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https://github.com/huggingface/datasets/pull/1828 | Add CelebA Dataset | Hi @lhoestq,
Thanks for your response.
I used `gc.collect()` inside the loop and that worked for me. I think since we are using a generator, and if I have something like `train[100000:100002]`, we will need to generate the first 1000001 examples and store. Ofcourse, this feature isn't a necessity right now, I suppose. | 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). | 53 | 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 @lhoestq,
Thanks for your response.
I used `gc.collect()` inside the loop and that worked for me. I think since we are using a generator, and if I have something like `train[100000:100002]`, we will need to generate the first 1000001 examples and store. Ofcourse, this feature isn't a necessity right now, I suppose. | [
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https://github.com/huggingface/datasets/pull/1824 | Add OSCAR dataset card | Hi @lhoestq! When are you planning to release the version with this dataset?
BTW: What a huge README file :astonished: | I started adding the dataset card for OSCAR !
For now it's just basic info for all the different configurations in `Dataset Structure`.
In particular the Data Splits section tells how may samples there are for each config. The Data Instances section show an example for each config, and it also shows the size in MB. Since the Data Instances section is very long the user has to click to expand the info. I was able to generate it thanks to the tools made by @madlag and @yjernite :D
Cc @pjox could you help me with the other sections ? (Dataset Description, Dataset Creation, Considerations for Using the Data, Additional Information)
| 20 | text: Add OSCAR dataset card
I started adding the dataset card for OSCAR !
For now it's just basic info for all the different configurations in `Dataset Structure`.
In particular the Data Splits section tells how may samples there are for each config. The Data Instances section show an example for each config, and it also shows the size in MB. Since the Data Instances section is very long the user has to click to expand the info. I was able to generate it thanks to the tools made by @madlag and @yjernite :D
Cc @pjox could you help me with the other sections ? (Dataset Description, Dataset Creation, Considerations for Using the Data, Additional Information)
Hi @lhoestq! When are you planning to release the version with this dataset?
BTW: What a huge README file :astonished: | [
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https://github.com/huggingface/datasets/pull/1823 | Add FewRel Dataset | Hi @lhoestq,
Sorry for the late response. What do you mean when you say "adding names to default config"? Should I handle "pid2name" in the same config as "default"? | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | 29 | text: Add FewRel Dataset
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
Hi @lhoestq,
Sorry for the late response. What do you mean when you say "adding names to default config"? Should I handle "pid2name" in the same config as "default"? | [
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https://github.com/huggingface/datasets/pull/1823 | Add FewRel Dataset | Yes I was thinking of having the pid2name field available in the default configuration (and therefore only have one config). What do you think ? | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | 25 | text: Add FewRel Dataset
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
Yes I was thinking of having the pid2name field available in the default configuration (and therefore only have one config). What do you think ? | [
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https://github.com/huggingface/datasets/pull/1823 | Add FewRel Dataset | Hi @lhoestq,
Sorry again, the last couple of weeks were a bit busy for me. I am wondering how do you want me to achieve that. Using a custom BuilderConfig which takes in whether it is the regular data or "pid2name"? "pid2name" is only useful for "train_wiki", "val_nyt" and "val_wiki". So, based on my understanding, it would look like this:
```python
wiki_data = load_dataset('few_rel','train_wiki')
id2name = load_dataset('few_rel','pid2name')
```
and this will be handled in the multiple configs.
A better alternative could be providing name of the relationship in only "train_wiki", "val_nyt" and "val_wiki" as an extra feature in the dataset, and doing away with "pid2name" entirely. I'll only download pid2name if any of those datasets are requested, and then during generation I'll return the list with the dataset under "names" feature. How does this sound?
EDIT:
There is one issue with the second approach, the entire pid2name is saved with all three datasets - "train_wiki", "val_nyt" and "val_wiki" ([see code below](https://github.com/huggingface/datasets/pull/1823#issuecomment-786402026)). In dummy data, I can address this by manually editing the pid2name to contain only a few id-name pairs, those matching with the examples in the corresponding example file. But this seems to be inefficient for the entire dataset - storing the same file in multiple places. | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | 208 | text: Add FewRel Dataset
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
Hi @lhoestq,
Sorry again, the last couple of weeks were a bit busy for me. I am wondering how do you want me to achieve that. Using a custom BuilderConfig which takes in whether it is the regular data or "pid2name"? "pid2name" is only useful for "train_wiki", "val_nyt" and "val_wiki". So, based on my understanding, it would look like this:
```python
wiki_data = load_dataset('few_rel','train_wiki')
id2name = load_dataset('few_rel','pid2name')
```
and this will be handled in the multiple configs.
A better alternative could be providing name of the relationship in only "train_wiki", "val_nyt" and "val_wiki" as an extra feature in the dataset, and doing away with "pid2name" entirely. I'll only download pid2name if any of those datasets are requested, and then during generation I'll return the list with the dataset under "names" feature. How does this sound?
EDIT:
There is one issue with the second approach, the entire pid2name is saved with all three datasets - "train_wiki", "val_nyt" and "val_wiki" ([see code below](https://github.com/huggingface/datasets/pull/1823#issuecomment-786402026)). In dummy data, I can address this by manually editing the pid2name to contain only a few id-name pairs, those matching with the examples in the corresponding example file. But this seems to be inefficient for the entire dataset - storing the same file in multiple places. | [
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https://github.com/huggingface/datasets/pull/1823 | Add FewRel Dataset | Okay, I apologize, I guess I finally understand what is required.
