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https://github.com/huggingface/datasets/issues/1773 | bug in loading datasets | Looks like an issue with your csv file. Did you use the right delimiter ?
Apparently at line 37 the CSV reader from pandas reads 2 fields instead of 1. | Hi,
I need to load a dataset, I use these commands:
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files={'train': 'sick/train.csv',
'test': 'sick/test.csv',
'validation': 'sick/validation.csv'})
print(dataset['validation'])
```
the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq
```
Using custom data configuration default
Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...
Traceback (most recent call last):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir
yield tmp_dir
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__
return self.get_chunk()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk
return self.read(nrows=size)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read
index, columns, col_dict = self._engine.read(nrows)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "write_sick.py", line 19, in <module>
'validation': 'sick/validation.csv'})
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare
self._save_info()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__
self.gen.throw(type, value, traceback)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir
shutil.rmtree(tmp_dir)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'
```
| 30 | bug in loading datasets
Hi,
I need to load a dataset, I use these commands:
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files={'train': 'sick/train.csv',
'test': 'sick/test.csv',
'validation': 'sick/validation.csv'})
print(dataset['validation'])
```
the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq
```
Using custom data configuration default
Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...
Traceback (most recent call last):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir
yield tmp_dir
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__
return self.get_chunk()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk
return self.read(nrows=size)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read
index, columns, col_dict = self._engine.read(nrows)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "write_sick.py", line 19, in <module>
'validation': 'sick/validation.csv'})
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare
self._save_info()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__
self.gen.throw(type, value, traceback)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir
shutil.rmtree(tmp_dir)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'
```
Looks like an issue with your csv file. Did you use the right delimiter ?
Apparently at line 37 the CSV reader from pandas reads 2 fields instead of 1. | [
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] |
https://github.com/huggingface/datasets/issues/1773 | bug in loading datasets | Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do
```python
from datasets import load_dataset
dataset = load_dataset('csv', data_files=data_files, sep="\t")
```
for example to use a tab separator.
You can see the full list of arguments here: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/csv/csv.py
(I've not found the list in the documentation though, we definitely must add them !) | Hi,
I need to load a dataset, I use these commands:
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files={'train': 'sick/train.csv',
'test': 'sick/test.csv',
'validation': 'sick/validation.csv'})
print(dataset['validation'])
```
the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq
```
Using custom data configuration default
Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...
Traceback (most recent call last):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir
yield tmp_dir
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__
return self.get_chunk()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk
return self.read(nrows=size)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read
index, columns, col_dict = self._engine.read(nrows)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "write_sick.py", line 19, in <module>
'validation': 'sick/validation.csv'})
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare
self._save_info()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__
self.gen.throw(type, value, traceback)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir
shutil.rmtree(tmp_dir)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'
```
| 64 | bug in loading datasets
Hi,
I need to load a dataset, I use these commands:
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files={'train': 'sick/train.csv',
'test': 'sick/test.csv',
'validation': 'sick/validation.csv'})
print(dataset['validation'])
```
the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq
```
Using custom data configuration default
Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...
Traceback (most recent call last):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir
yield tmp_dir
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__
return self.get_chunk()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk
return self.read(nrows=size)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read
index, columns, col_dict = self._engine.read(nrows)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "write_sick.py", line 19, in <module>
'validation': 'sick/validation.csv'})
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare
self._save_info()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__
self.gen.throw(type, value, traceback)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir
shutil.rmtree(tmp_dir)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'
```
Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do
```python
from datasets import load_dataset
dataset = load_dataset('csv', data_files=data_files, sep="\t")
```
for example to use a tab separator.
You can see the full list of arguments here: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/csv/csv.py
(I've not found the list in the documentation though, we definitely must add them !) | [
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https://github.com/huggingface/datasets/issues/1771 | Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py | Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library.
See PR #1726
We'll do a new release soon :) | Hi,
When I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset?
```
Traceback (most recent call last):
File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
max_retries=download_config.max_retries,
File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py
``` | 36 | Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py
Hi,
When I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset?
```
Traceback (most recent call last):
File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
max_retries=download_config.max_retries,
File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py
```
Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library.
See PR #1726
We'll do a new release soon :) | [
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https://github.com/huggingface/datasets/issues/1770 | how can I combine 2 dataset with different/same features? | Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)
For now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188 | to combine 2 dataset by one-one map like ds = zip(ds1, ds2):
ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'}
or different feature:
ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} | 45 | how can I combine 2 dataset with different/same features?
to combine 2 dataset by one-one map like ds = zip(ds1, ds2):
ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'}
or different feature:
ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}
Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)
For now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188 | [
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https://github.com/huggingface/datasets/issues/1770 | how can I combine 2 dataset with different/same features? | Good to hear.
Currently I did not use map , just fetch src and tgt from the 2 dataset and merge them.
It will be a release if you can deal with it at the backend.
Thanks. | to combine 2 dataset by one-one map like ds = zip(ds1, ds2):
ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'}
or different feature:
ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} | 37 | how can I combine 2 dataset with different/same features?
to combine 2 dataset by one-one map like ds = zip(ds1, ds2):
ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'}
or different feature:
ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}
Good to hear.
Currently I did not use map , just fetch src and tgt from the 2 dataset and merge them.
It will be a release if you can deal with it at the backend.
Thanks. | [
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https://github.com/huggingface/datasets/issues/1769 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 | Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ? | It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chinese-roberta-wwm-ext
--train_file
/nfs/volume-377-2/bert/data/test/train.txt
--output_dir
test
--do_train
--per_device_train_batch_size
2
--gradient_accumulation_steps
2
--learning_rate
1e-4
--max_steps
1000
--warmup_steps
10
--save_steps
1000
--save_total_limit
1
--seed
23333
--max_seq_length
512
--preprocessing_num_workers
2
--cache_dir
/nfs/volume-377-2/bert/data/test/cache
```
Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.
Full Traceback:
```
Traceback (most recent call last):
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module>
main()
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks
put(task)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump
StockPickler.dump(self, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump
self.save(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function
obj.__dict__, fkwdefaults), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce
save(cls)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type
obj.__bases__, _dict), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save
self.save_global(obj, rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union
```
| 21 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chinese-roberta-wwm-ext
--train_file
/nfs/volume-377-2/bert/data/test/train.txt
--output_dir
test
--do_train
--per_device_train_batch_size
2
--gradient_accumulation_steps
2
--learning_rate
1e-4
--max_steps
1000
--warmup_steps
10
--save_steps
1000
--save_total_limit
1
--seed
23333
--max_seq_length
512
--preprocessing_num_workers
2
--cache_dir
/nfs/volume-377-2/bert/data/test/cache
```
Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.
Full Traceback:
```
Traceback (most recent call last):
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module>
main()
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks
put(task)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump
StockPickler.dump(self, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump
self.save(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function
obj.__dict__, fkwdefaults), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce
save(cls)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type
obj.__bases__, _dict), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save
self.save_global(obj, rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union
```
Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ? | [
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0.041450586169958115,
0.13671056926250458,
-0.024166591465473175,
-0.45826801657676697,
0.23896338045597076,
0.01220790483057499,
-0.2194102257490158,
-0.09122022241353989,
0.3244311511516571,
0.14234265685081482,
-0.29269155859947205,
0.1541835516691208,
-0.34222474694252014
] |
https://github.com/huggingface/datasets/issues/1769 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 | > Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?
python==3.6.10
datasets==1.2.1
dill==0.3.2
pickle.format_version==4.0 | It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chinese-roberta-wwm-ext
--train_file
/nfs/volume-377-2/bert/data/test/train.txt
--output_dir
test
--do_train
--per_device_train_batch_size
2
--gradient_accumulation_steps
2
--learning_rate
1e-4
--max_steps
1000
--warmup_steps
10
--save_steps
1000
--save_total_limit
1
--seed
23333
--max_seq_length
512
--preprocessing_num_workers
2
--cache_dir
/nfs/volume-377-2/bert/data/test/cache
```
Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.
Full Traceback:
```
Traceback (most recent call last):
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module>
main()
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks
put(task)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump
StockPickler.dump(self, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump
self.save(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function
obj.__dict__, fkwdefaults), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce
save(cls)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type
obj.__bases__, _dict), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save
self.save_global(obj, rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union
```
| 26 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chinese-roberta-wwm-ext
--train_file
/nfs/volume-377-2/bert/data/test/train.txt
--output_dir
test
--do_train
--per_device_train_batch_size
2
--gradient_accumulation_steps
2
--learning_rate
1e-4
--max_steps
1000
--warmup_steps
10
--save_steps
1000
--save_total_limit
1
--seed
23333
--max_seq_length
512
--preprocessing_num_workers
2
--cache_dir
/nfs/volume-377-2/bert/data/test/cache
```
Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.
Full Traceback:
```
Traceback (most recent call last):
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module>
main()
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks
put(task)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump
StockPickler.dump(self, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump
self.save(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function
obj.__dict__, fkwdefaults), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce
save(cls)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type
obj.__bases__, _dict), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save
self.save_global(obj, rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union
```
> Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?
python==3.6.10
datasets==1.2.1
dill==0.3.2
pickle.format_version==4.0 | [
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] |
https://github.com/huggingface/datasets/issues/1769 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 | Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`.
However objects like `typing.Union[str, NoneType]` are not picklable in python <3.7.
Can you try to update your python version to python>=3.7 ?
| It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chinese-roberta-wwm-ext
--train_file
/nfs/volume-377-2/bert/data/test/train.txt
--output_dir
test
--do_train
--per_device_train_batch_size
2
--gradient_accumulation_steps
2
--learning_rate
1e-4
--max_steps
1000
--warmup_steps
10
--save_steps
1000
--save_total_limit
1
--seed
23333
--max_seq_length
512
--preprocessing_num_workers
2
--cache_dir
/nfs/volume-377-2/bert/data/test/cache
```
Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.
Full Traceback:
```
Traceback (most recent call last):
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module>
main()
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks
put(task)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump
StockPickler.dump(self, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump
self.save(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function
obj.__dict__, fkwdefaults), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce
save(cls)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type
obj.__bases__, _dict), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save
self.save_global(obj, rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union
```
| 41 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chinese-roberta-wwm-ext
--train_file
/nfs/volume-377-2/bert/data/test/train.txt
--output_dir
test
--do_train
--per_device_train_batch_size
2
--gradient_accumulation_steps
2
--learning_rate
1e-4
--max_steps
1000
--warmup_steps
10
--save_steps
1000
--save_total_limit
1
--seed
23333
--max_seq_length
512
--preprocessing_num_workers
2
--cache_dir
/nfs/volume-377-2/bert/data/test/cache
```
Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.
Full Traceback:
```
Traceback (most recent call last):
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module>
main()
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks
put(task)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump
StockPickler.dump(self, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump
self.save(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function
obj.__dict__, fkwdefaults), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce
save(cls)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type
obj.__bases__, _dict), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save
self.save_global(obj, rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union
```
Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`.
However objects like `typing.Union[str, NoneType]` are not picklable in python <3.7.
Can you try to update your python version to python>=3.7 ?
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https://github.com/huggingface/datasets/issues/1766 | Issues when run two programs compute the same metrics | Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace "default_experiment" with the experiment id that you provide in the arrow filename.
Also when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate. | I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:
```
File "train_matching_min.py", line 160, in <module>ch_9_label
avg_loss = valid(epoch, args.batch, args.validation, args.with_label)
File "train_matching_min.py", line 93, in valid
bleu += eval.compute()
File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute
return self.metric.compute()['score']
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute
self._finalize()
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize
self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths]))
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files
pa_table = self._read_files(files)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename
pa_table = f.read_all()
File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396
``` | 69 | Issues when run two programs compute the same metrics
I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:
```
File "train_matching_min.py", line 160, in <module>ch_9_label
avg_loss = valid(epoch, args.batch, args.validation, args.with_label)
File "train_matching_min.py", line 93, in valid
bleu += eval.compute()
File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute
return self.metric.compute()['score']
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute
self._finalize()
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize
self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths]))
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files
pa_table = self._read_files(files)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename
pa_table = f.read_all()
File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396
```
Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace "default_experiment" with the experiment id that you provide in the arrow filename.
