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Update multi_woz_v22 checksum
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Fix issue https://github.com/huggingface/datasets/issues/1876
The files were changed in https://github.com/budzianowski/multiwoz/pull/72
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Fixed typo seperate->separate
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Compatibility with Ubuntu 18 and GLIBC 2.27?
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[] | null | 2021-04-28T22:08:07Z
| 2021-04-29T07:42:42Z
| 2021-04-29T07:42:42Z
|
NONE
| null | null | null | null |
## Describe the bug
For use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https://www.digitalocean.com/community/questions/how-install-glibc-2-29-or-higher-in-ubuntu-18-04).
I'm not sure if there is anything that can be done about this, but I'd like to confirm that using huggingface/datasets requires either an upgrade to Ubuntu 19/20 or a hand-rolled install of a higher version of GLIBC.
## Steps to reproduce the bug
1. clone the transformers repo
2. move to examples/pytorch/language-modeling
3. run example command:
```python run_clm.py --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train --do_eval --output_dir /tmp/test-clm```
## Expected results
As described in the transformers repo.
## Actual results
```Traceback (most recent call last):
File "run_clm.py", line 34, in <module>
from transformers import (
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/__init__.py", line 2487, in __getattr__
return super().__getattr__(name)
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/file_utils.py", line 1699, in __getattr__
module = self._get_module(self._class_to_module[name])
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/__init__.py", line 2481, in _get_module
return importlib.import_module("." + module_name, self.__name__)
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/__init__.py", line 19, in <module>
from . import (
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/layoutlm/__init__.py", line 23, in <module>
from .tokenization_layoutlm import LayoutLMTokenizer
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/layoutlm/tokenization_layoutlm.py", line 19, in <module>
from ..bert.tokenization_bert import BertTokenizer
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/bert/tokenization_bert.py", line 23, in <module>
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/tokenization_utils.py", line 26, in <module>
from .tokenization_utils_base import (
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 68, in <module>
from tokenizers import AddedToken
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/tokenizers/__init__.py", line 79, in <module>
from .tokenizers import (
ImportError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by /home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/tokenizers/tokenizers.cpython-37m-x86_64-linux-gnu.so)
```
## Versions
Paste the output of the following code:
```
- Datasets: 1.6.1
- Python: 3.7.10 (default, Feb 26 2021, 18:47:35)
[GCC 7.3.0]
- Platform: Linux-4.15.0-128-generic-x86_64-with-debian-buster-sid
```
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Loss result inGptNeoForCasual
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| 2021-05-06T16:14:23Z
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NONE
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Is there any way you give the " loss" and "logits" results in the gpt neo api?
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Create CacheManager
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Perform refactoring to decouple cache functionality (method `as_dataset`).
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concatenate_datasets loads all the data into memory
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NONE
| null | null | null | null |
## Describe the bug
When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
## Steps to reproduce the bug
```python
from datasets import concatenate_datasets, load_from_disk
test_sampled_pro = load_from_disk("test_sampled_pro")
val_sampled_pro = load_from_disk("val_sampled_pro")
big_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])
# Loaded to memory
big_set.save_to_disk("big_set")
# Loaded to memory
big_set = concatenate_datasets([big_set, val_sampled_pro])
```
## Expected results
The data should be loaded into memory in batches and then saved directly to disk.
## Actual results
The entire data set is loaded into the memory and then saved to the hard disk.
## Versions
Paste the output of the following code:
```python
- Datasets: 1.6.1
- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)
[GCC 7.3.0]
- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10
```
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SNLI dataset has labels of -1
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[] | null | 2021-04-28T00:32:25Z
| 2021-05-17T13:34:18Z
| 2021-05-17T13:34:18Z
|
NONE
| null | null | null | null |
There are a number of rows with a label of -1 in the SNLI dataset. The dataset descriptions [here](https://nlp.stanford.edu/projects/snli/) and [here](https://github.com/huggingface/datasets/tree/master/datasets/snli) don't list -1 as a label possibility, and neither does the dataset viewer. As examples, see index 107 or 124 of the test set.
It isn't clear what these labels mean. I found a [line of code](https://github.com/huggingface/datasets/blob/80e59ef178d3bb2090d091bc32315c655eb0633d/datasets/snli/snli.py#L94) that seems to put them in but it seems still unclear why they are there. The current workaround is to just drop the rows from any model being trained.
Perhaps the documentation should be updated.
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MDExOlB1bGxSZXF1ZXN0NjI0NTkyMjQx
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Always update metadata in arrow schema
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[] | null | 2021-04-27T19:21:57Z
| 2022-06-03T08:31:19Z
| 2021-04-29T09:57:50Z
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MEMBER
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We store a redundant copy of the features in the metadata of the schema of the arrow table. This is used to recover the features when doing `Dataset.from_file`. These metadata are updated after each transfor, that changes the feature types.
For each function that transforms the feature types of the dataset, I added a step in the tests to make sure the metadata in the arrow schema are up to date.
I also added a line to update the metadata directly in the Dataset.__init__ method.
This way even a dataset instantiated with __init__ will have a table with the right metadata.
Fix #2271.
cc @mariosasko
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| 869,046,290
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MDExOlB1bGxSZXF1ZXN0NjI0NDcxODc1
| 2,273
|
Added CUAD metrics
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[] | null | 2021-04-27T16:49:12Z
| 2021-04-29T13:59:47Z
| 2021-04-29T13:59:47Z
|
CONTRIBUTOR
| null | null | 0
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`EM`, `F1`, `AUPR`, `Precision@80%Recall`, and `Precision@90%Recall` metrics supported for CUAD
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| 869,017,977
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MDU6SXNzdWU4NjkwMTc5Nzc=
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Bug in Dataset.class_encode_column
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| 2021-04-30T12:54:27Z
| 2021-04-30T12:54:27Z
|
MEMBER
| null | null | null | null |
## Describe the bug
All the rest of the columns except the one passed to `Dataset.class_encode_column` are discarded.
## Expected results
All the original columns should be kept.
This needs regression tests.
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MDU6SXNzdWU4NjkwMDIxNDE=
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Synchronize table metadata with features
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[] | null | 2021-04-27T15:55:13Z
| 2022-06-01T17:13:21Z
| 2022-06-01T17:13:21Z
|
MEMBER
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
As pointed out in this [comment](https://github.com/huggingface/datasets/pull/2145#discussion_r621326767):
> Metadata stored in the schema is just a redundant information regarding the feature types.
It is used when calling Dataset.from_file to know which feature types to use.
These metadata are stored in the schema of the pyarrow table by using `update_metadata_with_features`.
However this something that's almost never tested properly.
**Describe the solution you'd like**
We should find a way to always make sure that the metadata (in `self.data.schema.metadata`) are synced with the actual feature types (in `self.info.features`).
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Fix iterable interface expected by numpy
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Numpy expects the old iterable interface with `__getitem__` instead of `__iter__`.