Basically, using:
```python
few_rel = load_dataset('few_rel')
```
should give all the files. This seems difficult since "pid2name" has a different format. Any suggestions on this? | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | 36 | text: Add FewRel Dataset
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
Okay, I apologize, I guess I finally understand what is required.
Basically, using:
```python
few_rel = load_dataset('few_rel')
```
should give all the files. This seems difficult since "pid2name" has a different format. Any suggestions on this? | [
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https://github.com/huggingface/datasets/pull/1823 | Add FewRel Dataset | Hi @lhoestq,
Since pid2name has different features from the rest of the files, how will I add them to the same config?
Do we want to exclude pid2name totally and add "names" to every example? | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | 35 | text: Add FewRel Dataset
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
Hi @lhoestq,
Since pid2name has different features from the rest of the files, how will I add them to the same config?
Do we want to exclude pid2name totally and add "names" to every example? | [
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https://github.com/huggingface/datasets/pull/1823 | Add FewRel Dataset | If I understand correctly each sample in the "default" config has one relation, and each relation has corresponding names in pid2name.
Would it be possible to also include the names in the "default" configuration for each sample ? The names of one sample can be retrieved using the relation id no ? | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | 52 | text: Add FewRel Dataset
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
If I understand correctly each sample in the "default" config has one relation, and each relation has corresponding names in pid2name.
Would it be possible to also include the names in the "default" configuration for each sample ? The names of one sample can be retrieved using the relation id no ? | [
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https://github.com/huggingface/datasets/pull/1823 | Add FewRel Dataset | Yes, that can be done. But for some files, the name is already given instead of ID. Only "train_wiki", "val_wiki", "val_nyc" have IDs. For others, I can set the names equal to a list of key. | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | 36 | text: Add FewRel Dataset
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
Yes, that can be done. But for some files, the name is already given instead of ID. Only "train_wiki", "val_wiki", "val_nyc" have IDs. For others, I can set the names equal to a list of key. | [
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https://github.com/huggingface/datasets/pull/1823 | Add FewRel Dataset | I think that's fine as long as we mention this processing explicitly in the dataset card. | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | 16 | text: Add FewRel Dataset
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
I think that's fine as long as we mention this processing explicitly in the dataset card. | [
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https://github.com/huggingface/datasets/pull/1823 | Add FewRel Dataset | Hi @lhoestq,
I have added the changes. Please let me know in case of any remaining issues.
Thanks,
Gunjan | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | 19 | text: Add FewRel Dataset
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
Hi @lhoestq,
I have added the changes. Please let me know in case of any remaining issues.
Thanks,
Gunjan | [
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https://github.com/huggingface/datasets/pull/1815 | Add CCAligned Multilingual Dataset | Hi !
We already have some datasets that can have many many configurations possible.
To be able to support that, we allow to subclass BuilderConfig to add as many additional parameters as you may need.
This way users can load any language they want. For example the [bible_para](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py) dataset is a dataset for translation and therefore users should be able to provide any language pair. You can check how the subclass of BuilderConfig is defined [here](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py#L49).
For testing, only the configurations defined in the `BUILDER_CONFIGS` class attribute are used.
All the other configs combinations are not tested, but they can be used by users. If a config doesn't already exist in `BUILDER_CONFIGS`, then it is created on the fly.
For example in [bible_para](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py#L61), only 6 configs are defined in `BUILDER_CONFIGS`.
So what I would do in your case is have something like
```python
class CCAlignedConfig(datasets.BuilderConfig):
def __init__(self, *args, documents_or_sentences=None, language_code=None, **kwargs):
super().__init__(
*args,
name=f"{documents_or_sentences}-{language_code}",
**kwargs,
)
self.documents_or_sentences = documents_or_sentences
self.language_code = language_code
```
And of course, feel free to change/rename things if you want to. In particular I think we can improve the name of the parameter `documents_or_sentences` | Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review. | 188 | text: Add CCAligned Multilingual Dataset
Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review.
Hi !
We already have some datasets that can have many many configurations possible.
To be able to support that, we allow to subclass BuilderConfig to add as many additional parameters as you may need.
This way users can load any language they want. For example the [bible_para](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py) dataset is a dataset for translation and therefore users should be able to provide any language pair. You can check how the subclass of BuilderConfig is defined [here](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py#L49).
For testing, only the configurations defined in the `BUILDER_CONFIGS` class attribute are used.
All the other configs combinations are not tested, but they can be used by users. If a config doesn't already exist in `BUILDER_CONFIGS`, then it is created on the fly.
For example in [bible_para](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py#L61), only 6 configs are defined in `BUILDER_CONFIGS`.
So what I would do in your case is have something like
```python
class CCAlignedConfig(datasets.BuilderConfig):
def __init__(self, *args, documents_or_sentences=None, language_code=None, **kwargs):
super().__init__(
*args,
name=f"{documents_or_sentences}-{language_code}",
**kwargs,
)
self.documents_or_sentences = documents_or_sentences
self.language_code = language_code
```
And of course, feel free to change/rename things if you want to. In particular I think we can improve the name of the parameter `documents_or_sentences` | [
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https://github.com/huggingface/datasets/pull/1815 | Add CCAligned Multilingual Dataset | Hi @lhoestq,
Thanks a lot! I don't know why I didn't think about that. :P
I'll make these changes and update. | Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review. | 21 | text: Add CCAligned Multilingual Dataset
Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review.