Also when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate. | [
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https://github.com/huggingface/datasets/issues/1766 | Issues when run two programs compute the same metrics | Thank you for your response. I fixed the issue by set "keep_in_memory=True" when load_metric.
I cannot share the entire source code but below is the wrapper I wrote:
```python
class Evaluation:
def __init__(self, metric='sacrebleu'):
# self.metric = load_metric(metric, keep_in_memory=True)
self.metric = load_metric(metric)
def add(self, predictions, references):
self.metric.add_batch(predictions=predictions, references=references)
def compute(self):
return self.metric.compute()['score']
```
Then call the given wrapper as follows:
```python
eval = Evaluation(metric='sacrebleu')
for query, candidates, labels in tqdm(dataset):
predictions = net.generate(query)
references = [[s] for s in labels]
eval.add(predictions, references)
if n % 100 == 0:
bleu += eval.compute()
eval = Evaluation(metric='sacrebleu') | I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:
```
File "train_matching_min.py", line 160, in <module>ch_9_label
avg_loss = valid(epoch, args.batch, args.validation, args.with_label)
File "train_matching_min.py", line 93, in valid
bleu += eval.compute()
File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute
return self.metric.compute()['score']
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute
self._finalize()
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize
self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths]))
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files
pa_table = self._read_files(files)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename
pa_table = f.read_all()
File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396
``` | 94 | Issues when run two programs compute the same metrics
I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:
```
File "train_matching_min.py", line 160, in <module>ch_9_label
avg_loss = valid(epoch, args.batch, args.validation, args.with_label)
File "train_matching_min.py", line 93, in valid
bleu += eval.compute()
File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute
return self.metric.compute()['score']
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute
self._finalize()
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize
self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths]))
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files
pa_table = self._read_files(files)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename
pa_table = f.read_all()
File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396
```
Thank you for your response. I fixed the issue by set "keep_in_memory=True" when load_metric.
I cannot share the entire source code but below is the wrapper I wrote:
```python
class Evaluation:
def __init__(self, metric='sacrebleu'):
# self.metric = load_metric(metric, keep_in_memory=True)
self.metric = load_metric(metric)
def add(self, predictions, references):
self.metric.add_batch(predictions=predictions, references=references)
def compute(self):
return self.metric.compute()['score']
```
Then call the given wrapper as follows:
```python
eval = Evaluation(metric='sacrebleu')
for query, candidates, labels in tqdm(dataset):
predictions = net.generate(query)
references = [[s] for s in labels]
eval.add(predictions, references)
if n % 100 == 0:
bleu += eval.compute()
eval = Evaluation(metric='sacrebleu') | [
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https://github.com/huggingface/datasets/issues/1765 | Error iterating over Dataset with DataLoader | Instead of:
```python
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
```
It should be:
```python
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)
```
`batch_sampler` accepts a Sampler object or an Iterable, so you get an error. | I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113,
102]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}
```
When I try to iterate as in the docs, I get errors:
```
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
next(iter(dataloader))
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-05180ba8aa35> in <module>()
1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
----> 2 next(iter(dataloader))
3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader)
411 self._timeout = loader.timeout
412 self._collate_fn = loader.collate_fn
--> 413 self._sampler_iter = iter(self._index_sampler)
414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
415 self._persistent_workers = loader.persistent_workers
TypeError: 'int' object is not iterable
``` | 30 | Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113,
102]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}
```
When I try to iterate as in the docs, I get errors:
```
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
next(iter(dataloader))
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-05180ba8aa35> in <module>()
1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
----> 2 next(iter(dataloader))
3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader)
411 self._timeout = loader.timeout
412 self._collate_fn = loader.collate_fn
--> 413 self._sampler_iter = iter(self._index_sampler)
414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
415 self._persistent_workers = loader.persistent_workers
TypeError: 'int' object is not iterable
```
Instead of:
```python
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
```
It should be:
```python
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)
```
`batch_sampler` accepts a Sampler object or an Iterable, so you get an error. | [
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] |
https://github.com/huggingface/datasets/issues/1765 | Error iterating over Dataset with DataLoader | @mariosasko I thought that would fix it, but now I'm getting a different error:
```
/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)
return torch.tensor(x, **format_kwargs)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-20-3af1d82bf93a> in <module>()
1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)
----> 2 next(iter(dataloader))
5 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch)
53 storage = elem.storage()._new_shared(numel)
54 out = elem.new(storage)
---> 55 return torch.stack(batch, 0, out=out)
56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
57 and elem_type.__name__ != 'string_':
RuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1
```
Any thoughts what this means?I Do I need padding? | I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113,
102]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}
```
When I try to iterate as in the docs, I get errors:
```
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
next(iter(dataloader))
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-05180ba8aa35> in <module>()
1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
----> 2 next(iter(dataloader))
3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader)
411 self._timeout = loader.timeout
412 self._collate_fn = loader.collate_fn
--> 413 self._sampler_iter = iter(self._index_sampler)
414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
415 self._persistent_workers = loader.persistent_workers
TypeError: 'int' object is not iterable
``` | 169 | Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113,
102]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}
```
When I try to iterate as in the docs, I get errors:
```
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
next(iter(dataloader))
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-05180ba8aa35> in <module>()
1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
----> 2 next(iter(dataloader))
3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader)
411 self._timeout = loader.timeout
412 self._collate_fn = loader.collate_fn
--> 413 self._sampler_iter = iter(self._index_sampler)
414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
415 self._persistent_workers = loader.persistent_workers
TypeError: 'int' object is not iterable
```
@mariosasko I thought that would fix it, but now I'm getting a different error:
```
/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)
return torch.tensor(x, **format_kwargs)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-20-3af1d82bf93a> in <module>()
1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)
----> 2 next(iter(dataloader))
5 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch)
53 storage = elem.storage()._new_shared(numel)
54 out = elem.new(storage)
---> 55 return torch.stack(batch, 0, out=out)
56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
57 and elem_type.__name__ != 'string_':
RuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1
```
Any thoughts what this means?I Do I need padding? | [
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https://github.com/huggingface/datasets/issues/1765 | Error iterating over Dataset with DataLoader | Yes, padding is an answer.
This can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding. | I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113,
102]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}
```
When I try to iterate as in the docs, I get errors:
```
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
next(iter(dataloader))
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-05180ba8aa35> in <module>()
1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
----> 2 next(iter(dataloader))
3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader)
411 self._timeout = loader.timeout
412 self._collate_fn = loader.collate_fn
--> 413 self._sampler_iter = iter(self._index_sampler)
414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
415 self._persistent_workers = loader.persistent_workers
TypeError: 'int' object is not iterable
``` | 23 | Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113,
102]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}
```
When I try to iterate as in the docs, I get errors:
```
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
next(iter(dataloader))
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-05180ba8aa35> in <module>()
1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
----> 2 next(iter(dataloader))
3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader)
411 self._timeout = loader.timeout
412 self._collate_fn = loader.collate_fn
--> 413 self._sampler_iter = iter(self._index_sampler)
414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
415 self._persistent_workers = loader.persistent_workers
TypeError: 'int' object is not iterable
```
Yes, padding is an answer.
This can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding. | [
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https://github.com/huggingface/datasets/issues/1762 | Unable to format dataset to CUDA Tensors | Hi ! You can get CUDA tensors with
```python
dataset.set_format("torch", columns=columns, device="cuda")
```
Indeed `set_format` passes the `**kwargs` to `torch.tensor` | Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't support assignment:
```
columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']
samples.set_format(type='torch', columns = columns)
for column in columns:
samples[column].to(torch.device(self.config.device))
```
There should be an option to do so, or if there is already a way to do this, please let me know.
Thanks,
Gunjan | 20 | Unable to format dataset to CUDA Tensors
Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't support assignment:
```
columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']
samples.set_format(type='torch', columns = columns)
for column in columns:
samples[column].to(torch.device(self.config.device))
```
There should be an option to do so, or if there is already a way to do this, please let me know.
Thanks,
Gunjan
Hi ! You can get CUDA tensors with
```python
dataset.set_format("torch", columns=columns, device="cuda")
```
Indeed `set_format` passes the `**kwargs` to `torch.tensor` | [
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] |
https://github.com/huggingface/datasets/issues/1762 | Unable to format dataset to CUDA Tensors | Hi @lhoestq,
Thanks a lot. Is this true for all format types?
As in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'? | Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't support assignment:
```
columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']
samples.set_format(type='torch', columns = columns)
for column in columns:
samples[column].to(torch.device(self.config.device))
```
There should be an option to do so, or if there is already a way to do this, please let me know.
Thanks,
Gunjan | 38 | Unable to format dataset to CUDA Tensors
Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't support assignment:
```
columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']
samples.set_format(type='torch', columns = columns)
for column in columns:
samples[column].to(torch.device(self.config.device))
```
There should be an option to do so, or if there is already a way to do this, please let me know.
Thanks,
Gunjan
Hi @lhoestq,
Thanks a lot. Is this true for all format types?
As in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'? | [
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https://github.com/huggingface/datasets/issues/1762 | Unable to format dataset to CUDA Tensors | Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.
We don't support the kwargs for pandas on the other hand. | Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't support assignment:
```
columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']
samples.set_format(type='torch', columns = columns)
for column in columns:
samples[column].to(torch.device(self.config.device))
```
There should be an option to do so, or if there is already a way to do this, please let me know.
Thanks,
Gunjan | 26 | Unable to format dataset to CUDA Tensors
Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't support assignment:
```
columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']
samples.set_format(type='torch', columns = columns)
for column in columns:
samples[column].to(torch.device(self.config.device))
```
There should be an option to do so, or if there is already a way to do this, please let me know.
Thanks,
Gunjan
Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.
We don't support the kwargs for pandas on the other hand. | [
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] |
https://github.com/huggingface/datasets/issues/1762 | Unable to format dataset to CUDA Tensors | Thanks @lhoestq,
Would it be okay if I added this to the docs and made a PR? | Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't support assignment:
```
columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']
samples.set_format(type='torch', columns = columns)
for column in columns:
samples[column].to(torch.device(self.config.device))
```
There should be an option to do so, or if there is already a way to do this, please let me know.
Thanks,
Gunjan | 17 | Unable to format dataset to CUDA Tensors
Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't support assignment:
```
columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']
samples.set_format(type='torch', columns = columns)
for column in columns:
samples[column].to(torch.device(self.config.device))
```
There should be an option to do so, or if there is already a way to do this, please let me know.
Thanks,
Gunjan
Thanks @lhoestq,
Would it be okay if I added this to the docs and made a PR? | [
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https://github.com/huggingface/datasets/issues/1759 | wikipedia dataset incomplete | Hi !
From what pickle file fo you get this ?
I guess you mean the dataset loaded using `load_dataset` ? | Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche).
The pickle file however shows: französische Gemeinde mit Einwohnern (Stand).
Is it possible to fix this?
Best regards
Chris
| 21 | wikipedia dataset incomplete
Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche).
The pickle file however shows: französische Gemeinde mit Einwohnern (Stand).
Is it possible to fix this?
Best regards
Chris
Hi !
From what pickle file fo you get this ?
I guess you mean the dataset loaded using `load_dataset` ? | [
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] |
https://github.com/huggingface/datasets/issues/1759 | wikipedia dataset incomplete | yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. | Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche).
The pickle file however shows: französische Gemeinde mit Einwohnern (Stand).
Is it possible to fix this?
Best regards
Chris
| 28 | wikipedia dataset incomplete
Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche).
The pickle file however shows: französische Gemeinde mit Einwohnern (Stand).