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MDExOlB1bGxSZXF1ZXN0NjI0MzMwNDA3
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Fix query table with iterable
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The benchmark runs are failing on master because it tries to use an iterable to query the dataset.
However there's currently an issue caused by the use of `np.array` instead of `np.fromiter` on the iterable.
This PR fixes it
|
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MDExOlB1bGxSZXF1ZXN0NjI0MjQyODg1
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Don't use pyarrow 4.0.0 since it segfaults when casting a sliced ListArray of integers
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This test `tests/test_table.py::test_concatenation_table_cast` segfaults with the latest update of pyarrow 4.0.0.
Setting `pyarrow<4.0.0` for now. I'll open an issue on JIRA once I know more about the origin of the issue
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MDU6SXNzdWU4NjgyOTExMjk=
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DatasetDict save load Failing test in 1.6 not in 1.5
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[] | null | 2021-04-27T00:03:25Z
| 2021-05-28T15:27:34Z
| null |
NONE
| null | null | null | null |
## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a dataset dict from jsonl
path = '/test/foo'
ds_dict.save_to_disk(path)
ds_from_disk = DatasetDict.load_from_disk(path). ## <-- this is where I see the error on 1.6
```
## Expected results
Upgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.
## Actual results
```
# Infer features if None
inferred_features = Features.from_arrow_schema(arrow_table.schema)
if self.info.features is None:
self.info.features = inferred_features
# Infer fingerprint if None
if self._fingerprint is None:
self._fingerprint = generate_fingerprint(self)
# Sanity checks
assert self.features is not None, "Features can't be None in a Dataset object"
assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object"
if self.info.features.type != inferred_features.type:
> raise ValueError(
"External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format(
self.info.features, self.info.features.type, inferred_features, inferred_features.type
)
)
E ValueError: External features info don't match the dataset:
E Got
E {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}
E with type
E struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>
E
E but expected something like
E {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}
E with type
E struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>
../../../../../.virtualenvs/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:274: ValueError
```
## Versions
- Datasets: 1.6.1
- Python: 3.8.5 (default, Jan 26 2021, 10:01:04)
[Clang 12.0.0 (clang-1200.0.32.2)]
- Platform: macOS-10.15.7-x86_64-i386-64bit
```
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MDExOlB1bGxSZXF1ZXN0NjIzNDY1OTI5
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|
Make tests run faster
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| 2021-04-29T10:00:13Z
| 2021-04-29T10:00:04Z
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From 7min to 2min to run pytest.
Ideally we should keep the whole CI run time below 10min.
In this PR I removed the remote tests that were never used.
I also replaced nested parametrized tests with unit tests.
This makes me think that we could still add more high level tests to check for a few combinations of parameters (but not all of them since there are too many of them).
Let me know what you think
Finally in another PR we can also separate in two circleci jobs:
- the tests of the code code of the lib
- the tests of the all the dataset/metric scripts.
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| 867,490,646
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MDExOlB1bGxSZXF1ZXN0NjIzMTUyOTg5
| 2,265
|
Update black
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Latest black version 21.4b0 requires to reformat most dataset scripts and also the core code of the lib.
This makes the CI currently fail on master
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| 867,476,228
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MDExOlB1bGxSZXF1ZXN0NjIzMTQwODA1
| 2,264
|
Fix memory issue in multiprocessing: Don't pickle table index
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The table index is currently being pickled when doing multiprocessing, which brings all the record batches of the dataset in memory.
I fixed that by not pickling the index attributes. Therefore each process has to rebuild the index when unpickling the table.
Fix issue #2256
We'll do a patch release asap !
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|
test data added, dataset_infos updated
|
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| 2021-04-29T09:30:21Z
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Fixes #2262. Thanks for pointing out issue with dataset @jinmang2!
|
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MDU6SXNzdWU4NjczMjUzNTE=
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|
NewsPH NLI dataset script fails to access test data.
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| 2021-04-29T09:32:03Z
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NONE
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In Newsph-NLI Dataset (#1192), it fails to access test data.
According to the script below, the download manager will download the train data when trying to download the test data.
https://github.com/huggingface/datasets/blob/2a2dd6316af2cc7fdf24e4779312e8ee0c7ed98b/datasets/newsph_nli/newsph_nli.py#L71
If you download it according to the script above, you can see that train and test receive the same data as shown below.
```python
>>> from datasets import load_dataset
>>> newsph_nli = load_dataset(path="./datasets/newsph_nli.py")
>>> newsph_nli
DatasetDict({
train: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 420000
})
test: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 420000
})
validation: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 90000
})
})
>>> newsph_nli["train"][0]
{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',
'label': 1,
'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'}
>>> newsph_nli["test"][0]
{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',
'label': 1,
'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'}
```
In local, I modified the code of the source as below and got the correct result.
```python
71 test_path = os.path.join(download_path, "test.csv")
```
```python
>>> from datasets import load_dataset
>>> newsph_nli = load_dataset(path="./datasets/newsph_nli.py")
>>> newsph_nli
DatasetDict({
train: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 420000
})
test: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 9000
})
validation: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 90000
})
})
>>> newsph_nli["train"][0]
{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',
'label': 1,
'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'}
>>> newsph_nli["test"][0]
{'hypothesis': '-- JAI (@JaiPaller) September 13, 2019',
'label': 1,
'premise': 'Pinag-iingat ng Konsulado ng Pilipinas sa Dubai ang publiko, partikular ang mga donor, laban sa mga scam na gumagamit ng mga charitable organization.'}
```
I don't have experience with open source pull requests, so I suggest that you reflect them in the source.
Thank you for reading :)
|
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Improve ReadInstruction logic and update docs
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Improve ReadInstruction logic and docs.
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GooAQ dataset added
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@lhoestq here the dataset is stored with Git LFS. Should I add option for manual downloading of dataset using `git lfs pull` post repo cloning or can we accommodate this in the current `download_and_extract`?
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MDExOlB1bGxSZXF1ZXN0NjIyNjc2ODA0
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|
Add support for Split.ALL
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The title says it all.
