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https://github.com/huggingface/datasets/pull/2023 | Add Romanian to XQuAD | You're right, you needed the `--ignore_verifications` flag !
Thanks for updating them :)
Although I just noticed that the new dummy_data.zip files are quite big (170KB each) because they contain the json files of all the languages, while only one json file per language is necessary. Could you remove the unnecessary json files to reduce the size of the dummy_data.zip files if you don't mind ? | On Jan 18, XQuAD was updated with a new Romanian validation file ([xquad commit link](https://github.com/deepmind/xquad/commit/60cac411649156efb6aab9dd4c9cde787a2c0345))
| 66 | text: Add Romanian to XQuAD
On Jan 18, XQuAD was updated with a new Romanian validation file ([xquad commit link](https://github.com/deepmind/xquad/commit/60cac411649156efb6aab9dd4c9cde787a2c0345))
You're right, you needed the `--ignore_verifications` flag !
Thanks for updating them :)
Although I just noticed that the new dummy_data.zip files are quite big (170KB each) because they contain the json files of all the languages, while only one json file per language is necessary. Could you remove the unnecessary json files to reduce the size of the dummy_data.zip files if you don't mind ? | [
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https://github.com/huggingface/datasets/pull/2019 | Replace print with logging in dataset scripts | @lhoestq Maybe a script or even a test in `test_dataset_common.py` that verifies that a dataset script meets some set of quality standards (print calls and todos from the dataset script template are not present, etc.) could be added? | Replaces `print(...)` in the dataset scripts with the library logger. | 38 | text: Replace print with logging in dataset scripts
Replaces `print(...)` in the dataset scripts with the library logger.
@lhoestq Maybe a script or even a test in `test_dataset_common.py` that verifies that a dataset script meets some set of quality standards (print calls and todos from the dataset script template are not present, etc.) could be added? | [
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https://github.com/huggingface/datasets/pull/2018 | Md gender card update | Ahah that's what I wanted to say @lhoestq, thanks for fixing. Not used to review the Datasets side ;-) | I updated the descriptions of the datasets as they appear in the HF repo and the descriptions of the source datasets according to what I could find from the paper and the references. I'm still a little unclear about some of the fields of the different configs, and there was little info on the word list and name list. I'll contact the authors to see if they have any additional information or suggested changes. | 19 | text: Md gender card update
I updated the descriptions of the datasets as they appear in the HF repo and the descriptions of the source datasets according to what I could find from the paper and the references. I'm still a little unclear about some of the fields of the different configs, and there was little info on the word list and name list. I'll contact the authors to see if they have any additional information or suggested changes.
Ahah that's what I wanted to say @lhoestq, thanks for fixing. Not used to review the Datasets side ;-) | [
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https://github.com/huggingface/datasets/pull/2008 | Fix various typos/grammer in the docs | What do yo think of the documentation btw ?
What parts would you like to see improved ? | This PR:
* fixes various typos/grammer I came across while reading the docs
* adds the "Install with conda" installation instructions
Closes #1959 | 18 | text: Fix various typos/grammer in the docs
This PR:
* fixes various typos/grammer I came across while reading the docs
* adds the "Install with conda" installation instructions
Closes #1959
What do yo think of the documentation btw ?
What parts would you like to see improved ? | [
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https://github.com/huggingface/datasets/pull/2008 | Fix various typos/grammer in the docs | I like how concise and straightforward the docs are.
Few things that would further improve the docs IMO:
* the usage example of `Dataset.formatted_as` in https://huggingface.co/docs/datasets/master/processing.html
* the "Open in Colab" button would be nice where it makes sense (we can borrow this from the transformers project + link to HF Forum) | This PR:
* fixes various typos/grammer I came across while reading the docs
* adds the "Install with conda" installation instructions
Closes #1959 | 52 | text: Fix various typos/grammer in the docs
This PR:
* fixes various typos/grammer I came across while reading the docs
* adds the "Install with conda" installation instructions
Closes #1959
I like how concise and straightforward the docs are.
Few things that would further improve the docs IMO:
* the usage example of `Dataset.formatted_as` in https://huggingface.co/docs/datasets/master/processing.html
* the "Open in Colab" button would be nice where it makes sense (we can borrow this from the transformers project + link to HF Forum) | [
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https://github.com/huggingface/datasets/pull/2002 | MOROCO | @lhoestq Thank you for all the feedback. I've added the suggested changes in my last commit. | Add MOROCO to huggingface datasets. | 16 | text: MOROCO
Add MOROCO to huggingface datasets.
@lhoestq Thank you for all the feedback. I've added the suggested changes in my last commit. | [
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https://github.com/huggingface/datasets/pull/1998 | Add -DOCSTART- note to dataset card of conll-like datasets | Nice catch! Yes I didn't check the actual data, instead I was just looking for the `if line.startswith("-DOCSTART-")` pattern. | Closes #1983 | 19 | text: Add -DOCSTART- note to dataset card of conll-like datasets
Closes #1983
Nice catch! Yes I didn't check the actual data, instead I was just looking for the `if line.startswith("-DOCSTART-")` pattern. | [
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https://github.com/huggingface/datasets/pull/1991 | Adding the conllpp dataset | Thanks for the reviews! A note that I have addressed the comments, and waiting for a further review. | Adding the conllpp dataset, is a revision from https://github.com/huggingface/datasets/pull/1910. | 18 | text: Adding the conllpp dataset
Adding the conllpp dataset, is a revision from https://github.com/huggingface/datasets/pull/1910.
Thanks for the reviews! A note that I have addressed the comments, and waiting for a further review. | [
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] |
https://github.com/huggingface/datasets/pull/1985 | Optimize int precision | @lhoestq, are the tests OK? Some other cases I missed? Do you agree with this approach? | Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861. | 16 | text: Optimize int precision
Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861.
@lhoestq, are the tests OK? Some other cases I missed? Do you agree with this approach? | [
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https://github.com/huggingface/datasets/pull/1985 | Optimize int precision | I just tested this and it works like a charm :)
However tokenizing and then setting the format to "torch" to feed the tokens into a model doesn't seem to work anymore, since the pytorch tensors have the int32/int8 precisions instead of int64 that is required as model inputs.
For example:
```python
import torch
from datasets import Dataset
from transformers import BertModel, BertTokenizer
torch.set_grad_enabled(False)
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased")
dataset = Dataset.from_dict({"text": ["hello there !"]})
dataset = dataset.map(tokenizer, input_columns="text", remove_columns=dataset.column_names)
dataset = dataset.with_format("torch")
print(dataset.features)
# {'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
# 'input_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), # this should be int32 though
# 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)}
model(**dataset[:1])
# RuntimeError: Expected tensor for argument #1 'indices' to have scalar type Long; but got torch.CharTensor instead (while checking arguments for embedding)
dataset = dataset.with_format("torch", dtype=torch.int64)
model(**dataset[:1])
# works as expected
```
Pinging @sgugger here to make sure we take the right decision here.
Do we want the "torch" format to always return int64 ? Or does it have to keep the precision defined by the `dataset.features`
and therefore we would need to specify "torch" with `dtype=torch.int64` ? | Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861. | 188 | text: Optimize int precision
Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861.
I just tested this and it works like a charm :)
However tokenizing and then setting the format to "torch" to feed the tokens into a model doesn't seem to work anymore, since the pytorch tensors have the int32/int8 precisions instead of int64 that is required as model inputs.
For example:
```python
import torch
from datasets import Dataset
from transformers import BertModel, BertTokenizer
torch.set_grad_enabled(False)
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased")
dataset = Dataset.from_dict({"text": ["hello there !"]})
dataset = dataset.map(tokenizer, input_columns="text", remove_columns=dataset.column_names)
dataset = dataset.with_format("torch")
print(dataset.features)
# {'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
# 'input_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), # this should be int32 though
# 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)}
model(**dataset[:1])
# RuntimeError: Expected tensor for argument #1 'indices' to have scalar type Long; but got torch.CharTensor instead (while checking arguments for embedding)
dataset = dataset.with_format("torch", dtype=torch.int64)
model(**dataset[:1])
# works as expected
```
Pinging @sgugger here to make sure we take the right decision here.
Do we want the "torch" format to always return int64 ? Or does it have to keep the precision defined by the `dataset.features`
and therefore we would need to specify "torch" with `dtype=torch.int64` ? | [
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https://github.com/huggingface/datasets/pull/1985 | Optimize int precision | From a user perspective, I think it's fine if the "torch" format converts all ints types to `torch.int64` by default since it's what the model will need almost all the time. I don't see a case where you would want to keep the low precision at the top of my head, and one can always write a custom transform for an edge case. | Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861. | 63 | text: Optimize int precision
Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861.
From a user perspective, I think it's fine if the "torch" format converts all ints types to `torch.int64` by default since it's what the model will need almost all the time. I don't see a case where you would want to keep the low precision at the top of my head, and one can always write a custom transform for an edge case. | [
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https://github.com/huggingface/datasets/pull/1985 | Optimize int precision | Sounds good to me !
For consistency maybe we should make the float precision fixed as well (float32, I guess) | Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861. | 20 | text: Optimize int precision
Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861.
Sounds good to me !
For consistency maybe we should make the float precision fixed as well (float32, I guess) | [
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https://github.com/huggingface/datasets/pull/1985 | Optimize int precision | Yes I we should do the same for tensorflow as well since tf models would have the same issue
Thanks for adding this :) | Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861. | 24 | text: Optimize int precision
Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861.
Yes I we should do the same for tensorflow as well since tf models would have the same issue
Thanks for adding this :) | [
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] |
https://github.com/huggingface/datasets/pull/1982 | Fix NestedDataStructure.data for empty dict | still facing the same issue or similar:
from datasets import load_dataset
wtm14_test = load_dataset('wmt14',"de-en",cache_dir='./datasets')
~\.cache\huggingface\modules\datasets_modules\datasets\wmt14\43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\wmt_utils.py in _split_generators(self, dl_manager)
758 # Extract manually downloaded files.
759 manual_files = dl_manager.extract(manual_paths_dict)
--> 760 extraction_map = dict(downloaded_files, **manual_files)
761
762 for language in self.config.language_pair:
TypeError: type object argument after ** must be a mapping, not list | Fix #1981 | 52 | text: Fix NestedDataStructure.data for empty dict
Fix #1981
still facing the same issue or similar:
from datasets import load_dataset
wtm14_test = load_dataset('wmt14',"de-en",cache_dir='./datasets')
~\.cache\huggingface\modules\datasets_modules\datasets\wmt14\43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\wmt_utils.py in _split_generators(self, dl_manager)
758 # Extract manually downloaded files.
759 manual_files = dl_manager.extract(manual_paths_dict)
--> 760 extraction_map = dict(downloaded_files, **manual_files)
761
762 for language in self.config.language_pair:
TypeError: type object argument after ** must be a mapping, not list | [
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https://github.com/huggingface/datasets/pull/1982 | Fix NestedDataStructure.data for empty dict | Hi @sabania
We released a patch version that fixes this issue (1.4.1), can you try with the new version please ?
```
pip install --upgrade datasets
``` | Fix #1981 | 27 | text: Fix NestedDataStructure.data for empty dict
Fix #1981
Hi @sabania
We released a patch version that fixes this issue (1.4.1), can you try with the new version please ?
```
pip install --upgrade datasets
``` | [
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https://github.com/huggingface/datasets/pull/1980 | Loading all answers from drop | Nice thanks for the change !
This looks all good to me
Before we merge can you just update the dataset_infos.json file of drop ? You can do it by running
```
datasets-cli test ./datasets/drop --all_configs --save_infos --ignore_verifications
``` | Hello all,
I propose this change to the DROP loading script so that all answers are loaded no matter their type. Currently, only "span" answers are loaded, which excludes a significant amount of answers from drop (i.e. "number" and "date").
I updated the script with the version I use for my work. However, I couldn't find a way to verify that all is working when integrated with the datasets repo, since the `load_dataset` method seems to always download the script from github and not local files.
Note that 9 items from the train set have no answers, as well as 1 from the validation set. The script I propose simply do not load them.
Let me know if there is anything else I can do,
Clément | 39 | text: Loading all answers from drop
Hello all,
I propose this change to the DROP loading script so that all answers are loaded no matter their type. Currently, only "span" answers are loaded, which excludes a significant amount of answers from drop (i.e. "number" and "date").
I updated the script with the version I use for my work. However, I couldn't find a way to verify that all is working when integrated with the datasets repo, since the `load_dataset` method seems to always download the script from github and not local files.
Note that 9 items from the train set have no answers, as well as 1 from the validation set. The script I propose simply do not load them.
Let me know if there is anything else I can do,
Clément
Nice thanks for the change !
This looks all good to me
Before we merge can you just update the dataset_infos.json file of drop ? You can do it by running
```
datasets-cli test ./datasets/drop --all_configs --save_infos --ignore_verifications
``` | [
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https://github.com/huggingface/datasets/pull/1979 | Add article_id and process test set template for semeval 2020 task 11… | Thanks !
Now to fix the CI the only thing left is to add a dummy `test-task-tc-template.out` file inside the `dummy_data.zip` at `./datasets/sem_eval_2020_task_11/dummy/1.1.0`
It must contain the labels template for each dummy article of the test set included in `dummy_data.zip`
After that we should be good to merge this one :) | … dataset
- `article_id` is needed to create the submission file for the task at https://propaganda.qcri.org/semeval2020-task11/
- The `technique classification` task provides the span indices in a template for the test set that is necessary to complete the task. This PR implements processing of that template for the dataset. | 51 | text: Add article_id and process test set template for semeval 2020 task 11…
… dataset
- `article_id` is needed to create the submission file for the task at https://propaganda.qcri.org/semeval2020-task11/
- The `technique classification` task provides the span indices in a template for the test set that is necessary to complete the task. This PR implements processing of that template for the dataset.
Thanks !
Now to fix the CI the only thing left is to add a dummy `test-task-tc-template.out` file inside the `dummy_data.zip` at `./datasets/sem_eval_2020_task_11/dummy/1.1.0`
It must contain the labels template for each dummy article of the test set included in `dummy_data.zip`
After that we should be good to merge this one :) | [
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https://github.com/huggingface/datasets/pull/1979 | Add article_id and process test set template for semeval 2020 task 11… | @lhoestq Made the changes! The failure now seems to be unrelated to the changes. Any idea what's going on? | … dataset
- `article_id` is needed to create the submission file for the task at https://propaganda.qcri.org/semeval2020-task11/
- The `technique classification` task provides the span indices in a template for the test set that is necessary to complete the task. This PR implements processing of that template for the dataset. | 19 | text: Add article_id and process test set template for semeval 2020 task 11…
… dataset
- `article_id` is needed to create the submission file for the task at https://propaganda.qcri.org/semeval2020-task11/
- The `technique classification` task provides the span indices in a template for the test set that is necessary to complete the task. This PR implements processing of that template for the dataset.
@lhoestq Made the changes! The failure now seems to be unrelated to the changes. Any idea what's going on? | [
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https://github.com/huggingface/datasets/pull/1978 | Adding ro sts dataset | @lhoestq thank you very much for the quick review and useful comments!
I have tried to address them all, and a few comments that you left for ro_sts I have applied to the ro_sts_parallel as well (in read-me: fixed source_datasets, links to homepage, repository, leaderboard, thanks to me message, in ro_sts_parallel.py changed to camel case as well). In the ro_sts_parallel I have changed the order on the languages, also in the example, as you said order doesn't matter, but just to have them listed in the readme in the same order.
I have commented above on why we would like to keep them as separate datasets, hope it makes sense.
If there is anything else I should change please let me know.
Thanks again! | Adding [RO-STS](https://github.com/dumitrescustefan/RO-STS) dataset | 124 | text: Adding ro sts dataset
Adding [RO-STS](https://github.com/dumitrescustefan/RO-STS) dataset
@lhoestq thank you very much for the quick review and useful comments!
