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https://github.com/huggingface/datasets/issues/3333
|
load JSON files, get the errors
|
I think you may need to implement your own Trainer, from the `QuestionAnsweringTrainer` for example.
This way you can have the flexibility of saving all the inputs/output used at each step
|
Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command
`!python3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/`
change the dateset to load json by refering to https://huggingface.co/docs/datasets/loading.html
`dataset = datasets.load_dataset('json', data_files=args.dataset)`
Errors:
`Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/json/default-c1e124ad488911b8/0.0.0/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...
`
_Originally posted by @yanllearnn in https://github.com/huggingface/datasets/issues/730#issuecomment-981095050_
| 31 |
load JSON files, get the errors
|
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] |
https://github.com/huggingface/datasets/issues/3333
|
load JSON files, get the errors
|
> does there have any function to be overwritten to do this?
ok, I overwrote the compute_loss, thank you.
|
Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command
`!python3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/`
change the dateset to load json by refering to https://huggingface.co/docs/datasets/loading.html
`dataset = datasets.load_dataset('json', data_files=args.dataset)`
Errors:
`Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/json/default-c1e124ad488911b8/0.0.0/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...
`
_Originally posted by @yanllearnn in https://github.com/huggingface/datasets/issues/730#issuecomment-981095050_
| 19 |
load JSON files, get the errors
|
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] |
https://github.com/huggingface/datasets/issues/3333
|
load JSON files, get the errors
|
Hi, I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs
```
*********************** inputs: {'attention_mask': tensor([[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
...,
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25, 97, 93, 44, 25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390, ..., 0, 0, 0],
[ 101, 2054, 2515, ..., 0, 0, 0],
[ 101, 2054, 2106, ..., 0, 0, 0],
...,
[ 101, 2339, 2001, ..., 0, 0, 0],
[ 101, 2054, 2515, ..., 0, 0, 0],
[ 101, 2054, 2003, ..., 0, 0, 0]], device='cuda:0'), 'start_positions': tensor([ 20, 90, 89, 41, 25, 96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
...,
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0]], device='cuda:0')}
```
```
# This function preprocesses a question answering dataset, tokenizing the question and context text
# and finding the right offsets for the answer spans in the tokenized context (to use as labels).
# Adapted from https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py
def prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):
questions = [q.lstrip() for q in examples["question"]]
max_seq_length = tokenizer.model_max_length
# tokenize both questions and the corresponding context
# if the context length is longer than max_length, we split it to several
# chunks of max_length
tokenized_examples = tokenizer(
questions,
examples["context"],
truncation="only_second",
max_length=max_seq_length,
stride=min(max_seq_length // 2, 128),
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length"
)
# Since one example might give us several features if it has a long context,
# we need a map from a feature to its corresponding example.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# The offset mappings will give us a map from token to character position
# in the original context. This will help us compute the start_positions
# and end_positions to get the final answer string.
offset_mapping = tokenized_examples.pop("offset_mapping")
tokenized_examples["start_positions"] = []
tokenized_examples["end_positions"] = []
tokenized_examples["example_id"] = []
for i, offsets in enumerate(offset_mapping):
input_ids = tokenized_examples["input_ids"][i]
# We will label features not containing the answer the index of the CLS token.
cls_index = input_ids.index(tokenizer.cls_token_id)
sequence_ids = tokenized_examples.sequence_ids(i)
# from the feature idx to sample idx
sample_index = sample_mapping[i]
# get the answer for a feature
answers = examples["answers"][sample_index]
tokenized_examples["example_id"].append(examples["id"][sample_index])
if len(answers["answer_start"]) == 0:
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# Start/end character index of the answer in the text.
start_char = answers["answer_start"][0]
end_char = start_char + len(answers["text"][0])
# Start token index of the current span in the text.
token_start_index = 0
while sequence_ids[token_start_index] != 1:
token_start_index += 1
# End token index of the current span in the text.
token_end_index = len(input_ids) - 1
while sequence_ids[token_end_index] != 1:
token_end_index -= 1
# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
if not (offsets[token_start_index][0] <= start_char and
offsets[token_end_index][1] >= end_char):
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
# Note: we could go after the last offset if the answer is the last word (edge case).
while token_start_index < len(offsets) and \
offsets[token_start_index][0] <= start_char:
token_start_index += 1
tokenized_examples["start_positions"].append(
token_start_index - 1)
while offsets[token_end_index][1] >= end_char:
token_end_index -= 1
tokenized_examples["end_positions"].append(token_end_index + 1)
return tokenized_examples
```
|
Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command
`!python3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/`
change the dateset to load json by refering to https://huggingface.co/docs/datasets/loading.html
`dataset = datasets.load_dataset('json', data_files=args.dataset)`
Errors:
`Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/json/default-c1e124ad488911b8/0.0.0/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...