Hi @lhoestq,
Thanks a lot! I don't know why I didn't think about that. :P
I'll make these changes and update. | [
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https://github.com/huggingface/datasets/pull/1815 | Add CCAligned Multilingual Dataset | Hi @lhoestq,
I have tested and added dummy files. Request you to review.
Also, does this mean BUILDER_CONFIGS is only needed while testing? | Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review. | 23 | text: Add CCAligned Multilingual Dataset
Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review.
Hi @lhoestq,
I have tested and added dummy files. Request you to review.
Also, does this mean BUILDER_CONFIGS is only needed while testing? | [
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https://github.com/huggingface/datasets/pull/1815 | Add CCAligned Multilingual Dataset | Hi @lhoestq,
Sorry for the delay, I have added the changes from the review. For the ISO format language codes, I just selected the first two characters from the names, hoping those are correct. Let me know if you want me to verify :P
Thanks for taking the time to add such a detailed review. I'll keep all these changes in mind the next time I'm adding a dataset.
Thanks,
Gunjan | Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review. | 71 | text: Add CCAligned Multilingual Dataset
Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review.
Hi @lhoestq,
Sorry for the delay, I have added the changes from the review. For the ISO format language codes, I just selected the first two characters from the names, hoping those are correct. Let me know if you want me to verify :P
Thanks for taking the time to add such a detailed review. I'll keep all these changes in mind the next time I'm adding a dataset.
Thanks,
Gunjan | [
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https://github.com/huggingface/datasets/pull/1815 | Add CCAligned Multilingual Dataset | Hi @lhoestq,
I have changed the README, and added a single example per config. Even one example is long enough to make the files heavy. Hope that isn't an issue.
Thanks,
Gunjan | Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review. | 32 | text: Add CCAligned Multilingual Dataset
Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review.
Hi @lhoestq,
I have changed the README, and added a single example per config. Even one example is long enough to make the files heavy. Hope that isn't an issue.
Thanks,
Gunjan | [
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https://github.com/huggingface/datasets/pull/1809 | Add FreebaseQA dataset | Hi ! It looks like this PR contains changes about other datasets than freebase_qa such as DuoRC.
Can you remove these changes please ? | Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review. | 24 | text: Add FreebaseQA dataset
Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review.
Hi ! It looks like this PR contains changes about other datasets than freebase_qa such as DuoRC.
Can you remove these changes please ? | [
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https://github.com/huggingface/datasets/pull/1809 | Add FreebaseQA dataset | Hi @lhoestq,
I think this happened because of rebasing. I'm unable to remove the duorc commit from the branch. GEM, Arabic sarcasm datasets are also there. I can't see any merge conflicts, however. Before commiting I always rebase (shouldn't have done that).
Can you explain what is to be done? Should I create a clean PR? | Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review. | 56 | text: Add FreebaseQA dataset
Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review.
Hi @lhoestq,
I think this happened because of rebasing. I'm unable to remove the duorc commit from the branch. GEM, Arabic sarcasm datasets are also there. I can't see any merge conflicts, however. Before commiting I always rebase (shouldn't have done that).
Can you explain what is to be done? Should I create a clean PR? | [
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https://github.com/huggingface/datasets/pull/1809 | Add FreebaseQA dataset | Hi @gchhablani
I think you can simply create another branch and another PR.
If I understand correctly the github diff is messed up because you rebased instead of merge.
Rebasing is supposed to be used only before pushing the branch the first time, or github messes up the diff.
If you want to include changes from master on a branch that is already push you need to use git merge. | Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review. | 70 | text: Add FreebaseQA dataset
Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review.
Hi @gchhablani
I think you can simply create another branch and another PR.
If I understand correctly the github diff is messed up because you rebased instead of merge.
Rebasing is supposed to be used only before pushing the branch the first time, or github messes up the diff.
If you want to include changes from master on a branch that is already push you need to use git merge. | [
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https://github.com/huggingface/datasets/pull/1809 | Add FreebaseQA dataset | Thanks @lhoestq.
I understand the issue now. I missed the instructions on the template. Sorry for bothering you unnecessarily, I'm pretty new to contributing on GitHub. I'll make a fresh PR.
| Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review. | 31 | text: Add FreebaseQA dataset
Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review.
Thanks @lhoestq.
I understand the issue now. I missed the instructions on the template. Sorry for bothering you unnecessarily, I'm pretty new to contributing on GitHub. I'll make a fresh PR.
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https://github.com/huggingface/datasets/pull/1809 | Add FreebaseQA dataset | No problem, I'm not a big fan of this weird behavior tbh.
Thanks for making a new PR | Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review. | 18 | text: Add FreebaseQA dataset
Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review.
No problem, I'm not a big fan of this weird behavior tbh.
Thanks for making a new PR | [
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https://github.com/huggingface/datasets/pull/1809 | Add FreebaseQA dataset | @lhoestq Haha, well, it's not as weird as not reading the [instructions](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#open-a-pull-request-on-the-main-huggingface-repo-and-share-your-work).
