL Lab (Habash, Alhafni and Oudah), and Mind Lab (Antoun and Baly) met with the creator of the corpus, Yassine Benajiba, to consult with him and collectively agree on an exact split, and accepted minor corrections from the original dataset. Bashar Alhafni from CAMeL Lab working with Nizar Habash implemented the decisions provided in this release.*
- **Paper:** *(a) Benajiba, Yassine, Paolo Rosso, and José Miguel Benedí Ruiz. "Anersys: An Arabic named entity recognition system based on maximum entropy." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 143-153. Springer, Berlin, Heidelberg, 2007.
(b)Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. "CAMeL Tools: An Open Source Python Toolkit, for Arabic Natural Language Processing." In Proceedings of the Conference on Language Resources and Evaluation (LREC 2020), Marseille, 2020.*
- **Data:** *https://camel.abudhabi.nyu.edu/anercorp/*
- **Motivation:** This is the standard dataset for evaluating NER performance in Arabic*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | [
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https://github.com/huggingface/datasets/issues/3240 | Couldn't reach data file for disaster_response_messages | It looks like the dataset isn't available anymore on appen.com
The CSV files appear to still be available at https://www.kaggle.com/landlord/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ? | ## Describe the bug
Following command gives an ConnectionError.
## Steps to reproduce the bug
```python
disaster = load_dataset('disaster_response_messages')
```
## Error
```
ConnectionError: Couldn't reach https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv
```
## Expected results
It should load dataset without an error
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
| 42 | Couldn't reach data file for disaster_response_messages
## Describe the bug
Following command gives an ConnectionError.
## Steps to reproduce the bug
```python
disaster = load_dataset('disaster_response_messages')
```
## Error
```
ConnectionError: Couldn't reach https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv
```
## Expected results
It should load dataset without an error
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
It looks like the dataset isn't available anymore on appen.com
The CSV files appear to still be available at https://www.kaggle.com/landlord/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ? | [
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https://github.com/huggingface/datasets/issues/3236 | Loading of datasets changed in #3110 returns no examples | Hi @eladsegal, thanks for reporting.
I am sorry, but I can't reproduce the bug:
```
In [1]: from datasets import load_dataset
In [2]: ds = load_dataset("qasper")
Downloading: 5.11kB [00:00, ?B/s]
Downloading and preparing dataset qasper/qasper (download: 9.88 MiB, generated: 35.11 MiB, post-processed: Unknown size, total: 44.99 MiB) to .cache\qasper\qasper\0.1.0\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8...
Dataset qasper downloaded and prepared to .cache\qasper\qasper\0.1.0\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<?, ?it/s]
In [3]: ds
Out[3]:
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
This makes me suspect that the origin of the problem might be the cache: I didn't have this dataset in my cache, although I guess you already had it, before the code change introduced by #3110.
@lhoestq might it be possible that the code change introduced by #3110 makes "inaccessible" all previously cached TAR-based datasets?
- Before the caching system downloaded and extracted the tar dataset
- Now it only downloads the tar dataset (no extraction is done) | ## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 173 | Loading of datasets changed in #3110 returns no examples
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
Hi @eladsegal, thanks for reporting.
I am sorry, but I can't reproduce the bug:
```
In [1]: from datasets import load_dataset
In [2]: ds = load_dataset("qasper")
Downloading: 5.11kB [00:00, ?B/s]
Downloading and preparing dataset qasper/qasper (download: 9.88 MiB, generated: 35.11 MiB, post-processed: Unknown size, total: 44.99 MiB) to .cache\qasper\qasper\0.1.0\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8...
Dataset qasper downloaded and prepared to .cache\qasper\qasper\0.1.0\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<?, ?it/s]
In [3]: ds
Out[3]:
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
This makes me suspect that the origin of the problem might be the cache: I didn't have this dataset in my cache, although I guess you already had it, before the code change introduced by #3110.
@lhoestq might it be possible that the code change introduced by #3110 makes "inaccessible" all previously cached TAR-based datasets?
- Before the caching system downloaded and extracted the tar dataset
- Now it only downloads the tar dataset (no extraction is done) | [
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https://github.com/huggingface/datasets/issues/3236 | Loading of datasets changed in #3110 returns no examples | I can't reproduce either in my environment (macos, python 3.7).
In your case it generates zero examples. This can only happen if the extraction of the TAR archive doesn't output the right filenames. Indeed if the `qasper` script can't find the right file to load, it's currently ignored and it returns zero examples. This case was not even considered when #3110 was developed since we considered the file names to be deterministic - and not depend on your environment.
Therefore here is my hypothesis:
- either the cache is corrupted somehow with an empty TAR archive
- OR I suspect that the issue comes from python 3.8
| ## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 107 | Loading of datasets changed in #3110 returns no examples
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
I can't reproduce either in my environment (macos, python 3.7).
In your case it generates zero examples. This can only happen if the extraction of the TAR archive doesn't output the right filenames. Indeed if the `qasper` script can't find the right file to load, it's currently ignored and it returns zero examples. This case was not even considered when #3110 was developed since we considered the file names to be deterministic - and not depend on your environment.
Therefore here is my hypothesis:
- either the cache is corrupted somehow with an empty TAR archive
- OR I suspect that the issue comes from python 3.8
| [
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0.2818650603,
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] |
https://github.com/huggingface/datasets/issues/3236 | Loading of datasets changed in #3110 returns no examples | I just tried again on python 3.8 and I was able to reproduce the issue. Let me work on a fix | ## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 21 | Loading of datasets changed in #3110 returns no examples
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
I just tried again on python 3.8 and I was able to reproduce the issue. Let me work on a fix | [
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https://github.com/huggingface/datasets/issues/3236 | Loading of datasets changed in #3110 returns no examples | Ok I found the issue. It's not related to python 3.8 in itself though. This issue happens because your local installation of `datasets` is outdated compared to the changes to datasets in #3110
To fix this you just have to pull the latest changes from `master` :)
Let me know if that helps !
--------------
Here are more details about my investigation:
It's possible to reproduce this issue if you use `datasets<=1.15.1` or before b6469baa22c174b3906c631802a7016fedea6780 and if you load the dataset after revision b6469baa22c174b3906c631802a7016fedea6780. This is because `dl_manager.iter_archive` had issues at that time (and it was not used anywhere anyway).
In particular it was returning the absolute path to extracted files instead of the relative path of the file inside the archive. This was an issue because `dl_manager.iter_archive` isn't supposed to extract the TAR archive. Instead, it iterates over all the files inside the archive, without creating a directory with the extracted content.