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MDExOlB1bGxSZXF1ZXN0NjIyNjcxNTQy
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Fix incorrect update_metadata_with_features calls in ArrowDataset
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| 2021-04-26T16:54:04Z
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Fixes bugs in the `unpdate_metadata_with_features` calls (caused by changes in #2151)
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added metrics for CUAD
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| 2021-04-29T09:53:38Z
| 2021-04-27T16:16:32Z
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For now I've added F1, AUPR, Precision at 80% recall, and Precision at 90%. Last 3 metrics were reported in the [paper](https://arxiv.org/pdf/2103.06268.pdf). Please let me know if we require `exact_match` metric too here
|
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MDU6SXNzdWU4NjY3MDg2MDk=
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Running `datase.map` with `num_proc > 1` uses a lot of memory
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|
NONE
| null | null | null | null |
## Describe the bug
Running `datase.map` with `num_proc > 1` leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dstc8_datset = load_dataset("roskoN/dstc8-reddit-corpus", keep_in_memory=False)
def _prepare_sample(batch):
return {"input_ids": list(), "attention_mask": list()}
for split_name, dataset_split in list(dstc8_datset.items()):
print(f"Processing {split_name}")
encoded_dataset_split = dataset_split.map(
function=_prepare_sample,
batched=True,
num_proc=4,
remove_columns=dataset_split.column_names,
batch_size=10,
writer_batch_size=10,
keep_in_memory=False,
)
print(encoded_dataset_split)
path = f"./data/encoded_{split_name}"
encoded_dataset_split.save_to_disk(path)
```
## Expected results
Memory usage should stay within reasonable boundaries.
## Actual results
This is htop-output from running the provided script.
## Versions
```
- Datasets: 1.6.0
- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)
[GCC 7.3.0]
- Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10
```
Running on WSL2
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|
Task casting for text classification & question answering
|
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[] | null | 2021-04-23T16:00:41Z
| 2021-05-18T13:31:36Z
| 2021-05-18T13:31:35Z
|
CONTRIBUTOR
| null | null | 0
|
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This PR implements task preparation for a given task, in the continuation of #2143
Task taxonomy follows 🤗 Transformers's pipelines taxonomy: https://github.com/huggingface/transformers/tree/master/src/transformers/pipelines
Edit by @lewtun:
This PR implements support for the following tasks:
* `text-classification`
* `question-answering`
The intended usage is as follows:
```python
# Load a dataset with default column names / features
ds = load_dataset("dataset_name")
# Cast column names / features to schema. Casting is defined in the dataset's `DatasetInfo`
ds = ds.prepare_for_task(task="text-classification")
# Casting can also be realised during load
ds = load_dataset("dataset_name", task="text-classification")
# We can also combine shared tasks across dataset concatenation
ds1 = load_dataset("dataset_name_1", task="text-classification")
ds2 = load_dataset("dataset_name_2", task="text-classification")
# If the tasks have the same schema, so will `ds_concat`
ds_concat = concatenate_datasets([ds1, ds2])
```
Note that the current implementation assumes that `DatasetInfo.task_templates` has been pre-defined by the user / contributor when overriding the `MyDataset(GeneratorBasedBuilder)._info` function.
As pointed out by @SBrandeis, for evaluation we'll need a way to detect which datasets are already have a compatible schema so we don't have to edit hundreds of dataset scripts. One possibility is to check if the schema features are a subset of the dataset ones, e.g.
```python
squad = load_dataset("./datasets/squad", split="train")
qa = QuestionAnswering()
schema = Features({**qa.input_schema, **qa.label_schema})
assert all(item in squad.features.items() for item in schema.items())
```
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Update format, fingerprint and indices after add_item
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Added fingerprint and format update wrappers + update the indices by adding the index of the newly added item in the table.
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Perform minor refactoring: use config
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Slow dataloading with big datasets issue persists
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| 2024-01-26T15:10:28Z
| 2024-01-26T15:10:28Z
|
NONE
| null | null | null | null |
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
------------------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 517.96 | 100 % |
------------------------------------------------------------------------------------------------------------------------------------
model_backward | 0.26144 |100 | 26.144 | 5.0475 |
model_forward | 0.11123 |100 | 11.123 | 2.1474 |
get_train_batch | 0.097121 |100 | 9.7121 | 1.8751 |
```
3) Running with 600GB, datasets==1.6.0
```
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
------------------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 4563.2 | 100 % |
------------------------------------------------------------------------------------------------------------------------------------
get_train_batch | 5.1279 |100 | 512.79 | 11.237 |
model_backward | 4.8394 |100 | 483.94 | 10.605 |
model_forward | 0.12162 |100 | 12.162 | 0.26652 |
```
I see that `get_train_batch` lags when data is large. Could this be related to different issues?
I would be happy to provide necessary information to investigate.
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MDU6SXNzdWU4NjU4NDg3MDU=
| 2,251
|
while running run_qa.py, ran into a value error
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[] | null | 2021-04-23T07:51:03Z
| 2021-04-23T07:51:03Z
| null |
NONE
| null | null | null | null |
command:
python3 run_qa.py --model_name_or_path hyunwoongko/kobart --dataset_name squad_kor_v2 --do_train --do_eval --per_device_train_batch_size 8 --learning_rate 3e-5 --num_train_epochs 3 --max_seq_length 512 --doc_stride 128 --output_dir /tmp/debug_squad/
error:
ValueError: External features info don't match the dataset:
Got
{'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answer': {'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None), 'html_answer_start': Value(dtype='int32', id=None)}, 'url': Value(dtype='string', id=None), 'raw_html': Value(dtype='string', id=None)}
with type
struct<answer: struct<text: string, answer_start: int32, html_answer_start: int32>, context: string, id: string, question: string, raw_html: string, title: string, url: string>
but expected something like
{'answer': {'answer_start': Value(dtype='int32', id=None), 'html_answer_start': Value(dtype='int32', id=None), 'text': Value(dtype='string', id=None)}, 'context': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'raw_html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
with type
struct<answer: struct<answer_start: int32, html_answer_start: int32, text: string>, context: string, id: string, question: string, raw_html: string, title: string, url: string>
I didn't encounter this error 4 hours ago. any solutions for this kind of issue?
looks like gained dataset format refers to 'Data Fields', while expected refers to 'Data Instances'.
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| 2,250
|
some issue in loading local txt file as Dataset for run_mlm.py
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[] | null | 2021-04-22T19:39:13Z
| 2022-03-30T08:29:47Z
| 2022-03-30T08:29:47Z
|
NONE
| null | null | null | null |
first of all, I tried to load 3 .txt files as a dataset (sure that the directory and permission is OK.), I face with the below error.
> FileNotFoundError: [Errno 2] No such file or directory: 'c'
by removing one of the training .txt files It's fixed and although if I put all file as training it's ok
after this, my question is how could I use this defined Dataset for run_mlm.py for from scratch pretraining.
by using --train_file path_to_train_file just can use one .txt , .csv or, .json file. I tried to set my defined Dataset as --dataset_name but the below issue occurs.
> Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 336, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py", line 621, 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/master/datasets/dataset/dataset.py
> During handling of the above exception, another exception occurred:
> Traceback (most recent call last):
File "run_mlm.py", line 486, in <module>
main()
File "run_mlm.py", line 242, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 719, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 347, in prepare_module
combined_path, github_file_path
FileNotFoundError: Couldn't find file locally at dataset/dataset.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.6.0/datasets/dataset/dataset.py.
The file is also not present on the master branch on github.