`
_Originally posted by @yanllearnn in https://github.com/huggingface/datasets/issues/730#issuecomment-981095050_
| 576 |
load JSON files, get the errors
|
[
0.05790461599826813,
-0.11226317286491394,
-0.26398608088493347,
0.5209110975265503,
0.054915256798267365,
-0.03413403779268265,
0.041143517941236496,
0.45858925580978394,
0.30102455615997314,
0.2604897916316986,
0.1641225814819336,
0.20083747804164886,
0.05085420981049538,
-0.12169943004846573,
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0.16992264986038208,
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0.23610162734985352,
0.09127844870090485,
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0.41123658418655396,
0.015502090565860271,
0.4478663504123688,
0.0036557537969201803,
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0.18555043637752533,
0.44761037826538086,
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0.24036578834056854,
0.15343980491161346,
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0.15420196950435638,
0.4796506464481354,
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0.04112165793776512,
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0.22732950747013092,
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0.25288668274879456,
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0.7177602648735046,
0.2429312914609909,
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0.0763268768787384,
0.19327189028263092,
0.0544087290763855,
0.34792661666870117,
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] |
https://github.com/huggingface/datasets/issues/3331
|
AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'
|
Hi,
the fix was merged and will be available in the next release of `datasets`.
In the meantime, you can use it by installing `datasets` directly from master as follows:
```
pip install git+https://github.com/huggingface/datasets.git
```
|
## Describe the bug
I add a new question answering dataset to huggingface datasets manually. Here is the link: [luozhouyang/question-answering-datasets](https://huggingface.co/datasets/luozhouyang/question-answering-datasets)
But when I load the dataset, an error raised:
```bash
AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("luozhouyang/question-answering-datasets", data_files=["dureader_robust.train.json"])
```
## Expected results
Load dataset successfully without any error.
## Actual results
```bash
Traceback (most recent call last):
File "/mnt/home/zhouyang.lzy/github/naivenlp/naivenlp/tests/question_answering_tests/dataset_test.py", line 89, in test_load_dataset_with_hf
data_files=["dureader_robust.train.json"],
File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1616, in load_dataset
**config_kwargs,
File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1443, in load_dataset_builder
path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files
File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1157, in dataset_module_factory
raise e1 from None
File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1144, in dataset_module_factory
download_mode=download_mode,
File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 798, in get_module
raise FileNotFoundError(f"No data files or dataset script found in {self.path}")
AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: linux
- Python version: 3.6.13
- PyArrow version: 6.0.1
| 35 |
AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'
|
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] |
https://github.com/huggingface/datasets/issues/3329
|
Map function: Type error on iter #999
|
Hi, thanks for reporting.
It would be really helpful if you could provide the actual code of the `text_numbers_to_int` function so we can reproduce the error.
|
## Describe the bug
Using the map function, it throws a type error on iter #999
Here is the code I am calling:
```
dataset = datasets.load_dataset('squad')
dataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})
```
text_numbers_to_int returns the input text with numbers replaced in the format {'context': text}
It happens at
`
File "C:\Users\lonek\anaconda3\envs\ai\Lib\site-packages\datasets\arrow_writer.py", line 289, in <listcomp>
[row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col
`
The issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)
Here is an example of what self.current_examples should be
({'context': 'Super Bowl 50 was an...merals 50.'}, '')
Here is an example of what self.current_examples are when it throws the error:
('The Panthers used th... Marriott.', '')
| 26 |
Map function: Type error on iter #999
|
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] |
https://github.com/huggingface/datasets/issues/3329
|
Map function: Type error on iter #999
|
```
def text_numbers_to_int(text, column=""):
"""
Convert text numbers to int.