I have tried to address them all, and a few comments that you left for ro_sts I have applied to the ro_sts_parallel as well (in read-me: fixed source_datasets, links to homepage, repository, leaderboard, thanks to me message, in ro_sts_parallel.py changed to camel case as well). In the ro_sts_parallel I have changed the order on the languages, also in the example, as you said order doesn't matter, but just to have them listed in the readme in the same order.
I have commented above on why we would like to keep them as separate datasets, hope it makes sense.
If there is anything else I should change please let me know.
Thanks again! | [
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https://github.com/huggingface/datasets/pull/1978 | Adding ro sts dataset | @lhoestq I tried to adjust the ro_sts_parallel, locally when I run the tests they are passing, but somewhere it has the old name of rosts-parallel-ro-en which I am trying to change to ro_sts_parallel. I don't think I have left anything related to rosts-parallel-ro-en, but when the dataset_infos.json is regenerated it adds it. Could you please help me out, how can I fix this? Thanks in advance! | Adding [RO-STS](https://github.com/dumitrescustefan/RO-STS) dataset | 66 | text: Adding ro sts dataset
Adding [RO-STS](https://github.com/dumitrescustefan/RO-STS) dataset
@lhoestq I tried to adjust the ro_sts_parallel, locally when I run the tests they are passing, but somewhere it has the old name of rosts-parallel-ro-en which I am trying to change to ro_sts_parallel. I don't think I have left anything related to rosts-parallel-ro-en, but when the dataset_infos.json is regenerated it adds it. Could you please help me out, how can I fix this? Thanks in advance! | [
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https://github.com/huggingface/datasets/pull/1971 | Fix ArrowWriter closes stream at exit | Oh nice thanks for adding the context manager ! All the streams and RecordBatchWriter will be properly closed now. Hopefully this gives a better experience on windows on which it's super important to close stuff.
Not sure about the error, it looks like a process crashed silently.
Let me take a look | Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method.
Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit. | 52 | text: Fix ArrowWriter closes stream at exit
Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method.
Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit.
Oh nice thanks for adding the context manager ! All the streams and RecordBatchWriter will be properly closed now. Hopefully this gives a better experience on windows on which it's super important to close stuff.
Not sure about the error, it looks like a process crashed silently.
Let me take a look | [
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https://github.com/huggingface/datasets/pull/1971 | Fix ArrowWriter closes stream at exit | > Hopefully this gives a better experience on windows on which it's super important to close stuff.
Exactly! On Windows, you got:
> PermissionError: [WinError 32] The process cannot access the file because it is being used by another process
when trying to access the unclosed `stream` file, e.g. by `with incomplete_dir(self._cache_dir) as tmp_data_dir`: `shutil.rmtree(tmp_dir)`
The reason is: https://docs.python.org/3/library/os.html#os.remove
> On Windows, attempting to remove a file that is in use causes an exception to be raised; on Unix, the directory entry is removed but the storage allocated to the file is not made available until the original file is no longer in use.
| Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method.
Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit. | 104 | text: Fix ArrowWriter closes stream at exit
Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method.
Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit.
> Hopefully this gives a better experience on windows on which it's super important to close stuff.
Exactly! On Windows, you got:
> PermissionError: [WinError 32] The process cannot access the file because it is being used by another process
when trying to access the unclosed `stream` file, e.g. by `with incomplete_dir(self._cache_dir) as tmp_data_dir`: `shutil.rmtree(tmp_dir)`
The reason is: https://docs.python.org/3/library/os.html#os.remove
> On Windows, attempting to remove a file that is in use causes an exception to be raised; on Unix, the directory entry is removed but the storage allocated to the file is not made available until the original file is no longer in use.
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] |
https://github.com/huggingface/datasets/pull/1971 | Fix ArrowWriter closes stream at exit | The test passes on my windows. This was probably a circleCI issue. I re-ran the circleCI tests | Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method.
Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit. | 17 | text: Fix ArrowWriter closes stream at exit
Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method.
Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit.
The test passes on my windows. This was probably a circleCI issue. I re-ran the circleCI tests | [
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https://github.com/huggingface/datasets/pull/1966 | Fix metrics collision in separate multiprocessed experiments | Since the failure was originally intermittent, there is no 100% telling that the problem is gone.
But if my artificial race condition setup https://github.com/huggingface/datasets/issues/1942#issuecomment-787124529 is to be the litmus test then the problem has been fixed, as with this PR branch that particular race condition is taken care of correctly.
Thank you for taking care of this, @lhoestq - locking can be very tricky to do right! | As noticed in #1942 , there's a issue with locks if you run multiple separate evaluation experiments in a multiprocessed setup.
Indeed there is a time span in Metric._finalize() where the process 0 loses its lock before re-acquiring it. This is bad since the lock of the process 0 tells the other process that the corresponding cache file is available for writing/reading/deleting: we end up having one metric cache that collides with another one. This can raise FileNotFound errors when a metric tries to read the cache file and if the second conflicting metric deleted it.
To fix that I made sure that the lock file of the process 0 stays acquired from the cache file creation to the end of the metric computation. This way the other metrics can simply sample a new hashing name in order to avoid the collision.
Finally I added missing tests for separate experiments in distributed setup. | 67 | text: Fix metrics collision in separate multiprocessed experiments
As noticed in #1942 , there's a issue with locks if you run multiple separate evaluation experiments in a multiprocessed setup.
Indeed there is a time span in Metric._finalize() where the process 0 loses its lock before re-acquiring it. This is bad since the lock of the process 0 tells the other process that the corresponding cache file is available for writing/reading/deleting: we end up having one metric cache that collides with another one. This can raise FileNotFound errors when a metric tries to read the cache file and if the second conflicting metric deleted it.
To fix that I made sure that the lock file of the process 0 stays acquired from the cache file creation to the end of the metric computation. This way the other metrics can simply sample a new hashing name in order to avoid the collision.
Finally I added missing tests for separate experiments in distributed setup.
Since the failure was originally intermittent, there is no 100% telling that the problem is gone.
But if my artificial race condition setup https://github.com/huggingface/datasets/issues/1942#issuecomment-787124529 is to be the litmus test then the problem has been fixed, as with this PR branch that particular race condition is taken care of correctly.
Thank you for taking care of this, @lhoestq - locking can be very tricky to do right! | [
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https://github.com/huggingface/datasets/pull/1962 | Fix unused arguments | @lhoestq Re-added the arg. The ConnectionError in CI seems unrelated to this PR (the same test fails on master as well). | Noticed some args in the codebase are not used, so managed to find all such occurrences with Pylance and fix them. | 21 | text: Fix unused arguments
Noticed some args in the codebase are not used, so managed to find all such occurrences with Pylance and fix them.
@lhoestq Re-added the arg. The ConnectionError in CI seems unrelated to this PR (the same test fails on master as well). | [
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https://github.com/huggingface/datasets/pull/1960 | Allow stateful function in dataset.map | @lhoestq Added a test. If you can come up with a better stateful callable, I'm all ears 😄. | Removes the "test type" section in Dataset.map which would modify the state of the stateful function. Now, the return type of the map function is inferred after processing the first example.
Fixes #1940
@lhoestq Not very happy with the usage of `nonlocal`. Would like to hear your opinion on this. | 18 | text: Allow stateful function in dataset.map
Removes the "test type" section in Dataset.map which would modify the state of the stateful function. Now, the return type of the map function is inferred after processing the first example.
Fixes #1940
@lhoestq Not very happy with the usage of `nonlocal`. Would like to hear your opinion on this.
@lhoestq Added a test. If you can come up with a better stateful callable, I'm all ears 😄. | [
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https://github.com/huggingface/datasets/pull/1960 | Allow stateful function in dataset.map | Sorry I said earlier that it was good to have it inside the loop, my mistake ! | Removes the "test type" section in Dataset.map which would modify the state of the stateful function. Now, the return type of the map function is inferred after processing the first example.
Fixes #1940
@lhoestq Not very happy with the usage of `nonlocal`. Would like to hear your opinion on this. | 17 | text: Allow stateful function in dataset.map
Removes the "test type" section in Dataset.map which would modify the state of the stateful function. Now, the return type of the map function is inferred after processing the first example.
Fixes #1940
@lhoestq Not very happy with the usage of `nonlocal`. Would like to hear your opinion on this.
Sorry I said earlier that it was good to have it inside the loop, my mistake ! | [
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https://github.com/huggingface/datasets/pull/1960 | Allow stateful function in dataset.map | @lhoestq Okay, did some refactoring and now the "cache" part comes before the for loop. Thanks for the guidance.
Think this is ready for the final review. | Removes the "test type" section in Dataset.map which would modify the state of the stateful function. Now, the return type of the map function is inferred after processing the first example.
Fixes #1940
@lhoestq Not very happy with the usage of `nonlocal`. Would like to hear your opinion on this. | 27 | text: Allow stateful function in dataset.map
Removes the "test type" section in Dataset.map which would modify the state of the stateful function. Now, the return type of the map function is inferred after processing the first example.
Fixes #1940
@lhoestq Not very happy with the usage of `nonlocal`. Would like to hear your opinion on this.
@lhoestq Okay, did some refactoring and now the "cache" part comes before the for loop. Thanks for the guidance.
Think this is ready for the final review. | [
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https://github.com/huggingface/datasets/pull/1952 | Handle timeouts | I never said the calls were hanging indefinitely, what we need is quite different - in the firewalled env with a network, there should be no network calls or they should fail instantly.
To make this work I suppose on top of this PR we need:
1. `DATASETS_OFFLINE` env var to force set timeout to 0 globally (or to 0.0001 if 0 has a special meaning of no timeout)
2. `DATASETS_OFFLINE` should guard against failing network calls and not fail the program if it has all the data it needs locally.
Bottom line - if the logic wants to check online if the local file matches online dataset name, let it go wild, but it should fail instantly, recover and use the local file - if one is specified explicitly or cache if there is one. And only if neither was found only then assert.
I hope this makes sense and is doable.
I have started on the same approach for transformers https://github.com/huggingface/transformers/pull/10407
Thank you, @lhoestq | As noticed in https://github.com/huggingface/datasets/issues/1939, timeouts were not properly handled when loading a dataset.
This caused the connection to hang indefinitely when working in a firewalled environment cc @stas00
I added a default timeout, and included an option to our offline environment for tests to be able to simulate both connection errors and timeout errors (previously it was simulating connection errors only).
Now networks calls don't hang indefinitely.
The default timeout is set to 10sec (we might reduce it). | 166 | text: Handle timeouts
As noticed in https://github.com/huggingface/datasets/issues/1939, timeouts were not properly handled when loading a dataset.
This caused the connection to hang indefinitely when working in a firewalled environment cc @stas00
I added a default timeout, and included an option to our offline environment for tests to be able to simulate both connection errors and timeout errors (previously it was simulating connection errors only).
Now networks calls don't hang indefinitely.
The default timeout is set to 10sec (we might reduce it).
I never said the calls were hanging indefinitely, what we need is quite different - in the firewalled env with a network, there should be no network calls or they should fail instantly.
To make this work I suppose on top of this PR we need:
1. `DATASETS_OFFLINE` env var to force set timeout to 0 globally (or to 0.0001 if 0 has a special meaning of no timeout)
2. `DATASETS_OFFLINE` should guard against failing network calls and not fail the program if it has all the data it needs locally.
Bottom line - if the logic wants to check online if the local file matches online dataset name, let it go wild, but it should fail instantly, recover and use the local file - if one is specified explicitly or cache if there is one. And only if neither was found only then assert.
I hope this makes sense and is doable.
I have started on the same approach for transformers https://github.com/huggingface/transformers/pull/10407
Thank you, @lhoestq | [
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] |
https://github.com/huggingface/datasets/pull/1952 | Handle timeouts | Yes that was the first step to add DATASETS_OFFLINE :)
With this PR, if a request times out (which couldn't happen before because no time out was set), it falls back on the local files with no error.
As you said, setting the timeout to something like 1e-16 makes the requests fail instantly, which is one step forward. One last thing left is to disable request retries and everything will be instant ! | As noticed in https://github.com/huggingface/datasets/issues/1939, timeouts were not properly handled when loading a dataset.
This caused the connection to hang indefinitely when working in a firewalled environment cc @stas00
I added a default timeout, and included an option to our offline environment for tests to be able to simulate both connection errors and timeout errors (previously it was simulating connection errors only).
Now networks calls don't hang indefinitely.
The default timeout is set to 10sec (we might reduce it). | 73 | text: Handle timeouts
As noticed in https://github.com/huggingface/datasets/issues/1939, timeouts were not properly handled when loading a dataset.
This caused the connection to hang indefinitely when working in a firewalled environment cc @stas00
I added a default timeout, and included an option to our offline environment for tests to be able to simulate both connection errors and timeout errors (previously it was simulating connection errors only).
Now networks calls don't hang indefinitely.
The default timeout is set to 10sec (we might reduce it).
Yes that was the first step to add DATASETS_OFFLINE :)
With this PR, if a request times out (which couldn't happen before because no time out was set), it falls back on the local files with no error.
As you said, setting the timeout to something like 1e-16 makes the requests fail instantly, which is one step forward. One last thing left is to disable request retries and everything will be instant ! | [
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https://github.com/huggingface/datasets/pull/1952 | Handle timeouts | Ah, fantastic. Thank you for elucidating that this PR is part of a bigger master plan! | As noticed in https://github.com/huggingface/datasets/issues/1939, timeouts were not properly handled when loading a dataset.
This caused the connection to hang indefinitely when working in a firewalled environment cc @stas00
I added a default timeout, and included an option to our offline environment for tests to be able to simulate both connection errors and timeout errors (previously it was simulating connection errors only).
Now networks calls don't hang indefinitely.
The default timeout is set to 10sec (we might reduce it). | 16 | text: Handle timeouts
As noticed in https://github.com/huggingface/datasets/issues/1939, timeouts were not properly handled when loading a dataset.
This caused the connection to hang indefinitely when working in a firewalled environment cc @stas00
I added a default timeout, and included an option to our offline environment for tests to be able to simulate both connection errors and timeout errors (previously it was simulating connection errors only).
Now networks calls don't hang indefinitely.
The default timeout is set to 10sec (we might reduce it).
Ah, fantastic. Thank you for elucidating that this PR is part of a bigger master plan! | [
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https://github.com/huggingface/datasets/pull/1944 | Add Turkish News Category Dataset (270K - Lite Version) | > Thanks for changing to ClassLabel :)
> This is all good now !
>
> However I can see changes in other files than the ones for interpress_news_category_tr_lite, can you please fix that ?
> To do so you can create another branch and another PR to only include the interpress_news_category_tr_lite files.
>
> Maybe this happened because of a git rebase ? Once you've already pushed your code, please use git merge instead of rebase in order to avoid this.
Thanks for the feedback.
New PR https://github.com/huggingface/datasets/pull/1967 | This PR adds the Turkish News Categories Dataset (270K - Lite Version) dataset which is a text classification dataset by me, @basakbuluz and @serdarakyol.
This dataset contains the same news from the current [interpress_news_category_tr dataset](https://huggingface.co/datasets/interpress_news_category_tr) but contains less information, OCR errors are reduced, can be easily separated, and can be divided into 10 classes ("kültürsanat", "ekonomi", "siyaset", "eğitim", "dünya", "spor", "teknoloji", "magazin", "sağlık", "gündem") were rearranged.
@SBrandeis @lhoestq, can you please review this PR?
| 89 | text: Add Turkish News Category Dataset (270K - Lite Version)
This PR adds the Turkish News Categories Dataset (270K - Lite Version) dataset which is a text classification dataset by me, @basakbuluz and @serdarakyol.
This dataset contains the same news from the current [interpress_news_category_tr dataset](https://huggingface.co/datasets/interpress_news_category_tr) but contains less information, OCR errors are reduced, can be easily separated, and can be divided into 10 classes ("kültürsanat", "ekonomi", "siyaset", "eğitim", "dünya", "spor", "teknoloji", "magazin", "sağlık", "gündem") were rearranged.