Therefore if you want to use the datasets on `master`, make sure that you have an up-to-date local installation of `datasets` as well, or you may face incompatibilities like this. | ## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 183 | Loading of datasets changed in #3110 returns no examples
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
Ok I found the issue. It's not related to python 3.8 in itself though. This issue happens because your local installation of `datasets` is outdated compared to the changes to datasets in #3110
To fix this you just have to pull the latest changes from `master` :)
Let me know if that helps !
--------------
Here are more details about my investigation:
It's possible to reproduce this issue if you use `datasets<=1.15.1` or before b6469baa22c174b3906c631802a7016fedea6780 and if you load the dataset after revision b6469baa22c174b3906c631802a7016fedea6780. This is because `dl_manager.iter_archive` had issues at that time (and it was not used anywhere anyway).
In particular it was returning the absolute path to extracted files instead of the relative path of the file inside the archive. This was an issue because `dl_manager.iter_archive` isn't supposed to extract the TAR archive. Instead, it iterates over all the files inside the archive, without creating a directory with the extracted content.
Therefore if you want to use the datasets on `master`, make sure that you have an up-to-date local installation of `datasets` as well, or you may face incompatibilities like this. | [
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https://github.com/huggingface/datasets/issues/3236 | Loading of datasets changed in #3110 returns no examples | Thanks!
But what about code that is already using older version of datasets?
The reason I encountered this issue was that suddenly one of my repos with version 1.12.1 started getting 0 examples.
I handled it by adding `revision` to `load_dataset`, but I guess it would still be an issue for other users who doesn't know this. | ## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 57 | Loading of datasets changed in #3110 returns no examples
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
Thanks!
But what about code that is already using older version of datasets?
The reason I encountered this issue was that suddenly one of my repos with version 1.12.1 started getting 0 examples.
I handled it by adding `revision` to `load_dataset`, but I guess it would still be an issue for other users who doesn't know this. | [
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https://github.com/huggingface/datasets/issues/3236 | Loading of datasets changed in #3110 returns no examples | Hi, in 1.12.1 it uses the dataset scripts from that time, not the one on master.
It only uses the datasets from master if you installed `datasets` from source, or if the dataset isn't available in your local version (in this case it shows a warning and it loads from master).
| ## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 51 | Loading of datasets changed in #3110 returns no examples
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
Hi, in 1.12.1 it uses the dataset scripts from that time, not the one on master.
It only uses the datasets from master if you installed `datasets` from source, or if the dataset isn't available in your local version (in this case it shows a warning and it loads from master).
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https://github.com/huggingface/datasets/issues/3236 | Loading of datasets changed in #3110 returns no examples | OK, I understand the issue a bit better now.
I see I wasn't on 1.12.1, but on 1.12.1.dev0 and since it is a dev version it uses master.
So users that use an old dev version must specify revision or else they'll encounter this problem.
BTW, when I opened the issue I installed the latest master version with
```
pip install git+git://github.com/huggingface/datasets@master#egg=datasets
```
and also used `download_mode="force_redownload"`, and it still returned 0 examples.
Now I deleted all of the cache and ran the code again, and it worked.
I'm not sure what exactly happened here, but looks like it was due to a mix of an unofficial version and its cache.
Thanks again! | ## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 113 | Loading of datasets changed in #3110 returns no examples
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
OK, I understand the issue a bit better now.
I see I wasn't on 1.12.1, but on 1.12.1.dev0 and since it is a dev version it uses master.
So users that use an old dev version must specify revision or else they'll encounter this problem.
BTW, when I opened the issue I installed the latest master version with
```
pip install git+git://github.com/huggingface/datasets@master#egg=datasets
```
and also used `download_mode="force_redownload"`, and it still returned 0 examples.
Now I deleted all of the cache and ran the code again, and it worked.
I'm not sure what exactly happened here, but looks like it was due to a mix of an unofficial version and its cache.
Thanks again! | [
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https://github.com/huggingface/datasets/issues/3232 | The Xsum datasets seems not able to download. | > Hi ! On my side the URL is working fine, could you try again ?
I try it again and cannot download the file (might because of my location). Could you please provide another download link(such as google drive)? :> | ## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
| 41 | The Xsum datasets seems not able to download.
## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
> Hi ! On my side the URL is working fine, could you try again ?
I try it again and cannot download the file (might because of my location). Could you please provide another download link(such as google drive)? :> | [
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https://github.com/huggingface/datasets/issues/3232 | The Xsum datasets seems not able to download. | I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example. | ## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
| 41 | The Xsum datasets seems not able to download.
## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example. | [
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https://github.com/huggingface/datasets/issues/3232 | The Xsum datasets seems not able to download. | > I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.
:> ok. Thanks for your reply. | ## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
| 48 | The Xsum datasets seems not able to download.
## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
> I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.
:> ok. Thanks for your reply. | [
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https://github.com/huggingface/datasets/issues/3227 | Error in `Json(datasets.ArrowBasedBuilder)` class | I have additionally identified the source of the error, being that [this condition](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L124-L126) in the file
`python3.8/site-packages/datasets/packaged_modules/json/json.py` is not being entered correctly:
```python
if (
isinstance(e, pa.ArrowInvalid)
and "straddling" not in str(e)
or block_size > len(batch)
):
```
From what I can tell, in my case the block_size simply needs to be increased, but the error message does not contain "straddling" so the condition does trigger correctly and we fail to reach [the line to increase block_size](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L135).
Changing the condition above to simply
```python
if (
block_size > len(batch)
):
```
Fixes the error for me. I'm happy to create a PR containing this fix if the developers deem the other conditions unnecessary. | ## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
├── testdata
│ └── mydata.json
└── test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
| 113 | Error in `Json(datasets.ArrowBasedBuilder)` class
## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
├── testdata
│ └── mydata.json
└── test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
I have additionally identified the source of the error, being that [this condition](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L124-L126) in the file
`python3.8/site-packages/datasets/packaged_modules/json/json.py` is not being entered correctly:
```python
if (
isinstance(e, pa.ArrowInvalid)
and "straddling" not in str(e)
or block_size > len(batch)
):
```
From what I can tell, in my case the block_size simply needs to be increased, but the error message does not contain "straddling" so the condition does trigger correctly and we fail to reach [the line to increase block_size](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L135).