Is it possible to fix this?
Best regards
Chris
yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. | [
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] |
https://github.com/huggingface/datasets/issues/1759 | wikipedia dataset incomplete | The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell
There also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.
| Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche).
The pickle file however shows: französische Gemeinde mit Einwohnern (Stand).
Is it possible to fix this?
Best regards
Chris
| 48 | wikipedia dataset incomplete
Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche).
The pickle file however shows: französische Gemeinde mit Einwohnern (Stand).
Is it possible to fix this?
Best regards
Chris
The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell
There also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.
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https://github.com/huggingface/datasets/issues/1758 | dataset.search() (elastic) cannot reliably retrieve search results | Hi !
I tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.
Maybe this is because the index is not updated yet on the ElasticSearch side ? | I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.
I am indexing data that looks like the following from the HF SQuAD 2.0 data set:
```
['57318658e6313a140071d02b',
'56f7165e3d8e2e1400e3733a',
'570e2f6e0b85d914000d7d21',
'5727e58aff5b5019007d97d0',
'5a3b5a503ff257001ab8441f',
'57262fab271a42140099d725']
```
To reproduce the issue, try:
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# build base examples, features set of training data
shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list()
examples = load_dataset("squad_v2").shuffle(seed=1)['train']
features = load_dataset("squad_v2").shuffle(seed=1)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# reorder features by the training process
features = features.select(indices=shuffled_idx)
# get the example ids to match with the "example" data; get unique entries
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions in the examples data set; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# retrieve the example index
example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]
example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]
example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]
len(example_idx_k1) # should be 130319
len(example_idx_k2) # should be 130319
#trial 1 lengths:
# k=1: 130314
# k=3: 130319
# trial 2:
# just run k=3 first: 130310
# try k=1 after k=3: 130319
```
| 41 | dataset.search() (elastic) cannot reliably retrieve search results
I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.
I am indexing data that looks like the following from the HF SQuAD 2.0 data set:
```
['57318658e6313a140071d02b',
'56f7165e3d8e2e1400e3733a',
'570e2f6e0b85d914000d7d21',
'5727e58aff5b5019007d97d0',
'5a3b5a503ff257001ab8441f',
'57262fab271a42140099d725']
```
To reproduce the issue, try:
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# build base examples, features set of training data
shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list()
examples = load_dataset("squad_v2").shuffle(seed=1)['train']
features = load_dataset("squad_v2").shuffle(seed=1)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# reorder features by the training process
features = features.select(indices=shuffled_idx)
# get the example ids to match with the "example" data; get unique entries
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions in the examples data set; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# retrieve the example index
example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]
example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]
example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]
len(example_idx_k1) # should be 130319
len(example_idx_k2) # should be 130319
#trial 1 lengths:
# k=1: 130314
# k=3: 130319
# trial 2:
# just run k=3 first: 130310
# try k=1 after k=3: 130319
```
Hi !
I tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.
Maybe this is because the index is not updated yet on the ElasticSearch side ? | [
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https://github.com/huggingface/datasets/issues/1758 | dataset.search() (elastic) cannot reliably retrieve search results | Thanks for the feedback! I added a 30 second "sleep" and that seemed to work well! | I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.
I am indexing data that looks like the following from the HF SQuAD 2.0 data set:
```
['57318658e6313a140071d02b',
'56f7165e3d8e2e1400e3733a',
'570e2f6e0b85d914000d7d21',
'5727e58aff5b5019007d97d0',
'5a3b5a503ff257001ab8441f',
'57262fab271a42140099d725']
```
To reproduce the issue, try:
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# build base examples, features set of training data
shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list()
examples = load_dataset("squad_v2").shuffle(seed=1)['train']
features = load_dataset("squad_v2").shuffle(seed=1)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# reorder features by the training process
features = features.select(indices=shuffled_idx)
# get the example ids to match with the "example" data; get unique entries
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions in the examples data set; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# retrieve the example index
example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]
example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]
example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]
len(example_idx_k1) # should be 130319
len(example_idx_k2) # should be 130319
#trial 1 lengths:
# k=1: 130314
# k=3: 130319
# trial 2:
# just run k=3 first: 130310
# try k=1 after k=3: 130319
```
| 16 | dataset.search() (elastic) cannot reliably retrieve search results
I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.
I am indexing data that looks like the following from the HF SQuAD 2.0 data set:
```
['57318658e6313a140071d02b',
'56f7165e3d8e2e1400e3733a',
'570e2f6e0b85d914000d7d21',
'5727e58aff5b5019007d97d0',
'5a3b5a503ff257001ab8441f',
'57262fab271a42140099d725']
```
To reproduce the issue, try:
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# build base examples, features set of training data
shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list()
examples = load_dataset("squad_v2").shuffle(seed=1)['train']
features = load_dataset("squad_v2").shuffle(seed=1)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# reorder features by the training process
features = features.select(indices=shuffled_idx)
# get the example ids to match with the "example" data; get unique entries
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions in the examples data set; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# retrieve the example index
example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]
example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]
example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]
len(example_idx_k1) # should be 130319
len(example_idx_k2) # should be 130319
#trial 1 lengths:
# k=1: 130314
# k=3: 130319
# trial 2:
# just run k=3 first: 130310
# try k=1 after k=3: 130319
```
Thanks for the feedback! I added a 30 second "sleep" and that seemed to work well! | [
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https://github.com/huggingface/datasets/issues/1757 | FewRel | @dspoka Please check the following link : https://github.com/thunlp/FewRel
This link mentions two versions of the datasets. Also, this one seems to be the official link.
I am assuming this is the correct link and implementing based on the same. | ## Adding a Dataset
- **Name:** FewRel
- **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset
- **Paper:** @inproceedings{han2018fewrel,
title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},
author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong},
booktitle={EMNLP},
year={2018}}
- **Data:** https://github.com/ProKil/FewRel
- **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| 39 | FewRel
## Adding a Dataset
- **Name:** FewRel
- **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset
- **Paper:** @inproceedings{han2018fewrel,
title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},
author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong},
booktitle={EMNLP},
year={2018}}
- **Data:** https://github.com/ProKil/FewRel
- **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
@dspoka Please check the following link : https://github.com/thunlp/FewRel
This link mentions two versions of the datasets. Also, this one seems to be the official link.
I am assuming this is the correct link and implementing based on the same. | [
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] |
https://github.com/huggingface/datasets/issues/1755 | Using select/reordering datasets slows operations down immensely | Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad`
```
examples = examples.flatten_indices()
features = features.flatten_indices()
```
That helped quite a bit! | I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour.
The below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score.
The problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes.
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30):
all_start_logits, all_end_logits = starting_logits, ending_logits
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
predictions = collections.OrderedDict()
# Logging.
print(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
# Let's loop over all the examples!
for example_index, example in enumerate(tqdm(examples)):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_score = None # Only used if squad_v2 is True.
valid_answers = []
context = example["context"]
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_logits = all_start_logits[feature_index]
end_logits = all_end_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Update minimum null prediction.
cls_index = features[feature_index]["input_ids"].index(tokenizer.cls_token_id)
feature_null_score = start_logits[cls_index] + end_logits[cls_index]
if min_null_score is None or min_null_score < feature_null_score:
min_null_score = feature_null_score
# Go through all possibilities for the `n_best_size` greater start and end logits.
start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
for start_index in start_indexes:
for end_index in end_indexes:
# Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
# to part of the input_ids that are not in the context.
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or offset_mapping[end_index] is None
):
continue
# Don't consider answers with a length that is either < 0 or > max_answer_length.
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
start_char = offset_mapping[start_index][0]
end_char = offset_mapping[end_index][1]
valid_answers.append(
{
"score": start_logits[start_index] + end_logits[end_index],
"text": context[start_char: end_char]
}
)
if len(valid_answers) > 0:
best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0]
else:
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
best_answer = {"text": "", "score": 0.0}
# Let's pick our final answer: the best one or the null answer (only for squad_v2)
if not squad_v2:
predictions[example["id"]] = best_answer["text"]
else:
answer = best_answer["text"] if best_answer["score"] > min_null_score else ""
predictions[example["id"]] = answer
return predictions
# build base examples, features from training data
examples = load_dataset("squad_v2").shuffle(seed=5)['train']
features = load_dataset("squad_v2").shuffle(seed=5)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# sim some shuffled training indices that we want to use to re-order the data to compare how we did
shuffle_idx = np.arange(0, 131754)
np.random.shuffle(shuffle_idx)
# create a new dataset with rows selected following the training shuffle
features = features.select(indices=shuffle_idx)
# get unique example ids to match with the "example" data
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# search the examples for their index position
example_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
# drop the elastic search
examples.drop_index(index_name='id')
# put examples in the right order
examples = examples.select(indices=example_idx)
# generate some fake data
logits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)}
def score_squad(logits, n_best_size, max_answer):
# proceed with QA calculation
final_predictions = postprocess_qa_predictions(examples=examples,
features=features,
starting_logits=logits['starting_logits'],
ending_logits=logits['ending_logits'],
n_best_size=20,
max_answer_length=30)
metric = load_metric("squad_v2")
formatted_predictions = [{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in final_predictions.items()]
references = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples]
metrics = metric.compute(predictions=formatted_predictions, references=references)
return metrics
metrics = score_squad(logits, n_best_size=20, max_answer=30)
```
| 39 | Using select/reordering datasets slows operations down immensely
I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour.
The below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score.
The problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes.
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30):
all_start_logits, all_end_logits = starting_logits, ending_logits
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
predictions = collections.OrderedDict()
# Logging.
print(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
# Let's loop over all the examples!
for example_index, example in enumerate(tqdm(examples)):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_score = None # Only used if squad_v2 is True.
valid_answers = []
context = example["context"]
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_logits = all_start_logits[feature_index]
end_logits = all_end_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Update minimum null prediction.
cls_index = features[feature_index]["input_ids"].index(tokenizer.cls_token_id)
feature_null_score = start_logits[cls_index] + end_logits[cls_index]
if min_null_score is None or min_null_score < feature_null_score:
min_null_score = feature_null_score
# Go through all possibilities for the `n_best_size` greater start and end logits.
start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
for start_index in start_indexes:
for end_index in end_indexes:
# Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
# to part of the input_ids that are not in the context.
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or offset_mapping[end_index] is None
):
continue
# Don't consider answers with a length that is either < 0 or > max_answer_length.
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
start_char = offset_mapping[start_index][0]
end_char = offset_mapping[end_index][1]
valid_answers.append(
{
"score": start_logits[start_index] + end_logits[end_index],
"text": context[start_char: end_char]
}
)
if len(valid_answers) > 0:
best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0]
else:
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
best_answer = {"text": "", "score": 0.0}
# Let's pick our final answer: the best one or the null answer (only for squad_v2)
if not squad_v2:
predictions[example["id"]] = best_answer["text"]
else:
answer = best_answer["text"] if best_answer["score"] > min_null_score else ""
predictions[example["id"]] = answer
return predictions
# build base examples, features from training data
examples = load_dataset("squad_v2").shuffle(seed=5)['train']
features = load_dataset("squad_v2").shuffle(seed=5)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# sim some shuffled training indices that we want to use to re-order the data to compare how we did
shuffle_idx = np.arange(0, 131754)
np.random.shuffle(shuffle_idx)
# create a new dataset with rows selected following the training shuffle
features = features.select(indices=shuffle_idx)
# get unique example ids to match with the "example" data
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# search the examples for their index position
example_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
# drop the elastic search
examples.drop_index(index_name='id')
# put examples in the right order
examples = examples.select(indices=example_idx)
# generate some fake data
logits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)}
def score_squad(logits, n_best_size, max_answer):
# proceed with QA calculation
final_predictions = postprocess_qa_predictions(examples=examples,
features=features,
starting_logits=logits['starting_logits'],
ending_logits=logits['ending_logits'],
n_best_size=20,
max_answer_length=30)
metric = load_metric("squad_v2")
formatted_predictions = [{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in final_predictions.items()]
references = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples]
metrics = metric.compute(predictions=formatted_predictions, references=references)
return metrics
metrics = score_squad(logits, n_best_size=20, max_answer=30)
```
Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad`
```
examples = examples.flatten_indices()
features = features.flatten_indices()
```
That helped quite a bit! | [
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https://github.com/huggingface/datasets/issues/1747 | datasets slicing with seed | Hi :)
The slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data.