@SBrandeis @lhoestq, can you please review this PR?
> Thanks for changing to ClassLabel :)
> This is all good now !
>
> However I can see changes in other files than the ones for interpress_news_category_tr_lite, can you please fix that ?
> To do so you can create another branch and another PR to only include the interpress_news_category_tr_lite files.
>
> Maybe this happened because of a git rebase ? Once you've already pushed your code, please use git merge instead of rebase in order to avoid this.
Thanks for the feedback.
New PR https://github.com/huggingface/datasets/pull/1967 | [
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https://github.com/huggingface/datasets/pull/1943 | Implement Dataset from JSON and JSON Lines | Thanks @lhoestq. I was trying to follow @thomwolf suggestion about integrating that script but as `from_json` method...
> Note that I don't think this is necessary a breaking change, we can still keep the old scripts around
Do you think there is a better way of doing it?
I was trying to implement more or less the same logic as in the script, but I confess I assumed the target was in-memory only... | Implement `Dataset.from_jsonl`. | 73 | text: Implement Dataset from JSON and JSON Lines
Implement `Dataset.from_jsonl`.
Thanks @lhoestq. I was trying to follow @thomwolf suggestion about integrating that script but as `from_json` method...
> Note that I don't think this is necessary a breaking change, we can still keep the old scripts around
Do you think there is a better way of doing it?
I was trying to implement more or less the same logic as in the script, but I confess I assumed the target was in-memory only... | [
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https://github.com/huggingface/datasets/pull/1943 | Implement Dataset from JSON and JSON Lines | Basically, I was trying to reimplement `Json(datasets.ArrowBasedBuilder)._generate_tables`, and no writing to arrow file (I assumed only in-memory usage). I started with the first "else" clause...
I was planning to remove my `_cast_table_to_info_features` and use `paj.read_json(parse_options=...)` instead (like in the script). | Implement `Dataset.from_jsonl`. | 40 | text: Implement Dataset from JSON and JSON Lines
Implement `Dataset.from_jsonl`.
Basically, I was trying to reimplement `Json(datasets.ArrowBasedBuilder)._generate_tables`, and no writing to arrow file (I assumed only in-memory usage). I started with the first "else" clause...
I was planning to remove my `_cast_table_to_info_features` and use `paj.read_json(parse_options=...)` instead (like in the script). | [
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https://github.com/huggingface/datasets/pull/1943 | Implement Dataset from JSON and JSON Lines | What's the issue exactly ? Apparently it's correctly passed to as_dataset so I don't find the issue | Implement `Dataset.from_jsonl`. | 17 | text: Implement Dataset from JSON and JSON Lines
Implement `Dataset.from_jsonl`.
What's the issue exactly ? Apparently it's correctly passed to as_dataset so I don't find the issue | [
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] |
https://github.com/huggingface/datasets/pull/1943 | Implement Dataset from JSON and JSON Lines | <s>merging master into this branch should fix the CI issue :)</s>
Oops I didn't refresh the page sorry ^^'
Looks all good ! | Implement `Dataset.from_jsonl`. | 23 | text: Implement Dataset from JSON and JSON Lines
Implement `Dataset.from_jsonl`.
<s>merging master into this branch should fix the CI issue :)</s>
Oops I didn't refresh the page sorry ^^'
Looks all good ! | [
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https://github.com/huggingface/datasets/pull/1943 | Implement Dataset from JSON and JSON Lines | Good job ! I think we can merge after the last changes regarding the error message and the docstring above :) | Implement `Dataset.from_jsonl`. | 21 | text: Implement Dataset from JSON and JSON Lines
Implement `Dataset.from_jsonl`.
Good job ! I think we can merge after the last changes regarding the error message and the docstring above :) | [
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] |
https://github.com/huggingface/datasets/pull/1936 | [WIP] Adding Support for Reading Pandas Category | Thanks ! could you maybe add a few tests in test_arrow_dataset.py to make sure from_pandas works as expected with categorical types ?
In particular I'm pretty sure that if you now try to `cast` the dataset to the same features at its current features, it will break instead of just being a no-op.
This is because `features.type` returns an arrow int64 type for the classlabel column instead of the arrow dictionary type that you have in the arrow table. There are two issues in this case:
- it will try to replace the arrow type from dictionary to int64 instead of being a no-op
- it will crash because pyarrow is not able to cast a dictionary to int64 (even if it's actually possible do cast the column by hand by accessing the sub-array of the dictionary array containing the indices/integers)
It would be awesome to fix this case ! Ideally the arrow `pa_type` of classlabel ([here](https://github.com/huggingface/datasets/blob/7072e1becd69d421d863374b825e3da4c6551798/src/datasets/features.py#L558)) should be an arrow dictionary type. This should fix the issue. Then we can start working on backward compatibility.
Let me know if you have questions or if I can help.
In particular if there is some glue-ing to do I can take care of that if you want ;)
--------------
Also just a few information regarding the functions you mentioned
`int2str` and `str2int` are used by users to transforms the labels if they want to. Here sine ClassLabel is instantiated without the class names, they would crash. I was about to make a PR to disallow the creation of an empty ClassLabel feature type.
Therefore can you provide class_names= when creating the ClassLabel ?
`encode_example` is mostly used with a dataset builder (e.g. squad.py) so it's not used when using .from_pandas.
| @lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself. | 289 | text: [WIP] Adding Support for Reading Pandas Category
@lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself.
Thanks ! could you maybe add a few tests in test_arrow_dataset.py to make sure from_pandas works as expected with categorical types ?
In particular I'm pretty sure that if you now try to `cast` the dataset to the same features at its current features, it will break instead of just being a no-op.
This is because `features.type` returns an arrow int64 type for the classlabel column instead of the arrow dictionary type that you have in the arrow table. There are two issues in this case:
- it will try to replace the arrow type from dictionary to int64 instead of being a no-op
- it will crash because pyarrow is not able to cast a dictionary to int64 (even if it's actually possible do cast the column by hand by accessing the sub-array of the dictionary array containing the indices/integers)
It would be awesome to fix this case ! Ideally the arrow `pa_type` of classlabel ([here](https://github.com/huggingface/datasets/blob/7072e1becd69d421d863374b825e3da4c6551798/src/datasets/features.py#L558)) should be an arrow dictionary type. This should fix the issue. Then we can start working on backward compatibility.
Let me know if you have questions or if I can help.
In particular if there is some glue-ing to do I can take care of that if you want ;)
--------------
Also just a few information regarding the functions you mentioned
`int2str` and `str2int` are used by users to transforms the labels if they want to. Here sine ClassLabel is instantiated without the class names, they would crash. I was about to make a PR to disallow the creation of an empty ClassLabel feature type.
Therefore can you provide class_names= when creating the ClassLabel ?
`encode_example` is mostly used with a dataset builder (e.g. squad.py) so it's not used when using .from_pandas.
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https://github.com/huggingface/datasets/pull/1936 | [WIP] Adding Support for Reading Pandas Category | Got it - that's super helpful, I was trying to figure out what would break!
I think there are two issues we're discussing here:
1. modifying the pa_type of ClassLabel: totally agree with you on that one if that's OK from a back-compat perspective. (i.e. are users of `datasets` not supposed to access or use the .pa_type attribute of ClassLabel?)
2. creating a ClassLabel requires information that's not present on the pa.DictionaryType object: I think the crux of the problem is that at this line (https://github.com/huggingface/datasets/pull/1936/files#diff-54081ede051fd0a7ef65748c481cc06f90209f01bb89968747089d13a2ca052bR933) - you only have access to the `pa_type`, which is `DictionaryType[int8, string]`. I've unpacked it and looked at all of the available methods, and I don't believe that any of the actual values ("names") are present - those are stored on the `pyarrow.DictArray.dictionary` attribute (i.e. as data, not on the pyarrow.DataType) - so in order to actually be able to instantiate the ClassLabel with the names= parameter, we need to pass in more information to this method.
We *could* mostly accomplish this by modifying https://github.com/huggingface/datasets/pull/1936/files#diff-54081ede051fd0a7ef65748c481cc06f90209f01bb89968747089d13a2ca052bR909 to accept a pyarrow Table in addition to the type, and it's not too difficult to do, but it feels a little bit off to me:
- It feels a bit off that a "schema" definition will change depending on what data gets added to the dataset. In particular, if someone adds rows or concatenates two datasets, the ClassLabel "names" will also need to change, right? I think maybe we're getting around this because a Dataset is immutable (I think?) and so any new dataset is freshly constructed, but for example - I think this check wouldn't work for `ClassLabel`s if we were to compare the `Dataset.features` instead of the underlying pyarrow type https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L2664
- To that end I wonder if ClassLabel should actually just be the "type" akin to Category, and the "names" should be considered "data" and not part of the "type"? Similar to how pyarrow maintains two data objects - the array of indices and the array of string values.
With that in mind, I'm wondering if you *should* allow an empty ClassLabel (and`int2str`, etc. can be updated to have more descriptive error messages if labels aren't provided or inferred), and if the underlying data is a pa.DictionaryType, then the names can be inferred and applied at these points in the code:
- https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L274
- https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L686
- https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L673
I think perhaps the mismatch here is when the data is stored on disk as an int there should be a convenient way of saying "this is a dictionary and here are some explicitly provided labels", whereas when it's stored as a string, we'd ideally like to say "this is a Category and please condense the representation and automatically infer the labels".
Sorry for the long comment! Hopefully my thoughts make sense - thanks for taking the time to discuss! | @lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself. | 472 | text: [WIP] Adding Support for Reading Pandas Category
@lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself.
Got it - that's super helpful, I was trying to figure out what would break!
I think there are two issues we're discussing here:
1. modifying the pa_type of ClassLabel: totally agree with you on that one if that's OK from a back-compat perspective. (i.e. are users of `datasets` not supposed to access or use the .pa_type attribute of ClassLabel?)
2. creating a ClassLabel requires information that's not present on the pa.DictionaryType object: I think the crux of the problem is that at this line (https://github.com/huggingface/datasets/pull/1936/files#diff-54081ede051fd0a7ef65748c481cc06f90209f01bb89968747089d13a2ca052bR933) - you only have access to the `pa_type`, which is `DictionaryType[int8, string]`. I've unpacked it and looked at all of the available methods, and I don't believe that any of the actual values ("names") are present - those are stored on the `pyarrow.DictArray.dictionary` attribute (i.e. as data, not on the pyarrow.DataType) - so in order to actually be able to instantiate the ClassLabel with the names= parameter, we need to pass in more information to this method.
We *could* mostly accomplish this by modifying https://github.com/huggingface/datasets/pull/1936/files#diff-54081ede051fd0a7ef65748c481cc06f90209f01bb89968747089d13a2ca052bR909 to accept a pyarrow Table in addition to the type, and it's not too difficult to do, but it feels a little bit off to me:
- It feels a bit off that a "schema" definition will change depending on what data gets added to the dataset. In particular, if someone adds rows or concatenates two datasets, the ClassLabel "names" will also need to change, right? I think maybe we're getting around this because a Dataset is immutable (I think?) and so any new dataset is freshly constructed, but for example - I think this check wouldn't work for `ClassLabel`s if we were to compare the `Dataset.features` instead of the underlying pyarrow type https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L2664
- To that end I wonder if ClassLabel should actually just be the "type" akin to Category, and the "names" should be considered "data" and not part of the "type"? Similar to how pyarrow maintains two data objects - the array of indices and the array of string values.
With that in mind, I'm wondering if you *should* allow an empty ClassLabel (and`int2str`, etc. can be updated to have more descriptive error messages if labels aren't provided or inferred), and if the underlying data is a pa.DictionaryType, then the names can be inferred and applied at these points in the code:
- https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L274
- https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L686
- https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L673
I think perhaps the mismatch here is when the data is stored on disk as an int there should be a convenient way of saying "this is a dictionary and here are some explicitly provided labels", whereas when it's stored as a string, we'd ideally like to say "this is a Category and please condense the representation and automatically infer the labels".
Sorry for the long comment! Hopefully my thoughts make sense - thanks for taking the time to discuss! | [
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https://github.com/huggingface/datasets/pull/1936 | [WIP] Adding Support for Reading Pandas Category | Yes that makes sense. I completely forgot that the label names of an arrow Dictionary type were not stored in the type but in the DictionaryArray.
This is made me realize that it's actually pretty unpractical and I feel that handling this can add unnecessary complexity in the handling of dtypes.
More specifically:
- it's not possible to create a DictionaryArray from a call to pyarrow.array with python objects, which is the function we use to convert python objects to pyarrow objects (or we would need to convert the python objects to pandas categorical series beforehand but it doesn't work for nested types)
- casting nested types containing Dictionary types would require a lot of array manipulations since it's not compatible with pyarrow.array.cast
I feel like the original feature request (support of pandas Categorical) should be addressable without adding so much complexity to the library.
If we admit that we don't want to deal with arrow Dictionary type, maybe we can simply convert the pandas categorical series to an int64 series and set the feature type to the right ClassLabel in `from_pandas`. We can have the reverse operation in `to_pandas`. This way we don't need to support the arrow DictionaryType and so we can keep simple/accessible code for conversion from python to arrow and also for type casting. Let me know what you think.
In the future depending on the usage of the ClassLabel types with pandas/pyarrow we might reconsider this but for now I believe this simple solution is enough. | @lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself. | 251 | text: [WIP] Adding Support for Reading Pandas Category
@lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself.
Yes that makes sense. I completely forgot that the label names of an arrow Dictionary type were not stored in the type but in the DictionaryArray.
This is made me realize that it's actually pretty unpractical and I feel that handling this can add unnecessary complexity in the handling of dtypes.
More specifically:
- it's not possible to create a DictionaryArray from a call to pyarrow.array with python objects, which is the function we use to convert python objects to pyarrow objects (or we would need to convert the python objects to pandas categorical series beforehand but it doesn't work for nested types)
- casting nested types containing Dictionary types would require a lot of array manipulations since it's not compatible with pyarrow.array.cast
I feel like the original feature request (support of pandas Categorical) should be addressable without adding so much complexity to the library.
If we admit that we don't want to deal with arrow Dictionary type, maybe we can simply convert the pandas categorical series to an int64 series and set the feature type to the right ClassLabel in `from_pandas`. We can have the reverse operation in `to_pandas`. This way we don't need to support the arrow DictionaryType and so we can keep simple/accessible code for conversion from python to arrow and also for type casting. Let me know what you think.
In the future depending on the usage of the ClassLabel types with pandas/pyarrow we might reconsider this but for now I believe this simple solution is enough. | [
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https://github.com/huggingface/datasets/pull/1936 | [WIP] Adding Support for Reading Pandas Category | OK! I just whipped up the `from_pandas()` portion of this PR, and it works, though I'm not *super* familiar with the available APIs so I'm not sure if there's a more "vectorized" way of doing all of these updates - so happy to get some feedback and iterate!
Apologies for multiple commits - I realized how to solve a few different problems right after I gave up and pushed with the intent to ask for help :-)
I wanted to get some guidance on how to handle the reverse direction: I think there are two main areas to look at, `.to_pandas()` and also `.set_format('pandas')` and then pulling out a dataframe like so: `dataset[:]`. Is there a single place where I can handle both of these cases at once or do these need to be handled independently? | @lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself. | 136 | text: [WIP] Adding Support for Reading Pandas Category
@lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself.
OK! I just whipped up the `from_pandas()` portion of this PR, and it works, though I'm not *super* familiar with the available APIs so I'm not sure if there's a more "vectorized" way of doing all of these updates - so happy to get some feedback and iterate!