Changing the condition above to simply
```python
if (
block_size > len(batch)
):
```
Fixes the error for me. I'm happy to create a PR containing this fix if the developers deem the other conditions unnecessary. | [
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https://github.com/huggingface/datasets/issues/3227 | Error in `Json(datasets.ArrowBasedBuilder)` class | Hi ! I think the issue comes from the fact that your JSON file is not a valid JSON Lines file.
Each example should be on one single line.
Can you try fixing the format to have one line per example and try again ? | ## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
├── testdata
│ └── mydata.json
└── test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
| 45 | Error in `Json(datasets.ArrowBasedBuilder)` class
## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
├── testdata
│ └── mydata.json
└── test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
Hi ! I think the issue comes from the fact that your JSON file is not a valid JSON Lines file.
Each example should be on one single line.
Can you try fixing the format to have one line per example and try again ? | [
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https://github.com/huggingface/datasets/issues/3227 | Error in `Json(datasets.ArrowBasedBuilder)` class | :open_mouth: you're right, that did it! I just put everything on a single line (my file only has a single example) and that fixed the error. Thank you so much! | ## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
├── testdata
│ └── mydata.json
└── test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
| 30 | Error in `Json(datasets.ArrowBasedBuilder)` class
## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
├── testdata
│ └── mydata.json
└── test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
:open_mouth: you're right, that did it! I just put everything on a single line (my file only has a single example) and that fixed the error. Thank you so much! | [
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https://github.com/huggingface/datasets/issues/3210 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py | Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?
Maybe you're having this error because you don't have access to this URL from python ? | when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue? | 35 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py
when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue?
Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?
Maybe you're having this error because you don't have access to this URL from python ? | [
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https://github.com/huggingface/datasets/issues/3210 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py | You don't need authentication to access those github hosted files
Please check that you can access this URL from your browser and also from your terminal | when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue? | 26 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py
when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue?
You don't need authentication to access those github hosted files
Please check that you can access this URL from your browser and also from your terminal | [
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https://github.com/huggingface/datasets/issues/3204 | FileNotFoundError for TupleIE dataste | @mariosasko @lhoestq Could you give me an update on how to load the dataset after the fix?
Thanks. | Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
| 18 | FileNotFoundError for TupleIE dataste
Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
@mariosasko @lhoestq Could you give me an update on how to load the dataset after the fix?
Thanks. | [
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https://github.com/huggingface/datasets/issues/3204 | FileNotFoundError for TupleIE dataste | Hi @arda-vianai,
first, you can try:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all', revision="master")
```
If this doesn't work, your version of `datasets` is missing some features that are required to run the dataset script, so install the master version with the following command:
```
pip install git+https://github.com/huggingface/datasets.git
```
and then:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all')
```
should work (even without `revision`). | Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
| 64 | FileNotFoundError for TupleIE dataste
Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
Hi @arda-vianai,
first, you can try:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all', revision="master")
```
If this doesn't work, your version of `datasets` is missing some features that are required to run the dataset script, so install the master version with the following command:
```
pip install git+https://github.com/huggingface/datasets.git
```
and then:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all')
```
should work (even without `revision`). | [
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] |
https://github.com/huggingface/datasets/issues/3204 | FileNotFoundError for TupleIE dataste | @mariosasko
Thanks, it is working now. I actually did that before but I didn't restart the kernel. I restarted it and it works now. My bad!!!
Many thanks and great job!
-arda | Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
| 32 | FileNotFoundError for TupleIE dataste
Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
@mariosasko
Thanks, it is working now. I actually did that before but I didn't restart the kernel. I restarted it and it works now. My bad!!!
Many thanks and great job!
-arda | [
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https://github.com/huggingface/datasets/issues/3191 | Dataset viewer issue for '*compguesswhat*' | ```python
>>> import datasets
>>> dataset = datasets.load_dataset('compguesswhat', name='compguesswhat-original',split='train', streaming=True)
>>> next(iter(dataset))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
for key, example in self._iter():
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 494, in _iter
yield from ex_iterable
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/compguesswhat/4d08b9e0a8d1cf036c9626c93be4a759fdd9fcce050ea503ea14b075e830c799/compguesswhat.py", line 251, in _generate_examples
with gzip.open(filepath) as in_file:
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'zip://compguesswhat-original/0.2.0/compguesswhat.train.jsonl.gz::https://www.dropbox.com/s/l0nc13udml6vs0w/compguesswhat-original.zip?dl=1'
```
It's an issue with the streaming mode. Note that normal mode is used by the dataset viewer when streaming is failing, but only for the smallest datasets. This dataset is above the limit, hence the error.
Same case as https://github.com/huggingface/datasets/issues/3186#issuecomment-1096549774. | ## Dataset viewer issue for '*compguesswhat*'
**Link:** https://huggingface.co/datasets/compguesswhat
File not found
Am I the one who added this dataset ? No
| 137 | Dataset viewer issue for '*compguesswhat*'
## Dataset viewer issue for '*compguesswhat*'
**Link:** https://huggingface.co/datasets/compguesswhat
File not found
Am I the one who added this dataset ? No
```python
>>> import datasets
>>> dataset = datasets.load_dataset('compguesswhat', name='compguesswhat-original',split='train', streaming=True)
>>> next(iter(dataset))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
for key, example in self._iter():
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 494, in _iter
yield from ex_iterable
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/compguesswhat/4d08b9e0a8d1cf036c9626c93be4a759fdd9fcce050ea503ea14b075e830c799/compguesswhat.py", line 251, in _generate_examples
with gzip.open(filepath) as in_file:
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'zip://compguesswhat-original/0.2.0/compguesswhat.train.jsonl.gz::https://www.dropbox.com/s/l0nc13udml6vs0w/compguesswhat-original.zip?dl=1'
```
It's an issue with the streaming mode. Note that normal mode is used by the dataset viewer when streaming is failing, but only for the smallest datasets. This dataset is above the limit, hence the error.
Same case as https://github.com/huggingface/datasets/issues/3186#issuecomment-1096549774. | [
-0.1937608123,
-0.1040919349,
-0.0767582282,
0.3095780313,
0.132095933,
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https://github.com/huggingface/datasets/issues/3190 | combination of shuffle and filter results in a bug | Hi ! There was a regression in `datasets` 1.12 that introduced this bug. It has been fixed in #3019 in 1.13
Can you try to update `datasets` and try again ? | ## Describe the bug
Hi,
I would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq.
Thanks.