:param text: text numbers
:return: int
"""
try:
numbers = find_numbers(text)
if not numbers:
return text
result = ""
i, j = 0, 0
while i < len(text):
if j < len(numbers) and i == numbers[j][1]:
n = int(numbers[j][0]) if numbers[j][0] % 1 == 0 else float(numbers[j][0])
result += str(n)
i = numbers[j][2] #end
j += 1
else:
result += text[i]
i += 1
if column:
return{column: result}
else:
return {column: result}
except Exception as e:
print(e)
return {column: result}
```
|
## Describe the bug
Using the map function, it throws a type error on iter #999
Here is the code I am calling:
```
dataset = datasets.load_dataset('squad')
dataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})
```
text_numbers_to_int returns the input text with numbers replaced in the format {'context': text}
It happens at
`
File "C:\Users\lonek\anaconda3\envs\ai\Lib\site-packages\datasets\arrow_writer.py", line 289, in <listcomp>
[row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col
`
The issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)
Here is an example of what self.current_examples should be
({'context': 'Super Bowl 50 was an...merals 50.'}, '')
Here is an example of what self.current_examples are when it throws the error:
('The Panthers used th... Marriott.', '')
| 91 |
Map function: Type error on iter #999
|
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] |
https://github.com/huggingface/datasets/issues/3329
|
Map function: Type error on iter #999
|
Maybe this is because of the `return text` line ? I think it should return a dictionary rather than a string
|
## Describe the bug
Using the map function, it throws a type error on iter #999
Here is the code I am calling:
```
dataset = datasets.load_dataset('squad')
dataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})
```
text_numbers_to_int returns the input text with numbers replaced in the format {'context': text}
It happens at
`
File "C:\Users\lonek\anaconda3\envs\ai\Lib\site-packages\datasets\arrow_writer.py", line 289, in <listcomp>
[row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col
`
The issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)
Here is an example of what self.current_examples should be
({'context': 'Super Bowl 50 was an...merals 50.'}, '')
Here is an example of what self.current_examples are when it throws the error:
('The Panthers used th... Marriott.', '')
| 21 |
Map function: Type error on iter #999
|
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] |
https://github.com/huggingface/datasets/issues/3317
|
Add desc parameter to Dataset filter method
|
Hi,
`Dataset.map` allows more generic transforms compared to `Dataset.filter`, which purpose is very specific (to filter examples based on a condition). That's why I don't think we need the `desc` parameter there for consistency. #3196 has added descriptions to the `Dataset` methods that call `.map` internally, but not for the `filter` method, so we should do that.
Do you have a description in mind? Maybe `"Filtering the dataset"` or `"Filtering the indices"`? If yes, feel free to open a PR.
|
**Is your feature request related to a problem? Please describe.**
As I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?
**Describe the solution you'd like**
Add desc parameter to Dataset filter method
**Describe alternatives you've considered**
N/A
**Additional context**
N/A
| 80 |
Add desc parameter to Dataset filter method
|
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] |
https://github.com/huggingface/datasets/issues/3317
|
Add desc parameter to Dataset filter method
|
I'm personally ok with adding the `desc` parameter actually. Let's say you have different filters, it can be nice to differentiate between the different filters when they're running no ?
|
**Is your feature request related to a problem? Please describe.**
As I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?
**Describe the solution you'd like**
Add desc parameter to Dataset filter method
**Describe alternatives you've considered**
N/A
**Additional context**
N/A
| 30 |
Add desc parameter to Dataset filter method
|
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] |
https://github.com/huggingface/datasets/issues/3317
|
Add desc parameter to Dataset filter method
|
@mariosasko the use case is filtering of a dataset prior to tokenization and subsequent training. As the dataset is huge it's just a matter of giving a user (model trainer) some feedback on what's going on. Otherwise, feedback is given for all steps in training preparation and not for filtering and the filtering in my use case lasts about 4-5 minutes. And yes, if there are more filtering stages, as @lhoestq pointed out, it would be nice to give some feedback. I thought desc is there already and got confused when I got the script error.
|
**Is your feature request related to a problem? Please describe.**
As I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?
**Describe the solution you'd like**
Add desc parameter to Dataset filter method
**Describe alternatives you've considered**
N/A
**Additional context**
N/A
| 96 |
Add desc parameter to Dataset filter method
|
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] |
https://github.com/huggingface/datasets/issues/3317
|
Add desc parameter to Dataset filter method
|
I don't have a strong opinion on that, so having `desc` as a parameter is also OK.
|
**Is your feature request related to a problem? Please describe.**
As I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?