Apologies for multiple commits - I realized how to solve a few different problems right after I gave up and pushed with the intent to ask for help :-)
I wanted to get some guidance on how to handle the reverse direction: I think there are two main areas to look at, `.to_pandas()` and also `.set_format('pandas')` and then pulling out a dataframe like so: `dataset[:]`. Is there a single place where I can handle both of these cases at once or do these need to be handled independently? | [
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https://github.com/huggingface/datasets/pull/1936 | [WIP] Adding Support for Reading Pandas Category | Thanks ! This is awesome :)
Could you also add a test ? There is already `test_to_pandas` in test_arrow_dataset.py
Feel free to complete this test to make sure it works for Categorical :)
To make it work with the "pandas" formating (when you do `set_format("pandas")` and then query `dataset[0]`, `dataset[:]`, etc.), you can take a look and the `PandasFormatter` in formatting.py
It takes a pyarrow table as input of its formatting methods (one method for rows, one for columns and one for batches) and returns a pandas DataFrame (or a Series for the method for formatting a column). You can cast to Categorical in each one of the formatter methods and it should work directly when you use a pandas-formatted dataset.
This formatter can then also be used in `to_pandas` (currently it does `pa_table.to_pandas()` but `PandasFormatter().format_batch(pa_table)` can be used instead). | @lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself. | 140 | text: [WIP] Adding Support for Reading Pandas Category
@lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself.
Thanks ! This is awesome :)
Could you also add a test ? There is already `test_to_pandas` in test_arrow_dataset.py
Feel free to complete this test to make sure it works for Categorical :)
To make it work with the "pandas" formating (when you do `set_format("pandas")` and then query `dataset[0]`, `dataset[:]`, etc.), you can take a look and the `PandasFormatter` in formatting.py
It takes a pyarrow table as input of its formatting methods (one method for rows, one for columns and one for batches) and returns a pandas DataFrame (or a Series for the method for formatting a column). You can cast to Categorical in each one of the formatter methods and it should work directly when you use a pandas-formatted dataset.
This formatter can then also be used in `to_pandas` (currently it does `pa_table.to_pandas()` but `PandasFormatter().format_batch(pa_table)` can be used instead). | [
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https://github.com/huggingface/datasets/pull/1931 | add m_lama (multilingual lama) dataset | Hi, it seems I am somewhat stuck here. The failed test `ci/circleci: run_dataset_script_tests_pyarrow_1_WIN` seems to be caused by some broken connection (`ConnectionResetError: [WinError 10054] An existing connection was forcibly closed by the remote host`). Any help on this is appreciated.
Edit: Seems to be resolved now. | Add a multilingual (machine translated and automatically generated) version of the LAMA benchmark. For details see the paper https://arxiv.org/pdf/2102.00894.pdf | 46 | text: add m_lama (multilingual lama) dataset
Add a multilingual (machine translated and automatically generated) version of the LAMA benchmark. For details see the paper https://arxiv.org/pdf/2102.00894.pdf
Hi, it seems I am somewhat stuck here. The failed test `ci/circleci: run_dataset_script_tests_pyarrow_1_WIN` seems to be caused by some broken connection (`ConnectionResetError: [WinError 10054] An existing connection was forcibly closed by the remote host`). Any help on this is appreciated.
Edit: Seems to be resolved now. | [
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https://github.com/huggingface/datasets/pull/1931 | add m_lama (multilingual lama) dataset | I guess the `dummy_data.zip` is too large. I can reduce the languages that are contained there, but when testing it, it obviously throws an error, as not all files can be found. I guess I can either i) change the default value regarding which languages are loaded or ii) let the `_generate_examples` silently skip any language for which it cannot find files. Both solutions are not really pretty - is there another way around this? | Add a multilingual (machine translated and automatically generated) version of the LAMA benchmark. For details see the paper https://arxiv.org/pdf/2102.00894.pdf | 75 | text: add m_lama (multilingual lama) dataset
Add a multilingual (machine translated and automatically generated) version of the LAMA benchmark. For details see the paper https://arxiv.org/pdf/2102.00894.pdf
I guess the `dummy_data.zip` is too large. I can reduce the languages that are contained there, but when testing it, it obviously throws an error, as not all files can be found. I guess I can either i) change the default value regarding which languages are loaded or ii) let the `_generate_examples` silently skip any language for which it cannot find files. Both solutions are not really pretty - is there another way around this? | [
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https://github.com/huggingface/datasets/pull/1931 | add m_lama (multilingual lama) dataset | Thanks for the review and the constructive comments :) ! I tried to address them, and reduced the number of lines in the dummy data to 1 to reduce its size. | Add a multilingual (machine translated and automatically generated) version of the LAMA benchmark. For details see the paper https://arxiv.org/pdf/2102.00894.pdf | 31 | text: add m_lama (multilingual lama) dataset
Add a multilingual (machine translated and automatically generated) version of the LAMA benchmark. For details see the paper https://arxiv.org/pdf/2102.00894.pdf
Thanks for the review and the constructive comments :) ! I tried to address them, and reduced the number of lines in the dummy data to 1 to reduce its size. | [
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https://github.com/huggingface/datasets/pull/1930 | updated the wino_bias dataset | Hi @JieyuZhao ! Have you had a chance to add the different configurations ?
Thanks again for your help on this ! | Updated the wino_bias.py script.
- updated the data_url
- added different configurations for different data splits
- added the coreference_cluster to the data features | 22 | text: updated the wino_bias dataset
Updated the wino_bias.py script.
- updated the data_url
- added different configurations for different data splits
- added the coreference_cluster to the data features
Hi @JieyuZhao ! Have you had a chance to add the different configurations ?
Thanks again for your help on this ! | [
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https://github.com/huggingface/datasets/pull/1930 | updated the wino_bias dataset | > Hi @JieyuZhao ! Have you had a chance to add the different configurations ?
> Thanks again for your help on this !
Hi @lhoestq Yes, I've updated the code. Now the configuration will have dev/test splits. | Updated the wino_bias.py script.
- updated the data_url
- added different configurations for different data splits
- added the coreference_cluster to the data features | 38 | text: updated the wino_bias dataset
Updated the wino_bias.py script.
- updated the data_url
- added different configurations for different data splits
- added the coreference_cluster to the data features
> Hi @JieyuZhao ! Have you had a chance to add the different configurations ?
> Thanks again for your help on this !
Hi @lhoestq Yes, I've updated the code. Now the configuration will have dev/test splits. | [
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https://github.com/huggingface/datasets/pull/1930 | updated the wino_bias dataset | > Cool thanks !
> This looks perfect this way.
>
> Now we just need to update the dataset_infos.json (it contains the metadata of the dataset) and add dummy data to be able to test this script automatically.
>
> To update the dataset_infos.json you just need delete the current one at `./datasets/wino_biais/dataset_infos.json`, and then run this command:
>
> ```
> datasets-cli test ./datasets/wino_biais --save_infos --all_configs --ignore_verifications
> ```
>
> To add the dummy data there's also a tool to add them automatically.
> First delete the folder at `./datasets/wino_biais/dummy` and then run
>
> ```
> datasets-cli dummy_data ./datasets/wino_biais --auto_generate --match_text_files "*conll" --n_lines 15
> ```
>
> Let me know if you have questions :)
> Also don't forget to run `make style` to format the code properly.
Thanks for the instruction! I've updated the metadata and the dummy data and also do the formatting. Please let me know if more is needed. :) | Updated the wino_bias.py script.
- updated the data_url
- added different configurations for different data splits
- added the coreference_cluster to the data features | 159 | text: updated the wino_bias dataset
Updated the wino_bias.py script.
- updated the data_url
- added different configurations for different data splits
- added the coreference_cluster to the data features
> Cool thanks !
> This looks perfect this way.
>
> Now we just need to update the dataset_infos.json (it contains the metadata of the dataset) and add dummy data to be able to test this script automatically.
>
> To update the dataset_infos.json you just need delete the current one at `./datasets/wino_biais/dataset_infos.json`, and then run this command:
>
> ```
> datasets-cli test ./datasets/wino_biais --save_infos --all_configs --ignore_verifications
> ```
>
> To add the dummy data there's also a tool to add them automatically.
> First delete the folder at `./datasets/wino_biais/dummy` and then run
>
> ```
> datasets-cli dummy_data ./datasets/wino_biais --auto_generate --match_text_files "*conll" --n_lines 15
> ```
>
> Let me know if you have questions :)
> Also don't forget to run `make style` to format the code properly.
Thanks for the instruction! I've updated the metadata and the dummy data and also do the formatting. Please let me know if more is needed. :) | [
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https://github.com/huggingface/datasets/pull/1925 | Fix: Wiki_dpr - fix when with_embeddings is False or index_name is "no_index" | Hi @lhoestq ,
I am running into an issue now when trying to run RAG. Running exactly as described [here](https://huggingface.co/facebook/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage) I get the error below. Wondering if it's related to this.
Running Transformers 4.3.2 with datasets installed from source from `master` branch.
```bash
(venv) sergey_mkrtchyan datasets (master) $ python
Python 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)
[Clang 6.0 (clang-600.0.57)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
>>> tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
>>> retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
Using custom data configuration dummy.psgs_w100.nq.no_index-dummy=True,with_index=False
Reusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.no_index-dummy=True,with_index=False/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)
Using custom data configuration dummy.psgs_w100.nq.exact-50b6cda57ff32ab4
Reusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.exact-50b6cda57ff32ab4/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)
0%| | 0/10 [00:00<?, ?it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 425, in from_pretrained
return cls(
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 387, in __init__
self.init_retrieval()
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 458, in init_retrieval
self.index.init_index()
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 284, in init_index
self.dataset = load_dataset(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/load.py", line 750, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py", line 734, in as_dataset
datasets = utils.map_nested(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py", line 769, in _build_single_dataset
post_processed = self._post_process(ds, resources_paths)
File "/Users/sergey_mkrtchyan/.cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb/wiki_dpr.py", line 205, in _post_process
dataset.add_faiss_index("embeddings", custom_index=index)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/arrow_dataset.py", line 2516, in add_faiss_index
super().add_faiss_index(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py", line 416, in add_faiss_index
faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=faiss_verbose)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py", line 281, in add_vectors
self.faiss_index.add(vecs)
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/__init__.py", line 104, in replacement_add
self.add_c(n, swig_ptr(x))
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/swigfaiss.py", line 3263, in add
return _swigfaiss.IndexHNSW_add(self, n, x)
RuntimeError: Error in virtual void faiss::IndexHNSW::add(faiss::Index::idx_t, const float *) at /Users/runner/work/faiss-wheels/faiss-wheels/faiss/faiss/IndexHNSW.cpp:356: Error: 'is_trained' failed
>>>
```
The error message is hinting that it could be related to this, but I might be wrong. Any ideas?
Edit: Can confirm it's working fine with datasets==1.2.0
Double Edit: Did some further digging. The issue is related to this commit: 8c5220307c33f00e01c3bf7b8. I opened a separate issue #1941 for proper tracking. | Fix the bugs noticed in #1915
There was a bug when `with_embeddings=False` where the configuration name was the same as if `with_embeddings=True`, which led the dataset builder to do bad verifications (for example it used to expect to download the embeddings for `with_embeddings=False`).
Another issue was that setting `index_name="no_index"` didn't set `with_index` to False.
I fixed both of them and added dummy data for those configurations for testing. | 306 | text: Fix: Wiki_dpr - fix when with_embeddings is False or index_name is "no_index"
Fix the bugs noticed in #1915
There was a bug when `with_embeddings=False` where the configuration name was the same as if `with_embeddings=True`, which led the dataset builder to do bad verifications (for example it used to expect to download the embeddings for `with_embeddings=False`).
Another issue was that setting `index_name="no_index"` didn't set `with_index` to False.
I fixed both of them and added dummy data for those configurations for testing.
Hi @lhoestq ,
I am running into an issue now when trying to run RAG. Running exactly as described [here](https://huggingface.co/facebook/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage) I get the error below. Wondering if it's related to this.
Running Transformers 4.3.2 with datasets installed from source from `master` branch.
```bash
(venv) sergey_mkrtchyan datasets (master) $ python
Python 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)
[Clang 6.0 (clang-600.0.57)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
>>> tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
>>> retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
Using custom data configuration dummy.psgs_w100.nq.no_index-dummy=True,with_index=False
Reusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.no_index-dummy=True,with_index=False/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)
Using custom data configuration dummy.psgs_w100.nq.exact-50b6cda57ff32ab4
Reusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.exact-50b6cda57ff32ab4/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)
0%| | 0/10 [00:00<?, ?it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 425, in from_pretrained
return cls(
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 387, in __init__
self.init_retrieval()
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 458, in init_retrieval
self.index.init_index()
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 284, in init_index
self.dataset = load_dataset(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/load.py", line 750, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py", line 734, in as_dataset
datasets = utils.map_nested(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py", line 769, in _build_single_dataset
post_processed = self._post_process(ds, resources_paths)
File "/Users/sergey_mkrtchyan/.cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb/wiki_dpr.py", line 205, in _post_process
dataset.add_faiss_index("embeddings", custom_index=index)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/arrow_dataset.py", line 2516, in add_faiss_index
super().add_faiss_index(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py", line 416, in add_faiss_index
faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=faiss_verbose)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py", line 281, in add_vectors
self.faiss_index.add(vecs)
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/__init__.py", line 104, in replacement_add
self.add_c(n, swig_ptr(x))
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/swigfaiss.py", line 3263, in add
return _swigfaiss.IndexHNSW_add(self, n, x)
RuntimeError: Error in virtual void faiss::IndexHNSW::add(faiss::Index::idx_t, const float *) at /Users/runner/work/faiss-wheels/faiss-wheels/faiss/faiss/IndexHNSW.cpp:356: Error: 'is_trained' failed
>>>
```
The error message is hinting that it could be related to this, but I might be wrong. Any ideas?
Edit: Can confirm it's working fine with datasets==1.2.0
Double Edit: Did some further digging. The issue is related to this commit: 8c5220307c33f00e01c3bf7b8. I opened a separate issue #1941 for proper tracking. | [
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https://github.com/huggingface/datasets/pull/1921 | Standardizing datasets dtypes | @lhoestq - apologies for the multiple PRs, my previous one (#1905) got mangled due to some merge conflicts that I had trouble resolving so I just cherry-picked my changes onto a fresh branch here. | This PR follows up on discussion in #1900 to have an explicit set of basic dtypes for datasets.
This moves away from str(pyarrow.DataType) as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes.
I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here, with `float32` and `float64` acting as the official datasets dtypes, which resolves the tension between `double` being the pyarrow dtype and `float64` being the pyarrow type factory function. | 34 | text: Standardizing datasets dtypes
This PR follows up on discussion in #1900 to have an explicit set of basic dtypes for datasets.
This moves away from str(pyarrow.DataType) as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes.
I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here, with `float32` and `float64` acting as the official datasets dtypes, which resolves the tension between `double` being the pyarrow dtype and `float64` being the pyarrow type factory function.
@lhoestq - apologies for the multiple PRs, my previous one (#1905) got mangled due to some merge conflicts that I had trouble resolving so I just cherry-picked my changes onto a fresh branch here. | [
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https://github.com/huggingface/datasets/pull/1920 | Fix save_to_disk issue | So I was curious why the issue reported at #1919 wasn't caught in [this test](https://github.com/huggingface/datasets/blob/248104c4bdb2e01c036b7578867199191fbff181/tests/test_arrow_dataset.py#L209), so I did some digging.
I tried to save to a temporary directory (just like in the test), like this:
```python
with tempfile.TemporaryDirectory() as requested_tempdir:
squad.save_to_disk(requested_tempdir) # no error
```
and it executes succesfuly without problems.
So why does it work, but this doesn't?
```python
squad.save_to_disk("./squad") # error
```
It's because `save_to_disk` also creates a temporary directory (let's call it `tempdir`), and since `tempdir` and `requested_tempdir` share the same parents, the `Path.joinpath` method [(here)](https://github.com/huggingface/datasets/blob/248104c4bdb2e01c036b7578867199191fbff181/src/datasets/arrow_dataset.py#L469) will keep `requested_tempdir` as it is and the *train* directory will be created under `requested_tempdir` and hence no errors will arise.
But in the second case (where we are saving to a local dir), the *train* directory is created under *squad* which in turn is created under `tempdir`, not under `.` (current dir).