Best regards
Rabeeh
## Steps to reproduce the bug
```python
import numpy as np
import datasets
datasets = datasets.load_dataset('super_glue', 'rte', script_version="master")
shuffled_data = datasets["train"].shuffle(seed=42)
for label in range(2):
print("label ", label)
data = shuffled_data.filter(lambda example: int(example['label']) == label)
print("length ", len(data), np.unique(data['label']))
```
## Expected results
Filtering per label, should only return the data with that specific label.
## Actual results
As you can see, filtered data per label, has still two labels of [0, 1]
```
label 0
length 1249 [0 1]
label 1
length 1241 [0 1]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.7.11
- PyArrow version: 5.0.0
| 31 | combination of shuffle and filter results in a bug
## Describe the bug
Hi,
I would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq.
Thanks.
Best regards
Rabeeh
## Steps to reproduce the bug
```python
import numpy as np
import datasets
datasets = datasets.load_dataset('super_glue', 'rte', script_version="master")
shuffled_data = datasets["train"].shuffle(seed=42)
for label in range(2):
print("label ", label)
data = shuffled_data.filter(lambda example: int(example['label']) == label)
print("length ", len(data), np.unique(data['label']))
```
## Expected results
Filtering per label, should only return the data with that specific label.
## Actual results
As you can see, filtered data per label, has still two labels of [0, 1]
```
label 0
length 1249 [0 1]
label 1
length 1241 [0 1]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.7.11
- PyArrow version: 5.0.0
Hi ! There was a regression in `datasets` 1.12 that introduced this bug. It has been fixed in #3019 in 1.13
Can you try to update `datasets` and try again ? | [
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https://github.com/huggingface/datasets/issues/3189 | conll2003 incorrect label explanation | Hi @BramVanroy,
since these fields are of type `ClassLabel` (you can check this with `dset.features`), you can inspect the possible values with:
```python
dset.features[field_name].feature.names # .feature because it's a sequence of labels
```
and to find the mapping between names and integers, use:
```python
dset.features[field_name].feature.int2str(value_or_values_list) # map integer value to string value
# or
dset.features[field_name].feature.str2int(value_or_values_list) # map string value to integer value
```
| In the [conll2003](https://huggingface.co/datasets/conll2003#data-fields) README, the labels are described as follows
> - `id`: a `string` feature.
> - `tokens`: a `list` of `string` features.
> - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4).
> - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
> - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8).
First of all, it would be great if we can get a list of ALL possible pos_tags.
Second, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0.
EDIT: not really a bug, sorry for mistagging. | 63 | conll2003 incorrect label explanation
In the [conll2003](https://huggingface.co/datasets/conll2003#data-fields) README, the labels are described as follows
> - `id`: a `string` feature.
> - `tokens`: a `list` of `string` features.
> - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4).
> - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
> - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8).
First of all, it would be great if we can get a list of ALL possible pos_tags.
Second, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0.
EDIT: not really a bug, sorry for mistagging.
Hi @BramVanroy,
since these fields are of type `ClassLabel` (you can check this with `dset.features`), you can inspect the possible values with:
```python
dset.features[field_name].feature.names # .feature because it's a sequence of labels
```
and to find the mapping between names and integers, use:
```python
dset.features[field_name].feature.int2str(value_or_values_list) # map integer value to string value
# or
dset.features[field_name].feature.str2int(value_or_values_list) # map string value to integer value
```
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] |
https://github.com/huggingface/datasets/issues/3188 | conll2002 issues | Hi ! Thanks for reporting :)
This is related to https://github.com/huggingface/datasets/issues/2742, I'm working on it. It should fix the viewer for around 80 datasets.
| **Link:** https://huggingface.co/datasets/conll2002
The dataset viewer throws a server error when trying to preview the dataset.
```
Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet
```
In addition, the "point of contact" has encoding issues and does not work when clicked.
Am I the one who added this dataset ? No, @lhoestq did | 24 | conll2002 issues
**Link:** https://huggingface.co/datasets/conll2002
The dataset viewer throws a server error when trying to preview the dataset.
```
Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet
```
In addition, the "point of contact" has encoding issues and does not work when clicked.
Am I the one who added this dataset ? No, @lhoestq did
Hi ! Thanks for reporting :)
This is related to https://github.com/huggingface/datasets/issues/2742, I'm working on it. It should fix the viewer for around 80 datasets.
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https://github.com/huggingface/datasets/issues/3188 | conll2002 issues | Ah, hadn't seen that sorry.
The scrambled "point of contact" is a separate issue though, I think. | **Link:** https://huggingface.co/datasets/conll2002
The dataset viewer throws a server error when trying to preview the dataset.
```
Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet
```
In addition, the "point of contact" has encoding issues and does not work when clicked.
Am I the one who added this dataset ? No, @lhoestq did | 17 | conll2002 issues
**Link:** https://huggingface.co/datasets/conll2002
The dataset viewer throws a server error when trying to preview the dataset.
```
Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet
```
In addition, the "point of contact" has encoding issues and does not work when clicked.
Am I the one who added this dataset ? No, @lhoestq did
Ah, hadn't seen that sorry.
The scrambled "point of contact" is a separate issue though, I think. | [
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https://github.com/huggingface/datasets/issues/3186 | Dataset viewer for nli_tr | It's an issue with the streaming mode:
```python
>>> import datasets
>>> dataset = datasets.load_dataset('nli_tr', name='snli_tr',split='test', streaming=True)
>>> next(iter(dataset))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
for key, example in self._iter():
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 494, in _iter
yield from ex_iterable
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/nli_tr/c2ddd0c0a70caddac6a81c2dae5ca7939f00060d517d08f1983927818dba6521/nli_tr.py", line 155, in _generate_examples
with codecs.open(filepath, encoding="utf-8") as f:
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/codecs.py", line 905, in open
file = builtins.open(filename, mode, buffering)
FileNotFoundError: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_test.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip'
```
Note that normal mode is used by the dataset viewer when streaming is failing, but only for the smallest datasets. `nli_tr` is above the limit, hence the error. | ## Dataset viewer issue for '*nli_tr*'
**Link:** https://huggingface.co/datasets/nli_tr
Hello,
Thank you for the new dataset preview feature that will help the users to view the datasets online.
We just noticed that the dataset viewer widget in the `nli_tr` dataset shows the error below. The error must be due to a temporary problem that may have blocked access to the dataset through the dataset viewer. But the dataset is currently accessible through the link in the error message. May we kindly ask if it would be possible to rerun the job so that it can access the dataset for the dataset viewer function?
Thank you.