You can shuffle and then take a subset of your dataset with
```python
# shuffle and take the first 100 examples
dataset = dataset.shuffle(seed=42).select(range(100))
```
You can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows | Hi
I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq | 50 | datasets slicing with seed
Hi
I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq
Hi :)
The slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data.
You can shuffle and then take a subset of your dataset with
```python
# shuffle and take the first 100 examples
dataset = dataset.shuffle(seed=42).select(range(100))
```
You can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows | [
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https://github.com/huggingface/datasets/issues/1747 | datasets slicing with seed | thank you so much
On Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <[email protected]>
wrote:
> Hi :)
> The slicing API doesn't shuffle the data.
> You can shuffle and then take a subset of your dataset with
>
> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))
>
> You can find more information about shuffling and selecting rows in the
> documentation:
> https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/1747#issuecomment-762278134>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ>
> .
>
| Hi
I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq | 103 | datasets slicing with seed
Hi
I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq
thank you so much
On Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <[email protected]>
wrote:
> Hi :)
> The slicing API doesn't shuffle the data.
> You can shuffle and then take a subset of your dataset with
>
> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))
>
> You can find more information about shuffling and selecting rows in the
> documentation:
> https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/1747#issuecomment-762278134>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ>
> .
>
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https://github.com/huggingface/datasets/issues/1745 | difference between wsc and wsc.fixed for superglue | From the description given in the dataset script for `wsc.fixed`:
```
This version fixes issues where the spans are not actually substrings of the text.
``` | Hi
I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq | 26 | difference between wsc and wsc.fixed for superglue
Hi
I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq
From the description given in the dataset script for `wsc.fixed`:
```
This version fixes issues where the spans are not actually substrings of the text.
``` | [
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https://github.com/huggingface/datasets/issues/1743 | Issue while Creating Custom Metric | Currently it's only possible to define the features for the two columns `references` and `predictions`.
The data for these columns can then be passed to `metric.add_batch` and `metric.compute`.
Instead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.
For example
```python
features = datasets.Features({
'predictions':datasets.Sequence(datasets.Value("int32")),
"references": datasets.Sequence({
"references_ids": datasets.Value("int32"),
"offset_mapping": datasets.Value("int32"),
'text': datasets.Value('string'),
"ground": datasets.Value("int32")
}),
})
```
Another option would be to simply have the two features like
```python
features = datasets.Features({
'predictions':datasets.Sequence(datasets.Value("int32")),
"references": datasets.Sequence(datasets.Value("int32")),
})
```
and keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).
What is the metric you would like to implement ?
I'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users.
What do you think ? | Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}),
# Homepage of the metric for documentation
homepage="http://metric.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_metric"],
reference_urls=["http://path.to.reference.url/new_metric"]
)
def _compute(self,predictions,references,text,offset_mapping,spans):
pred_spans = []
for i,preds in enumerate(predictions):
current_preds = []
for j,token_preds in enumerate(preds):
if (preds>0.5):
current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))
pred_spans.append(current_spans)
return {
"Token Wise F1": f1_score(references,predictions,labels=[0,1]),
"Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])
}
```
I believe this is not correct. But that's not the issue I am facing right now. I get this error :
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-144-ed7349b50821> in <module>()
----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] )
2 frames
/usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch)
802 encoded_batch = {}
803 if set(batch) != set(self):
--> 804 print(batch)
805 print(self)
806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self)))
ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}
```
On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.
How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?
Thanks,
Gunjan | 151 | Issue while Creating Custom Metric
Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}),
# Homepage of the metric for documentation
homepage="http://metric.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_metric"],
reference_urls=["http://path.to.reference.url/new_metric"]
)
def _compute(self,predictions,references,text,offset_mapping,spans):
pred_spans = []
for i,preds in enumerate(predictions):
current_preds = []
for j,token_preds in enumerate(preds):
if (preds>0.5):
current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))
pred_spans.append(current_spans)
return {
"Token Wise F1": f1_score(references,predictions,labels=[0,1]),
"Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])
}
```
I believe this is not correct. But that's not the issue I am facing right now. I get this error :
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-144-ed7349b50821> in <module>()
----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] )
2 frames
/usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch)
802 encoded_batch = {}
803 if set(batch) != set(self):
--> 804 print(batch)
805 print(self)
806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self)))
ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}
```
On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.
How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?
Thanks,
Gunjan
Currently it's only possible to define the features for the two columns `references` and `predictions`.
The data for these columns can then be passed to `metric.add_batch` and `metric.compute`.
Instead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.
For example
```python
features = datasets.Features({
'predictions':datasets.Sequence(datasets.Value("int32")),
"references": datasets.Sequence({
"references_ids": datasets.Value("int32"),
"offset_mapping": datasets.Value("int32"),
'text': datasets.Value('string'),
"ground": datasets.Value("int32")
}),
})
```
Another option would be to simply have the two features like
```python
features = datasets.Features({
'predictions':datasets.Sequence(datasets.Value("int32")),
"references": datasets.Sequence(datasets.Value("int32")),
})
```
and keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).
What is the metric you would like to implement ?
I'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users.
What do you think ? | [
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https://github.com/huggingface/datasets/issues/1743 | Issue while Creating Custom Metric | Hi @lhoestq,
I am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.
Including them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know. | Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}),
# Homepage of the metric for documentation
homepage="http://metric.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_metric"],
reference_urls=["http://path.to.reference.url/new_metric"]
)
def _compute(self,predictions,references,text,offset_mapping,spans):
pred_spans = []
for i,preds in enumerate(predictions):
current_preds = []
for j,token_preds in enumerate(preds):
if (preds>0.5):
current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))
pred_spans.append(current_spans)
return {
"Token Wise F1": f1_score(references,predictions,labels=[0,1]),
"Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])
}
```
I believe this is not correct. But that's not the issue I am facing right now. I get this error :
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-144-ed7349b50821> in <module>()
----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] )
2 frames
/usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch)
802 encoded_batch = {}
803 if set(batch) != set(self):
--> 804 print(batch)
805 print(self)
806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self)))
ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}
```
On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.
How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?
Thanks,
Gunjan | 75 | Issue while Creating Custom Metric
Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}),
# Homepage of the metric for documentation
homepage="http://metric.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_metric"],
reference_urls=["http://path.to.reference.url/new_metric"]
)
def _compute(self,predictions,references,text,offset_mapping,spans):
pred_spans = []
for i,preds in enumerate(predictions):
current_preds = []
for j,token_preds in enumerate(preds):
if (preds>0.5):
current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))
pred_spans.append(current_spans)
return {
"Token Wise F1": f1_score(references,predictions,labels=[0,1]),
"Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])
}
```
I believe this is not correct. But that's not the issue I am facing right now. I get this error :
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-144-ed7349b50821> in <module>()
----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] )
2 frames
/usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch)
802 encoded_batch = {}
803 if set(batch) != set(self):
--> 804 print(batch)
805 print(self)
806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self)))
ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}
```
On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.
How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?
Thanks,
Gunjan
Hi @lhoestq,
I am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.
Including them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know. | [
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https://github.com/huggingface/datasets/issues/1733 | connection issue with glue, what is the data url for glue? | Hello @juliahane, which config of GLUE causes you trouble?
The URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py | Hi
my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not
thanks | 20 | connection issue with glue, what is the data url for glue?
Hi
my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not
thanks
Hello @juliahane, which config of GLUE causes you trouble?
The URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py | [
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https://github.com/huggingface/datasets/issues/1731 | Couldn't reach swda.py | Hi @yangp725,
The SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.
You can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471).
Let me know if this helps ! | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py
| 54 | Couldn't reach swda.py
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py
Hi @yangp725,
The SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.
You can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471).
Let me know if this helps ! | [
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https://github.com/huggingface/datasets/issues/1729 | Is there support for Deep learning datasets? | Hi @ZurMaD!
Thanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617
MNIST is also on the way: #1730
If you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated 🚀
| I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets | 55 | Is there support for Deep learning datasets?
I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets
Hi @ZurMaD!
Thanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617
MNIST is also on the way: #1730
If you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated 🚀
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https://github.com/huggingface/datasets/issues/1728 | Add an entry to an arrow dataset | Hi @ameet-1997,
I think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets
For your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.
Let me know If this helps! | Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
| 43 | Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
Hi @ameet-1997,
I think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets
For your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.
Let me know If this helps! | [
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https://github.com/huggingface/datasets/issues/1728 | Add an entry to an arrow dataset | That's a great idea! Thank you so much!
When I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?
Code:
``` python
combined_dataset = concatenate_datasets([datasets, modified_dataset])
```
Error:
```
AttributeError: 'DatasetDict' object has no attribute 'features'
```
Output:
```
(Pdb) datasets
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
num_rows: 493
})
})
(Pdb) modified_dataset
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
num_rows: 493
})
})
```
The error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution? | Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
| 123 | Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
That's a great idea! Thank you so much!
When I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?
Code:
``` python
combined_dataset = concatenate_datasets([datasets, modified_dataset])
```
Error:
```
AttributeError: 'DatasetDict' object has no attribute 'features'
```
Output:
```
(Pdb) datasets
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
num_rows: 493
})
})
(Pdb) modified_dataset
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
num_rows: 493
})
})
```
The error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution? | [
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https://github.com/huggingface/datasets/issues/1728 | Add an entry to an arrow dataset | You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`
Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases. | Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
| 42 | Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`
Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases. | [
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https://github.com/huggingface/datasets/issues/1728 | Add an entry to an arrow dataset | > Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.
My opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.
For instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split="train"` is given for example it's always returning a Dataset. | Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
| 96 | Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.
My opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.
For instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split="train"` is given for example it's always returning a Dataset. | [
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?! | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 18 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?! | [
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb:
```
!pip install git+https://github.com/google-research/bleurt.git
references = ["foo bar baz", "one two three"]
bleurt_metric = load_metric('bleurt')
predictions = ["foo bar", "four five six"]
bleurt_metric.compute(predictions=predictions, references=references)
``` | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 39 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb:
```
!pip install git+https://github.com/google-research/bleurt.git
references = ["foo bar baz", "one two three"]
bleurt_metric = load_metric('bleurt')
predictions = ["foo bar", "four five six"]
bleurt_metric.compute(predictions=predictions, references=references)
``` | [
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | @aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?
I tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error.
However, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.
This made me suspect that the problem is somehow related to the Jupyter notebook.
More environment details:
```
OS: Ubuntu Linux 18.04
conda==4.8.3
python==3.8.5
datasets==1.3.0
tensorflow==2.4.0
BLEURT==0.0.1
notebook==6.2.0
``` | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 112 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
@aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?
I tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error.
However, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.
This made me suspect that the problem is somehow related to the Jupyter notebook.
More environment details:
```
OS: Ubuntu Linux 18.04
conda==4.8.3
python==3.8.5
datasets==1.3.0
tensorflow==2.4.0
BLEURT==0.0.1
notebook==6.2.0
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.
Running this before calling bleurt fixes it:
```
import sys
sys.argv = sys.argv[:1]
```
Not the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?
This is the output of `print(sys.argv)` when running on colab:
```
['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']
``` | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 71 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.
Running this before calling bleurt fixes it:
```
import sys
sys.argv = sys.argv[:1]
```
Not the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?