So, all of this probably doesn't help solving the issue but it might help creating a better test, and it also makes me wonder why are we saving to a temporary dir in `save_to_disk` anyway? I mean, won't it be removed with all its contents upon execution completion? what's the point then? | Fixes #1919
| 194 | text: Fix save_to_disk issue
Fixes #1919
So I was curious why the issue reported at #1919 wasn't caught in [this test](https://github.com/huggingface/datasets/blob/248104c4bdb2e01c036b7578867199191fbff181/tests/test_arrow_dataset.py#L209), so I did some digging.
I tried to save to a temporary directory (just like in the test), like this:
```python
with tempfile.TemporaryDirectory() as requested_tempdir:
squad.save_to_disk(requested_tempdir) # no error
```
and it executes succesfuly without problems.
So why does it work, but this doesn't?
```python
squad.save_to_disk("./squad") # error
```
It's because `save_to_disk` also creates a temporary directory (let's call it `tempdir`), and since `tempdir` and `requested_tempdir` share the same parents, the `Path.joinpath` method [(here)](https://github.com/huggingface/datasets/blob/248104c4bdb2e01c036b7578867199191fbff181/src/datasets/arrow_dataset.py#L469) will keep `requested_tempdir` as it is and the *train* directory will be created under `requested_tempdir` and hence no errors will arise.
But in the second case (where we are saving to a local dir), the *train* directory is created under *squad* which in turn is created under `tempdir`, not under `.` (current dir).
So, all of this probably doesn't help solving the issue but it might help creating a better test, and it also makes me wonder why are we saving to a temporary dir in `save_to_disk` anyway? I mean, won't it be removed with all its contents upon execution completion? what's the point then? | [
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https://github.com/huggingface/datasets/pull/1913 | Add keep_linebreaks parameter to text loader | Just so I understand how it can be used in practice, do you have an example showing how to load a text dataset with this option? | As asked in #870 and https://github.com/huggingface/transformers/issues/10269 there should be a parameter to keep the linebreaks when loading a text dataset.
cc @sgugger @jncasey | 26 | text: Add keep_linebreaks parameter to text loader
As asked in #870 and https://github.com/huggingface/transformers/issues/10269 there should be a parameter to keep the linebreaks when loading a text dataset.
cc @sgugger @jncasey
Just so I understand how it can be used in practice, do you have an example showing how to load a text dataset with this option? | [
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https://github.com/huggingface/datasets/pull/1913 | Add keep_linebreaks parameter to text loader | Sure ! Here is an example:
```python
from datasets import load_dataset
load_dataset("text", keep_linebreaks=True, data_files=...)
```
I'll update the documentation to explain this | As asked in #870 and https://github.com/huggingface/transformers/issues/10269 there should be a parameter to keep the linebreaks when loading a text dataset.
cc @sgugger @jncasey | 22 | text: Add keep_linebreaks parameter to text loader
As asked in #870 and https://github.com/huggingface/transformers/issues/10269 there should be a parameter to keep the linebreaks when loading a text dataset.
cc @sgugger @jncasey
Sure ! Here is an example:
```python
from datasets import load_dataset
load_dataset("text", keep_linebreaks=True, data_files=...)
```
I'll update the documentation to explain this | [
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https://github.com/huggingface/datasets/pull/1910 | Adding CoNLLpp dataset. | It looks like this PR now includes changes to many other files than the ones for CoNLLpp.
To fix that feel free to create another branch and another PR.
This was probably caused by a git rebase. You can avoid this issue by using git merge if you've already pushed your branch. | 52 | text: Adding CoNLLpp dataset.
It looks like this PR now includes changes to many other files than the ones for CoNLLpp.
To fix that feel free to create another branch and another PR.
This was probably caused by a git rebase. You can avoid this issue by using git merge if you've already pushed your branch. | [
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|
https://github.com/huggingface/datasets/pull/1905 | Standardizing datasets.dtypes | Also - I took a stab at updating the docs, but I'm not sure how to actually check the outputs to see if it's formatted properly. | This PR was further branched off of jdy-str-to-pyarrow-parsing, so it depends on https://github.com/huggingface/datasets/pull/1900 going first for the diff to be up-to-date (I'm not sure if there's a way for me to use jdy-str-to-pyarrow-parsing as a base branch while having it appear in the pull requests here).
This moves away from `str(pyarrow.DataType)` as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes.
I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here. | 26 | text: Standardizing datasets.dtypes
This PR was further branched off of jdy-str-to-pyarrow-parsing, so it depends on https://github.com/huggingface/datasets/pull/1900 going first for the diff to be up-to-date (I'm not sure if there's a way for me to use jdy-str-to-pyarrow-parsing as a base branch while having it appear in the pull requests here).
This moves away from `str(pyarrow.DataType)` as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes.
I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here.
Also - I took a stab at updating the docs, but I'm not sure how to actually check the outputs to see if it's formatted properly. | [
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https://github.com/huggingface/datasets/pull/1903 | Initial commit for the addition of TIMIT dataset | @lhoestq Thank you so much for your comments and for patiently reviewing the code. Have _hopefully_ included all the suggested changes. Let me know if any more changes are required.
Sorry the code had lots of silly errors from my side!:' Will be more careful from next time! :)
| Below points needs to be addressed:
- Creation of dummy dataset is failing
- Need to check on the data representation
- License is not creative commons. Copyright: Portions © 1993 Trustees of the University of Pennsylvania
Also the links (_except the download_) point to the ami corpus! ;-)
@patrickvonplaten Requesting your comments, will be happy to address them! | 49 | text: Initial commit for the addition of TIMIT dataset
Below points needs to be addressed:
- Creation of dummy dataset is failing
- Need to check on the data representation
- License is not creative commons. Copyright: Portions © 1993 Trustees of the University of Pennsylvania
Also the links (_except the download_) point to the ami corpus! ;-)
@patrickvonplaten Requesting your comments, will be happy to address them!
@lhoestq Thank you so much for your comments and for patiently reviewing the code. Have _hopefully_ included all the suggested changes. Let me know if any more changes are required.
Sorry the code had lots of silly errors from my side!:' Will be more careful from next time! :)
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https://github.com/huggingface/datasets/pull/1900 | Issue #1895: Bugfix for string_to_arrow timestamp[ns] support | OK! Thank you for the review - I will follow up with a separate PR for the comments here (https://github.com/huggingface/datasets/pull/1900#discussion_r578319725)! | Should resolve https://github.com/huggingface/datasets/issues/1895
The main part of this PR adds additional parsing in `string_to_arrow` to convert the timestamp dtypes that result from `str(pa_type)` back into the pa.DataType TimestampType.
While adding unit-testing, I noticed that support for the double/float types also don't invert correctly, so I added them, which I believe would hypothetically make this section of `Value` redundant:
```
def __post_init__(self):
if self.dtype == "double": # fix inferred type
self.dtype = "float64"
if self.dtype == "float": # fix inferred type
self.dtype = "float32"
```
However, since I think Value.dtype is part of the public interface, removing that would result in a backward-incompatible change, so I didn't muck with that.
The rest of the PR consists of docstrings that I added while developing locally so I could keep track of which functions were supposed to be inverses of each other, and thought I'd include them initially in case you want to keep them around, but I'm happy to delete or remove any of them at your request! | 20 | text: Issue #1895: Bugfix for string_to_arrow timestamp[ns] support
Should resolve https://github.com/huggingface/datasets/issues/1895
The main part of this PR adds additional parsing in `string_to_arrow` to convert the timestamp dtypes that result from `str(pa_type)` back into the pa.DataType TimestampType.
While adding unit-testing, I noticed that support for the double/float types also don't invert correctly, so I added them, which I believe would hypothetically make this section of `Value` redundant:
```
def __post_init__(self):
if self.dtype == "double": # fix inferred type
self.dtype = "float64"
if self.dtype == "float": # fix inferred type
self.dtype = "float32"
```
However, since I think Value.dtype is part of the public interface, removing that would result in a backward-incompatible change, so I didn't muck with that.
The rest of the PR consists of docstrings that I added while developing locally so I could keep track of which functions were supposed to be inverses of each other, and thought I'd include them initially in case you want to keep them around, but I'm happy to delete or remove any of them at your request!
OK! Thank you for the review - I will follow up with a separate PR for the comments here (https://github.com/huggingface/datasets/pull/1900#discussion_r578319725)! | [
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https://github.com/huggingface/datasets/pull/1887 | Implement to_csv for Dataset | @lhoestq I stumbled upon an interesting failure when adding tests for CSV serialization of `ArrayXD` features (see the failing unit tests in the CI)
It's due to the fact that booleans cannot be converted from arrow format to numpy without copy: https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy | cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory | 42 | text: Implement to_csv for Dataset
cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory
@lhoestq I stumbled upon an interesting failure when adding tests for CSV serialization of `ArrayXD` features (see the failing unit tests in the CI)
It's due to the fact that booleans cannot be converted from arrow format to numpy without copy: https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy | [
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https://github.com/huggingface/datasets/pull/1887 | Implement to_csv for Dataset | Good catch ! I must be able to fix that one by allowing copies for this kind of arrays.
This is the kind of surprise you get sometimes when playing with arrow x) | cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory | 33 | text: Implement to_csv for Dataset
cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory
Good catch ! I must be able to fix that one by allowing copies for this kind of arrays.
This is the kind of surprise you get sometimes when playing with arrow x) | [
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] |
https://github.com/huggingface/datasets/pull/1887 | Implement to_csv for Dataset | Raising this error for booleans was introduced in https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22 without much explanations unfortunately.
So "no copy" only works for primitive types - except booleans.
This is confirmed in the source code at https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/array.pxi#L621
I'm opening a PR to allow copies for booleans... | cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory | 42 | text: Implement to_csv for Dataset
cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory
Raising this error for booleans was introduced in https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22 without much explanations unfortunately.
So "no copy" only works for primitive types - except booleans.
This is confirmed in the source code at https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/array.pxi#L621
I'm opening a PR to allow copies for booleans... | [
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https://github.com/huggingface/datasets/pull/1887 | Implement to_csv for Dataset | I just merged the fix for boolean ArrayXD, feel free to merge from master to see if it fixes the ci :) | cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory | 22 | text: Implement to_csv for Dataset
cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory
I just merged the fix for boolean ArrayXD, feel free to merge from master to see if it fixes the ci :) | [
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https://github.com/huggingface/datasets/pull/1887 | Implement to_csv for Dataset | @lhoestq unfirtunately, arrays of strings (or any other non-primitive type) require a copy too
A list of primitive types can be found here: https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/types.pxi#L821
pyarrow provides a `is_primitive` function to check whether a type is primitive , I used it to set `zero_copy_only`
Also, `PandasArrayExtensionArray.isna` was using `numpy.isnan` which fails for arrays of strings. I replaced it with `pandas.isna`. Let me know what you think! :) | cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory | 66 | text: Implement to_csv for Dataset
cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory
@lhoestq unfirtunately, arrays of strings (or any other non-primitive type) require a copy too
A list of primitive types can be found here: https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/types.pxi#L821
pyarrow provides a `is_primitive` function to check whether a type is primitive , I used it to set `zero_copy_only`
Also, `PandasArrayExtensionArray.isna` was using `numpy.isnan` which fails for arrays of strings. I replaced it with `pandas.isna`. Let me know what you think! :) | [
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https://github.com/huggingface/datasets/pull/1886 | Common voice | Does it make sense to make the domains as the different languages?
A problem is that you need to download the datasets from the browser.
One idea would be to either contact Mozilla regarding API access to the dataset or make use of a headless browser for downloading the datasets (might be hard since we have to figure out how to host them). An even more creative idea would be to host the dataset inside a torrent and figure out a way to download specific datasets from within that torrent.
Here is some information about the download authorization. They are hosting the data on S3.
https://docs.aws.amazon.com/AmazonS3/latest/API/sigv4-auth-using-authorization-header.html
Here is an example of how a download link looks.
https://mozilla-common-voice-datasets.s3.dualstack.us-west-2.amazonaws.com/cv-corpus-6.1-2020-12-11/nl.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQ3GQRTO3ND4UAQXB%2F20210217%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20210217T080740Z&X-Amz-Expires=43200&X-Amz-Security-Token=FwoGZXIvYXdzEGIaDCC6ALh%2FwIK9ovvRdCKSBCs5WaSJNsZ2h0SnhpnWFv4yiAJHJTe%2BY6pBcCqadRMs0RABHeQ2n1QDACJ5V9WOqIHfMfT0AI%2Bfe6iFkTGLgRrJOMYpgV%2FmIBcXCjeb72r4ZvudMA8tprkSxZsEh53bJkIDQx1tXqfpz0yoefM0geD3461suEGhHnLIyiwffrUpRg%2BkNZN9%2FLZZXpF5F2pogieKKV533Jetkd1xlWOR%2Bem9R2bENu2RV563XX3JvbWxSYN9IHkVT1xwd4ZiOpUtX7%2F2RoluJUKw%2BUPpyml3J%2FOPPGdr7CyPLjqNxdq9ceRi8lRybty64XvNYZGt45VNTQ3pkTTz4VpUCJAGkgxq95Ve%2BOwW%2Fsc8JtblTFKrH11vej62NB7C0n7JPPS4SLKXHKW%2B7ZbybcNf3BnsAVouPdsGTMslcgkD81b9trnjyXJdOZkzdHUf2KcWVXVceEsZnMhcCZQ1cJpI7qXPEk8QrKCQcNByPLHmPIEdHpj9IrIBKDkl2qO7VX7CCB65WDt2eZRltOcNHXWVFXFktMdQOQztI1j0XSZz2iOX4jPKKaqz193VEytlAqmehNi8pePOnxkP9Z1SP7d3I6rayuBF3phmpHxw499tY3ECYYgoCnJ6QSFa3KxMjFmEpQlmjxuwEMHd4CDL2FJYGcCiIxbCcL1r8ZE3%2BbGdcu7PRsVCHX3Huh%2FqGIaF4h40FgteN6teyKCHKOebs4EGMipb9xmEMZ9ZbVopz4bkhLdMTrjKon9w624Xem0MTPqN7XY%2BB6lRgrW8rd4%3D&X-Amz-Signature=28eabdfce72a472a70b0f9e1e2c37fe1471b5ec8ed60614fbe900bfa97ae1ac8&X-Amz-SignedHeaders=host
It could be that we simply need to make a http-request with the right parameters and we can download the datasets. | Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information | 138 | text: Common voice
Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information
Does it make sense to make the domains as the different languages?
A problem is that you need to download the datasets from the browser.
One idea would be to either contact Mozilla regarding API access to the dataset or make use of a headless browser for downloading the datasets (might be hard since we have to figure out how to host them). An even more creative idea would be to host the dataset inside a torrent and figure out a way to download specific datasets from within that torrent.