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MDExOlB1bGxSZXF1ZXN0NjIxMzU1MzE3
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Allow downloading/processing/caching only specific splits
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| 2021-04-22T17:51:44Z
| 2022-07-06T15:19:48Z
| null |
MEMBER
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Allow downloading/processing/caching only specific splits without downloading/processing/caching the other splits.
This PR implements two steps to handle only specific splits:
- it allows processing/caching only specific splits into Arrow files
- for some simple cases, it allows downloading only specific splits (which is more intricate as it depends on the user-defined method `_split_generators`)
This PR makes several assumptions:
- `DownloadConfig` contains the configuration settings for downloading
- the parameter `split` passed to `load_dataset` is just a parameter for loading (from cache), not for downloading
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Implement Dataset to JSON
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Implement instantiation of Dataset from Parquet file.
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Faster map w/ input_columns & faster slicing w/ Iterable keys
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@lhoestq Fixes #2193
- `map` now uses `with_format` to only load needed columns in memory when `input_columns` is set
- Slicing datasets with Iterables of indices now uses a new `Table.fast_gather` method, implemented with `np.searchsorted`, to find the appropriate batch indices all at once. `pa.concat_tables` is no longer used for this; we just call `pa.Table.from_batches` with a list of all the batch slices.
Together these changes have sped up batched `map()` calls over subsets of columns quite considerably in my initial testing.
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Add `key` type and duplicates verification with hashing
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Closes #2230
There is currently no verification for the data type and the uniqueness of the keys yielded by the `dataset_builder`.
This PR is currently a work in progress with the following goals:
- [x] Adding `hash_salt` to `ArrowWriter` so that the keys belonging to different splits have different hash
- [x] Add `key` arrtibute to `ArrowWriter.write()` for hashing
- [x] Add a hashing class which takes an input key of certain type (`str`/`int`/anything convertible to string) and produces a 128-bit hash using `hashlib.md5`
- [x] Creating a function giving a custom error message when non-unique keys are found
**[This will take care of type-checking for keys]**
- [x] Checking for duplicate keys in `writer.write()` for each batch
[**NOTE**: This PR is currently concerned with `GeneratorBasedBuilder` only, for simplification. A subsequent PR will be made in future for `ArrowBasedBuilder`]
@lhoestq Thank you for the feedback. It would be great to have your guidance on this!
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| 2022-07-06T15:19:48Z
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Implement specific cache directories (datasets, metrics and modules) per test function call.
Currently, the cache directories are set within the temporary test directory, but they are shared across all test function calls.
This PR implements specific cache directories for each test function call, so that tests are atomic and there are no side effects.
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Map is slow and processes batches one after another
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[] | null | 2021-04-20T14:58:20Z
| 2021-05-03T17:54:33Z
| 2021-05-03T17:54:32Z
|
NONE
| null | null | null | null |
## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry.
I process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.
pseudo code:
```python
ds = datasets.load_from_disk("path")
new_dataset = ds.map(work, batched=True, ...) # fast uses all processes
final_dataset = new_dataset.map(work2, batched=True, ...) # slow starts one process after another
```
## Expected results
Second stage should be as fast as the first stage.
## Versions
Paste the output of the following code:
- Datasets: 1.5.0
- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)
- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10
Do you guys have any idea? Thanks a lot!
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Link to datasets viwer on Quick Tour page returns "502 Bad Gateway"
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| null | null | null | null |
Link to datasets viwer (https://huggingface.co/datasets/viewer/) on Quick Tour page (https://huggingface.co/docs/datasets/quicktour.html) returns "502 Bad Gateway"
The same error with https://huggingface.co/datasets/viewer/?dataset=glue&config=mrpc
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|
Add SLR32 to OpenSLR
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[] | null | 2021-04-20T11:02:45Z
| 2021-04-23T16:21:24Z
| 2021-04-23T15:36:15Z
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CONTRIBUTOR
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I would like to add SLR32 to OpenSLR. It contains four South African languages: Afrikaans, Sesotho, Setswana and isiXhosa
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Clarify how to load wikihow
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Explain clearer how to load the dataset in the manual download instructions.
En relation with #2239.
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Error loading wikihow dataset
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[] | null | 2021-04-19T21:02:31Z
| 2021-04-20T16:33:11Z
| 2021-04-20T16:33:11Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
When attempting to load wikihow into a dataset with
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
I get the message:
```
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
at the end of a [full stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2).
## Steps to reproduce the bug
I have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https://huggingface.co/datasets/wikihow) says to use
```python
from datasets import load_dataset
dataset = load_dataset('wikihow')
```
to load the dataset. I do so and I get the message
```
AssertionError: The dataset wikihow with config all requires manual data.
Please follow the manual download instructions: You need to manually download two wikihow files. An overview of which files to download can be seen at https://github.com/mahnazkoupaee/WikiHow-Dataset.
You need to download the following two files manually:
1) https://ucsb.app.box.com/s/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path/to/folder>/wikihowAll.csv
2) https://ucsb.app.box.com/s/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path/to/folder>/wikihowSep.csv
The <path/to/folder> can e.g. be "~/manual_wikihow_data".
Wikihow can then be loaded using the following command `datasets.load_dataset("wikihow", data_dir="<path/to/folder>")`.
.
Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path/to/manual/data>')
```
So I create a directory `./wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory.
Then I run
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
that's when I get the [stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2)
## Expected results
I expected it to load the downloaded files into a dataset.
## Actual results
```python
Using custom data configuration default-data_dir=.%2Fwikihow
Downloading and preparing dataset wikihow/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/azureuser/.cache/huggingface/datasets/wikihow/default-data_dir=.%2Fwikihow/0.0.0/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2... ---------------------------------------------------------------------------
AttributeError
Traceback (most recent call last)
<ipython-input-9-5e4d40142f30> in <module>
----> 1 dataset = load_dataset('wikihow',data_dir='./wikihow')
~/.local/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)
745 try_from_hf_gcs=try_from_hf_gcs,
746 base_path=base_path,-->
747 use_auth_token=use_auth_token,
748 )
749
~/.local/lib/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
577 if not downloaded_from_gcs:
578 self._download_and_prepare( -->
579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
580 )
581 # Sync info
~/.local/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
632 split_dict = SplitDict(dataset_name=self.name)
633 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) -->
634 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
635
636 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/wikihow/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2/wikihow.py in _split_generators(self, dl_manager)
132
133 path_to_manual_file = os.path.join(
--> 134 os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename
135 )
136
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
## Versions
Paste the output of the following code:
```python
import datasets
import sys
import platform
print(f"""
- Datasets: {datasets.__version__}
- Python: {sys.version}
- Platform: {platform.platform()}
""")
```
```
- Datasets: 1.5.0
- Python: 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0]
- Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic
```
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New intent classification dataset from https://github.com/xliuhw/NLU-Evaluation-Data
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Update Dataset.dataset_size after transformed with map
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MEMBER
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After loading a dataset, if we transform it by using `.map` its `dataset_size` attirbute is not updated.