**Describe the solution you'd like**
Add desc parameter to Dataset filter method
**Describe alternatives you've considered**
N/A
**Additional context**
N/A
| 17 |
Add desc parameter to Dataset filter method
|
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] |
https://github.com/huggingface/datasets/issues/3313
|
TriviaQA License Mismatch
|
Hi ! You're completely right, this must be mentioned in the dataset card.
If you're interesting in contributing, feel free to open a pull request to mention this in the `trivia_qa` dataset card in the "Licensing Information" section at https://github.com/huggingface/datasets/blob/master/datasets/trivia_qa/README.md
|
## Describe the bug
TriviaQA Webpage at http://nlp.cs.washington.edu/triviaqa/ says they do not own the copyright to the data. However, Huggingface datasets at https://huggingface.co/datasets/trivia_qa mentions that the dataset is released under Apache License
Is the License Information on HuggingFace correct?
| 40 |
TriviaQA License Mismatch
|
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0.006154949776828289,
0.07161474227905273
] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
Hi ! Are you having this issue only with this specific dataset, or it also happens with other ones like `squad` ?
|
## 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
| 22 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
@lhoestq It happens also on `squad`. It successfully downloads the whole dataset and then crashes on:
```
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
```
I tested it on Ubuntu and its working OK. Didn't test on non-preview version of Windows 11, `Windows-10-10.0.22504-SP0` is a preview version, not sure if this is causing it.
|
## 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
| 61 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
I see the same error in Windows-10.0.19042 as of a few days ago:
`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`
python 3.8.12 h7840368_2_cpython conda-forge
boto3 1.20.11 pyhd8ed1ab_0 conda-forge
botocore 1.23.11 pyhd8ed1ab_0 conda-forge
...but I am not using `datasets` (although I might take a look now that I know about it!)
The error has occurred a few times over the last two days, but not consistently enough for me to get it with DEBUG. If there is any interest I can report back here, but it seems not unique to `datasets`.
|
## 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
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?
|
## 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
| 25 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
> I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?
Agreed, this issue is not likely a bug in datasets, since I get the identical error without datasets installed.
|
## 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
| 45 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
Will close this issue. Bug in `aws-c-io` shouldn't be in `datasets` repo. Nevertheless, it can be useful to know that it happens. Thanks @leehaust @lhoestq
|
## 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
| 25 |
Fatal error condition occurred in aws-c-io
|
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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
|
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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
|
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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
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
Any updates on this issue? I started receiving the same error a few days ago on the amazon reviews
|
## 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
| 19 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
Hi,
I also ran into this issue, Windows only. It caused our massive binary to minidump left and right, very annoying.
When the program is doing an exit, the destructors in the exit-handlers want to do cleanup, leading to code in event_loop.c, on line 73-ish:
AWS_FATAL_ASSERT(
aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) ==
AWS_OP_SUCCESS);
The fatal_assert end in an abort/minidump.
Digging through the code, I found that aws_thread_launch in the Windows version (aws-c-common/source/windows/thread.c) has only ONE reason to return anything other than AWS_OP_SUCCESS:
return aws_raise_error(AWS_ERROR_THREAD_INSUFFICIENT_RESOURCE);
on line 263, when CreateThread fails. Our conclusion was that, apparently, Windows dislikes launching a new thread while already handling the exit-handlers. And while I appreciate the the fatal_assert is there in case of problems, the cure here is worse than the problem.
I "fixed" this in our (Windows) environment by (bluntly) removing the AWS_FATAL_ASSERT. If Windows cannot start a thread, the program is in deep trouble anyway and the chances of that actually happening are acceptable (to us).
The exit is going to clean up all resources anyway.
A neater fix would probably be to detect somehow that the program is actually in the process of exiting and then not bother (on windows, anyway) to start a cleanup thread. Alternatively, try to start the thread but not fatal-assert when it fails during exit. Or perhaps Windows can be convinced somehow to start the thread under these circumstances?