This is the output of `print(sys.argv)` when running on colab:
```
['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase. | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 25 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase. | [
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https://github.com/huggingface/datasets/issues/1725 | load the local dataset | You should rephrase your question or give more examples and details on what you want to do.
it’s not possible to understand it and help you with only this information. | your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot! | 30 | load the local dataset
your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot!
You should rephrase your question or give more examples and details on what you want to do.
it’s not possible to understand it and help you with only this information. | [
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https://github.com/huggingface/datasets/issues/1725 | load the local dataset | sorry for that.
i want to know how could i load the train set and the test set from the local ,which api or function should i use .
| your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot! | 29 | load the local dataset
your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot!
sorry for that.
i want to know how could i load the train set and the test set from the local ,which api or function should i use .
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https://github.com/huggingface/datasets/issues/1725 | load the local dataset | thanks a lot
i find that the problem is i dont use vpn...
so i have to keep my net work even if i want to load the local data ? | your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot! | 31 | load the local dataset
your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot!
thanks a lot
i find that the problem is i dont use vpn...
so i have to keep my net work even if i want to load the local data ? | [
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https://github.com/huggingface/datasets/issues/1724 | could not run models on a offline server successfully | Hi @lkcao !
Your issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py.
Then you can change the line 221 of `run_mlm_new.py` into:
```python
datasets = load_dataset('/path/to/text.py', data_files=data_files)
```
Where `/path/to/text.py` is the path on the server where you saved the `text.py` script. | Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... | 64 | could not run models on a offline server successfully
Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
Hi @lkcao !
Your issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py.
Then you can change the line 221 of `run_mlm_new.py` into:
```python
datasets = load_dataset('/path/to/text.py', data_files=data_files)
```
Where `/path/to/text.py` is the path on the server where you saved the `text.py` script. | [
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https://github.com/huggingface/datasets/issues/1724 | could not run models on a offline server successfully | We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline | Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... | 24 | could not run models on a offline server successfully
Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline | [
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https://github.com/huggingface/datasets/issues/1724 | could not run models on a offline server successfully | The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)
You can now use them offline
```python
datasets = load_dataset('text', data_files=data_files)
```
We'll do a new release soon | Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... | 38 | could not run models on a offline server successfully
Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)
You can now use them offline
```python
datasets = load_dataset('text', data_files=data_files)
```
We'll do a new release soon | [
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https://github.com/huggingface/datasets/issues/1724 | could not run models on a offline server successfully | > The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)
> You can now use them offline
>
> ```python
> datasets = load_dataset('text', data_files=data_files)
> ```
>
> We'll do a new release soon
so the new version release now? | Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... | 52 | could not run models on a offline server successfully
Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
> The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)
> You can now use them offline
>
> ```python
> datasets = load_dataset('text', data_files=data_files)
> ```
>
> We'll do a new release soon
so the new version release now? | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Thanks for reporting !
I was able to reproduce thanks to your code and find the origin of the bug.
The cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.
I'm opening a PR to fix this.
Also we plan to do a new release in the coming days so you can expect the fix to be available soon.
Note that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to. | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 129 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Thanks for reporting !
I was able to reproduce thanks to your code and find the origin of the bug.
The cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.
I'm opening a PR to fix this.
Also we plan to do a new release in the coming days so you can expect the fix to be available soon.
Note that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to. | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Thanks for the fast reply, waiting for the fix :)
I tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:
```
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}
)
```
and got an error:
```
multiprocess.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 157, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/venv/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper
out = func(self, *args, **kwargs)
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1491, in _map_single
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False)
File "/usr/lib/python3.6/tempfile.py", line 690, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/usr/lib/python3.6/tempfile.py", line 401, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "test.py", line 48, in <module>
cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}
File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in map
transformed_shards = [r.get() for r in results]
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'
```
| Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 229 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Thanks for the fast reply, waiting for the fix :)
I tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:
```
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}
)
```
and got an error:
```
multiprocess.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 157, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/venv/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper
out = func(self, *args, **kwargs)
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1491, in _map_single
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False)
File "/usr/lib/python3.6/tempfile.py", line 690, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/usr/lib/python3.6/tempfile.py", line 401, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "test.py", line 48, in <module>
cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}
File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in map
transformed_shards = [r.get() for r in results]
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'
```
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https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | The documentation says
```
cache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the
results of the computation instead of the automatically generated cache file name.
You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.
```
What is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like
```python
cache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}
``` | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 90 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
The documentation says
```
cache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the
results of the computation instead of the automatically generated cache file name.
You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.
```
What is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like
```python
cache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}
``` | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Managed to get `cache_file_names` working and caching works well with it
Had to make a small modification for it to work:
```
cache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}
``` | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 31 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Managed to get `cache_file_names` working and caching works well with it
Had to make a small modification for it to work:
```
cache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}
``` | [
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https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.
I can confirm that this is how it works in `datasets==1.1.3` | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 41 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.
I can confirm that this is how it works in `datasets==1.1.3` | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 18 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path | [
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https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
# CHANGE
print('hello')
# CHANGE
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
...
```
I am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird
Edit: accidently clicked the close issue button | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 136 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
# CHANGE
print('hello')
# CHANGE
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
...
```
I am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird
Edit: accidently clicked the close issue button | [
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https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.
Not sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.
Moreover we're already ignoring the line definition for lambda functions. | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 86 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.
Not sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.
Moreover we're already ignoring the line definition for lambda functions. | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Sounds great, thank you for your quick responses and help! Looking forward for the next release. | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 16 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Sounds great, thank you for your quick responses and help! Looking forward for the next release. | [
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https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!
```python
# Datasets
train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))
if args.dev_split >= len(train):
raise ValueError("Not enough dev files")
dev = []
state = random.Random(1001)
for _ in range(args.dev_split):
dev.append(train.pop(state.randint(0, len(train) - 1)))
max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)
def tokenize_function(examples):
return tokenizer(examples['text'], return_special_tokens_mask=True)
def group_texts(examples):
# Concatenate all texts from our dataset and generate chunks of max_seq_length
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# Truncate (not implementing padding)
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_seq_length
result = {
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
datasets = load_dataset(
'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]},
cache_dir=args.data_cache_dir)
datasets = datasets.map(tokenize_function,
batched=True, remove_columns=['text'],
cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},
load_from_cache_file=not args.overwrite_cache)
datasets = datasets.map(group_texts,
batched=True,
cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},
load_from_cache_file=not args.overwrite_cache)
```
And this is the log
```
04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1
04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s]
04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped
04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped
```
| Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 274 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!
```python
# Datasets
train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))
if args.dev_split >= len(train):
raise ValueError("Not enough dev files")
dev = []
state = random.Random(1001)
for _ in range(args.dev_split):
dev.append(train.pop(state.randint(0, len(train) - 1)))
max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)
def tokenize_function(examples):
return tokenizer(examples['text'], return_special_tokens_mask=True)
def group_texts(examples):
# Concatenate all texts from our dataset and generate chunks of max_seq_length
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# Truncate (not implementing padding)
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_seq_length
result = {
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
datasets = load_dataset(
'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]},
cache_dir=args.data_cache_dir)
datasets = datasets.map(tokenize_function,
batched=True, remove_columns=['text'],
cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},
load_from_cache_file=not args.overwrite_cache)
datasets = datasets.map(group_texts,
batched=True,
cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},
load_from_cache_file=not args.overwrite_cache)
```
And this is the log
```
04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1
04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s]
04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped
04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped
```
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https://github.com/huggingface/datasets/issues/1717 | SciFact dataset - minor changes | Hi Dave,
You are more than welcome to open a PR to make these changes! 🤗
You will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Pinging also @lhoestq for the Google cloud matter. | Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave | 44 | SciFact dataset - minor changes
Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave
Hi Dave,
You are more than welcome to open a PR to make these changes! 🤗
You will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Pinging also @lhoestq for the Google cloud matter. | [
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https://github.com/huggingface/datasets/issues/1717 | SciFact dataset - minor changes | > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
Sure ! Also feel free to ping us for reviews or if we can help :)
> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
What makes you think that ?
Afaik there's no scifact on our google storage
| Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave | 91 | SciFact dataset - minor changes
Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave
> I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
Sure ! Also feel free to ping us for reviews or if we can help :)
> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
What makes you think that ?
Afaik there's no scifact on our google storage
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] |
https://github.com/huggingface/datasets/issues/1717 | SciFact dataset - minor changes |
> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
>
> Sure ! Also feel free to ping us for reviews or if we can help :)
>
OK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.
> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
>
> What makes you think that ?
> Afaik there's no scifact on our google storage
You're right, I had the data cached on my machine somewhere.
| Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave | 152 | SciFact dataset - minor changes
Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave
> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
>
> Sure ! Also feel free to ping us for reviews or if we can help :)
>
OK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.
> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
>
> What makes you think that ?
> Afaik there's no scifact on our google storage
You're right, I had the data cached on my machine somewhere.
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https://github.com/huggingface/datasets/issues/1713 | Installation using conda | Great! Did you guys have a timeframe in mind for the next release?
Thank you for all the great work in developing this library. | Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. | 24 | Installation using conda
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
Great! Did you guys have a timeframe in mind for the next release?
Thank you for all the great work in developing this library. | [
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https://github.com/huggingface/datasets/issues/1713 | Installation using conda | I think we can have `datasets` on conda by next week. Will see what I can do! | Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. | 17 | Installation using conda
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
I think we can have `datasets` on conda by next week. Will see what I can do! | [
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https://github.com/huggingface/datasets/issues/1713 | Installation using conda | `datasets` has been added to the huggingface channel thanks to @LysandreJik :)
It depends on conda-forge though
```
conda install -c huggingface -c conda-forge datasets
``` | Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. | 26 | Installation using conda
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
`datasets` has been added to the huggingface channel thanks to @LysandreJik :)
It depends on conda-forge though
```
conda install -c huggingface -c conda-forge datasets
``` | [
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https://github.com/huggingface/datasets/issues/1710 | IsADirectoryError when trying to download C4 | I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.
Here it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.
Valid files are paths to local files or URLs to remote files. | **TLDR**:
I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.
How can the problem be fixed?
**VERBOSE**:
I use Python version 3.7 and have the following dependencies listed in my project:
```
datasets==1.2.0
apache-beam==2.26.0
```
When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:
```
from datasets import load_dataset
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
```
I get the following stacktrace:
```
/Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...
Traceback (most recent call last):
File "/Users/fredriko/source/misc/main.py", line 3, in <module>
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare
pipeline=pipeline,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators
file_paths = dl_manager.download_and_extract(files_to_download)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums
self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict
with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/'
Process finished with exit code 1
``` | 50 | IsADirectoryError when trying to download C4
**TLDR**:
I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.
How can the problem be fixed?
**VERBOSE**:
I use Python version 3.7 and have the following dependencies listed in my project:
```
datasets==1.2.0
apache-beam==2.26.0
```
When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:
```
from datasets import load_dataset
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
```
I get the following stacktrace:
```
/Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...
Traceback (most recent call last):
File "/Users/fredriko/source/misc/main.py", line 3, in <module>
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare
pipeline=pipeline,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators
file_paths = dl_manager.download_and_extract(files_to_download)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums
self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict
with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/'
Process finished with exit code 1
```
I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.
Here it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.
Valid files are paths to local files or URLs to remote files. | [
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https://github.com/huggingface/datasets/issues/1706 | Error when downloading a large dataset on slow connection. | Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ?
It looks like the downloaded file is corrupted and can't be extracted using `tarfile`.
Could you try loading it again with
```python
import datasets
datasets.load_dataset("openwebtext", download_mode="force_redownload")
``` | I receive the following error after about an hour trying to download the `openwebtext` dataset.