Emrah
------------------------------------------
Server Error
Status code: 404
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_train.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip
------------------------------------------
Am I the one who added this dataset ? Yes
| 119 | Dataset viewer for nli_tr
## Dataset viewer issue for '*nli_tr*'
**Link:** https://huggingface.co/datasets/nli_tr
Hello,
Thank you for the new dataset preview feature that will help the users to view the datasets online.
We just noticed that the dataset viewer widget in the `nli_tr` dataset shows the error below. The error must be due to a temporary problem that may have blocked access to the dataset through the dataset viewer. But the dataset is currently accessible through the link in the error message. May we kindly ask if it would be possible to rerun the job so that it can access the dataset for the dataset viewer function?
Thank you.
Emrah
------------------------------------------
Server Error
Status code: 404
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_train.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip
------------------------------------------
Am I the one who added this dataset ? Yes
It's an issue with the streaming mode:
```python
>>> import datasets
>>> dataset = datasets.load_dataset('nli_tr', name='snli_tr',split='test', streaming=True)
>>> next(iter(dataset))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
for key, example in self._iter():
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 494, in _iter
yield from ex_iterable
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/nli_tr/c2ddd0c0a70caddac6a81c2dae5ca7939f00060d517d08f1983927818dba6521/nli_tr.py", line 155, in _generate_examples
with codecs.open(filepath, encoding="utf-8") as f:
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/codecs.py", line 905, in open
file = builtins.open(filename, mode, buffering)
FileNotFoundError: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_test.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip'
```
Note that normal mode is used by the dataset viewer when streaming is failing, but only for the smallest datasets. `nli_tr` is above the limit, hence the error. | [
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https://github.com/huggingface/datasets/issues/3185 | 7z dataset preview not implemented? | It's a bug in the dataset viewer: the dataset cannot be downloaded in streaming mode, but since the dataset is relatively small, the dataset viewer should have fallback to normal mode. Working on a fix. | ## Dataset viewer issue for dataset 'samsum'
**Link:** https://huggingface.co/datasets/samsum
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol '7z' for file at 'https://arxiv.org/src/1911.12237v2/anc/corpus.7z' is not implemented yet
| 35 | 7z dataset preview not implemented?
## Dataset viewer issue for dataset 'samsum'
**Link:** https://huggingface.co/datasets/samsum
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol '7z' for file at 'https://arxiv.org/src/1911.12237v2/anc/corpus.7z' is not implemented yet
It's a bug in the dataset viewer: the dataset cannot be downloaded in streaming mode, but since the dataset is relatively small, the dataset viewer should have fallback to normal mode. Working on a fix. | [
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https://github.com/huggingface/datasets/issues/3181 | `None` converted to `"None"` when loading a dataset | Hi @eladsegal, thanks for reporting.
@mariosasko I saw you are already working on this, but maybe my comment will be useful to you.
All values are casted to their corresponding feature type (including `None` values). For example if the feature type is `Value("bool")`, `None` is casted to `False`.
It is true that strings were an exception, but this was recently fixed by @lhoestq (see #3158). | ## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 65 | `None` converted to `"None"` when loading a dataset
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
Hi @eladsegal, thanks for reporting.
@mariosasko I saw you are already working on this, but maybe my comment will be useful to you.
All values are casted to their corresponding feature type (including `None` values). For example if the feature type is `Value("bool")`, `None` is casted to `False`.
It is true that strings were an exception, but this was recently fixed by @lhoestq (see #3158). | [
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https://github.com/huggingface/datasets/issues/3181 | `None` converted to `"None"` when loading a dataset | Thanks for reporting.
This is actually a breaking change that I think can cause issues when users preprocess their data. String columns used to be nullable. Maybe we can correct https://github.com/huggingface/datasets/pull/3158 to keep the None values and avoid this breaking change ?
EDIT: the other types (bool, int, etc) can also become nullable IMO | ## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 54 | `None` converted to `"None"` when loading a dataset
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
Thanks for reporting.
This is actually a breaking change that I think can cause issues when users preprocess their data. String columns used to be nullable. Maybe we can correct https://github.com/huggingface/datasets/pull/3158 to keep the None values and avoid this breaking change ?
EDIT: the other types (bool, int, etc) can also become nullable IMO | [
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https://github.com/huggingface/datasets/issues/3181 | `None` converted to `"None"` when loading a dataset | So what would be the best way to handle a feature that can have a null value in some of the instances? So far I used `None`.
Using the empty string won't be a good option, as it can be an actual value in the data and is not the same as not having a value at all. | ## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 58 | `None` converted to `"None"` when loading a dataset
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
So what would be the best way to handle a feature that can have a null value in some of the instances? So far I used `None`.
Using the empty string won't be a good option, as it can be an actual value in the data and is not the same as not having a value at all. | [
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] |
https://github.com/huggingface/datasets/issues/3181 | `None` converted to `"None"` when loading a dataset | Hi @eladsegal,
Use `None`. As @albertvillanova correctly pointed out, this change in conversion was introduced (by mistake) in #3158. To avoid it, install the earlier revision with:
```
pip install git+https://github.com/huggingface/datasets.git@8107844ec0e7add005db0585c772ee20adc01a5e
```
I'm making all the feature types nullable as we speak, and the fix will be merged probably early next week. | ## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 52 | `None` converted to `"None"` when loading a dataset
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
Hi @eladsegal,
Use `None`. As @albertvillanova correctly pointed out, this change in conversion was introduced (by mistake) in #3158. To avoid it, install the earlier revision with:
```
pip install git+https://github.com/huggingface/datasets.git@8107844ec0e7add005db0585c772ee20adc01a5e
```
I'm making all the feature types nullable as we speak, and the fix will be merged probably early next week. | [
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https://github.com/huggingface/datasets/issues/3181 | `None` converted to `"None"` when loading a dataset | https://github.com/huggingface/datasets/pull/3195 fixed it, we'll do a new release soon :)
For now feel free to install `datasets` from the master branch | ## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 21 | `None` converted to `"None"` when loading a dataset
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
https://github.com/huggingface/datasets/pull/3195 fixed it, we'll do a new release soon :)
For now feel free to install `datasets` from the master branch | [
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https://github.com/huggingface/datasets/issues/3181 | `None` converted to `"None"` when loading a dataset | Thanks, but unfortunately looks like it isn't fixed yet 😢
[notebook for 1.14.0](https://colab.research.google.com/drive/1SV3sFXPJMWSQgbm4pr9Y1Q8OJ4JYKcDo?usp=sharing)
[notebook for master](https://colab.research.google.com/drive/145wDpuO74MmsuI0SVLcI1IswG6aHpyhi?usp=sharing) | ## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 16 | `None` converted to `"None"` when loading a dataset
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
Thanks, but unfortunately looks like it isn't fixed yet 😢
[notebook for 1.14.0](https://colab.research.google.com/drive/1SV3sFXPJMWSQgbm4pr9Y1Q8OJ4JYKcDo?usp=sharing)
[notebook for master](https://colab.research.google.com/drive/145wDpuO74MmsuI0SVLcI1IswG6aHpyhi?usp=sharing) | [
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https://github.com/huggingface/datasets/issues/3181 | `None` converted to `"None"` when loading a dataset | Oh, sorry. I deleted the fix by accident when I was resolving a merge conflict. Let me fix this real quick. | ## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 21 | `None` converted to `"None"` when loading a dataset
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
Oh, sorry. I deleted the fix by accident when I was resolving a merge conflict. Let me fix this real quick. | [
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-0.3202935457,
-0.2545708716,
0.4859703779,
-0.1807729304,
-0.1767816395,
-0.254753828,
0.2028669566,
0.0377263017,
-0.3065603375,
-0.6241031289,
-0.1227760911,
0.4069283605,
-0.3267411888,
-0.214220643,
0.111270003,
-0.1418680549,
0.2245862037,
-0.3807933331,
0.221859917,
0.044005055,
0.01985191,
0.0425349027,
-0.4498520494
] |
https://github.com/huggingface/datasets/issues/3178 | "Property couldn't be hashed properly" even though fully picklable | After some digging, I found that this is caused by `dill` and using `recurse=True)` when trying to dump the object. The problem also occurs without multiprocessing. I can only find [the following information](https://dill.readthedocs.io/en/latest/dill.html#dill._dill.dumps) about this:
> If recurse=True, then objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. This is needed for functions defined via exec().