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Request to add StrategyQA dataset
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NONE
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## Request to add StrategyQA dataset
- **Name:** StrategyQA
- **Description:** open-domain QA [(project page)](https://allenai.org/data/strategyqa)
- **Paper:** [url](https://arxiv.org/pdf/2101.02235.pdf)
- **Data:** [here](https://allenai.org/data/strategyqa)
- **Motivation:** uniquely-formulated dataset that also includes a question-decomposition breakdown and associated Wikipedia annotations for each step. Good for multi-hop reasoning modeling.
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Update README.md
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Adding relevant citations (paper accepted at AAAI 2020 & EMNLP 2020) to the benchmark
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Fix bash snippet formatting in ADD_NEW_DATASET.md
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COLLABORATOR
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This PR indents the paragraphs around the bash snippets in ADD_NEW_DATASET.md to fix formatting.
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Fix `xnli` dataset tuple key
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| 2021-04-19T08:56:42Z
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CONTRIBUTOR
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Closes #2229
The `xnli` dataset yields a tuple key in case of `ar` which is inconsistant with the acceptable key types (str/int).
The key was thus ported to `str` keeping the original information intact.
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Start filling GLUE dataset card
|
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The dataset card was pretty much empty.
I added the descriptions (mainly from TFDS since the script is the same), and I also added the tasks tags as well as examples for a subset of the tasks.
cc @sgugger
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MDExOlB1bGxSZXF1ZXN0NjE2ODYyNTEx
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Fix map when removing columns on a formatted dataset
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This should fix issue #2226
The `remove_columns` argument was ignored on formatted datasets
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Keys yielded while generating dataset are not being checked
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[] | null | 2021-04-16T13:29:47Z
| 2021-05-10T17:31:21Z
| 2021-05-10T17:31:21Z
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CONTRIBUTOR
| null | null | null | null |
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196
Even after having a tuple as key, the dataset is generated without any warning.
Also, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):
```
>>> import datasets
>>> nik = datasets.load_dataset('anli')
Downloading and preparing dataset anli/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\Users\nikhil\.cache\huggingface\datasets\anli\plain_text\0.1.0\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...
0 examples [00:00, ? examples/s]1 {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: "Rete filoviaria di Parma" ) forms part of the public transport network of the city and "comune" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}
2021-04-16 12:38:14.483968: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
1 examples [00:01, 1.87s/ examples]1 {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Sharron Macready was a popular character through the 1980's.", 'label': 'neutral', 'reason': ''}
1 {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Bastedo didn't keep any pets because of her views on animal rights.", 'label': 'neutral', 'reason': ''}
1 {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}
1 {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}
```
Here also, the dataset was generated successfuly even hough it had same keys without any warning.
The reason appears to stem from here:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L988
Here, although it has access to every key, but it is not being checked and the example is written directly:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L992
I would like to take this issue if you allow me. Thank You!
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`xnli` dataset creating a tuple key while yielding instead of `str` or `int`
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| 2021-04-19T08:56:42Z
| 2021-04-19T08:56:42Z
|
CONTRIBUTOR
| null | null | null | null |
When using `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196
Since, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset.
I'm up for sending a fix for this, I think we can simply use `file_idx + "_" + row_idx` as a unique key instead of a tuple.
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MDExOlB1bGxSZXF1ZXN0NjE2ODE2MTQz
| 2,228
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[WIP] Add ArrayXD support for fixed size list.
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Add support for fixed size list for ArrayXD when shape is known . See https://github.com/huggingface/datasets/issues/2146
Since offset are not stored anymore, the file size is now roughly equal to the actual data size.
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MDExOlB1bGxSZXF1ZXN0NjE2Nzk1NjMx
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Use update_metadata_with_features decorator in class_encode_column method
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Following @mariosasko 's comment
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Batched map fails when removing all columns
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NONE
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Hi @lhoestq ,
I'm hijacking this issue, because I'm currently trying to do the approach you recommend:
> Currently the optimal setup for single-column computations is probably to do something like
>
> ```python
> result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names)
> ```
Here is my code: (see edit, in which I added a simplified version
```
This is the error:
```bash
pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000
```
I wonder why this error occurs, when I delete every column? Can you give me a hint?
### Edit:
I preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error? I can iterate over the
complete dataset and print every sample before calling map. There seems to be no other problem with the dataset.
I tried to simplify the code that crashes:
```python
# works
log.debug(dataset.column_names)
log.debug(dataset)
for i, sample in enumerate(dataset):
log.debug(i, sample)
# crashes
counted_dataset = dataset.map(
lambda x: {"a": list(range(20))},
input_columns=column,
remove_columns=dataset.column_names,
load_from_cache_file=False,
num_proc=num_workers,
batched=True,
)
```
```
pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000
```
Edit2:
May this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:
```python
# crashes
counted_dataset = dataset.map(
lambda x: {"a": list(range(20))},
input_columns=column,
remove_columns=dataset.column_names,
load_from_cache_file=False,
num_proc=num_workers,
batched=True,
features=datasets.Features(
{
"a": datasets.Sequence(datasets.Value("int32"))
}
)
)
```
```
File "env/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1704, in _map_single
writer.write_batch(batch)
File "env/lib/python3.8/site-packages/datasets/arrow_writer.py", line 312, in write_batch
col_type = schema.field(col).type if schema is not None else None
File "pyarrow/types.pxi", line 1341, in pyarrow.lib.Schema.field
KeyError: 'Column tokens does not exist in schema'
```
_Originally posted by @villmow in https://github.com/huggingface/datasets/issues/2193#issuecomment-820230874_
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fixed one instance of 'train' to 'test'
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I believe this should be 'test' instead of 'train'
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MDU6SXNzdWU4NTc5ODMzNjE=
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Raise error if Windows max path length is not disabled
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On startup, raise an error if Windows max path length is not disabled; ask the user to disable it.
Linked to discussion in #2220.
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MDExOlB1bGxSZXF1ZXN0NjE1MjE4MDIz
| 2,223
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Set test cache config
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Currently, running the tests populates the default cache directory `"~/.cache"`.
This PR monkey-patches the config to set the cache directory within the temporary test directory, avoiding side effects.
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MDExOlB1bGxSZXF1ZXN0NjE1MTk5MTM5
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Fix too long WindowsFileLock name
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Fix WindowsFileLock name longer than allowed MAX_PATH by shortening the basename.
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MDExOlB1bGxSZXF1ZXN0NjE1MTg4MTE5
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Add SLR70 - SLR80 and SLR86 to OpenSLR dataset
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I would like to add SLR70, SLR71, SLR72, SLR73, SLR74, SLR75, SLR76, SLR77, SLR78, SLR79, SLR80 and SLR86 to OpenSLR dataset. The languages are:
Nigerian English, Chilean Spanish, Columbian Spanish, Peruvian Spanish, Puerto Rico Spanish, Venezuelan Spanish, Basque, Galician, Gujarati and Kannada.