The code used is:
```python
import datasets
datasets.load_dataset("openwebtext")
```
> Traceback (most recent call last): [4/28]
> File "<stdin>", line 1, in <module>
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset
> ignore_verifications=ignore_verifications,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare
> dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 570, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> File "/home/lucadiliello/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02/openwebtext.py", line 62, in _split_generators
> dl_dir = dl_manager.download_and_extract(_URL)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
> return self.extract(self.download(url_or_urls))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 235, in extract
> num_proc=num_proc,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
> return function(data_struct)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
> tar_file.extractall(output_path_extracted)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2000, in extractall
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2042, in extract
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2112, in _extract_member
> self.makefile(tarinfo, targetpath)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2161, in makefile
> copyfileobj(source, target, tarinfo.size, ReadError, bufsize)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 253, in copyfileobj
> buf = src.read(remainder)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/lzma.py", line 200, in read
> return self._buffer.read(size)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 68, in readinto
> data = self.read(len(byte_view))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 99, in read
> raise EOFError("Compressed file ended before the "
> EOFError: Compressed file ended before the end-of-stream marker was reached
| 44 | Error when downloading a large dataset on slow connection.
I receive the following error after about an hour trying to download the `openwebtext` dataset.
The code used is:
```python
import datasets
datasets.load_dataset("openwebtext")
```
> Traceback (most recent call last): [4/28]
> File "<stdin>", line 1, in <module>
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset
> ignore_verifications=ignore_verifications,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare
> dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 570, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> File "/home/lucadiliello/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02/openwebtext.py", line 62, in _split_generators
> dl_dir = dl_manager.download_and_extract(_URL)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
> return self.extract(self.download(url_or_urls))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 235, in extract
> num_proc=num_proc,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
> return function(data_struct)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
> tar_file.extractall(output_path_extracted)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2000, in extractall
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2042, in extract
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2112, in _extract_member
> self.makefile(tarinfo, targetpath)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2161, in makefile
> copyfileobj(source, target, tarinfo.size, ReadError, bufsize)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 253, in copyfileobj
> buf = src.read(remainder)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/lzma.py", line 200, in read
> return self._buffer.read(size)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 68, in readinto
> data = self.read(len(byte_view))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 99, in read
> raise EOFError("Compressed file ended before the "
> EOFError: Compressed file ended before the end-of-stream marker was reached
Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ?
It looks like the downloaded file is corrupted and can't be extracted using `tarfile`.
Could you try loading it again with
```python
import datasets
datasets.load_dataset("openwebtext", download_mode="force_redownload")
``` | [
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] |
https://github.com/huggingface/datasets/issues/1701 | Some datasets miss dataset_infos.json or dummy_data.zip | Thanks for reporting.
We should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.
For c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.
| While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json :
```
c4
lm1b
reclor
wikihow
```
And some does not have a dummy_data.zip :
```
kor_nli
math_dataset
mlqa
ms_marco
newsgroup
qa4mre
qangaroo
reddit_tifu
super_glue
trivia_qa
web_of_science
wmt14
wmt15
wmt16
wmt17
wmt18
wmt19
xtreme
```
But it seems that some of those last do have a "dummy" directory .
| 42 | Some datasets miss dataset_infos.json or dummy_data.zip
While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json :
```
c4
lm1b
reclor
wikihow
```
And some does not have a dummy_data.zip :
```
kor_nli
math_dataset
mlqa
ms_marco
newsgroup
qa4mre
qangaroo
reddit_tifu
super_glue
trivia_qa
web_of_science
wmt14
wmt15
wmt16
wmt17
wmt18
wmt19
xtreme
```
But it seems that some of those last do have a "dummy" directory .
Thanks for reporting.
We should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.
For c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.
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https://github.com/huggingface/datasets/issues/1687 | Question: Shouldn't .info be a part of DatasetDict? | We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work. | Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets.
For instance:
```
>>> ds = datasets.load_dataset("conll2002", "es")
>>> ds.info
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'DatasetDict' object has no attribute 'info'
```
I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets.
Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.
It is entirely possible that I am missing another perspective | 26 | Question: Shouldn't .info be a part of DatasetDict?
Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets.
For instance:
```
>>> ds = datasets.load_dataset("conll2002", "es")
>>> ds.info
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'DatasetDict' object has no attribute 'info'
```
I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets.
Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.
It is entirely possible that I am missing another perspective
We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work. | [
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https://github.com/huggingface/datasets/issues/1687 | Question: Shouldn't .info be a part of DatasetDict? | Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). | Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets.
For instance:
```
>>> ds = datasets.load_dataset("conll2002", "es")
>>> ds.info
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'DatasetDict' object has no attribute 'info'
```
I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets.
Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.
It is entirely possible that I am missing another perspective | 27 | Question: Shouldn't .info be a part of DatasetDict?
Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets.
For instance:
```
>>> ds = datasets.load_dataset("conll2002", "es")
>>> ds.info
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'DatasetDict' object has no attribute 'info'
```
I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets.
Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.
It is entirely possible that I am missing another perspective
Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). | [
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https://github.com/huggingface/datasets/issues/1686 | Dataset Error: DaNE contains empty samples at the end | One the PR is merged the fix will be available in the next release of `datasets`.
If you don't want to wait the next release you can still load the script from the master branch with
```python
load_dataset("dane", script_version="master")
``` | The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors.
```python
>>> import datasets
[...]
>>> dataset = datasets.load_dataset("dane")
[...]
>>> dataset["test"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
>>> dataset["train"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
```
Best,
Kenneth | 40 | Dataset Error: DaNE contains empty samples at the end
The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors.
```python
>>> import datasets
[...]
>>> dataset = datasets.load_dataset("dane")
[...]
>>> dataset["test"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
>>> dataset["train"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
```
Best,
Kenneth
One the PR is merged the fix will be available in the next release of `datasets`.
If you don't want to wait the next release you can still load the script from the master branch with
```python
load_dataset("dane", script_version="master")
``` | [
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https://github.com/huggingface/datasets/issues/1683 | `ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext | Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.
To fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two. | It seems to fail the final batch ):
steps to reproduce:
```
from datasets import load_dataset
from elasticsearch import Elasticsearch
import torch
from transformers import file_utils, set_seed
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
MAX_SEQ_LENGTH = 256
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", cache_dir="../datasets/")
ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(
"facebook/dpr-ctx_encoder-single-nq-base",
cache_dir="..datasets/"
)
dataset = load_dataset('text',
data_files='data/raw/ARC_Corpus.txt',
cache_dir='../datasets')
torch.set_grad_enabled(False)
ds_with_embeddings = dataset.map(
lambda example: {
'embeddings': ctx_encoder(
**ctx_tokenizer(
example["text"],
padding='max_length',
truncation=True,
max_length=MAX_SEQ_LENGTH,
return_tensors="pt"
)
)[0][0].numpy(),
},
batched=True,
load_from_cache_file=False,
batch_size=1000
)
```
ARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip)
And then the error:
```
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-13-67d139bb2ed3> in <module>
14 batched=True,
15 load_from_cache_file=False,
---> 16 batch_size=1000
17 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1257 fn_kwargs=fn_kwargs,
1258 new_fingerprint=new_fingerprint,
-> 1259 update_data=update_data,
1260 )
1261 else:
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
155 }
156 # apply actual function
--> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
159 # re-apply format to the output
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
161 # Call actual function
162
--> 163 out = func(self, *args, **kwargs)
164
165 # Update fingerprint of in-place transforms + update in-place history of transforms
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)
1526 if update_data:
1527 batch = cast_to_python_objects(batch)
-> 1528 writer.write_batch(batch)
1529 if update_data:
1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)
277 typed_sequence_examples[col] = typed_sequence
--> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples)
279 self.write_table(pa_table)
280
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Column 1 named text expected length 768 but got length 1000
``` | 68 | `ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext
It seems to fail the final batch ):
steps to reproduce:
```
from datasets import load_dataset
from elasticsearch import Elasticsearch
import torch
from transformers import file_utils, set_seed
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
MAX_SEQ_LENGTH = 256
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", cache_dir="../datasets/")
ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(
"facebook/dpr-ctx_encoder-single-nq-base",
cache_dir="..datasets/"
)
dataset = load_dataset('text',
data_files='data/raw/ARC_Corpus.txt',
cache_dir='../datasets')
torch.set_grad_enabled(False)
ds_with_embeddings = dataset.map(
lambda example: {
'embeddings': ctx_encoder(
**ctx_tokenizer(
example["text"],
padding='max_length',
truncation=True,
max_length=MAX_SEQ_LENGTH,
return_tensors="pt"
)
)[0][0].numpy(),
},
batched=True,
load_from_cache_file=False,
batch_size=1000
)
```
ARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip)
And then the error:
```
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-13-67d139bb2ed3> in <module>
14 batched=True,
15 load_from_cache_file=False,
---> 16 batch_size=1000
17 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1257 fn_kwargs=fn_kwargs,
1258 new_fingerprint=new_fingerprint,
-> 1259 update_data=update_data,
1260 )
1261 else:
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
155 }
156 # apply actual function
--> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
159 # re-apply format to the output
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
161 # Call actual function
162
--> 163 out = func(self, *args, **kwargs)
164
165 # Update fingerprint of in-place transforms + update in-place history of transforms
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)
1526 if update_data:
1527 batch = cast_to_python_objects(batch)
-> 1528 writer.write_batch(batch)
1529 if update_data:
1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)
277 typed_sequence_examples[col] = typed_sequence
--> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples)
279 self.write_table(pa_table)
280
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Column 1 named text expected length 768 but got length 1000
```
Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.
To fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two. | [
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https://github.com/huggingface/datasets/issues/1681 | Dataset "dane" missing | Hi @KennethEnevoldsen ,
I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.
For now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:
pip install git+https://github.com/huggingface/datasets.git@master | the `dane` dataset appear to be missing in the latest version (1.1.3).
```python
>>> import datasets
>>> datasets.__version__
'1.1.3'
>>> "dane" in datasets.list_datasets()
True
```
As we can see it should be present, but doesn't seem to be findable when using `load_dataset`.
```python
>>> datasets.load_dataset("dane")
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset
module_path, hash = prepare_module(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
```
This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data. | 56 | Dataset "dane" missing
the `dane` dataset appear to be missing in the latest version (1.1.3).
```python
>>> import datasets
>>> datasets.__version__
'1.1.3'
>>> "dane" in datasets.list_datasets()
True
```
As we can see it should be present, but doesn't seem to be findable when using `load_dataset`.
```python
>>> datasets.load_dataset("dane")
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset
module_path, hash = prepare_module(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
```
This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.
Hi @KennethEnevoldsen ,
I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.
For now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:
pip install git+https://github.com/huggingface/datasets.git@master | [
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https://github.com/huggingface/datasets/issues/1681 | Dataset "dane" missing | The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `dane` with
```python
from datasets import load_dataset
dataset = load_dataset("dane")
``` | the `dane` dataset appear to be missing in the latest version (1.1.3).
```python
>>> import datasets
>>> datasets.__version__
'1.1.3'
>>> "dane" in datasets.list_datasets()
True
```
As we can see it should be present, but doesn't seem to be findable when using `load_dataset`.
```python
>>> datasets.load_dataset("dane")
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset
module_path, hash = prepare_module(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
```
This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data. | 52 | Dataset "dane" missing
the `dane` dataset appear to be missing in the latest version (1.1.3).
```python
>>> import datasets
>>> datasets.__version__
'1.1.3'
>>> "dane" in datasets.list_datasets()
True
```
As we can see it should be present, but doesn't seem to be findable when using `load_dataset`.
```python
>>> datasets.load_dataset("dane")
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset
module_path, hash = prepare_module(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
```
This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.
The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `dane` with
```python
from datasets import load_dataset
dataset = load_dataset("dane")
``` | [
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https://github.com/huggingface/datasets/issues/1679 | Can't import cc100 dataset | cc100 was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `cc100` with
```python
from datasets import load_dataset
lang = "en"
dataset = load_dataset("cc100", lang=lang, split="train")
``` | There is some issue to import cc100 dataset.