@xhochy : The problem is Windows-only, the aws_thread_launch has two implementations (posix and windows). The problem is in the windows CreateThread which fails.
|
## 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
| 254 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
I also encountered the same problem, but I made an error in the multi gpu training environment on Linux, and the single gpu training environment will not make an error.
i use accelerate package to do multi gpu training.
|
## 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
| 39 |
Fatal error condition occurred in aws-c-io
|
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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
Any updates for your issue because I'm getting the same one
|
## 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
| 130 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
Potentially related AWS issue: https://github.com/aws/aws-sdk-cpp/issues/1809
Ran into this issue today while training a BPE tokenizer on a dataset.
Train code:
```python
"""Train a ByteLevelBPETokenizer based on a given dataset. The dataset must be on the HF Hub.
This script is adaptated from the Transformers example in https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling
"""
from os import PathLike
from pathlib import Path
from typing import Sequence, Union
from datasets import load_dataset
from tokenizers import ByteLevelBPETokenizer
def train_tokenizer(dataset_name: str = "oscar", dataset_config_name: str = "unshuffled_deduplicated_nl",
dataset_split: str = "train", dataset_textcol: str = "text",
vocab_size: int = 50265, min_frequency: int = 2,
special_tokens: Sequence[str] = ("<s>", "<pad>", "</s>", "<unk>", "<mask>"),
dout: Union[str, PathLike] = "."):
# load dataset
dataset = load_dataset(dataset_name, dataset_config_name, split=dataset_split)
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1024):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size][dataset_textcol]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=vocab_size, min_frequency=min_frequency,
special_tokens=special_tokens)
# Save to disk
pdout = Path(dout).resolve()
pdout.mkdir(exist_ok=True, parents=True)
tokenizer.save_model(str(pdout))
def main():
import argparse
cparser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
cparser.add_argument("dataset_name", help="Name of dataset to use for tokenizer training")
cparser.add_argument("--dataset_config_name", default=None,
help="Name of the config to use for tokenizer training")
cparser.add_argument("--dataset_split", default=None,
help="Name of the split to use for tokenizer training (typically 'train')")
cparser.add_argument("--dataset_textcol", default="text",
help="Name of the text column to use for tokenizer training")
cparser.add_argument("--vocab_size", type=int, default=50265, help="Vocabulary size")
cparser.add_argument("--min_frequency", type=int, default=2, help="Minimal frequency of tokens")
cparser.add_argument("--special_tokens", nargs="+", default=["<s>", "<pad>", "</s>", "<unk>", "<mask>"],
help="Special tokens to add. Useful for specific training objectives. Note that if you wish"
" to use this tokenizer with a default transformers.BartConfig, then make sure that the"
" order of at least these special tokens are correct: BOS (0), padding (1), EOS (2)")
cparser.add_argument("--dout", default=".", help="Path to directory to save tokenizer.json file")
train_tokenizer(**vars(cparser.parse_args()))
if __name__ == "__main__":
main()
```
Command:
```sh
$WDIR="your_tokenizer"
python prepare_tokenizer.py dbrd --dataset_config_name plain_text --dataset_split unsupervised --dout $WDIR
```
Output:
```
Reusing dataset dbrd (cache/datasets/dbrd/plain_text/3.0.0/2b12e31348489dfe586c2d0f40694e5d9f9454c9468457ac9f1b51abf686eeb3)
[00:00:30] Pre-processing sequences ████████ 0 / 0
[00:00:00] Tokenize words ████████ 333319 / 333319
[00:01:06] Count pairs ████████ 333319 / 333319
[00:00:03] Compute merges ████████ 50004 / 50004
Fatal error condition occurred in /opt/vcpkg/buildtrees/aws-c-io/src/9e6648842a-364b708815.clean/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:
################################################################################
venv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x200af06) [0x155106589f06]
venv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x20028e5) [0x1551065818e5]
venv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x1f27e09) [0x1551064a6e09]
venv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x15510658aa3d]
venv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x1f25948) [0x1551064a4948]
venv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x15510658aa3d]
venv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x1ee0b46) [0x15510645fb46]
venv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x194546a) [0x155105ec446a]
/lib64/libc.so.6(+0x39b0c) [0x1551075b8b0c]
/lib64/libc.so.6(on_exit+0) [0x1551075b8c40]
/lib64/libc.so.6(__libc_start_main+0xfa) [0x1551075a249a]
python(_start+0x2e) [0x4006ce]
Aborted (core dumped)
```
Running on datasets==2.4.0 and pyarrow==9.0.0 on RHEL 8.
|
## 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
| 384 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
There is also a discussion here https://issues.apache.org/jira/browse/ARROW-15141 where it is suggested for conda users to use an older version of aws-sdk-cpp: `aws-sdk-cpp=1.8.186`
|
## 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
| 22 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
First of all, I’d never call a downgrade a solution, at most a (very) temporary workaround.