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MDExOlB1bGxSZXF1ZXN0NjE1MTM4NDQz
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Fix infinite loop in WindowsFileLock
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Raise exception to avoid infinite loop.
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MDExOlB1bGxSZXF1ZXN0NjE0NzYxMzA3
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Added CUAD dataset
|
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Dataset link : https://github.com/TheAtticusProject/cuad/
Working on README.md currently.
Closes #2084 and [#1](https://github.com/TheAtticusProject/cuad/issues/1).
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MDU6SXNzdWU4NTcyMzg0MzU=
| 2,218
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Duplicates in the LAMA dataset
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[] | null | 2021-04-13T18:59:49Z
| 2021-04-14T21:42:27Z
| null |
NONE
| null | null | null | null |
I observed duplicates in the LAMA probing dataset, see a minimal code below.
```
>>> import datasets
>>> dataset = datasets.load_dataset('lama')
No config specified, defaulting to: lama/trex
Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc)
>>> train_dataset = dataset['train']
>>> train_dataset[0]
{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}
>>> train_dataset[1]
{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}
```
I checked the original data available at https://dl.fbaipublicfiles.com/LAMA/data.zip. This particular duplicated comes from:
```
{"uuid": "40b2ed1c-0961-482e-844e-32596b6117c8", "obj_uri": "Q150", "obj_label": "French", "sub_uri": "Q441235", "sub_label": "Louis Jules Trochu", "predicate_id": "P103", "evidences": [{"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}, {"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}]}
```
What is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?
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Revert breaking change in cache_files property
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[] | null | 2021-04-13T14:20:04Z
| 2021-04-14T14:24:24Z
| 2021-04-14T14:24:23Z
|
MEMBER
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#2025 changed the format of `Dataset.cache_files`.
Before it was formatted like
```python
[{"filename": "path/to/file.arrow", "start": 0, "end": 1337}]
```
and it was changed to
```python
["path/to/file.arrow"]
```
since there's no start/end offsets available anymore.
To make this less breaking, I'm setting the format back to a list of dicts:
```python
[{"filename": "path/to/file.arrow"}]
```
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added real label for glue/mrpc to test set
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[] | null | 2021-04-13T13:20:20Z
| 2021-04-13T13:53:20Z
| 2021-04-13T13:53:19Z
|
CONTRIBUTOR
| null | null | 0
|
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Added real label to `glue.py` `mrpc` task for test split.
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Add datasets SLR35 and SLR36 to OpenSLR
|
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| 2021-04-13T14:05:14Z
| 2021-04-13T14:05:14Z
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I would like to add [SLR35](https://openslr.org/35/) (18GB) and [SLR36](https://openslr.org/36/) (22GB) which are Large Javanese and Sundanese ASR training data set collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia.
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load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
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[] | null | 2021-04-12T20:26:01Z
| 2021-04-23T15:20:02Z
| 2021-04-23T15:20:02Z
|
NONE
| null | null | null | null |
I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_metric
>>> metric = load_metric("glue", "sst2")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 502, in load_metric
File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 66, in import_main_class
File "/ext3/miniconda3/lib/python3.8/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
File "<frozen importlib._bootstrap>", line 991, in _find_and_load
File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 671, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 783, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/ns4008/.cache/huggingface/modules/datasets_modules/metrics/glue/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de/glue.py", line 105, in <module>
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
AttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
```
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| 856,025,320
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MDExOlB1bGxSZXF1ZXN0NjEzNjcwODk2
| 2,213
|
Fix lc_quad download checksum
|
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| 2021-04-14T22:04:54Z
| 2021-04-14T13:42:25Z
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|
Fixes #2211
|
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| 855,999,133
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MDU6SXNzdWU4NTU5OTkxMzM=
| 2,212
|
Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset
|
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[] | null | 2021-04-12T13:49:56Z
| 2023-10-03T16:09:19Z
| 2023-10-03T16:09:18Z
|
NONE
| null | null | null | null |
I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data configuration default
Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to /root/.cache/huggingface/datasets/fquad/default/0.1.0/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-48-a2721797e23b> in <module>()
----> 1 fquad = load_dataset("fquad")
11 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
614 raise FileNotFoundError("Couldn't find file at {}".format(url))
615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
--> 616 raise ConnectionError("Couldn't reach {}".format(url))
617
618 # Try a second time
ConnectionError: Couldn't reach https://storage.googleapis.com/illuin/fquad/train.json.zip
```
Does anyone know why that is and how to fix it?
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MDU6SXNzdWU4NTU5ODg0MTA=
| 2,211
|
Getting checksum error when trying to load lc_quad dataset
|
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[] | null | 2021-04-12T13:38:58Z
| 2021-04-14T13:42:25Z
| 2021-04-14T13:42:25Z
|
NONE
| null | null | null | null |
I'm having issues loading the [lc_quad](https://huggingface.co/datasets/fquad) dataset by running:
```Python
lc_quad = load_dataset("lc_quad")
```
which is giving me the following error:
```
Using custom data configuration default
Downloading and preparing dataset lc_quad/default (download: 3.69 MiB, generated: 19.77 MiB, post-processed: Unknown size, total: 23.46 MiB) to /root/.cache/huggingface/datasets/lc_quad/default/2.0.0/5a98fe174603f5dec6df07edf1c2b4d2317210d2ad61f5a393839bca4d64e5a7...
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-42-404ace83f73c> in <module>()
----> 1 lc_quad = load_dataset("lc_quad")
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
37 if len(bad_urls) > 0:
38 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls))
40 logger.info("All the checksums matched successfully" + for_verification_name)
41
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/AskNowQA/LC-QuAD2.0/archive/master.zip']
```
Does anyone know why this could be and how I fix it?
|
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MDU6SXNzdWU4NTU3MDk0MDA=
| 2,210
|
dataloading slow when using HUGE dataset
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[] | null | 2021-04-12T08:33:02Z
| 2021-04-13T02:03:05Z
| 2021-04-13T02:03:05Z
|
NONE
| null | null | null | null |
Hi,
When I use datasets with 600GB data, the dataloading speed increases significantly.
I am experimenting with two datasets, and one is about 60GB and the other 600GB.
Simply speaking, my code uses `datasets.set_format("torch")` function and let pytorch-lightning handle ddp training.
When looking at the pytorch-lightning supported profile of two different runs, I see that fetching a batch(`get_train_batch`) consumes an unreasonable amount of time when data is large. What could be the cause?