Also, I'm enjoying adding new datasets so it's all cool :) | Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review. | 23 | text: Add FreebaseQA dataset
Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review.
@lhoestq Haha, well, it's not as weird as not reading the [instructions](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#open-a-pull-request-on-the-main-huggingface-repo-and-share-your-work).
Also, I'm enjoying adding new datasets so it's all cool :) | [
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https://github.com/huggingface/datasets/pull/1802 | add github of contributors | @lhoestq Can you confirm if this format is fine? I will update cards based on your feedback. | This PR will add contributors GitHub id at the end of every dataset cards. | 17 | text: add github of contributors
This PR will add contributors GitHub id at the end of every dataset cards.
@lhoestq Can you confirm if this format is fine? I will update cards based on your feedback. | [
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https://github.com/huggingface/datasets/pull/1802 | add github of contributors | On HuggingFace side we also have a mapping of hf user => github user (GitHub info used to be required when signing up until not long ago – cc @gary149 @beurkinger) so we can also add a link to HF profile | This PR will add contributors GitHub id at the end of every dataset cards. | 41 | text: add github of contributors
This PR will add contributors GitHub id at the end of every dataset cards.
On HuggingFace side we also have a mapping of hf user => github user (GitHub info used to be required when signing up until not long ago – cc @gary149 @beurkinger) so we can also add a link to HF profile | [
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https://github.com/huggingface/datasets/pull/1801 | [GEM] Updated the source link of the data to update correct tokenized version. | @mounicam we'll keep the original version in the Turk dataset proper, and use the updated file in the GEM aggregated dataset which I'll add later today
@lhoestq do not merge, I'll close when I've submitted the GEM dataset PR :) | 40 | text: [GEM] Updated the source link of the data to update correct tokenized version.
@mounicam we'll keep the original version in the Turk dataset proper, and use the updated file in the GEM aggregated dataset which I'll add later today
@lhoestq do not merge, I'll close when I've submitted the GEM dataset PR :) | [
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|
https://github.com/huggingface/datasets/pull/1798 | Add Arabic sarcasm dataset | @lhoestq thanks for the comments - I believe these are now addressed. I re-generated the datasets_info.json and dummy data | This MIT license dataset: https://github.com/iabufarha/ArSarcasm
Via https://sites.google.com/view/ar-sarcasm-sentiment-detection/ | 19 | text: Add Arabic sarcasm dataset
This MIT license dataset: https://github.com/iabufarha/ArSarcasm
Via https://sites.google.com/view/ar-sarcasm-sentiment-detection/
@lhoestq thanks for the comments - I believe these are now addressed. I re-generated the datasets_info.json and dummy data | [
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https://github.com/huggingface/datasets/pull/1795 | Custom formatting for lazy map + arrow data extraction refactor | This PR is amazing!!!
I only looked at `arrow_dataset.py` and `formatting/formatting.py` but those look good to me.
My only (tiny) concern is the name of the function: I don't think it's self-evident that `set_format` applies a generic transformation, and some people might not look too far into the doc.
Maybe we could have an `apply_transform` or `process_columns` method which is called by `set_format` (to keep backward compatibility)? | Hi !
This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions.
While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly.
A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`.
### What's new:
You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`.
A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch.
Here is an example to tokenize and pad tokens on-the-fly when accessing the samples:
```python
from datasets import load_dataset
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def encode(batch):
return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
dataset = load_dataset("glue", "mrpc", split="train")
dataset.set_format(transform=encode)
dataset.format
# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
dataset[:2]
# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}
```
Let me know what you think of this API !
We can still change it if we want to.
Especially @sgugger since this may be useful when using `datasets` to train models.
EDIT: this was changed to `dataset.set_transform(encode)`
-------------------
Note:
I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them.
Some bugs I found and fixed:
- certain slices/ranges were not supported because negative ids were passed to pyarrow
- formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy)
- on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32
The unit tests for those are now really extensive :) | 67 | text: Custom formatting for lazy map + arrow data extraction refactor
Hi !
This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions.
While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly.
A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`.
### What's new:
You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`.
A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch.
Here is an example to tokenize and pad tokens on-the-fly when accessing the samples:
```python
from datasets import load_dataset
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def encode(batch):
return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
dataset = load_dataset("glue", "mrpc", split="train")
dataset.set_format(transform=encode)
dataset.format
# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
dataset[:2]
# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}
```
Let me know what you think of this API !
We can still change it if we want to.
Especially @sgugger since this may be useful when using `datasets` to train models.
EDIT: this was changed to `dataset.set_transform(encode)`
-------------------
Note:
I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them.
Some bugs I found and fixed:
- certain slices/ranges were not supported because negative ids were passed to pyarrow
- formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy)
- on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32
The unit tests for those are now really extensive :)
This PR is amazing!!!
I only looked at `arrow_dataset.py` and `formatting/formatting.py` but those look good to me.
My only (tiny) concern is the name of the function: I don't think it's self-evident that `set_format` applies a generic transformation, and some people might not look too far into the doc.