In the utils, this is explicitly enabled
https://github.com/huggingface/datasets/blob/df63614223bf1dd1feb267d39d741bada613352c/src/datasets/utils/py_utils.py#L327-L330
Is this really necessary? Is there a way around it? Also pinging the spaCy team in case this is easy to solve on their end. (I hope so.) | ## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 108 | "Property couldn't be hashed properly" even though fully picklable
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
After some digging, I found that this is caused by `dill` and using `recurse=True)` when trying to dump the object. The problem also occurs without multiprocessing. I can only find [the following information](https://dill.readthedocs.io/en/latest/dill.html#dill._dill.dumps) about this:
> If recurse=True, then objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. This is needed for functions defined via exec().
In the utils, this is explicitly enabled
https://github.com/huggingface/datasets/blob/df63614223bf1dd1feb267d39d741bada613352c/src/datasets/utils/py_utils.py#L327-L330
Is this really necessary? Is there a way around it? Also pinging the spaCy team in case this is easy to solve on their end. (I hope so.) | [
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https://github.com/huggingface/datasets/issues/3178 | "Property couldn't be hashed properly" even though fully picklable | Hi ! Thanks for reporting
Yes `recurse=True` is necessary to be able to hash all the objects that are passed to the `map` function
EDIT: hopefully this object can be serializable soon, but otherwise we can consider adding more control to the user on how to hash objects that are not serializable (as mentioned in https://github.com/huggingface/datasets/issues/3044#issuecomment-948818210) | ## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 56 | "Property couldn't be hashed properly" even though fully picklable
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
Hi ! Thanks for reporting
Yes `recurse=True` is necessary to be able to hash all the objects that are passed to the `map` function
EDIT: hopefully this object can be serializable soon, but otherwise we can consider adding more control to the user on how to hash objects that are not serializable (as mentioned in https://github.com/huggingface/datasets/issues/3044#issuecomment-948818210) | [
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https://github.com/huggingface/datasets/issues/3178 | "Property couldn't be hashed properly" even though fully picklable | I submitted a PR to spacy that should fix this issue (linked above). I'll leave this open until that PR is merged. | ## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 22 | "Property couldn't be hashed properly" even though fully picklable
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
I submitted a PR to spacy that should fix this issue (linked above). I'll leave this open until that PR is merged. | [
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0.0118968263,
-0.1175985262,
0.0271564759,
-0.1152742282,
-0.1859707236,
0.0591473356,
-0.4235768616,
0.3720368445,
-0.162925899,
-0.0777222514,
-0.1368248016,
-0.4115264714,
0.3600969613,
0.1749584526,
0.2012335807,
0.4671104252,
0.5732735991,
-0.0765715986,
0.1230083928,
-0.3830678463,
-0.2349012792,
-0.1785785854,
0.3983487487,
-0.1059957221,
0.0254362319,
-0.041132655,
0.176678732,
-0.4238360822,
0.1713766158,
-0.3041483164,
-0.0763450265,
-0.1750941426,
0.2838480473,
-0.0343426019,
0.0743419528,
-0.2653817534,
0.4218532741,
-0.1129603907,
0.3408609033,
-0.2827915251,
-0.4867301285,
0.4779389501,
-0.5187080503,
-0.171952188,
0.0099418517,
0.1570142359,
0.2735477984,
-0.1657187194,
-0.4894462526,
-0.4313940704,
0.3024000525,
-0.1926945299,
-0.1562816948,
0.0346319228,
0.2694478929,
-0.0029802034,
-0.0985471159,
0.561190486,
-0.1310111433,
-0.224154368,
0.5392277241,
0.0612847582
] |
https://github.com/huggingface/datasets/issues/3178 | "Property couldn't be hashed properly" even though fully picklable | @lhoestq After some testing I find that even with the updated spaCy, no cache files are used. I do not get any warnings though, but I can see that map is run every time I run the code. Do you have thoughts about why? If you want to try the tests below, make sure to install spaCy from [here](https://github.com/BramVanroy/spaCy) and installing the base model with `python -m spacy download en_core_web_sm`.