* 60GB data
```
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
------------------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 200.33 | 100 % |
------------------------------------------------------------------------------------------------------------------------------------
run_training_epoch | 71.994 |1 | 71.994 | 35.937 |
run_training_batch | 0.64373 |100 | 64.373 | 32.133 |
optimizer_step_and_closure_0 | 0.64322 |100 | 64.322 | 32.108 |
training_step_and_backward | 0.61004 |100 | 61.004 | 30.452 |
model_backward | 0.37552 |100 | 37.552 | 18.745 |
model_forward | 0.22813 |100 | 22.813 | 11.387 |
training_step | 0.22759 |100 | 22.759 | 11.361 |
get_train_batch | 0.066385 |100 | 6.6385 | 3.3138 |
```
* 600GB data
```
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
------------------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 3285.6 | 100 % |
------------------------------------------------------------------------------------------------------------------------------------
run_training_epoch | 1397.9 |1 | 1397.9 | 42.546 |
run_training_batch | 7.2596 |100 | 725.96 | 22.095 |
optimizer_step_and_closure_0 | 7.2589 |100 | 725.89 | 22.093 |
training_step_and_backward | 7.223 |100 | 722.3 | 21.984 |
model_backward | 6.9662 |100 | 696.62 | 21.202 |
get_train_batch | 6.322 |100 | 632.2 | 19.241 |
model_forward | 0.24902 |100 | 24.902 | 0.75789 |
training_step | 0.2485 |100 | 24.85 | 0.75633 |
```
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Add code of conduct to the project
|
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Add code of conduct to the project and link it from README and CONTRIBUTING.
This was already done in `transformers`.
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Remove Python2 leftovers
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This PR removes Python2 leftovers since this project aims for Python3.6+ (and as of 2020 Python2 is no longer officially supported)
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making labels consistent across the datasets
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NONE
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Hi
For accessing the labels one can type
```
>>> a.features['label']
ClassLabel(num_classes=3, names=['entailment', 'neutral', 'contradiction'], names_file=None, id=None)
```
The labels however are not consistent with the actual labels sometimes, for instance in case of XNLI, the actual labels are 0,1,2, but if one try to access as above they are entailment, neutral,contradiction,
it would be great to have the labels consistent.
thanks
|
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MDU6SXNzdWU4NTUyNTI0MTU=
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Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
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| 2021-11-10T12:18:30Z
| 2021-11-10T12:04:28Z
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NONE
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I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_single
writer.write(example)
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 296, in write
self.write_on_file()
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 270, in write_on_file
pa_array = pa.array(typed_sequence)
File "pyarrow/array.pxi", line 222, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 108, in __arrow_array__
out = out.cast(pa.list_(self.optimized_int_type))
File "pyarrow/array.pxi", line 810, in pyarrow.lib.Array.cast
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/pyarrow/compute.py", line 281, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 465, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 294, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127
Do you have any idea about it?
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Updating citation information on LinCE readme
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Hi!
I just updated the citation information in this PR. It had an additional bibtex from one of the datasets used in LinCE and then the LinCE bibtex. I removed the former and added a link that shows the full list of citations for each dataset.
Thanks!
|
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Add configurable options to `seqeval` metric
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| 2021-04-15T13:49:46Z
| 2021-04-15T13:49:46Z
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Fixes #2148
Adds options to use strict mode, different schemes of evaluation, sample weight and adjust zero_division behavior, if encountered.
`seqeval` provides schemes as objects, hence dynamic import from string, to avoid making the user do the import (thanks to @albertvillanova for the `importlib` idea).
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MDExOlB1bGxSZXF1ZXN0NjEyODg4MzA5
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|
updated banking77 train and test data
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Add classes GenerateMode, DownloadConfig and Version to the documentation
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Add documentation for classes `GenerateMode`, `DownloadConfig` and `Version`.
Update the docstring of `load_dataset` to create cross-reference links to the classes.
Related to #2187.
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MDExOlB1bGxSZXF1ZXN0NjEyNDM1NTE3
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Fix ArrowWriter overwriting features in ArrowBasedBuilder
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This should fix the issues with CSV loading experienced in #2153 and #2200.
The CSV builder is an ArrowBasedBuilder that had an issue with its ArrowWriter used to write the arrow file from the csv data.
The writer wasn't initialized with the features passed by the user. Therefore the writer was inferring the features from the arrow data, discarding the features passed by the user.
I fixed that and I updated the tests
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_prepare_split will overwrite DatasetBuilder.info.features
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Hi, here is my issue:
I initialized a Csv datasetbuilder with specific features:
```
def get_dataset_features(data_args):
features = {}
if data_args.text_features:
features.update({text_feature: hf_features.Value("string") for text_feature in data_args.text_features.strip().split(",")})
if data_args.num_features:
features.update({text_feature: hf_features.Value("float32") for text_feature in data_args.num_features.strip().split(",")})
if data_args.label_classes:
features["label"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(","))
else:
features["label"] = hf_features.Value("float32")
return hf_features.Features(features)
datasets = load_dataset(extension,
data_files=data_files,
sep=data_args.delimiter,
header=data_args.header,
column_names=data_args.column_names.split(",") if data_args.column_names else None,
features=get_dataset_features(data_args=data_args))
```
The `features` is printout as below before `builder_instance.as_dataset` is called:
```
{'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}
````
But after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to:
```
{'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}
```
After digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`.
But `ArrowWriter` is initailized without passing `features`.
So my concern is:
It's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function?
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MDExOlB1bGxSZXF1ZXN0NjEyMzY0ODU3
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|
Fix backward compatibility in Dataset.load_from_disk
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Fix backward compatibility when loading from disk an old dataset saved to disk with indices using key "_indices_data_files".
Related to #2195.
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MDExOlB1bGxSZXF1ZXN0NjEyMzE0MTIz
| 2,198
|
added file_permission in load_dataset
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[] | null | 2021-04-09T09:39:06Z
| 2021-04-16T14:11:46Z
| 2021-04-16T14:11:46Z
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As discussed in #2065 I've added `file_permission` argument in `load_dataset`.
Added mainly 2 things here:
1) Permission of downloaded datasets when converted to .arrow files can be changed with argument `file_permission` argument in `load_dataset` (default is 0o644 only)
2) Incase the user uses `map` later on to generate another cache file of dataset, it ensures the permissions of newly generated file are similar to that of` *-train.arrow` file inside cache_dir for that dataset.
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fix missing indices_files in load_form_disk
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This should fix #2195
`load_from_disk` was failing if there was no "_indices_files" field in state.json. This can happen if the dataset has no indices mapping
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MDU6SXNzdWU4NTQxMjYxMTQ=
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|
`load_dataset` caches two arrow files?
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[] | null | 2021-04-09T03:49:19Z
| 2021-04-12T05:25:29Z
| 2021-04-12T05:25:29Z
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NONE
| null | null | null | null |
Hi,
I am using datasets to load large json file of 587G.
I checked the cached folder and found that there are two arrow files created:
* `cache-ed205e500a7dc44c.arrow` - 355G
* `json-train.arrow` - 582G
Why is the first file created?