Maybe we could have an `apply_transform` or `process_columns` method which is called by `set_format` (to keep backward compatibility)? | [
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https://github.com/huggingface/datasets/pull/1795 | Custom formatting for lazy map + arrow data extraction refactor | What about something like `.set_format` and `.set_transform` ?
- set_format would be the same as right now, i.e. defined by a format type.
- set_transform would define the transformation that is applied on output batches on-the-fly.
I was also thinking about `._with_format` and `.with_transform`. It could be their equivalent but would create a **new** dataset with the corresponding format or transform ? I know @sgugger was interested in something like that. | Hi !
This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions.
While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly.
A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`.
### What's new:
You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`.
A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch.
Here is an example to tokenize and pad tokens on-the-fly when accessing the samples:
```python
from datasets import load_dataset
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def encode(batch):
return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
dataset = load_dataset("glue", "mrpc", split="train")
dataset.set_format(transform=encode)
dataset.format
# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
dataset[:2]
# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}
```
Let me know what you think of this API !
We can still change it if we want to.
Especially @sgugger since this may be useful when using `datasets` to train models.
EDIT: this was changed to `dataset.set_transform(encode)`
-------------------
Note:
I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them.
Some bugs I found and fixed:
- certain slices/ranges were not supported because negative ids were passed to pyarrow
- formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy)
- on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32
The unit tests for those are now really extensive :) | 71 | text: Custom formatting for lazy map + arrow data extraction refactor
Hi !
This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions.
While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly.
A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`.
### What's new:
You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`.
A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch.
Here is an example to tokenize and pad tokens on-the-fly when accessing the samples:
```python
from datasets import load_dataset
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def encode(batch):
return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
dataset = load_dataset("glue", "mrpc", split="train")
dataset.set_format(transform=encode)
dataset.format
# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
dataset[:2]
# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}
```
Let me know what you think of this API !
We can still change it if we want to.
Especially @sgugger since this may be useful when using `datasets` to train models.
EDIT: this was changed to `dataset.set_transform(encode)`
-------------------
Note:
I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them.
Some bugs I found and fixed:
- certain slices/ranges were not supported because negative ids were passed to pyarrow
- formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy)
- on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32
The unit tests for those are now really extensive :)
What about something like `.set_format` and `.set_transform` ?
- set_format would be the same as right now, i.e. defined by a format type.
- set_transform would define the transformation that is applied on output batches on-the-fly.
I was also thinking about `._with_format` and `.with_transform`. It could be their equivalent but would create a **new** dataset with the corresponding format or transform ? I know @sgugger was interested in something like that. | [
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https://github.com/huggingface/datasets/pull/1795 | Custom formatting for lazy map + arrow data extraction refactor | I like all those options as well (as long as the `_` in `_with_format` is a typo ;-) ) | Hi !
This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions.
While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly.
A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`.
### What's new:
You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`.
A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch.
Here is an example to tokenize and pad tokens on-the-fly when accessing the samples:
```python
from datasets import load_dataset
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def encode(batch):
return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
dataset = load_dataset("glue", "mrpc", split="train")
dataset.set_format(transform=encode)
dataset.format
# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
dataset[:2]
# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}
```
Let me know what you think of this API !
We can still change it if we want to.
Especially @sgugger since this may be useful when using `datasets` to train models.
EDIT: this was changed to `dataset.set_transform(encode)`
-------------------
Note:
I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them.
Some bugs I found and fixed:
- certain slices/ranges were not supported because negative ids were passed to pyarrow
- formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy)
- on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32
The unit tests for those are now really extensive :) | 19 | text: Custom formatting for lazy map + arrow data extraction refactor
Hi !
This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions.
While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly.
A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`.
### What's new:
You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`.
A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch.
Here is an example to tokenize and pad tokens on-the-fly when accessing the samples:
```python
from datasets import load_dataset
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def encode(batch):
return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
dataset = load_dataset("glue", "mrpc", split="train")
dataset.set_format(transform=encode)
dataset.format
# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
dataset[:2]
# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}
```
Let me know what you think of this API !
We can still change it if we want to.
Especially @sgugger since this may be useful when using `datasets` to train models.
EDIT: this was changed to `dataset.set_transform(encode)`
-------------------
Note:
I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them.
Some bugs I found and fixed:
- certain slices/ranges were not supported because negative ids were passed to pyarrow
- formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy)
- on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32
The unit tests for those are now really extensive :)
I like all those options as well (as long as the `_` in `_with_format` is a typo ;-) ) | [
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] |
https://github.com/huggingface/datasets/pull/1795 | Custom formatting for lazy map + arrow data extraction refactor | I took all your suggestions into account, thanks :)
Let me know if you have more comments | Hi !
This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions.
While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly.
A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`.
### What's new:
You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`.
A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch.
Here is an example to tokenize and pad tokens on-the-fly when accessing the samples:
```python
from datasets import load_dataset
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def encode(batch):
return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
dataset = load_dataset("glue", "mrpc", split="train")
dataset.set_format(transform=encode)
dataset.format
# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
dataset[:2]
# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}
```
Let me know what you think of this API !
We can still change it if we want to.
Especially @sgugger since this may be useful when using `datasets` to train models.
EDIT: this was changed to `dataset.set_transform(encode)`
-------------------
Note:
I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them.