```python
from functools import partial
from pathlib import Path
import spacy
from datasets import Dataset
import datasets
datasets.logging.set_verbosity_debug()
def tokenize(nlp, l):
return {"tok": [t.text for t in nlp(l["text"])]}
def main():
fin = r"some/file/with/many/lines"
lines = Path(fin).read_text(encoding="utf-8").splitlines()
nlp = spacy.load("en_core_web_sm")
ds = Dataset.from_dict({"text": lines, "text_id": list(range(len(lines)))})
tok = partial(tokenize, nlp)
ds = ds.map(tok, load_from_cache_file=True)
print(ds[0:2])
if __name__ == '__main__':
main()
```
... or with load_dataset (here I get the message that `load_dataset` can reuse the dataset, but still I see all samples being processed via the tqdm progressbar):
```python
from functools import partial
import spacy
from datasets import load_dataset
import datasets
datasets.logging.set_verbosity_debug()
def tokenize(nlp, sample):
return {"tok": [t.text for t in nlp(sample["text"])]}
def main():
fin = r"some/file/with/many/lines"
nlp = spacy.load("en_core_web_sm")
tok_func = partial(tokenize, nlp)
ds = load_dataset('text', data_files=fin)
ds = ds["train"].map(tok_func)
print(ds[0:2])
if __name__ == '__main__':
main()
``` | ## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 207 | "Property couldn't be hashed properly" even though fully picklable
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
@lhoestq After some testing I find that even with the updated spaCy, no cache files are used. I do not get any warnings though, but I can see that map is run every time I run the code. Do you have thoughts about why? If you want to try the tests below, make sure to install spaCy from [here](https://github.com/BramVanroy/spaCy) and installing the base model with `python -m spacy download en_core_web_sm`.
```python
from functools import partial
from pathlib import Path
import spacy
from datasets import Dataset
import datasets
datasets.logging.set_verbosity_debug()
def tokenize(nlp, l):
return {"tok": [t.text for t in nlp(l["text"])]}
def main():
fin = r"some/file/with/many/lines"
lines = Path(fin).read_text(encoding="utf-8").splitlines()
nlp = spacy.load("en_core_web_sm")
ds = Dataset.from_dict({"text": lines, "text_id": list(range(len(lines)))})
tok = partial(tokenize, nlp)
ds = ds.map(tok, load_from_cache_file=True)
print(ds[0:2])
if __name__ == '__main__':
main()
```
... or with load_dataset (here I get the message that `load_dataset` can reuse the dataset, but still I see all samples being processed via the tqdm progressbar):
```python
from functools import partial
import spacy
from datasets import load_dataset
import datasets
datasets.logging.set_verbosity_debug()
def tokenize(nlp, sample):
return {"tok": [t.text for t in nlp(sample["text"])]}
def main():
fin = r"some/file/with/many/lines"
nlp = spacy.load("en_core_web_sm")
tok_func = partial(tokenize, nlp)
ds = load_dataset('text', data_files=fin)
ds = ds["train"].map(tok_func)
print(ds[0:2])
if __name__ == '__main__':
main()
``` | [
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https://github.com/huggingface/datasets/issues/3178 | "Property couldn't be hashed properly" even though fully picklable | It looks like every time you load `en_core_web_sm` you get a different python object:
```python
import spacy
from datasets.fingerprint import Hasher
nlp1 = spacy.load("en_core_web_sm")
nlp2 = spacy.load("en_core_web_sm")
Hasher.hash(nlp1), Hasher.hash(nlp2)
# ('f6196a33882fea3b', 'a4c676a071f266ff')
```
Here is a list of attributes that have different hashes for `nlp1` and `nlp2`:
- tagger
- parser
- entity
- pipeline (it's the list of the three attributes above)
I just took a look at the tagger for example and I found subtle differences (there may be other differences though):
```python
nlp1.tagger.model.tok2vec.embed.id, nlp2.tagger.model.tok2vec.embed.id
# (1721, 2243)
```
We can try to find all the differences and find the best way to hash those objects properly | ## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 109 | "Property couldn't be hashed properly" even though fully picklable
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
It looks like every time you load `en_core_web_sm` you get a different python object:
```python
import spacy
from datasets.fingerprint import Hasher
nlp1 = spacy.load("en_core_web_sm")
nlp2 = spacy.load("en_core_web_sm")
Hasher.hash(nlp1), Hasher.hash(nlp2)
# ('f6196a33882fea3b', 'a4c676a071f266ff')
```
Here is a list of attributes that have different hashes for `nlp1` and `nlp2`:
- tagger
- parser
- entity
- pipeline (it's the list of the three attributes above)
I just took a look at the tagger for example and I found subtle differences (there may be other differences though):
```python
nlp1.tagger.model.tok2vec.embed.id, nlp2.tagger.model.tok2vec.embed.id
# (1721, 2243)
```
We can try to find all the differences and find the best way to hash those objects properly | [
-0.0547037572,
-0.0296596158,
0.1383616775,
0.1674575657,
0.2572079003,
-0.1886388958,
0.3345493674,
0.0546137951,
0.0525931679,
0.1248953864,
0.0389523171,
0.5095518827,
-0.2360349447,
0.3779696524,
-0.1779329479,
0.0818778947,
0.0879114941,
-0.0570349731,
0.0878298432,
-0.1197966412,
-0.0697439685,
-0.0256732684,
-0.3639787436,
0.0150344642,
-0.5814357996,
-0.2471628636,
0.1233717129,
-0.0043504587,
-0.1769182086,
-0.3019955456,
0.1507757157,
-0.0842573196,
-0.0198123567,
0.0914173946,
-0.0001276944,
-0.0400400944,
0.2713608742,
-0.0208546668,
-0.0257947762,
-0.4120488167,
0.1907008439,
-0.3608407974,
0.0740303248,
-0.211347267,
-0.1636205316,
0.3611479104,
-0.2467535585,
-0.2143102884,
0.260607183,
0.0114906086,
0.0392241217,
0.3941231072,
0.0432313383,
0.2574501336,
-0.0588377379,
-0.0148635246,
-0.4733221233,
0.109114565,
0.1186906546,
0.1557988673,
-0.0484532043,
-0.019749077,
0.0093573071,
-0.1011296362,
-0.0401526913,
0.1538345963,
-0.237302959,
-0.0516985804,
0.1653799862,
0.0052834945,
0.0333908945,
-0.3204625547,
-0.3859625161,
-0.1491619647,
0.0474647768,
-0.5314664841,
0.4600654542,
0.0664499253,
0.1255585104,
0.1659148633,
-0.0848168954,
0.0914708599,
0.1008479819,
0.164870128,
0.2551167011,
0.4194827676,
-0.0190647002,
0.2278356701,
-0.0405594781,
-0.1689594835,
0.2658732235,
0.0705322027,
0.1795156002,
0.0836890414,
-0.0195625331,
-0.0307151452,
-0.1670056432,
0.2477212697,
0.2644006908,
-0.0484205522,