Furthermore: This bug also happens outside pyarrow, I incorporate AWS in a standalone Windows C-program and that crashes during exit.
From: Bo-Ru (Roy) Lu ***@***.***>
Sent: Thursday, 15 September 2022 01:12
To: huggingface/datasets ***@***.***>
Cc: Ruurd Beerstra ***@***.***>; Comment ***@***.***>
Subject: Re: [huggingface/datasets] Fatal error condition occurred in aws-c-io (Issue #3310)
Sent by an external sender. Please be cautious about clicking on links and opening attachments.
--------------------------------------------------------------------------------------------------------------------------------
Downgrading pyarrow to 6.0.1 solves the issue.
—
Reply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/3310#issuecomment-1247390774>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AKYUE3WBCSMHKJOOA2RQELLV6JLSVANCNFSM5IQ3WG7Q>.
You are receiving this because you commented.Message ID: ***@***.******@***.***>>
|
## 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
| 111 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
> First of all, I’d never call a downgrade a solution, at most a (very) temporary workaround.
Very much so! It looks like an apparent fix for the underlying problem [might](https://github.com/awslabs/aws-c-io/pull/515) have landed, but it sounds like it might still be a bit of a [lift](https://github.com/aws/aws-sdk-cpp/issues/1809#issuecomment-1289859795) to get it into aws-sdk-cpp.
> Downgrading pyarrow to 6.0.1 solves the issue for me.
Sidenote: On conda-forge side, all recent pyarrow releases (all the way up to v9 and soon v10) have carried the respective pin and will not run into this issue.
```
conda install -c conda-forge pyarrow
```
|
## 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
| 97 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
For pip people, I confirmed that installing the nightly version of pyarrow also solves this by: `pip install --extra-index-url https://pypi.fury.io/arrow-nightlies/ --prefer-binary --pre pyarrow --upgrade`. (See https://arrow.apache.org/docs/python/install.html#installing-nightly-packages)
Any version after https://github.com/apache/arrow/pull/14157 would work fine.
|
## 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
| 33 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
> Furthermore: This bug also happens outside pyarrow, I incorporate AWS in a standalone Windows C-program and that crashes during exit.
Do you have a reproducer you could share? I'd like to test if the new versions that supposedly solve this actually do, but we don't have a way to test it...
|
## 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
| 52 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
Hi,
No – sorry. It is part of a massive eco-system which cannot easily be shared.
But I think the problem was summarized quite clearly: Windows does not allow a CreateThread while doing ExitProcess.
The cleanup that gets called as part of the exit handler code tries to start a thread, the fatal-assert on that causes the crash, and in windows we get a very big dump file.
The fix I applied simply removes that fatal assert, that solves the problem for me.
I did not delve into the what the thread was trying to achieve and if that might cause issues when not executed during exit of the process. We did not notice anything of the kind.
However, we *did* notice the many, many gigabytes of accumulated dumps of hundreds of processes 😊
I’ll try and upgrade to the latest AWS version and report my findings, but that will be after I return from a month of vacationing…
* Regards – Ruurd Beerstra
From: h-vetinari ***@***.***>
Sent: Friday, 28 October 2022 02:09
To: huggingface/datasets ***@***.***>
Cc: Ruurd Beerstra ***@***.***>; Comment ***@***.***>
Subject: Re: [huggingface/datasets] Fatal error condition occurred in aws-c-io (Issue #3310)
Sent by an external sender. Please be cautious about clicking on links and opening attachments.
--------------------------------------------------------------------------------------------------------------------------------
Furthermore: This bug also happens outside pyarrow, I incorporate AWS in a standalone Windows C-program and that crashes during exit.
Do you have a reproducer you could share? I'd like to test if the new versions that supposedly solve this actually do, but we don't have a way to test it...
—
Reply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/3310#issuecomment-1294251331>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AKYUE3SHHPC5AT7KQ4GDAJDWFMKRTANCNFSM5IQ3WG7Q>.