Some bugs I found and fixed:
- certain slices/ranges were not supported because negative ids were passed to pyarrow
- formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy)
- on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32
The unit tests for those are now really extensive :) | 17 | text: Custom formatting for lazy map + arrow data extraction refactor
Hi !
This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions.
While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly.
A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`.
### What's new:
You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`.
A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch.
Here is an example to tokenize and pad tokens on-the-fly when accessing the samples:
```python
from datasets import load_dataset
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def encode(batch):
return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
dataset = load_dataset("glue", "mrpc", split="train")
dataset.set_format(transform=encode)
dataset.format
# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
dataset[:2]
# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}
```
Let me know what you think of this API !
We can still change it if we want to.
Especially @sgugger since this may be useful when using `datasets` to train models.
EDIT: this was changed to `dataset.set_transform(encode)`
-------------------
Note:
I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them.
Some bugs I found and fixed:
- certain slices/ranges were not supported because negative ids were passed to pyarrow
- formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy)
- on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32
The unit tests for those are now really extensive :)
I took all your suggestions into account, thanks :)
Let me know if you have more comments | [
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https://github.com/huggingface/datasets/pull/1792 | Allow loading dataset in-memory | > ring how to test their difference...
Hmm I don't think pyarrow exposes an API to check if a Table comes from a file that is memory-mapped. In particular since all the buffer/memory logic is in the C++ part of pyarrow.
Otherwise we can still check the difference of RAM used when loading a big chunk of data. | Allow loading datasets either from:
- memory-mapped file (current implementation)
- from file descriptor, copying data to physical memory
Close #708 | 58 | text: Allow loading dataset in-memory
Allow loading datasets either from:
- memory-mapped file (current implementation)
- from file descriptor, copying data to physical memory
Close #708
> ring how to test their difference...
Hmm I don't think pyarrow exposes an API to check if a Table comes from a file that is memory-mapped. In particular since all the buffer/memory logic is in the C++ part of pyarrow.
Otherwise we can still check the difference of RAM used when loading a big chunk of data. | [
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https://github.com/huggingface/datasets/pull/1792 | Allow loading dataset in-memory | > Hmm I don't think pyarrow exposes an API to check if a Table comes from a file that is memory-mapped. In particular since all the buffer/memory logic is in the C++ part of pyarrow.
>
> Otherwise we can still check the difference of RAM used when loading a big chunk of data.
@lhoestq I think I found a way: `pa.total_allocated_bytes()` :smirk: | Allow loading datasets either from:
- memory-mapped file (current implementation)
- from file descriptor, copying data to physical memory
Close #708 | 63 | text: Allow loading dataset in-memory
Allow loading datasets either from:
- memory-mapped file (current implementation)
- from file descriptor, copying data to physical memory
Close #708
> Hmm I don't think pyarrow exposes an API to check if a Table comes from a file that is memory-mapped. In particular since all the buffer/memory logic is in the C++ part of pyarrow.
>
> Otherwise we can still check the difference of RAM used when loading a big chunk of data.
@lhoestq I think I found a way: `pa.total_allocated_bytes()` :smirk: | [
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https://github.com/huggingface/datasets/pull/1780 | Update SciFact URL | Hi ! The error you get is the result of some verifications the library is doing when loading a dataset that already has some metadata in the dataset_infos.json. You can ignore the verifications with
```
python datasets-cli test datasets/scifact --save_infos --all_configs --ignore_verifications
```
This will update the dataset_infos.json :) | Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
``` | 49 | text: Update SciFact URL
Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
```
Hi ! The error you get is the result of some verifications the library is doing when loading a dataset that already has some metadata in the dataset_infos.json. You can ignore the verifications with
```
python datasets-cli test datasets/scifact --save_infos --all_configs --ignore_verifications
```
This will update the dataset_infos.json :) | [
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-0.023801960051059723,
0.0002656387514434755,
0.3309022784233093,
0.4473645091056824,
-0.07118293642997742,
0.3717612624168396,
-0.38933849334716797,
-0.19299595057964325,
0.6477616429328918,
-0.21286790072917938,
-0.21725283563137054,
0.29040637612342834,
0.33699750900268555,
-0.052696071565151215,
-0.14458124339580536,
-0.292357861995697,
0.21775001287460327,
0.19633536040782928,
0.357758104801178,
-0.27308928966522217,
0.27951017022132874,
-0.20034898817539215,
0.028310474008321762,
0.11597256362438202,
0.42178767919540405,
0.23872160911560059,
-0.2664662301540375,
0.025057267397642136,
-0.0944799929857254
] |
https://github.com/huggingface/datasets/pull/1780 | Update SciFact URL | Nice, I ran that command and `dataset_infos` seems to have been updated appropriately; I added this to the PR. But when I try to load the dataset it still seems like it's getting a path to the old URL somehow. I `pip install -e`'d my fork of the repo, so I'm not sure why `load_dataset` is still looking for the old version of the file. Stack trace below.
```
In [1]: import datasets
In [2]: ds = datasets.load_dataset("scifact", "claims")
Downloading: 7.34kB [00:00, 2.58MB/s]
Downloading: 3.38kB [00:00, 1.36MB/s]
Downloading and preparing dataset scifact/claims (download: 2.72 MiB, generated: 258.64 KiB, post-processed: Unknown size, total: 2.97 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/claims/1.0.0/2bb675b2003716a061a4d8ce27fab32ab7f6d010016bab08ffaccea3c14ec6e7...