If I delete it, would I still be able to `load_from_disk`?
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KeyError: '_indices_files' in `arrow_dataset.py`
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[] | null | 2021-04-09T01:37:12Z
| 2021-04-09T09:55:09Z
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NONE
| null | null | null | null |
After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset.
Trace:
```
Traceback (most recent call last):
File "load_data.py", line 11, in <module>
dataset = load_from_disk(SRC)
File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/load.py", line 784, in load_from_disk
return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)
File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/dataset_dict.py", line 692, in load_from_disk
dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)
File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 634, in load_from_disk
if state["_indices_files"]:
KeyError: '_indices_files'
```
I believe this is the line causing the error since there may not be a `_indices_files` key in the older versions:
https://github.com/huggingface/datasets/blob/b70141e3c5149430951773aaa0155555c5fb3e76/src/datasets/arrow_dataset.py#L634
May I suggest using `state.get()` instead of directly indexing the dictionary?
@lhoestq
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py3.7: TypeError: can't pickle _LazyModule objects
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CONTRIBUTOR
| null | null | null | null |
While this works fine with py3.8, under py3.7, with a totally new conda env and transformers install:
```
git clone https://github.com/huggingface/transformers
cd transformers
pip install -e .[testing]
export BS=1; rm -rf /tmp/test-clm; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python \
examples/language-modeling/run_clm.py --model_name_or_path distilgpt2 --dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 --do_train --max_train_samples 1 \
--per_device_train_batch_size $BS --output_dir /tmp/test-clm --block_size 128 --logging_steps 1 \
--fp16
```
```
Traceback (most recent call last):
File "examples/language-modeling/run_clm.py", line 453, in <module>
main()
File "examples/language-modeling/run_clm.py", line 336, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1259, in map
update_data=update_data,
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 157, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 158, in wrapper
self._fingerprint, transform, kwargs_for_fingerprint
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 105, in update_fingerprint
hasher.update(transform_args[key])
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 57, in update
self.m.update(self.hash(value).encode("utf-8"))
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 53, in hash
return cls.hash_default(value)
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 46, in hash_default
return cls.hash_bytes(dumps(value))
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 389, in dumps
dump(obj, file)
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 361, in dump
Pickler(file, recurse=True).dump(obj)
File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump
StockPickler.dump(self, obj)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 437, in dump
self.save(obj)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 556, in save_function
obj=obj,
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 524, in save
rv = reduce(self.proto)
TypeError: can't pickle _LazyModule objects
```
```
$ python --version
Python 3.7.4
$ python -m torch.utils.collect_env
Collecting environment information...
PyTorch version: 1.8.0.dev20210110+cu110
Is debug build: False
CUDA used to build PyTorch: 11.0
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.2 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
Clang version: 10.0.0-4ubuntu1
CMake version: version 3.16.3
```
Thanks.
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Filtering/mapping on one column is very slow
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CONTRIBUTOR
| null | null | null | null |
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that— I'm not very familiar with the pyarrow API.
I know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.
PS: This is definitely not a "dataset request." I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.
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Fix typo in huggingface hub
|
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pip knows how to resolve to `huggingface_hub`, but conda doesn't!
The `packaging` dependency is also required for the build to complete.
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Refactorize tests to use Dataset as context manager
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Refactorize Dataset tests to use Dataset as context manager.
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MDU6SXNzdWU4NTMxODE1NjQ=
| 2,190
|
News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
|
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[] | null | 2021-04-08T07:53:43Z
| 2021-05-24T10:03:55Z
| 2021-05-24T10:03:55Z
|
NONE
| null | null | null | null |
I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi.
```
train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]')
val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]')
# filtering out examples that are not ar-en translations but ar-hi
val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True)
```
* I'm fairly new to using datasets so I might be doing something wrong
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| 2,189
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save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object.
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[] | null | 2021-04-08T04:42:53Z
| 2022-06-01T16:32:15Z
| 2022-06-01T16:32:15Z
|
NONE
| null | null | null | null |
As you can see, it saves the entire dataset.
@lhoestq
You can check by going through the following example,
```
from datasets import load_from_disk,concatenate_datasets
loaded_data=load_from_disk('/home/gsir059/HNSW-ori/my_knowledge_dataset')
n=20
kb_list=[loaded_data.shard(n, i, contiguous=True) for i in range(n)]
final_dataset=concatenate_datasets([kb_list[1],kb_list[2]])
final_dataset.save_to_disk('/home/gsir059/haha/k.arrow')
```
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Duplicate data in Timit dataset
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[] | null | 2021-04-08T04:21:54Z
| 2021-04-08T12:13:19Z
| 2021-04-08T12:13:19Z
|
NONE
| null | null | null | null |
I ran a simple code to list all texts in Timit dataset and the texts were all the same.
Is this dataset corrupted?
**Code:**
timit = load_dataset("timit_asr")
print(*timit['train']['text'], sep='\n')
**Result:**
Would such an act of refusal be useful?
Would such an act of refusal be useful?
Would such an act of refusal be useful?
Would such an act of refusal be useful?
...
...
Would such an act of refusal be useful?
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Question (potential issue?) related to datasets caching
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open
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[] | null | 2021-04-08T00:16:28Z
| 2023-01-03T18:30:38Z
| null |
NONE
| null | null | null | null |
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.builder - Using custom data configuration default-888a87931cbc5877
04/07/2021 18:34:42 - WARNING - datasets.builder - Reusing dataset csv (xxxx/cache-transformers/datasets/csv/default-888a87931cbc5877/0.0.0/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93
```
Can you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!
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|
GEM: new challenge sets
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[] | null | 2021-04-07T21:39:07Z
| 2021-04-07T21:56:35Z
| 2021-04-07T21:56:35Z
|
MEMBER
| null | null | 0
|
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This PR updates the GEM dataset to:
- remove extraneous fields in WikiAuto after https://github.com/huggingface/datasets/pull/2171 fixed the source
- add context and services to Schema Guided Dialog
- Add new or update challenge sets for MLSUM ES and DE, XSUM, and SGD
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.map() and distributed training
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[] | null | 2021-04-07T18:22:14Z
| 2021-10-23T07:11:15Z
| 2021-04-09T15:38:31Z
|
CONTRIBUTOR
| null | null | null | null |
Hi,
I have a question regarding distributed training and the `.map` call on a dataset.
I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_path=my_path)
[...]
def tokenize_function(examples):
return tokenizer(examples[text_column_name])
logger.info("Mapping dataset to tokenized dataset.")
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=True,
)
```
I am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path/train` (there is only a train split).
When I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.
Everything so far was done by launching a **single process script**.
I now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files.
I tried adding the `cache_file_name` argument: `cache_file_name={"train": my_path/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.
**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.
- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_clm.py) in my case)
- I am using 1.5.0 version of datasets if that matters.
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