Here is some information about the download authorization. They are hosting the data on S3.
https://docs.aws.amazon.com/AmazonS3/latest/API/sigv4-auth-using-authorization-header.html
Here is an example of how a download link looks.
https://mozilla-common-voice-datasets.s3.dualstack.us-west-2.amazonaws.com/cv-corpus-6.1-2020-12-11/nl.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQ3GQRTO3ND4UAQXB%2F20210217%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20210217T080740Z&X-Amz-Expires=43200&X-Amz-Security-Token=FwoGZXIvYXdzEGIaDCC6ALh%2FwIK9ovvRdCKSBCs5WaSJNsZ2h0SnhpnWFv4yiAJHJTe%2BY6pBcCqadRMs0RABHeQ2n1QDACJ5V9WOqIHfMfT0AI%2Bfe6iFkTGLgRrJOMYpgV%2FmIBcXCjeb72r4ZvudMA8tprkSxZsEh53bJkIDQx1tXqfpz0yoefM0geD3461suEGhHnLIyiwffrUpRg%2BkNZN9%2FLZZXpF5F2pogieKKV533Jetkd1xlWOR%2Bem9R2bENu2RV563XX3JvbWxSYN9IHkVT1xwd4ZiOpUtX7%2F2RoluJUKw%2BUPpyml3J%2FOPPGdr7CyPLjqNxdq9ceRi8lRybty64XvNYZGt45VNTQ3pkTTz4VpUCJAGkgxq95Ve%2BOwW%2Fsc8JtblTFKrH11vej62NB7C0n7JPPS4SLKXHKW%2B7ZbybcNf3BnsAVouPdsGTMslcgkD81b9trnjyXJdOZkzdHUf2KcWVXVceEsZnMhcCZQ1cJpI7qXPEk8QrKCQcNByPLHmPIEdHpj9IrIBKDkl2qO7VX7CCB65WDt2eZRltOcNHXWVFXFktMdQOQztI1j0XSZz2iOX4jPKKaqz193VEytlAqmehNi8pePOnxkP9Z1SP7d3I6rayuBF3phmpHxw499tY3ECYYgoCnJ6QSFa3KxMjFmEpQlmjxuwEMHd4CDL2FJYGcCiIxbCcL1r8ZE3%2BbGdcu7PRsVCHX3Huh%2FqGIaF4h40FgteN6teyKCHKOebs4EGMipb9xmEMZ9ZbVopz4bkhLdMTrjKon9w624Xem0MTPqN7XY%2BB6lRgrW8rd4%3D&X-Amz-Signature=28eabdfce72a472a70b0f9e1e2c37fe1471b5ec8ed60614fbe900bfa97ae1ac8&X-Amz-SignedHeaders=host
It could be that we simply need to make a http-request with the right parameters and we can download the datasets. | [
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https://github.com/huggingface/datasets/pull/1886 | Common voice | > Wow, this looks great already! It's really a difficult dataset so thanks a lot for opening a PR.
> I think the tagging tool is not too important for now and we can take a look at that later!
>
> At the moment, it would be very good to correctly generate some dummy data for all the possible languages. I think the structure of the `.tsv` file as you've noted in the PR is the one we want to use as the structure for `features = datasets.Features(`
>
> The splits `'Train"`, `"Test"`, `"Validation"` look great to me! Because this is a special dataset that also has files called `"Invalidated"` I think the best option is to also add those as splits, _i.e._ `"other"`, `"invalidated"`, `"reported"`, `"validated"` . Those split names can be gives as shown here for example:
>
> https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L124
>
> Also putting @lhoestq in cc here to hear his opinion on the different splits. @lhoestq Common Voicie is a crowd collected dataset where if a collected data sample did not receive enough "up_votes" from the community -> then it is (If I understood it correctly) marked as invalid -> hence the file `"invalidated.tsv"`. I think this is still useful data, so I would include it what do you think?
>
> @BirgerMoell let me know if you have any more questions :-)
I think reporting is a separate feature. People can help annotate the data and then they can report things while annotating.
https://commonvoice.mozilla.org/sv-SE/listen
Here is the interface that shows reporting and the thumbs up and down which gives upvotes and downvotes.
<img src="https://i.imgur.com/utWjszt.png" height="800px">
| Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information | 271 | text: Common voice
Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information
> Wow, this looks great already! It's really a difficult dataset so thanks a lot for opening a PR.
> I think the tagging tool is not too important for now and we can take a look at that later!
>
> At the moment, it would be very good to correctly generate some dummy data for all the possible languages. I think the structure of the `.tsv` file as you've noted in the PR is the one we want to use as the structure for `features = datasets.Features(`
>
> The splits `'Train"`, `"Test"`, `"Validation"` look great to me! Because this is a special dataset that also has files called `"Invalidated"` I think the best option is to also add those as splits, _i.e._ `"other"`, `"invalidated"`, `"reported"`, `"validated"` . Those split names can be gives as shown here for example:
>
> https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L124
>
> Also putting @lhoestq in cc here to hear his opinion on the different splits. @lhoestq Common Voicie is a crowd collected dataset where if a collected data sample did not receive enough "up_votes" from the community -> then it is (If I understood it correctly) marked as invalid -> hence the file `"invalidated.tsv"`. I think this is still useful data, so I would include it what do you think?
>
> @BirgerMoell let me know if you have any more questions :-)
I think reporting is a separate feature. People can help annotate the data and then they can report things while annotating.
https://commonvoice.mozilla.org/sv-SE/listen
Here is the interface that shows reporting and the thumbs up and down which gives upvotes and downvotes.
<img src="https://i.imgur.com/utWjszt.png" height="800px">
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https://github.com/huggingface/datasets/pull/1886 | Common voice | I added splits and features. I'm not sure how you want me to generate dummy data for all the languages? | Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information | 20 | text: Common voice
Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information
I added splits and features. I'm not sure how you want me to generate dummy data for all the languages? | [
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https://github.com/huggingface/datasets/pull/1886 | Common voice | Hey @BirgerMoell,
I tweaked your dataset file a bit to have a first working version. To test this dataset downloading script, you can do the following:
- 1) Download the Common Voice Georgian dataset from https://commonvoice.mozilla.org/en/datasets (It's pretty small which is why I chose it)
- 2) Run the following command using this branch:
```python
from datasets import load_dataset
ds = load_dataset("./../datasets/datasets/common_voice", "Georgian", data_dir="./cv-corpus-6.1-2020-12-11/ka/", split="train")
```
Note that I'm loading a local version of the dataset script (`"./../datasets/datasets/common_voice/"` points to the folder in your branch) and that I also insert the downloaded data with the `data_dir` arg.
-> You'll see that the data is correctly loaded and that `ds` contains all the information we need.
Now there are a lot of different datasets on Common Voice, so it probably takes too much time to test all of those, but maybe you can test whether the current script works as well *e.g.* for Swedish, 3,4 other languages.
It would be very nice if we can use the exact same structure for all languages, meaning that we don't have to change the `datasets.Features(...)` structure depending on the language, but can use the exact same one for every language.
If everything works as expected we can then go over to cleaning the script and seeing how to add dummy data tests for it. | Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information | 220 | text: Common voice
Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information
Hey @BirgerMoell,
I tweaked your dataset file a bit to have a first working version. To test this dataset downloading script, you can do the following:
- 1) Download the Common Voice Georgian dataset from https://commonvoice.mozilla.org/en/datasets (It's pretty small which is why I chose it)
- 2) Run the following command using this branch:
```python
from datasets import load_dataset
ds = load_dataset("./../datasets/datasets/common_voice", "Georgian", data_dir="./cv-corpus-6.1-2020-12-11/ka/", split="train")
```
Note that I'm loading a local version of the dataset script (`"./../datasets/datasets/common_voice/"` points to the folder in your branch) and that I also insert the downloaded data with the `data_dir` arg.
-> You'll see that the data is correctly loaded and that `ds` contains all the information we need.
Now there are a lot of different datasets on Common Voice, so it probably takes too much time to test all of those, but maybe you can test whether the current script works as well *e.g.* for Swedish, 3,4 other languages.
It would be very nice if we can use the exact same structure for all languages, meaning that we don't have to change the `datasets.Features(...)` structure depending on the language, but can use the exact same one for every language.
If everything works as expected we can then go over to cleaning the script and seeing how to add dummy data tests for it. | [
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https://github.com/huggingface/datasets/pull/1883 | Add not-in-place implementations for several dataset transforms | I can take a look at dictionary_encode_column tomorrow.
Although it's likely that it doesn't work then. It was added at the beginning of the lib and never tested nor used afaik. | Should we deprecate in-place versions of such methods? | 31 | text: Add not-in-place implementations for several dataset transforms
Should we deprecate in-place versions of such methods?
I can take a look at dictionary_encode_column tomorrow.
Although it's likely that it doesn't work then. It was added at the beginning of the lib and never tested nor used afaik. | [
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https://github.com/huggingface/datasets/pull/1882 | Create Remote Manager | @lhoestq I have refactorized the logic. Instead of the previous hierarchy call (local temp file opening -> remote call -> use again temp local file logic but from within the remote caller scope), now it is flattened. Schematically:
```python
with src.open() as src_file, dst.open() as dst_file:
src_file.fetch(dst_file)
```
I have created `RemotePath` (analogue to Path) with method `.open()` that returns `FtpFile`/`HttpFile` (analogue to file-like).
Now I am going to implement `RemotePath.exists()` method (analogue to the Path's method) to check if remote resource is accessible, using `Ftp/Http.head()`. | Refactoring to separate the concern of remote (HTTP/FTP requests) management. | 86 | text: Create Remote Manager
Refactoring to separate the concern of remote (HTTP/FTP requests) management.
@lhoestq I have refactorized the logic. Instead of the previous hierarchy call (local temp file opening -> remote call -> use again temp local file logic but from within the remote caller scope), now it is flattened. Schematically:
```python
with src.open() as src_file, dst.open() as dst_file:
src_file.fetch(dst_file)
```
I have created `RemotePath` (analogue to Path) with method `.open()` that returns `FtpFile`/`HttpFile` (analogue to file-like).
Now I am going to implement `RemotePath.exists()` method (analogue to the Path's method) to check if remote resource is accessible, using `Ftp/Http.head()`. | [
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https://github.com/huggingface/datasets/pull/1882 | Create Remote Manager | Quick update on this one:
we discussed offline with @albertvillanova on this PR and I think using `fsspec` can help a lot, since it already implements many parts of the abstraction we need to have nice download tools for both http and ftp (and others !) | Refactoring to separate the concern of remote (HTTP/FTP requests) management. | 46 | text: Create Remote Manager
Refactoring to separate the concern of remote (HTTP/FTP requests) management.
Quick update on this one:
we discussed offline with @albertvillanova on this PR and I think using `fsspec` can help a lot, since it already implements many parts of the abstraction we need to have nice download tools for both http and ftp (and others !) | [
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https://github.com/huggingface/datasets/pull/1879 | Replace flatten_nested | Hi @lhoestq. If you agree to merge this, I will start separating the logic for NestedDataStructure.map ;) | Replace `flatten_nested` with `NestedDataStructure.flatten`.
This is a first step towards having all NestedDataStructure logic as a separated concern, independent of the caller/user of the data structure.
Eventually, all checks (whether the underlying data is list, dict, etc.) will be only inside this class.
I have also generalized the flattening, and now it handles multiple levels of nesting. | 17 | text: Replace flatten_nested
Replace `flatten_nested` with `NestedDataStructure.flatten`.
This is a first step towards having all NestedDataStructure logic as a separated concern, independent of the caller/user of the data structure.
Eventually, all checks (whether the underlying data is list, dict, etc.) will be only inside this class.
I have also generalized the flattening, and now it handles multiple levels of nesting.
Hi @lhoestq. If you agree to merge this, I will start separating the logic for NestedDataStructure.map ;) | [
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https://github.com/huggingface/datasets/pull/1878 | Add LJ Speech dataset | Hey @anton-l,
Thanks a lot for the very clean integration!
1) I think we should now start having "automatic-speech-recognition" as a label in the dataset tagger (@yjernite is it easy to add?). But we can surely add this dataset with the tag you've added and then later change the label to `asr`
2) That's perfect! Yeah good question - we're currently thinking about a better design with @lhoestq
3) Again tagging @yjernite & @lhoestq here - guess we should add this license though! | This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/)
As requested by #1841
The ASR format is based on #1767
There are a couple of quirks that should be addressed:
- I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list?
- Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo?
- The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well?
Pinging @patrickvonplaten to review | 83 | text: Add LJ Speech dataset
This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/)
As requested by #1841
The ASR format is based on #1767
There are a couple of quirks that should be addressed:
- I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list?
- Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo?
- The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well?
Pinging @patrickvonplaten to review
Hey @anton-l,
Thanks a lot for the very clean integration!
1) I think we should now start having "automatic-speech-recognition" as a label in the dataset tagger (@yjernite is it easy to add?). But we can surely add this dataset with the tag you've added and then later change the label to `asr`
2) That's perfect! Yeah good question - we're currently thinking about a better design with @lhoestq
3) Again tagging @yjernite & @lhoestq here - guess we should add this license though! | [
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https://github.com/huggingface/datasets/pull/1878 | Add LJ Speech dataset | Thanks @anton-l for adding this one :)
About the points you mentioned:
1. Sure as soon as we've updated the tag sets in https://github.com/huggingface/datasets-tagging/blob/main/task_set.json, we can update the tags in this dataset card and also in the other audio dataset card.
2. For now we just try to have them as small as possible but we may switch to S3/LFS at one point indeed
3. If it's not part of the license set at https://github.com/huggingface/datasets-tagging/blob/main/license_set.json we can add it to this license set
For now it's ok to have the other-* tags but we'll update them very soon | This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/)
As requested by #1841
The ASR format is based on #1767
There are a couple of quirks that should be addressed:
- I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list?
- Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo?
- The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well?
Pinging @patrickvonplaten to review | 98 | text: Add LJ Speech dataset
This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/)
As requested by #1841
The ASR format is based on #1767
There are a couple of quirks that should be addressed:
- I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list?
- Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo?
- The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well?
Pinging @patrickvonplaten to review
Thanks @anton-l for adding this one :)
About the points you mentioned:
1. Sure as soon as we've updated the tag sets in https://github.com/huggingface/datasets-tagging/blob/main/task_set.json, we can update the tags in this dataset card and also in the other audio dataset card.
2. For now we just try to have them as small as possible but we may switch to S3/LFS at one point indeed
3. If it's not part of the license set at https://github.com/huggingface/datasets-tagging/blob/main/license_set.json we can add it to this license set
For now it's ok to have the other-* tags but we'll update them very soon | [
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https://github.com/huggingface/datasets/pull/1878 | Add LJ Speech dataset | Let's merge this one and then we'll update the tags for the audio datasets. We'll probably also add something like this:
```
type:
- text
- audio
```
Thank you so much for adding this one, good job ! | This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/)
As requested by #1841
The ASR format is based on #1767
There are a couple of quirks that should be addressed:
- I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list?
- Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo?
- The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well?
Pinging @patrickvonplaten to review | 39 | text: Add LJ Speech dataset
This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/)
As requested by #1841
The ASR format is based on #1767
There are a couple of quirks that should be addressed:
- I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list?
- Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo?
- The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well?
Pinging @patrickvonplaten to review
Let's merge this one and then we'll update the tags for the audio datasets. We'll probably also add something like this:
```
type:
- text
- audio
```
Thank you so much for adding this one, good job ! | [
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https://github.com/huggingface/datasets/pull/1874 | Adding Europarl Bilingual dataset | is there a way to check errors without subscribing to CircleCI? Because they want access to private repositories when logging. | Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
| 20 | text: Adding Europarl Bilingual dataset
Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
is there a way to check errors without subscribing to CircleCI? Because they want access to private repositories when logging. | [
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https://github.com/huggingface/datasets/pull/1874 | Adding Europarl Bilingual dataset | I think you need to be logged in to check the errors unfortunately. Feel free to create an account with bitbucket maybe if you don't want it to access your private github repos | Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
| 33 | text: Adding Europarl Bilingual dataset
Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
I think you need to be logged in to check the errors unfortunately. Feel free to create an account with bitbucket maybe if you don't want it to access your private github repos | [
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https://github.com/huggingface/datasets/pull/1874 | Adding Europarl Bilingual dataset | I've resolved some requirements, but I cannot create dummy data. The dataset works as follows: for each language pair `<lang1>-<lang2>` 3 files are downloaded:
- dataset for `<lang1>`
- dataset for `<lang2>`
- alignments between `<lang1>` and `<lang2>`
Suppose we work with the `bg-cs` language pair. Then, the dataset will download three `gzip` files which should be decompressed. I do not understand the relation between the folders created by the script to create dummy data and the original data provided by the download manager. | Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
| 84 | text: Adding Europarl Bilingual dataset
Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
I've resolved some requirements, but I cannot create dummy data. The dataset works as follows: for each language pair `<lang1>-<lang2>` 3 files are downloaded:
- dataset for `<lang1>`
- dataset for `<lang2>`
- alignments between `<lang1>` and `<lang2>`
Suppose we work with the `bg-cs` language pair. Then, the dataset will download three `gzip` files which should be decompressed. I do not understand the relation between the folders created by the script to create dummy data and the original data provided by the download manager. | [
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https://github.com/huggingface/datasets/pull/1874 | Adding Europarl Bilingual dataset | Hi ! Indeed the data files structure of this dataset looks very specific.