```
from datasets import load_dataset
dataset = load_dataset("cc100")
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
280 raise FileNotFoundError(
281 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 282 combined_path, github_file_path, file_path
283 )
284 )
FileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py | 45 | Can't import cc100 dataset
There is some issue to import cc100 dataset.
```
from datasets import load_dataset
dataset = load_dataset("cc100")
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
280 raise FileNotFoundError(
281 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 282 combined_path, github_file_path, file_path
283 )
284 )
FileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
cc100 was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `cc100` with
```python
from datasets import load_dataset
lang = "en"
dataset = load_dataset("cc100", lang=lang, split="train")
``` | [
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] |
https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | The pile dataset would be very nice.
Benchmarks show that pile trained models achieve better results than most of actually trained models | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 22 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
The pile dataset would be very nice.
Benchmarks show that pile trained models achieve better results than most of actually trained models | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo.
However, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage. | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 45 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo.
However, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage. | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)
I'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 35 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)
I'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too! | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 47 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too! | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:
```python
# Install master branch of `datasets`
pip install git+https://github.com/huggingface/datasets.git#egg=datasets[streaming]
pip install zstandard
from datasets import load_dataset
dset = load_dataset("json", data_files="https://the-eye.eu/public/AI/pile/train/00.jsonl.zst", streaming=True, split="train")
next(iter(dset))
# {'meta': {'pile_set_name': 'Pile-CC'},
# 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web ... '}
```
Next step is to add the Pile as a "canonical" dataset that can be streamed without specifying the file names explicitly :) | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 92 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:
```python
# Install master branch of `datasets`
pip install git+https://github.com/huggingface/datasets.git#egg=datasets[streaming]
pip install zstandard
from datasets import load_dataset
dset = load_dataset("json", data_files="https://the-eye.eu/public/AI/pile/train/00.jsonl.zst", streaming=True, split="train")
next(iter(dset))
# {'meta': {'pile_set_name': 'Pile-CC'},
# 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web ... '}
```
Next step is to add the Pile as a "canonical" dataset that can be streamed without specifying the file names explicitly :) | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | > Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!
Hi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https://the-eye.eu/public/AI/pile_preliminary_components/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.
My current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train / dev / test splits that EleutherAI released [here](https://the-eye.eu/public/AI/pile/), e.g.
```python
from datasets import load_dataset
# Load a single component
youtube_subtitles = load_dataset("the_pile", "youtube_subtitles")
# Load the train / dev / test splits of the whole corpus
dset = load_dataset("the_pile", "all")
```
Ideally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.
cc @lhoestq | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 199 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
> Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!
Hi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https://the-eye.eu/public/AI/pile_preliminary_components/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.
My current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train / dev / test splits that EleutherAI released [here](https://the-eye.eu/public/AI/pile/), e.g.
```python
from datasets import load_dataset
# Load a single component
youtube_subtitles = load_dataset("the_pile", "youtube_subtitles")
# Load the train / dev / test splits of the whole corpus
dset = load_dataset("the_pile", "all")
```
Ideally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.
cc @lhoestq | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https://github.com/huggingface/datasets/pull/2817#issuecomment-901874049) :) | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 21 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https://github.com/huggingface/datasets/pull/2817#issuecomment-901874049) :) | [
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https://github.com/huggingface/datasets/issues/1674 | dutch_social can't be loaded | Hi @koenvandenberge and @alighofrani95!
The datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.
Meanwhile, you can still load the datasets using one of the techniques described in this issue: #1641
Let me know if this helps! | Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
``` | 59 | dutch_social can't be loaded
Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
```
Hi @koenvandenberge and @alighofrani95!
The datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.
Meanwhile, you can still load the datasets using one of the techniques described in this issue: #1641
Let me know if this helps! | [
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https://github.com/huggingface/datasets/issues/1674 | dutch_social can't be loaded | I just did the release :)
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `dutch_social` with
```python
from datasets import load_dataset
dataset = load_dataset("dutch_social")
``` | Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
``` | 36 | dutch_social can't be loaded
Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
```
I just did the release :)
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `dutch_social` with
```python
from datasets import load_dataset
dataset = load_dataset("dutch_social")
``` | [
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https://github.com/huggingface/datasets/issues/1674 | dutch_social can't be loaded | @lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently? | Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
``` | 28 | dutch_social can't be loaded
Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
```
@lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently? | [
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https://github.com/huggingface/datasets/issues/1674 | dutch_social can't be loaded | Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 thread. | Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
``` | 49 | dutch_social can't be loaded
Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
```
Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 thread. | [
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https://github.com/huggingface/datasets/issues/1673 | Unable to Download Hindi Wikipedia Dataset | Currently this dataset is only available when the library is installed from source since it was added after the last release.
We pin the dataset version with the library version so that people can have a reproducible dataset and processing when pinning the library.
We'll see if we can provide access to newer datasets with a warning that they are newer than your library version, that would help in cases like yours. | I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
| 72 | Unable to Download Hindi Wikipedia Dataset
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
Currently this dataset is only available when the library is installed from source since it was added after the last release.
We pin the dataset version with the library version so that people can have a reproducible dataset and processing when pinning the library.
We'll see if we can provide access to newer datasets with a warning that they are newer than your library version, that would help in cases like yours. | [
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https://github.com/huggingface/datasets/issues/1673 | Unable to Download Hindi Wikipedia Dataset | So for now, should i try and install the library from source and then try out the same piece of code? Will it work then, considering both the versions will match then? | I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
| 32 | Unable to Download Hindi Wikipedia Dataset
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
So for now, should i try and install the library from source and then try out the same piece of code? Will it work then, considering both the versions will match then? | [
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https://github.com/huggingface/datasets/issues/1673 | Unable to Download Hindi Wikipedia Dataset | Hey, so i tried installing the library from source using the commands : **git clone https://github.com/huggingface/datasets**, **cd datasets** and then **pip3 install -e .**. But i still am facing the same error that file is not found. Please advise.
The Datasets library version now is 1.1.3 by installing from source as compared to the earlier 1.0.3 that i had loaded using pip command but I am still getting same error
| I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
| 71 | Unable to Download Hindi Wikipedia Dataset
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
Hey, so i tried installing the library from source using the commands : **git clone https://github.com/huggingface/datasets**, **cd datasets** and then **pip3 install -e .**. But i still am facing the same error that file is not found. Please advise.
The Datasets library version now is 1.1.3 by installing from source as compared to the earlier 1.0.3 that i had loaded using pip command but I am still getting same error
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https://github.com/huggingface/datasets/issues/1673 | Unable to Download Hindi Wikipedia Dataset | Looks like the wikipedia dump for hindi at the date of 05/05/2020 is not available anymore.
You can try to load a more recent version of wikipedia
```python
from datasets import load_dataset
d = load_dataset("wikipedia", language="hi", date="20210101", split="train", beam_runner="DirectRunner")
``` | I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
| 40 | Unable to Download Hindi Wikipedia Dataset
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
Looks like the wikipedia dump for hindi at the date of 05/05/2020 is not available anymore.
You can try to load a more recent version of wikipedia
```python
from datasets import load_dataset
d = load_dataset("wikipedia", language="hi", date="20210101", split="train", beam_runner="DirectRunner")
``` | [
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] |
https://github.com/huggingface/datasets/issues/1672 | load_dataset hang on file_lock | Having the same issue with `datasets 1.1.3` of `1.5.0` (both tracebacks look the same) and `kilt_wikipedia`, Ubuntu 20.04
```py
In [1]: from datasets import load_dataset
In [2]: wikipedia = load_dataset('kilt_wikipedia')['full']
Downloading: 7.37kB [00:00, 2.74MB/s]
Downloading: 3.33kB [00:00, 1.44MB/s]
^C---------------------------------------------------------------------------
OSError Traceback (most recent call last)
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)
380 try:
--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
382 except (IOError, OSError):
OSError: [Errno 37] No locks available
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-2-f412d3d46ec9> in <module>
----> 1 wikipedia = load_dataset('kilt_wikipedia')['full']
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, sav
e_infos, script_version, **config_kwargs)
601 hash=hash,
602 features=features,
--> 603 **config_kwargs,
604 )
605
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, *args, **kwargs)
841 def __init__(self, *args, **kwargs):
842 self._writer_batch_size = kwargs.pop("writer_batch_size", self._writer_batch_size)
--> 843 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
844
845 @abc.abstractmethod
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)
174 os.makedirs(self._cache_dir_root, exist_ok=True)
175 lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, "_") + ".lock")
--> 176 with FileLock(lock_path):
177 if os.path.exists(self._cache_dir): # check if data exist
178 if len(os.listdir(self._cache_dir)) > 0:
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in __enter__(self)
312
313 def __enter__(self):
--> 314 self.acquire()
315 return self
316
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in acquire(self, timeout, poll_intervall)
261 if not self.is_locked:
262 logger().debug("Attempting to acquire lock %s on %s", lock_id, lock_filename)
--> 263 self._acquire()
264
265 if self.is_locked:
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)
379
380 try:
--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
382 except (IOError, OSError):
383 os.close(fd)
KeyboardInterrupt:
``` | I am trying to load the squad dataset. Fails on Windows 10 but succeeds in Colab.
Transformers: 3.3.1
Datasets: 1.0.2
Windows 10 (also tested in WSL)
```
datasets.logging.set_verbosity_debug()
datasets.
train_dataset = load_dataset('squad', split='train')
valid_dataset = load_dataset('squad', split='validation')
train_dataset.features
```
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py not found in cache or force_download set to True, downloading to C:\Users\simpl\.cache\huggingface\datasets\tmpzj_o_6u7
Downloading:
5.24k/? [00:00<00:00, 134kB/s]
storing https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py in cache at C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
creating metadata file for C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
Checking C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py to C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.json
No config specified, defaulting to first: squad/plain_text
```
Interrupting the jupyter kernel we are in a file lock.
In Google Colab the download is ok. In contrast to a local run in colab dataset_infos.json is downloaded
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/dataset_infos.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/datasets/tmptl9ha_ad
Downloading:
2.19k/? [00:00<00:00, 26.2kB/s]
``` | 234 | load_dataset hang on file_lock
I am trying to load the squad dataset. Fails on Windows 10 but succeeds in Colab.
Transformers: 3.3.1
Datasets: 1.0.2
Windows 10 (also tested in WSL)
```
datasets.logging.set_verbosity_debug()
datasets.
train_dataset = load_dataset('squad', split='train')
valid_dataset = load_dataset('squad', split='validation')
train_dataset.features
```
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py not found in cache or force_download set to True, downloading to C:\Users\simpl\.cache\huggingface\datasets\tmpzj_o_6u7
Downloading:
5.24k/? [00:00<00:00, 134kB/s]
storing https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py in cache at C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
creating metadata file for C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
Checking C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py to C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.json
No config specified, defaulting to first: squad/plain_text
```
Interrupting the jupyter kernel we are in a file lock.