You are receiving this because you commented.Message ID: ***@***.******@***.***>>
|
## 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
| 281 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
> No – sorry. It is part of a massive eco-system which cannot easily be shared.
OK, was worth a try...
> The fix I applied simply removes that fatal assert, that solves the problem for me.
This seems to be what https://github.com/awslabs/aws-c-io/pull/515 did upstream.
> I’ll try and upgrade to the latest AWS version and report my findings, but that will be after I return from a month of vacationing…
caution: aws-sdk-cpp hasn't yet upgraded its bundled(?) aws-c-io and hence doesn't contain the fix AFAICT
|
## 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
| 86 |
Fatal error condition occurred in aws-c-io
|
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https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
Hi, I also encountered the same problem, but I made an error on Ubuntu without using `datasets` as @Crabzmatic he wrote.
At that time, I find my version of pyarrow is 9.0.0, which is different from as follow:
> https://github.com/huggingface/datasets/issues/3310#issuecomment-1247390774
> Downgrading pyarrow to 6.0.1 solves the issue for me.
>
> `pip install pyarrow==6.0.1`
As it happens, I found this error message when I introduced the [`Trainer`](https://huggingface.co/docs/transformers/main_classes/trainer) of HuggingFace
For example, I write following code:
```python
from transformers import Trainer
print('Hugging Face')
```
I get the following error message:
```python
Hugging Face
Fatal error condition occurred in /opt/vcpkg/buildtrees/aws-c-io/src/9e6648842a-364b708815.clean/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/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x200af06) [0x7fa9add1df06]
/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x20028e5) [0x7fa9add158e5]
/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x1f27e09) [0x7fa9adc3ae09]
/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x7fa9add1ea3d]
/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x1f25948) [0x7fa9adc38948]
/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x7fa9add1ea3d]
/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x1ee0b46) [0x7fa9adbf3b46]
/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x194546a) [0x7fa9ad65846a]
/lib/x86_64-linux-gnu/libc.so.6(+0x468d7) [0x7faa2fcfe8d7]
/lib/x86_64-linux-gnu/libc.so.6(on_exit+0) [0x7faa2fcfea90]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xfa) [0x7faa2fcdc0ba]
/home/ubuntu/anaconda3/envs/pytorch38/bin/python(+0x1f9ad7) [0x5654571d1ad7]
```
But, when I remove the `Trainer` module from transformers, **everthing is OK**.
So Why ?
**Environment info**
- Platform: Ubuntu 18
- Python version: 3.8
- PyArrow version: 9.0.0
- transformers: 4.22.1
- simpletransformers: 0.63.9
|
## 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
| 171 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
> I get the following error message:
Not sure what's going on, but that shouldn't happen, especially as we're pinning to a version that should avoid this.
Can you please open an issue https://github.com/conda-forge/arrow-cpp-feedstock, including the requested output of `conda list` & `conda info`?
|
## 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
| 44 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
pyarrow 10.0.1 was just released in conda-forge, which is the first release where we're building against aws-sdk-cpp 1.9.* again after more than a year. Since we cannot test the failure reported here on our infra, I'd be very grateful if someone could verify that the problem does or doesn't reappear. 🙃
```
conda install -c conda-forge pyarrow=10
```
|
## 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
| 58 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
> pyarrow 10.0.1 was just released in conda-forge, which is the first release where we're building against aws-sdk-cpp 1.9.* again after more than a year. Since we cannot test the failure reported here on our infra, I'd be very grateful if someone could verify that the problem does or doesn't reappear. 🙃
>
> ```
> conda install -c conda-forge pyarrow=10
> ```
The problem is gone after I install the new version. Thanks!
pip install pyarrow==10
|
## 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
| 77 |
Fatal error condition occurred in aws-c-io
|
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] |
https://github.com/huggingface/datasets/issues/3310
|
Fatal error condition occurred in aws-c-io
|
@liuchaoqun, with `pip install pyarrow` you don't get aws-bindings, they're too complicated to package into wheels as far as I know. And even if they're packaged, at the time of the release of pyarrow 10 it would have still been pinned to aws 1.8 for the same reasons as in this issue.