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-2-9a50b954d89a> in <module>
----> 1 ds = datasets.load_dataset("scifact", "claims")
~/proj/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
672
673 # Download and prepare data
--> 674 builder_instance.download_and_prepare(
675 download_config=download_config,
676 download_mode=download_mode,
~/proj/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
560 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
561 if not downloaded_from_gcs:
--> 562 self._download_and_prepare(
563 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
564 )
~/proj/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
616 split_dict = SplitDict(dataset_name=self.name)
617 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 618 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
619
620 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/scifact/2bb675b2003716a061a4d8ce27fab32ab7f6d010016bab08ffaccea3c14ec6e7/scifact.py in _split_generators(self, dl_manager)
92 # dl_manager is a datasets.download.DownloadManager that can be used to
93 # download and extract URLs
---> 94 dl_dir = dl_manager.download_and_extract(_URL)
95
96 if self.config.name == "corpus":
~/proj/datasets/src/datasets/utils/download_manager.py in download_and_extract(self, url_or_urls)
256 extracted_path(s): `str`, extracted paths of given URL(s).
257 """
--> 258 return self.extract(self.download(url_or_urls))
259
260 def get_recorded_sizes_checksums(self):
~/proj/datasets/src/datasets/utils/download_manager.py in download(self, url_or_urls)
177
178 start_time = datetime.now()
--> 179 downloaded_path_or_paths = map_nested(
180 download_func,
181 url_or_urls,
~/proj/datasets/src/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)
223 # Singleton
224 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 225 return function(data_struct)
226
227 disable_tqdm = bool(logger.getEffectiveLevel() > INFO)
~/proj/datasets/src/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
348 if is_remote_url(url_or_filename):
349 # URL, so get it from the cache (downloading if necessary)
--> 350 output_path = get_from_cache(
351 url_or_filename,
352 cache_dir=cache_dir,
~/proj/datasets/src/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries)
631 elif response is not None and response.status_code == 404:
632 raise FileNotFoundError("Couldn't find file at {}".format(url))
--> 633 raise ConnectionError("Couldn't reach {}".format(url))
634
635 # Try a second time
ConnectionError: Couldn't reach https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz
``` | Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
``` | 390 | text: Update SciFact URL
Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
```
Nice, I ran that command and `dataset_infos` seems to have been updated appropriately; I added this to the PR. But when I try to load the dataset it still seems like it's getting a path to the old URL somehow. I `pip install -e`'d my fork of the repo, so I'm not sure why `load_dataset` is still looking for the old version of the file. Stack trace below.
```
In [1]: import datasets
In [2]: ds = datasets.load_dataset("scifact", "claims")
Downloading: 7.34kB [00:00, 2.58MB/s]
Downloading: 3.38kB [00:00, 1.36MB/s]
Downloading and preparing dataset scifact/claims (download: 2.72 MiB, generated: 258.64 KiB, post-processed: Unknown size, total: 2.97 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/claims/1.0.0/2bb675b2003716a061a4d8ce27fab32ab7f6d010016bab08ffaccea3c14ec6e7...
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-2-9a50b954d89a> in <module>
----> 1 ds = datasets.load_dataset("scifact", "claims")
~/proj/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
672
673 # Download and prepare data
--> 674 builder_instance.download_and_prepare(
675 download_config=download_config,
676 download_mode=download_mode,
~/proj/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
560 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
561 if not downloaded_from_gcs:
--> 562 self._download_and_prepare(
563 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
564 )
~/proj/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
616 split_dict = SplitDict(dataset_name=self.name)
617 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 618 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
619
620 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/scifact/2bb675b2003716a061a4d8ce27fab32ab7f6d010016bab08ffaccea3c14ec6e7/scifact.py in _split_generators(self, dl_manager)
92 # dl_manager is a datasets.download.DownloadManager that can be used to
93 # download and extract URLs
---> 94 dl_dir = dl_manager.download_and_extract(_URL)
95
96 if self.config.name == "corpus":
~/proj/datasets/src/datasets/utils/download_manager.py in download_and_extract(self, url_or_urls)
256 extracted_path(s): `str`, extracted paths of given URL(s).