In Google Colab the download is ok. In contrast to a local run in colab dataset_infos.json is downloaded
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/dataset_infos.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/datasets/tmptl9ha_ad
Downloading:
2.19k/? [00:00<00:00, 26.2kB/s]
```
Having the same issue with `datasets 1.1.3` of `1.5.0` (both tracebacks look the same) and `kilt_wikipedia`, Ubuntu 20.04
```py
In [1]: from datasets import load_dataset
In [2]: wikipedia = load_dataset('kilt_wikipedia')['full']
Downloading: 7.37kB [00:00, 2.74MB/s]
Downloading: 3.33kB [00:00, 1.44MB/s]
^C---------------------------------------------------------------------------
OSError Traceback (most recent call last)
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)
380 try:
--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
382 except (IOError, OSError):
OSError: [Errno 37] No locks available
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-2-f412d3d46ec9> in <module>
----> 1 wikipedia = load_dataset('kilt_wikipedia')['full']
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, sav
e_infos, script_version, **config_kwargs)
601 hash=hash,
602 features=features,
--> 603 **config_kwargs,
604 )
605
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, *args, **kwargs)
841 def __init__(self, *args, **kwargs):
842 self._writer_batch_size = kwargs.pop("writer_batch_size", self._writer_batch_size)
--> 843 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
844
845 @abc.abstractmethod
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)
174 os.makedirs(self._cache_dir_root, exist_ok=True)
175 lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, "_") + ".lock")
--> 176 with FileLock(lock_path):
177 if os.path.exists(self._cache_dir): # check if data exist
178 if len(os.listdir(self._cache_dir)) > 0:
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in __enter__(self)
312
313 def __enter__(self):
--> 314 self.acquire()
315 return self
316
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in acquire(self, timeout, poll_intervall)
261 if not self.is_locked:
262 logger().debug("Attempting to acquire lock %s on %s", lock_id, lock_filename)
--> 263 self._acquire()
264
265 if self.is_locked:
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)
379
380 try:
--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
382 except (IOError, OSError):
383 os.close(fd)
KeyboardInterrupt:
``` | [
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https://github.com/huggingface/datasets/issues/1671 | connection issue | Also, mayjor issue for me is the format issue, even if I go through changing the whole code to use load_from_disk, then if I do
d = datasets.load_from_disk("imdb")
d = d["train"][:10] => the format of this is no more in datasets format
this is different from you call load_datasets("train[10]")
could you tell me how I can make the two datastes the same format @lhoestq
| Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
| 64 | connection issue
Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
Also, mayjor issue for me is the format issue, even if I go through changing the whole code to use load_from_disk, then if I do
d = datasets.load_from_disk("imdb")
d = d["train"][:10] => the format of this is no more in datasets format
this is different from you call load_datasets("train[10]")
could you tell me how I can make the two datastes the same format @lhoestq
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https://github.com/huggingface/datasets/issues/1671 | connection issue | > `
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))`
Do you have an internet connection on the machine ? Is there a proxy that might block requests to aws ?
> I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
HF_HOME is used to specify the directory for the cache files of this library.
You can use save_to_disk and load_from_disk without changing the HF_HOME:
```python
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
imdb = datasets.load_from_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
```
> could you tell me how I can make the two datastes the same format
Indeed they returns different things:
- `load_dataset` returns a `Dataset` object if the split is specified, or a `DatasetDict` if no split is given. Therefore `load_datasets("imdb", split="train[10]")` returns a `Dataset` object containing 10 elements.
- doing `d["train"][:10]` on a DatasetDict "d" gets the train split `d["train"]` as a `Dataset` object and then gets the first 10 elements as a dictionary | Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
| 210 | connection issue
Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
> `
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))`
Do you have an internet connection on the machine ? Is there a proxy that might block requests to aws ?
> I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
HF_HOME is used to specify the directory for the cache files of this library.
You can use save_to_disk and load_from_disk without changing the HF_HOME:
```python
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
imdb = datasets.load_from_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
```
> could you tell me how I can make the two datastes the same format
Indeed they returns different things:
- `load_dataset` returns a `Dataset` object if the split is specified, or a `DatasetDict` if no split is given. Therefore `load_datasets("imdb", split="train[10]")` returns a `Dataset` object containing 10 elements.
- doing `d["train"][:10]` on a DatasetDict "d" gets the train split `d["train"]` as a `Dataset` object and then gets the first 10 elements as a dictionary | [
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https://github.com/huggingface/datasets/issues/1670 | wiki_dpr pre-processing performance | Hi ! And thanks for the tips :)
Indeed currently `wiki_dpr` takes some time to be processed.
Multiprocessing for dataset generation is definitely going to speed up things.
Regarding the index note that for the default configurations, the index is downloaded instead of being built, which avoid spending time on constructing the index. However in other cases it would be awesome to make the construction faster.
Any contribution that can help things faster are welcome. In particular in you have some code that can build a wiki_dpr IVF PQ index in a sharded GPU setup and would like to share it, we can add it to an `examples` folder. In particular since faiss is becoming the library of reference for dataset indexing for tasks like Open Domain Question Answering.
| I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422 | 129 | wiki_dpr pre-processing performance
I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422
Hi ! And thanks for the tips :)
Indeed currently `wiki_dpr` takes some time to be processed.
Multiprocessing for dataset generation is definitely going to speed up things.
Regarding the index note that for the default configurations, the index is downloaded instead of being built, which avoid spending time on constructing the index. However in other cases it would be awesome to make the construction faster.
Any contribution that can help things faster are welcome. In particular in you have some code that can build a wiki_dpr IVF PQ index in a sharded GPU setup and would like to share it, we can add it to an `examples` folder. In particular since faiss is becoming the library of reference for dataset indexing for tasks like Open Domain Question Answering.
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https://github.com/huggingface/datasets/issues/1670 | wiki_dpr pre-processing performance | I'd be happy to contribute something when I get the time, probably adding multiprocessing and / or cython support to wiki_dpr. I've written cythonized apache beam code before as well.
For sharded index building, I used the FAISS example code for indexing 1 billion vectors as a start. I'm sure you're aware that the documentation isn't great, but the source code is fairly easy to follow. | I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422 | 66 | wiki_dpr pre-processing performance
I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422
I'd be happy to contribute something when I get the time, probably adding multiprocessing and / or cython support to wiki_dpr. I've written cythonized apache beam code before as well.
For sharded index building, I used the FAISS example code for indexing 1 billion vectors as a start. I'm sure you're aware that the documentation isn't great, but the source code is fairly easy to follow. | [
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https://github.com/huggingface/datasets/issues/1662 | Arrow file is too large when saving vector data | Hi !
The arrow file size is due to the embeddings. Indeed if they're stored as float32 then the total size of the embeddings is
20 000 000 vectors * 768 dimensions * 4 bytes per dimension ~= 60GB
If you want to reduce the size you can consider using quantization for example, or maybe using dimension reduction techniques.
| I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file? | 59 | Arrow file is too large when saving vector data
I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file?
Hi !
The arrow file size is due to the embeddings. Indeed if they're stored as float32 then the total size of the embeddings is
20 000 000 vectors * 768 dimensions * 4 bytes per dimension ~= 60GB
If you want to reduce the size you can consider using quantization for example, or maybe using dimension reduction techniques.
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https://github.com/huggingface/datasets/issues/1662 | Arrow file is too large when saving vector data | Thanks for your reply @lhoestq.
I want to save original embedding for these sentences for subsequent calculations. So does arrow have a way to save in a compressed format to reduce the size of the file? | I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file? | 36 | Arrow file is too large when saving vector data
I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file?
Thanks for your reply @lhoestq.
I want to save original embedding for these sentences for subsequent calculations. So does arrow have a way to save in a compressed format to reduce the size of the file? | [
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https://github.com/huggingface/datasets/issues/1647 | NarrativeQA fails to load with `load_dataset` | Hi @eric-mitchell,
I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.
For now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:
`pip install git+https://github.com/huggingface/datasets.git@master` | When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https://huggingface.co/datasets/narrativeqa), I receive a cascade of exceptions, ending with
FileNotFoundError: Couldn't find file locally at narrativeqa/narrativeqa.py, or remotely at
https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/narrativeqa/narrativeqa.py or
https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/narrativeqa/narrativeqa.py
Workaround: manually copy the `narrativeqa.py` builder into my local directory with
curl https://raw.githubusercontent.com/huggingface/datasets/master/datasets/narrativeqa/narrativeqa.py -o narrativeqa.py
and load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10. | 55 | NarrativeQA fails to load with `load_dataset`
When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https://huggingface.co/datasets/narrativeqa), I receive a cascade of exceptions, ending with
FileNotFoundError: Couldn't find file locally at narrativeqa/narrativeqa.py, or remotely at
https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/narrativeqa/narrativeqa.py or
https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/narrativeqa/narrativeqa.py
Workaround: manually copy the `narrativeqa.py` builder into my local directory with
curl https://raw.githubusercontent.com/huggingface/datasets/master/datasets/narrativeqa/narrativeqa.py -o narrativeqa.py
and load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10.
Hi @eric-mitchell,
I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.
For now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:
`pip install git+https://github.com/huggingface/datasets.git@master` | [
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] |
https://github.com/huggingface/datasets/issues/1647 | NarrativeQA fails to load with `load_dataset` | Update: HuggingFace did an intermediate release yesterday just before the v2.0.
To load it you can just update `datasets`
`pip install --upgrade datasets` | When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https://huggingface.co/datasets/narrativeqa), I receive a cascade of exceptions, ending with
FileNotFoundError: Couldn't find file locally at narrativeqa/narrativeqa.py, or remotely at
https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/narrativeqa/narrativeqa.py or
https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/narrativeqa/narrativeqa.py
Workaround: manually copy the `narrativeqa.py` builder into my local directory with
curl https://raw.githubusercontent.com/huggingface/datasets/master/datasets/narrativeqa/narrativeqa.py -o narrativeqa.py
and load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10. | 23 | NarrativeQA fails to load with `load_dataset`
When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https://huggingface.co/datasets/narrativeqa), I receive a cascade of exceptions, ending with
FileNotFoundError: Couldn't find file locally at narrativeqa/narrativeqa.py, or remotely at
https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/narrativeqa/narrativeqa.py or
https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/narrativeqa/narrativeqa.py
Workaround: manually copy the `narrativeqa.py` builder into my local directory with
curl https://raw.githubusercontent.com/huggingface/datasets/master/datasets/narrativeqa/narrativeqa.py -o narrativeqa.py
and load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10.
Update: HuggingFace did an intermediate release yesterday just before the v2.0.
To load it you can just update `datasets`
`pip install --upgrade datasets` | [
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https://github.com/huggingface/datasets/issues/1644 | HoVeR dataset fails to load | Hover was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `hover` with
```python
from datasets import load_dataset
dataset = load_dataset("hover")
``` | Hi! I'm getting an error when trying to load **HoVeR** dataset. Another one (**SQuAD**) does work for me. I'm using the latest (1.1.3) version of the library.
Steps to reproduce the error:
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("hover")
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at hover/hover.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
``` | 40 | HoVeR dataset fails to load
Hi! I'm getting an error when trying to load **HoVeR** dataset. Another one (**SQuAD**) does work for me. I'm using the latest (1.1.3) version of the library.
Steps to reproduce the error:
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("hover")
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at hover/hover.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
```
Hover was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `hover` with
```python
from datasets import load_dataset
dataset = load_dataset("hover")
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] |
https://github.com/huggingface/datasets/issues/1641 | muchocine dataset cannot be dowloaded | I have encountered the same error with `v1.0.1` and `v1.0.2` on both Windows and Linux environments. However, cloning the repo and using the path to the dataset's root directory worked for me. Even after having the dataset cached - passing the path is the only way (for now) to load the dataset.
```python
from datasets import load_dataset
dataset = load_dataset("squad") # Works
dataset = load_dataset("code_search_net", "python") # Error
dataset = load_dataset("covid_qa_deepset") # Error
path = "/huggingface/datasets/datasets/{}/"
dataset = load_dataset(path.format("code_search_net"), "python") # Works
dataset = load_dataset(path.format("covid_qa_deepset")) # Works
```
| ```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
``` | 88 | muchocine dataset cannot be dowloaded
```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
```
I have encountered the same error with `v1.0.1` and `v1.0.2` on both Windows and Linux environments. However, cloning the repo and using the path to the dataset's root directory worked for me. Even after having the dataset cached - passing the path is the only way (for now) to load the dataset.
```python
from datasets import load_dataset
dataset = load_dataset("squad") # Works
dataset = load_dataset("code_search_net", "python") # Error
dataset = load_dataset("covid_qa_deepset") # Error
path = "/huggingface/datasets/datasets/{}/"
dataset = load_dataset(path.format("code_search_net"), "python") # Works
dataset = load_dataset(path.format("covid_qa_deepset")) # Works
```
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] |
Subsets and Splits