|
## 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
| 52 |
Fatal error condition occurred in aws-c-io
|
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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
|
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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
|
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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
|
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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`
|
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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
|
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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
|
[
-0.5723398327827454,
-0.2938041388988495,
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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
|
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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
|
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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
|
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-0.3928442895412445,
0.29272010922431946,
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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
|
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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
|
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] |
https://github.com/huggingface/datasets/issues/3299
|
Add option to find unique elements in nested sequences when calling `Dataset.unique`
|
Hi! Not yet, would you be interested in contributing a PR? I can give you some pointers if needed.
|
It would be nice to have an option to flatten nested sequences to find unique elements stored in them when calling `Dataset.unique`. ~~Currently, `Dataset.unique` only supports finding unique sequences and not unique elements in that situation.~~
| 19 |
Add option to find unique elements in nested sequences when calling `Dataset.unique`
|
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] |
https://github.com/huggingface/datasets/issues/3299
|
Add option to find unique elements in nested sequences when calling `Dataset.unique`
|
@mariosasko did this ever get implemented? Willing to help if you are still up for it.
|
It would be nice to have an option to flatten nested sequences to find unique elements stored in them when calling `Dataset.unique`. ~~Currently, `Dataset.unique` only supports finding unique sequences and not unique elements in that situation.~~
| 16 |
Add option to find unique elements in nested sequences when calling `Dataset.unique`
|
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] |
https://github.com/huggingface/datasets/issues/3299
|
Add option to find unique elements in nested sequences when calling `Dataset.unique`
|
@dcruiz01 No, but here is an example of how to do this with the existing API:
```python
ds = Dataset.from_dict({"tokens": [["a", "b"], ["c", "a"], ["c", "e"]]})
def flatten_tokens(pa_table):
return pa.table([pc.list_flatten(pa_table["tokens"])], ["flat_tokens"])
ds = ds.with_format("arrow")
ds = ds.map(flatten_tokens, batched=True)
ds = ds.with_format(None)
unique_tokens = ds.unique("flat_tokens")
```
When I think about it, `.unique` on `Sequence(Value(...))` should return unique sequences/arrays, not unique elements of these sequences...
|
It would be nice to have an option to flatten nested sequences to find unique elements stored in them when calling `Dataset.unique`. ~~Currently, `Dataset.unique` only supports finding unique sequences and not unique elements in that situation.~~
| 63 |
Add option to find unique elements in nested sequences when calling `Dataset.unique`
|
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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
|
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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
|
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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
|
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] |
https://github.com/huggingface/datasets/issues/3297
|
.map() cache is wrongfully reused - only happens when the mapping function is imported
|
For anyone interested, I see that with `dill==0.3.6` the workaround I suggested doesn't work anymore.
I opened an issue about it: https://github.com/uqfoundation/dill/issues/572.
|
## 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
| 22 |
.map() cache is wrongfully reused - only happens when the mapping function is imported
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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] |
https://github.com/huggingface/datasets/issues/3285
|
Add IEMOCAP dataset
|
@mariosasko has this been solved? I would like to use login and custom verification for training on my private 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).
| 20 |
Add IEMOCAP dataset
|
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] |
https://github.com/huggingface/datasets/issues/3285
|
Add IEMOCAP dataset
|
@mariosasko Thanks, but no. I would like to keep my HuggingFace Dataset private and train a model on it. Is this possible?
|
## 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).
| 22 |
Add IEMOCAP 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
|
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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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0.006481632590293884,
0.12766903638839722,
0.02781207673251629,
-0.03415180742740631,
0.1067160815000534,
0.21197834610939026,
0.03702661767601967,
0.20967890322208405,
0.1986742913722992,
-0.02539270743727684,
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0.40005558729171753,
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0.1835893839597702,
-0.2076832801103592,
-0.040791403502225876,
0.4279969036579132,
0.24057981371879578,
0.023175250738859177,
-0.11577194929122925,
0.4127350151538849,
-0.3214280605316162,
-0.2153785228729248,
-0.3908314108848572,
0.30091309547424316,
-0.2778129577636719,
-0.2592480480670929,
-0.16299474239349365,
-0.019802985712885857,
0.16454926133155823,
0.16483622789382935,
0.16743271052837372,
0.17344138026237488,
-0.441453754901886,
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0.14678935706615448,
0.38610997796058655,
0.0018131224205717444,
-0.08891995251178741,
-0.1492684930562973
] |
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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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'
|
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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'
|
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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'
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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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
|
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