0.007160774,
0.1911672354,
-0.33025226,
0.0856915861,
0.1315112412,
-0.265206784,
0.2935669422,
0.4055650532,
-0.0374953933,
0.0583079755,
-0.4280025661,
0.0356123522,
0.0313389413,
-0.0029143307,
0.114661321,
0.1762257218,
0.202796936,
-0.1554870158,
-0.1113362238,
0.2510730624,
-0.4037123024,
-0.0514079742,
0.1103123873,
0.1127123684,
-0.1159863397,
-0.2407061011,
-0.192309469,
0.241377309,
-0.337374419,
-0.3447399735,
-0.0766617879,
0.0834250227,
-0.4073736072,
-0.0222928561,
0.0333841667,
0.1963319182,
0.377796948,
0.1117832139,
-0.1122461334,
0.1048943773,
-0.0382502154,
-0.2463395,
0.1190531179,
0.0087581398,
-0.159172371,
0.1909403354,
0.1781331003,
-0.4805569351,
-0.2565742433,
0.0611921549,
-0.2101026773,
0.0271804929,
-0.0223347694,
-0.0561760366,
-0.3674880564,
-0.103926219,
-0.010629083,
0.2302388847,
0.3314209282,
0.0433118492,
0.0690918565,
-0.3608968258,
-0.3603518009,
-0.2394995093,
0.0475333519,
0.2380011231,
-0.2766398787,
-0.2168492526,
-0.2440198362,
0.0189822223,
0.9000562429,
0.2183276415,
-0.0556712486,
0.0234984681,
-0.2343845814,
0.7594913244,
0.0905847177,
-0.1478681713,
-0.5776648521,
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-0.0203919969,
0.1384986192,
0.0229974706,
0.0227617323,
0.3518770039,
0.0264439993,
0.3190077543,
0.3026783466,
0.1961114109,
-0.302872628,
-0.3790924549,
-0.142558828,
0.3757954538,
-0.3132485449,
0.1351573169,
0.1308348775,
0.3068951368,
0.4246308804,
0.2254492939,
0.2583018839,
0.1783137918,
-0.0941027328,
0.3221184313,
-0.0918224007,
-0.1540010273,
-0.1793636531,
-0.1915613562,
0.2066816986,
0.023735825,
0.2731806338,
-0.3610523045,
-0.1430864185,
0.0524439774,
0.176856339,
-0.1652963012,
-0.3081740141,
-0.0823015496,
0.0367540531,
-0.1271738857,
0.0828393549,
-0.1993957609,
-0.0555443875,
-0.1569062918,
0.0780029073,
0.119815439,
0.0761380196,
-0.1625960618,
-0.4827044308,
-0.1736913025,
0.2461585402,
0.3014327884,
0.0034809171,
-0.2449755818,
-0.0888495892,
0.1731424034,
-0.3360346854,
-0.1316605508,
0.2585936785,
0.3164643347,
0.0204325486,
0.0327867754,
0.010144542,
-0.0102995848,
-0.0306296796,
0.2863678038,
0.284411788,
-0.1854794919,
0.0013758254,
-0.1170924157,
0.1922469139,
0.4385982752,
-0.0604097769,
0.005020828,
-0.4889424145,
0.1553365141,
-0.0615307167,
0.4590315819,
0.3527908623,
-0.3520230651,
0.3137389719,
0.7528020144,
0.1944844574,
0.3439007401,
-0.0378361195,
0.0523134544,
0.0474330261,
0.1562965661,
-0.0160856824,
0.1846254468,
-0.0083616348,
0.1808769405,
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https://github.com/huggingface/datasets/issues/3178 | "Property couldn't be hashed properly" even though fully picklable | Thanks for searching! I went looking, and found that this is an implementation detail of thinc
https://github.com/explosion/thinc/blob/68691e303ae68cae4bc803299016f1fc064328bf/thinc/model.py#L96-L98
Presumably (?) exactly to distinguish between different parts in memory when multiple models are loaded. Do not think that this can be changed on their end - but I will ask what exactly it is for (I'm curious).
Do you think it is overkill to write something into the hasher explicitly to deal with spaCy models? It seems like something that is beneficial to many, but I do not know if you are open to adding third-party-specific ways to deal with this. If you are, I can have a look for this specific case how we can ignore `thinc.Model.id` from the hasher. | ## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 119 | "Property couldn't be hashed properly" even though fully picklable
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
Thanks for searching! I went looking, and found that this is an implementation detail of thinc
https://github.com/explosion/thinc/blob/68691e303ae68cae4bc803299016f1fc064328bf/thinc/model.py#L96-L98
Presumably (?) exactly to distinguish between different parts in memory when multiple models are loaded. Do not think that this can be changed on their end - but I will ask what exactly it is for (I'm curious).
Do you think it is overkill to write something into the hasher explicitly to deal with spaCy models? It seems like something that is beneficial to many, but I do not know if you are open to adding third-party-specific ways to deal with this. If you are, I can have a look for this specific case how we can ignore `thinc.Model.id` from the hasher. | [
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] |
https://github.com/huggingface/datasets/issues/3178 | "Property couldn't be hashed properly" even though fully picklable | It can be even simpler to hash the bytes of the pipeline instead
```python
nlp1.to_bytes() == nlp2.to_bytes() # True
```
IMO we should integrate the custom hashing for spacy models into `datasets` (we use a custom Pickler for that).
What could be done on Spacy's side instead (if they think it's nice to have) is to implement a custom pickling for these classes using `to_bytes`/`from_bytes` to have deterministic pickle dumps.
Finally I think it would be nice in the future to add an API to let `datasets` users control this kind of things. Something like being able to define your own hashing if you use complex objects.
```python
@datasets.register_hash(spacy.language.Language)
def hash_spacy_language(nlp):
return Hasher.hash(nlp.to_bytes())
``` | ## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 114 | "Property couldn't be hashed properly" even though fully picklable
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
It can be even simpler to hash the bytes of the pipeline instead
```python
nlp1.to_bytes() == nlp2.to_bytes() # True
```
IMO we should integrate the custom hashing for spacy models into `datasets` (we use a custom Pickler for that).
What could be done on Spacy's side instead (if they think it's nice to have) is to implement a custom pickling for these classes using `to_bytes`/`from_bytes` to have deterministic pickle dumps.
Finally I think it would be nice in the future to add an API to let `datasets` users control this kind of things. Something like being able to define your own hashing if you use complex objects.
```python
@datasets.register_hash(spacy.language.Language)
def hash_spacy_language(nlp):
return Hasher.hash(nlp.to_bytes())
``` | [
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-0.1310111433,
-0.224154368,
0.5392277241,
0.0612847582
] |
Subsets and Splits