The command `datasets-cli dummy_data ./datasets/europarl_bilingual` shows some instructions for each split but let me add more details.
First things to know is that the dummy data files need to be uncompressed data, so for example for the file `bg.zip` you should actually have one folder with all the xml files in it instead. In the same way, `bg-cs.xml.gz` must be replaced by an actual uncompressed xml file.
Let's take the bg-cs config as an example. To make the dummy data you need to:
- go to `./datasets/europarl_bilingual/dummy/bg-cs/8.0.0` and create a folder named `dummy_data`. Then go inside this folder
- create a text file named `bg-cs.xml.gz` containing xml content (so without .gz compression). The xml content must have the same structure as the original `bg-cs.zml` but only include 1 `linkGrp` entry. You can pick one entry from the original `bg-cs.xml` file. Let's say this entry is about this file: `ep-06-01-16-003.xml`
- create a folder named `bg.zip` and inside this folder add one file Europarl/raw/bg/ep-06-01-16-003.xml. You can pick the xml file from the original `bg.zip` archive.
- create a folder named `cs.zip` and inside this folder add one file Europarl/raw/cs/ep-06-01-16-003.xml. You can pick the xml file from the original `cs.zip` archive.
- zip the `dummy_data` into `dummy_data.zip`
At this point you have dummy data files to generate 1 example which is what we want to be able to test the dataset script `europarl_bilingual.py` with pytest.
In particular this will make this test pass:
```
pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_europarl_bilingual
```
Ideally it would be awesome to have dummy data for all the different configs so if we manage to make a script that generates all of it automatically that would be perfect. However since the structure is not trivial, another option would be to only have the dummy data for only 1 or 2 configs, like what we do for [bible_para](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py) for example. In `bible_para` only a few configurations are tested. As you can see there is only 6 configs in the `BUILDER_CONFIGS` attribute. All the other configs can still be used, here is what is said inside the dataset card of bible_para:
```
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/bible-uedin.php
E.g.
`dataset = load_dataset("bible_para", lang1="fi", lang2="hi")`
```
In this case the configuration "fi-hi" is simply created on the fly, instead of being picked from the `BUILDER_CONFIGS` list.
I hope this helps, let me know if you have questions or if I can help | Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
| 441 | text: Adding Europarl Bilingual dataset
Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
Hi ! Indeed the data files structure of this dataset looks very specific.
The command `datasets-cli dummy_data ./datasets/europarl_bilingual` shows some instructions for each split but let me add more details.
First things to know is that the dummy data files need to be uncompressed data, so for example for the file `bg.zip` you should actually have one folder with all the xml files in it instead. In the same way, `bg-cs.xml.gz` must be replaced by an actual uncompressed xml file.
Let's take the bg-cs config as an example. To make the dummy data you need to:
- go to `./datasets/europarl_bilingual/dummy/bg-cs/8.0.0` and create a folder named `dummy_data`. Then go inside this folder
- create a text file named `bg-cs.xml.gz` containing xml content (so without .gz compression). The xml content must have the same structure as the original `bg-cs.zml` but only include 1 `linkGrp` entry. You can pick one entry from the original `bg-cs.xml` file. Let's say this entry is about this file: `ep-06-01-16-003.xml`
- create a folder named `bg.zip` and inside this folder add one file Europarl/raw/bg/ep-06-01-16-003.xml. You can pick the xml file from the original `bg.zip` archive.
- create a folder named `cs.zip` and inside this folder add one file Europarl/raw/cs/ep-06-01-16-003.xml. You can pick the xml file from the original `cs.zip` archive.
- zip the `dummy_data` into `dummy_data.zip`
At this point you have dummy data files to generate 1 example which is what we want to be able to test the dataset script `europarl_bilingual.py` with pytest.
In particular this will make this test pass:
```
pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_europarl_bilingual
```
Ideally it would be awesome to have dummy data for all the different configs so if we manage to make a script that generates all of it automatically that would be perfect. However since the structure is not trivial, another option would be to only have the dummy data for only 1 or 2 configs, like what we do for [bible_para](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py) for example. In `bible_para` only a few configurations are tested. As you can see there is only 6 configs in the `BUILDER_CONFIGS` attribute. All the other configs can still be used, here is what is said inside the dataset card of bible_para:
```
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/bible-uedin.php
E.g.
`dataset = load_dataset("bible_para", lang1="fi", lang2="hi")`
```
In this case the configuration "fi-hi" is simply created on the fly, instead of being picked from the `BUILDER_CONFIGS` list.
I hope this helps, let me know if you have questions or if I can help | [
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https://github.com/huggingface/datasets/pull/1874 | Adding Europarl Bilingual dataset | I already created the scripts to create reduced versions of the data. What I didn't understand was how to put files in the dummy_data folder because, as you noticed, some file decompress to a nested tree structure. I will now try again with your suggestions! | Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
| 45 | text: Adding Europarl Bilingual dataset
Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
I already created the scripts to create reduced versions of the data. What I didn't understand was how to put files in the dummy_data folder because, as you noticed, some file decompress to a nested tree structure. I will now try again with your suggestions! | [
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] |
https://github.com/huggingface/datasets/pull/1874 | Adding Europarl Bilingual dataset | Thanks a lot !!
Since the set of all the dummy data files is quite big I only kept a few of them. If we had kept them all the size of the `datasets` repo would have increased too much :/
So I did the same as for `bible_para`: only keep a few configurations in BUILDER_CONFIGS and have all the other pairs loadable with the lang1 and lang2 parameters like this:
`dataset = load_dataset("europarl_bilingual", lang1="fi", lang2="fr")` | Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
| 76 | text: Adding Europarl Bilingual dataset
Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
Thanks a lot !!
Since the set of all the dummy data files is quite big I only kept a few of them. If we had kept them all the size of the `datasets` repo would have increased too much :/
So I did the same as for `bible_para`: only keep a few configurations in BUILDER_CONFIGS and have all the other pairs loadable with the lang1 and lang2 parameters like this:
`dataset = load_dataset("europarl_bilingual", lang1="fi", lang2="fr")` | [
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https://github.com/huggingface/datasets/pull/1870 | Implement Dataset add_item | Thanks @lhoestq for your remarks. Yes, I agree there are still many issues to be tackled... This PR is just a starting point, so that we can discuss how Dataset should be generalized. | Implement `Dataset.add_item`.
Close #1854. | 33 | text: Implement Dataset add_item
Implement `Dataset.add_item`.
Close #1854.
Thanks @lhoestq for your remarks. Yes, I agree there are still many issues to be tackled... This PR is just a starting point, so that we can discuss how Dataset should be generalized. | [
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https://github.com/huggingface/datasets/pull/1865 | Updated OPUS Open Subtitles Dataset with metadata information | Hi !
About the problems you mentioned:
- Saving the infos is only done for the configurations inside the BUILDER_CONFIGS. Otherwise you would need to run the scripts on ALL language pairs, which is not what we want.
- Moreover when you're on your branch, please specify the path to your local version of the dataset script, like "./datasets/open_subtitles". Otherwise the dataset is loaded from the master branch on github.
Hope that clarifies things a bit
And of course feel free to add methods or classmethods to your builder.
| Close #1844
Problems:
- I ran `python datasets-cli test datasets/open_subtitles --save_infos --all_configs`, hence the change in `dataset_infos.json`, but it appears that the metadata features have not been added for all pairs. Any idea why that might be?
- Possibly related to the above, I tried doing `pip uninstall datasets && pip install -e ".[dev]"` after the changes, and loading the dataset via `load_dataset("open_subtitles", lang1='hi', lang2='it')` to check if the update worked, but the loaded dataset did not contain the metadata fields (neither in the features nor doing `next(iter(dataset['train']))`). What step(s) did I miss?
Questions:
- Is it ok to have a `classmethod` in there? I have not seen any in the few other datasets I have checked. I could make it a local method of the `_generate_examples` method, but I'd rather not duplicate the logic... | 89 | text: Updated OPUS Open Subtitles Dataset with metadata information
Close #1844
Problems:
- I ran `python datasets-cli test datasets/open_subtitles --save_infos --all_configs`, hence the change in `dataset_infos.json`, but it appears that the metadata features have not been added for all pairs. Any idea why that might be?
- Possibly related to the above, I tried doing `pip uninstall datasets && pip install -e ".[dev]"` after the changes, and loading the dataset via `load_dataset("open_subtitles", lang1='hi', lang2='it')` to check if the update worked, but the loaded dataset did not contain the metadata fields (neither in the features nor doing `next(iter(dataset['train']))`). What step(s) did I miss?
Questions:
- Is it ok to have a `classmethod` in there? I have not seen any in the few other datasets I have checked. I could make it a local method of the `_generate_examples` method, but I'd rather not duplicate the logic...
Hi !
About the problems you mentioned:
- Saving the infos is only done for the configurations inside the BUILDER_CONFIGS. Otherwise you would need to run the scripts on ALL language pairs, which is not what we want.
- Moreover when you're on your branch, please specify the path to your local version of the dataset script, like "./datasets/open_subtitles". Otherwise the dataset is loaded from the master branch on github.
Hope that clarifies things a bit
And of course feel free to add methods or classmethods to your builder.
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] |
https://github.com/huggingface/datasets/pull/1860 | Add loading from the Datasets Hub + add relative paths in download manager | I just added the steps to share a dataset on the datasets hub. It's highly inspired by the steps to share a model in the `transformers` doc.
Moreover once the new huggingface_hub is released we can update the version in the setup.py. We also need to update the command to create a dataset repo in the documentation
I added a few more tests with the "lhoestq/test" dataset I added on the hub and it works fine :) | With the new Datasets Hub on huggingface.co it's now possible to have a dataset repo with your own script and data.
For example: https://huggingface.co/datasets/lhoestq/custom_squad/tree/main contains one script and two json files.
You can load it using
```python
from datasets import load_dataset
d = load_dataset("lhoestq/custom_squad")
```
To be able to use the data files that live right next to the dataset script on the repo in the hub, I added relative paths support for the DownloadManager. For example in the repo mentioned above, there are two json files that can be downloaded via
```python
_URLS = {
"train": "train-v1.1.json",
"dev": "dev-v1.1.json",
}
downloaded_files = dl_manager.download_and_extract(_URLS)
```
To make it work, I set the `base_path` of the DownloadManager to be the parent path of the dataset script (which comes from either a local path or a remote url).
I also had to add the auth header of the requests to huggingface.co for private datasets repos. The token is fetched from [huggingface_hub](https://github.com/huggingface/huggingface_hub). | 77 | text: Add loading from the Datasets Hub + add relative paths in download manager
With the new Datasets Hub on huggingface.co it's now possible to have a dataset repo with your own script and data.
For example: https://huggingface.co/datasets/lhoestq/custom_squad/tree/main contains one script and two json files.
You can load it using
```python
from datasets import load_dataset
d = load_dataset("lhoestq/custom_squad")
```
To be able to use the data files that live right next to the dataset script on the repo in the hub, I added relative paths support for the DownloadManager. For example in the repo mentioned above, there are two json files that can be downloaded via
```python
_URLS = {
"train": "train-v1.1.json",
"dev": "dev-v1.1.json",
}
downloaded_files = dl_manager.download_and_extract(_URLS)
```
To make it work, I set the `base_path` of the DownloadManager to be the parent path of the dataset script (which comes from either a local path or a remote url).
I also had to add the auth header of the requests to huggingface.co for private datasets repos. The token is fetched from [huggingface_hub](https://github.com/huggingface/huggingface_hub).
I just added the steps to share a dataset on the datasets hub. It's highly inspired by the steps to share a model in the `transformers` doc.
Moreover once the new huggingface_hub is released we can update the version in the setup.py. We also need to update the command to create a dataset repo in the documentation
I added a few more tests with the "lhoestq/test" dataset I added on the hub and it works fine :) | [
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https://github.com/huggingface/datasets/pull/1850 | Add cord 19 dataset | Before merging I might tweak a little bit the dummy data to avoid having to check if the `document_parses` and `embeddings` directories exist or not. I'll do that later today | Initial version only reading the metadata in CSV.
### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
### Extras:
- [x] add more metadata
- [x] add full text
- [x] add pre-computed document embedding | 30 | text: Add cord 19 dataset
Initial version only reading the metadata in CSV.
### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
### Extras:
- [x] add more metadata
- [x] add full text
- [x] add pre-computed document embedding
Before merging I might tweak a little bit the dummy data to avoid having to check if the `document_parses` and `embeddings` directories exist or not. I'll do that later today | [
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https://github.com/huggingface/datasets/pull/1846 | Make DownloadManager downloaded/extracted paths accessible | First I was thinking of the dict, which makes sense for .download, mapping URL to downloaded path. However does this make sense for .extract, mapping the downloaded path to the extracted path? I ask this because the user did not chose the downloaded path, so this is completely unknown for them... | Make accessible the file paths downloaded/extracted by DownloadManager.
Close #1831.
The approach:
- I set these paths as DownloadManager attributes: these are DownloadManager's concerns
- To access to these from DatasetBuilder, I set the DownloadManager instance as DatasetBuilder attribute: object composition | 51 | text: Make DownloadManager downloaded/extracted paths accessible
Make accessible the file paths downloaded/extracted by DownloadManager.
Close #1831.
The approach:
- I set these paths as DownloadManager attributes: these are DownloadManager's concerns
- To access to these from DatasetBuilder, I set the DownloadManager instance as DatasetBuilder attribute: object composition
First I was thinking of the dict, which makes sense for .download, mapping URL to downloaded path. However does this make sense for .extract, mapping the downloaded path to the extracted path? I ask this because the user did not chose the downloaded path, so this is completely unknown for them... | [
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https://github.com/huggingface/datasets/pull/1846 | Make DownloadManager downloaded/extracted paths accessible | There could be several situations:
- download a file with no extraction
- download a file and extract it
- download a file, extract it and then inside the output folder extract some more files
- extract a local file (for datasets with data that are manually downloaded for example)
- extract a local file, and then inside the output folder extract some more files
So I think it's ok to have `downloaded_paths` as a dict url -> downloaded_path and `extracted_paths` as a dict local_path -> extracted_path. | Make accessible the file paths downloaded/extracted by DownloadManager.
Close #1831.
The approach:
- I set these paths as DownloadManager attributes: these are DownloadManager's concerns
- To access to these from DatasetBuilder, I set the DownloadManager instance as DatasetBuilder attribute: object composition | 87 | text: Make DownloadManager downloaded/extracted paths accessible
Make accessible the file paths downloaded/extracted by DownloadManager.
Close #1831.
The approach:
- I set these paths as DownloadManager attributes: these are DownloadManager's concerns
- To access to these from DatasetBuilder, I set the DownloadManager instance as DatasetBuilder attribute: object composition
There could be several situations:
- download a file with no extraction
- download a file and extract it
- download a file, extract it and then inside the output folder extract some more files
- extract a local file (for datasets with data that are manually downloaded for example)
- extract a local file, and then inside the output folder extract some more files
So I think it's ok to have `downloaded_paths` as a dict url -> downloaded_path and `extracted_paths` as a dict local_path -> extracted_path. | [
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https://github.com/huggingface/datasets/pull/1845 | Enable logging propagation and remove logging handler | Thank you @lhoestq. This logging configuration makes more sense to me.
Once propagation is allowed, the end-user can customize logging behavior and add custom handlers to the proper top logger in the hierarchy.
And I also agree with following the best practices and removing any custom handlers:
- it is the end user who has to implement any custom handlers
- indeed, the previous logging problem with TensorFlow was due to the fact that absl did not follow best practices and had implemented a custom handler
Our errors/warnings will be displayed anyway, even if we do not implement any custom handler. Since Python 3.2, logging has a built-in "default" handler (logging.lastResort) with the expected default behavior (sending error/warning messages to sys.stderr), which is used only if the end user has not configured any custom handler. | We used to have logging propagation disabled because of this issue: https://github.com/tensorflow/tensorflow/issues/26691
But since it's now fixed we should re-enable it. This is important to keep the default logging behavior for users, and propagation is also needed for pytest fixtures as asked in #1826
I also removed the handler that was added since, according to the logging [documentation](https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library):
> It is strongly advised that you do not add any handlers other than NullHandler to your library’s loggers. This is because the configuration of handlers is the prerogative of the application developer who uses your library. The application developer knows their target audience and what handlers are most appropriate for their application: if you add handlers ‘under the hood’, you might well interfere with their ability to carry out unit tests and deliver logs which suit their requirements.
It could have been useful if we wanted to have a custom formatter for the logging but I think it's more important to keep the logging as default to not interfere with the users' logging management.
Therefore I also removed the two methods `datasets.logging.enable_default_handler` and `datasets.logging.disable_default_handler`.
cc @albertvillanova this should let you use capsys/caplog in pytest
cc @LysandreJik @sgugger if you want to do the same in `transformers` | 135 | text: Enable logging propagation and remove logging handler
We used to have logging propagation disabled because of this issue: https://github.com/tensorflow/tensorflow/issues/26691
But since it's now fixed we should re-enable it. This is important to keep the default logging behavior for users, and propagation is also needed for pytest fixtures as asked in #1826
I also removed the handler that was added since, according to the logging [documentation](https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library):
> It is strongly advised that you do not add any handlers other than NullHandler to your library’s loggers. This is because the configuration of handlers is the prerogative of the application developer who uses your library. The application developer knows their target audience and what handlers are most appropriate for their application: if you add handlers ‘under the hood’, you might well interfere with their ability to carry out unit tests and deliver logs which suit their requirements.
It could have been useful if we wanted to have a custom formatter for the logging but I think it's more important to keep the logging as default to not interfere with the users' logging management.
Therefore I also removed the two methods `datasets.logging.enable_default_handler` and `datasets.logging.disable_default_handler`.
cc @albertvillanova this should let you use capsys/caplog in pytest
cc @LysandreJik @sgugger if you want to do the same in `transformers`
Thank you @lhoestq. This logging configuration makes more sense to me.
Once propagation is allowed, the end-user can customize logging behavior and add custom handlers to the proper top logger in the hierarchy.
And I also agree with following the best practices and removing any custom handlers:
- it is the end user who has to implement any custom handlers
- indeed, the previous logging problem with TensorFlow was due to the fact that absl did not follow best practices and had implemented a custom handler
Our errors/warnings will be displayed anyway, even if we do not implement any custom handler. Since Python 3.2, logging has a built-in "default" handler (logging.lastResort) with the expected default behavior (sending error/warning messages to sys.stderr), which is used only if the end user has not configured any custom handler. | [
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https://github.com/huggingface/datasets/pull/1834 | Fixes base_url of limit dataset | OK, apparently it is a lot more complicated than simply changing the URL? Going to make an issue. | `test.json` is not available in the master branch of the repository anymore. Linking to a specific commit. | 18 | text: Fixes base_url of limit dataset
`test.json` is not available in the master branch of the repository anymore. Linking to a specific commit.
OK, apparently it is a lot more complicated than simply changing the URL? Going to make an issue. | [
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https://github.com/huggingface/datasets/pull/1833 | Add OSCAR dataset card | @lhoestq Thanks for the suggestions! I agree with all of them. Should I accept them one by one or can I accept them all at once? When I try to load the whole diff GitHub is complaining and it does no render them well (probably my browser?) 😅 | I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824). | 48 | text: Add OSCAR dataset card
I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824).
@lhoestq Thanks for the suggestions! I agree with all of them. Should I accept them one by one or can I accept them all at once? When I try to load the whole diff GitHub is complaining and it does no render them well (probably my browser?) 😅 | [
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https://github.com/huggingface/datasets/pull/1833 | Add OSCAR dataset card | I just merged the tables as suggested 😄 . However I noticed something weird, the train sizes are identical for both the original and deduplicated files ... This is not normal, in general the original files are almost twice as big as the deduplicated ones 🤔 | I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824). | 46 | text: Add OSCAR dataset card
I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824).
I just merged the tables as suggested 😄 . However I noticed something weird, the train sizes are identical for both the original and deduplicated files ... This is not normal, in general the original files are almost twice as big as the deduplicated ones 🤔 | [
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https://github.com/huggingface/datasets/pull/1833 | Add OSCAR dataset card | Good catch @pjox ! I just checked and this is because the scripts doesn't handle having several blank lines in a row.
Blank lines introduced by deduplication are currently not ignored so we end up with the same number of examples in the dataset as the original version (but with empty examples...)
I fixed that in this [commit](https://github.com/huggingface/datasets/commit/837a152e4724adc5308e2c4481908c00a8d93383). I'm re-running the metadata generation for deduplicated configs. | I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824). | 66 | text: Add OSCAR dataset card
I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824).
Good catch @pjox ! I just checked and this is because the scripts doesn't handle having several blank lines in a row.
Blank lines introduced by deduplication are currently not ignored so we end up with the same number of examples in the dataset as the original version (but with empty examples...)
I fixed that in this [commit](https://github.com/huggingface/datasets/commit/837a152e4724adc5308e2c4481908c00a8d93383). I'm re-running the metadata generation for deduplicated configs. | [
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https://github.com/huggingface/datasets/pull/1833 | Add OSCAR dataset card | > I got the new sizes today, will update the dataset_infos.json and the dataset card tomorrow
great, I just wanted to report that I got error message "NonMatchingSplitsSizesError" when I tried to load one of the oscar dataset. | I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824). | 38 | text: Add OSCAR dataset card
I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824).
> I got the new sizes today, will update the dataset_infos.json and the dataset card tomorrow
great, I just wanted to report that I got error message "NonMatchingSplitsSizesError" when I tried to load one of the oscar dataset. | [
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https://github.com/huggingface/datasets/pull/1833 | Add OSCAR dataset card | Ok I see you're having this issue because I haven't updated the sizes yet ! I'm opening a PR
I just checked and indeed there's an issue with the `deduplicated` configurations since the commit I mentioned above.
I'm fixing this by using the new sizes I got yesterday :)
| I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824). | 49 | text: Add OSCAR dataset card
I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824).
Ok I see you're having this issue because I haven't updated the sizes yet ! I'm opening a PR
I just checked and indeed there's an issue with the `deduplicated` configurations since the commit I mentioned above.
I'm fixing this by using the new sizes I got yesterday :)
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https://github.com/huggingface/datasets/pull/1833 | Add OSCAR dataset card | I just updated the size in the table @pjox it should be good now :)
I also updated the sizes in the dataset_infos.json in https://github.com/huggingface/datasets/pull/1868 (merged) | I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824). | 26 | text: Add OSCAR dataset card
I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824).
I just updated the size in the table @pjox it should be good now :)
I also updated the sizes in the dataset_infos.json in https://github.com/huggingface/datasets/pull/1868 (merged) | [
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https://github.com/huggingface/datasets/pull/1828 | Add CelebA Dataset | Hi @gchhablani! Thanks for all the contributions! We definitely want more image datasets, but Face datasets are tricky in general, in this one includes predicting attributes such as Attractiveness, Gender, or Race, which can be pretty problematic.
Would you be up for starting with only object classification or object detection datasets instead? (Your CIFAR-100 contribution will be super useful for example!) | Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10). | 61 | text: Add CelebA Dataset
Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10).
Hi @gchhablani! Thanks for all the contributions! We definitely want more image datasets, but Face datasets are tricky in general, in this one includes predicting attributes such as Attractiveness, Gender, or Race, which can be pretty problematic.
Would you be up for starting with only object classification or object detection datasets instead? (Your CIFAR-100 contribution will be super useful for example!) | [
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https://github.com/huggingface/datasets/pull/1828 | Add CelebA Dataset | Hi @yjernite, You're welcome. I am enjoying adding new datasets :)
By "pretty problematic", are you referring to the ethical issues? I used TFDS's [CelebA](https://github.com/tensorflow/datasets/blob/5ef7861470896acb6f74dacba85036001e4f1b8c/tensorflow_datasets/image/celeba.py#L91) as a reference. Here they mention in a "Note" that CelebA "may contain potential bias". Can we not do the same? I skipped the note for now, and we can add it. However, if you feel this isn't the right time, then I won't pursue this further.
But, can this issue be handled at a later stage? Does this also apply for my Hateful Memes Issue #1810?
Also, how can I
1. load a part of the dataset? since `load_dataset(<>,split='train[10:20]')` still loads all the examples.
2. make `datasets_infos.json` for huge datasets which have a single configuration?
I will ofcourse be looking for other datasets to add regardless.
| Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10). | 132 | text: Add CelebA Dataset
Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10).
Hi @yjernite, You're welcome. I am enjoying adding new datasets :)
By "pretty problematic", are you referring to the ethical issues? I used TFDS's [CelebA](https://github.com/tensorflow/datasets/blob/5ef7861470896acb6f74dacba85036001e4f1b8c/tensorflow_datasets/image/celeba.py#L91) as a reference. Here they mention in a "Note" that CelebA "may contain potential bias". Can we not do the same? I skipped the note for now, and we can add it. However, if you feel this isn't the right time, then I won't pursue this further.
But, can this issue be handled at a later stage? Does this also apply for my Hateful Memes Issue #1810?
Also, how can I
1. load a part of the dataset? since `load_dataset(<>,split='train[10:20]')` still loads all the examples.
2. make `datasets_infos.json` for huge datasets which have a single configuration?
I will ofcourse be looking for other datasets to add regardless.
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https://github.com/huggingface/datasets/pull/1828 | Add CelebA Dataset | It's definitely a thorny question. The short answer is: Hateful Memes and hate speech detection datasets are different since their use case is specifically to train systems to identify and hopefully remove hateful content, whereas the purpose of a dataset that has an Attractiveness score as output is implicitly to train more models to rate "Attractiveness".
As far as warning about the "potential biases", I do not think it is quite enough, especially because it is hard to guarantee that every potential user will read the documentation (it is also an insufficient warning.)
Note that we do have higher standards for the dataset cards of hate speech and hateful memes datasets, so if you do choose to add that one yourself we will ask that you summarize the relevant literature in the Social Impact section.
If you really need to add this dataset for your own research for the explicit purpose of studying these biases, you can add it as a community provided dataset following https://huggingface.co/docs/datasets/master/share_dataset.html#sharing-a-community-provided-dataset but I'd recommend just skipping it for now. | Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10). | 174 | text: Add CelebA Dataset
Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10).
It's definitely a thorny question. The short answer is: Hateful Memes and hate speech detection datasets are different since their use case is specifically to train systems to identify and hopefully remove hateful content, whereas the purpose of a dataset that has an Attractiveness score as output is implicitly to train more models to rate "Attractiveness".
As far as warning about the "potential biases", I do not think it is quite enough, especially because it is hard to guarantee that every potential user will read the documentation (it is also an insufficient warning.)
Note that we do have higher standards for the dataset cards of hate speech and hateful memes datasets, so if you do choose to add that one yourself we will ask that you summarize the relevant literature in the Social Impact section.
If you really need to add this dataset for your own research for the explicit purpose of studying these biases, you can add it as a community provided dataset following https://huggingface.co/docs/datasets/master/share_dataset.html#sharing-a-community-provided-dataset but I'd recommend just skipping it for now. | [
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https://github.com/huggingface/datasets/pull/1828 | Add CelebA Dataset | So currently you do need to download the whole dataset when using it, we are working on making it easier to stream parts of it from a remote host. You can also use the filesystem integration if local storage is an issue:
https://huggingface.co/docs/datasets/master/filesystems.html
| Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10). | 43 | text: Add CelebA Dataset
Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10).
So currently you do need to download the whole dataset when using it, we are working on making it easier to stream parts of it from a remote host. You can also use the filesystem integration if local storage is an issue:
https://huggingface.co/docs/datasets/master/filesystems.html
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https://github.com/huggingface/datasets/pull/1828 | Add CelebA Dataset | I don't think we have a great solution for `dataset_infos.json` with a single very large config when storage space is an issue, but it should be solved by the same upcoming feature mentioned above | Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10). | 34 | text: Add CelebA Dataset
Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10).
I don't think we have a great solution for `dataset_infos.json` with a single very large config when storage space is an issue, but it should be solved by the same upcoming feature mentioned above | [
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https://github.com/huggingface/datasets/pull/1828 | Add CelebA Dataset | Okay, then I won't pursue this one further. I'll keep this branch on my repository just in case the possibility of adding this dataset comes up in the future.
> So currently you do need to download the whole dataset when using it, we are working on making it easier to stream parts of it from a remote host. You can also use the filesystem integration if local storage is an issue:
> https://huggingface.co/docs/datasets/master/filesystems.html
After downloading the whole dataset (around 1.4GB), it still loads all the examples despite using `split='train[:10%]'` or `split='train[10:20]'`.
EDIT: I think this would happen only when the examples are generated for the first time and saved to the cache. Streaming parts of the data from a remote host sounds amazing! But, would that also allow for streaming examples of the data from the local cache? (without saving all the examples the first time).
What I used:
`d = load_dataset('./datasets/celeb_a',split='train[:10]')`
Output:
`570 examples [01:33, 6.25 examples/s]` and it keeps going.
EDIT 2: After a few thousand images, I get the following error:
```python
OSError: [Errno 24] Too many open files: '~/.cache/huggingface/datasets/celeb_a/default/1.1.0/01f9dca66039ab7c40b91b09af47a5fa8c3e49dc8d55df50da55b14116229207.incomplete'
```
I understand this is because of the way I load the images :
```python
Image.open(<path>)
```
What could be better alternative? I am only asking in case I face the same issues in the future. | Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10). | 220 | text: Add CelebA Dataset
Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10).
Okay, then I won't pursue this one further. I'll keep this branch on my repository just in case the possibility of adding this dataset comes up in the future.
> So currently you do need to download the whole dataset when using it, we are working on making it easier to stream parts of it from a remote host. You can also use the filesystem integration if local storage is an issue:
> https://huggingface.co/docs/datasets/master/filesystems.html
After downloading the whole dataset (around 1.4GB), it still loads all the examples despite using `split='train[:10%]'` or `split='train[10:20]'`.
EDIT: I think this would happen only when the examples are generated for the first time and saved to the cache. Streaming parts of the data from a remote host sounds amazing! But, would that also allow for streaming examples of the data from the local cache? (without saving all the examples the first time).
What I used:
`d = load_dataset('./datasets/celeb_a',split='train[:10]')`
Output:
`570 examples [01:33, 6.25 examples/s]` and it keeps going.
EDIT 2: After a few thousand images, I get the following error:
```python
OSError: [Errno 24] Too many open files: '~/.cache/huggingface/datasets/celeb_a/default/1.1.0/01f9dca66039ab7c40b91b09af47a5fa8c3e49dc8d55df50da55b14116229207.incomplete'
```
I understand this is because of the way I load the images :
```python
Image.open(<path>)
```
What could be better alternative? I am only asking in case I face the same issues in the future. | [
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https://github.com/huggingface/datasets/pull/1828 | Add CelebA Dataset | Just some addition about loading only a subset of the data:
Currently if even you specify `split='train[:10]'`, it downloads and generate the full dataset, so that you can pick another part afterward if you want to. We may change that in the future and use streaming.
And about your open files issue, you can try to close each image file after reading its content. | Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10). | 64 | text: Add CelebA Dataset
Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10).
Just some addition about loading only a subset of the data:
Currently if even you specify `split='train[:10]'`, it downloads and generate the full dataset, so that you can pick another part afterward if you want to. We may change that in the future and use streaming.
And about your open files issue, you can try to close each image file after reading its content. | [
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