257 """
--> 258 return self.extract(self.download(url_or_urls))
259
260 def get_recorded_sizes_checksums(self):
~/proj/datasets/src/datasets/utils/download_manager.py in download(self, url_or_urls)
177
178 start_time = datetime.now()
--> 179 downloaded_path_or_paths = map_nested(
180 download_func,
181 url_or_urls,
~/proj/datasets/src/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)
223 # Singleton
224 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 225 return function(data_struct)
226
227 disable_tqdm = bool(logger.getEffectiveLevel() > INFO)
~/proj/datasets/src/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
348 if is_remote_url(url_or_filename):
349 # URL, so get it from the cache (downloading if necessary)
--> 350 output_path = get_from_cache(
351 url_or_filename,
352 cache_dir=cache_dir,
~/proj/datasets/src/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries)
631 elif response is not None and response.status_code == 404:
632 raise FileNotFoundError("Couldn't find file at {}".format(url))
--> 633 raise ConnectionError("Couldn't reach {}".format(url))
634
635 # Try a second time
ConnectionError: Couldn't reach https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz
``` | [
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https://github.com/huggingface/datasets/pull/1780 | Update SciFact URL | Hi ! This may be because you need to point `load_dataset` to the path of the dataset script that has the updated url:
```python
load_dataset("./datasets/scifact", "claims")
```
If you don't use a path to the updated script, then the old one is used by deffault | Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
``` | 45 | text: Update SciFact URL
Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
```
Hi ! This may be because you need to point `load_dataset` to the path of the dataset script that has the updated url:
```python
load_dataset("./datasets/scifact", "claims")
```
If you don't use a path to the updated script, then the old one is used by deffault | [
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https://github.com/huggingface/datasets/pull/1780 | Update SciFact URL | One more question about the way the code is being preprocessed. The way I've formatted the data, each entry is a claim, which may be associated with multiple evidence documents (similar to FEVER):
```
# My way
{'id': 70,
'claim': 'Activation of PPM1D suppresses p53 function.',
'evidence': {'5956380': [{'sentences': [5, 6], 'label': 'SUPPORT'}],
'4414547': [{'sentences': [5], 'label': 'SUPPORT'}]},
'cited_doc_ids': [5956380, 4414547]}
```
In the Hugginface data, each entry is a single claim / evidence document pair. So, the above entry is converted into two separate entries, like so:
```
# huggingface
[{'cited_doc_ids': [5956380, 4414547],
'claim': 'Activation of PPM1D suppresses p53 function.',
'evidence_doc_id': '5956380',
'evidence_label': 'SUPPORT',
'evidence_sentences': [5, 6],
'id': 70},
{'cited_doc_ids': [5956380, 4414547],
'claim': 'Activation of PPM1D suppresses p53 function.',
'evidence_doc_id': '4414547',
'evidence_label': 'SUPPORT',
'evidence_sentences': [5],
'id': 70}]
```
Was this done by design? If not, would you mind if I modify the Huggingface code so that it more closely matches the format that people will get if they download the data from the SciFact repo? | Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
``` | 166 | text: Update SciFact URL
Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
```
One more question about the way the code is being preprocessed. The way I've formatted the data, each entry is a claim, which may be associated with multiple evidence documents (similar to FEVER):
```
# My way
{'id': 70,
'claim': 'Activation of PPM1D suppresses p53 function.',
'evidence': {'5956380': [{'sentences': [5, 6], 'label': 'SUPPORT'}],
'4414547': [{'sentences': [5], 'label': 'SUPPORT'}]},
'cited_doc_ids': [5956380, 4414547]}
```
In the Hugginface data, each entry is a single claim / evidence document pair. So, the above entry is converted into two separate entries, like so:
```
# huggingface
[{'cited_doc_ids': [5956380, 4414547],
'claim': 'Activation of PPM1D suppresses p53 function.',
'evidence_doc_id': '5956380',
'evidence_label': 'SUPPORT',
'evidence_sentences': [5, 6],
'id': 70},
{'cited_doc_ids': [5956380, 4414547],
'claim': 'Activation of PPM1D suppresses p53 function.',
'evidence_doc_id': '4414547',
'evidence_label': 'SUPPORT',
'evidence_sentences': [5],
'id': 70}]
```
Was this done by design? If not, would you mind if I modify the Huggingface code so that it more closely matches the format that people will get if they download the data from the SciFact repo? | [
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https://github.com/huggingface/datasets/pull/1780 | Update SciFact URL | Yes if you think the format is not convenient for training or evaluation we can change it.
Also I think we're doing something similar for FEVER: one example = one (claim, sentence) pair.
Let's merge this PR first and then feel free to open a new PR to change the format :) | Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
``` | 52 | text: Update SciFact URL
Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
```
Yes if you think the format is not convenient for training or evaluation we can change it.
Also I think we're doing something similar for FEVER: one example = one (claim, sentence) pair.
Let's merge this PR first and then feel free to open a new PR to change the format :) | [
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https://github.com/huggingface/datasets/pull/1780 | Update SciFact URL | Thanks for merging!
I don't have super-strong feelings one way or the other in terms of the data, I think it's probably fine. I may revisit later. | Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
``` | 27 | text: Update SciFact URL
Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
```
Thanks for merging!
I don't have super-strong feelings one way or the other in terms of the data, I think it's probably fine. I may revisit later. | [
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https://github.com/huggingface/datasets/pull/1778 | Narrative QA Manual | @lhoestq sorry I opened a new pull request because of some issues with the previous code base. This pull request is originally from #1364 | Submitting the manual version of Narrative QA script which requires a manual download from the original repository | 24 | text: Narrative QA Manual
Submitting the manual version of Narrative QA script which requires a manual download from the original repository
@lhoestq sorry I opened a new pull request because of some issues with the previous code base. This pull request is originally from #1364 | [
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https://github.com/huggingface/datasets/pull/1778 | Narrative QA Manual | Excellent comments. Thanks for those valuable suggestions. I changed everything as you have pointed out :) | Submitting the manual version of Narrative QA script which requires a manual download from the original repository | 16 | text: Narrative QA Manual
Submitting the manual version of Narrative QA script which requires a manual download from the original repository
Excellent comments. Thanks for those valuable suggestions. I changed everything as you have pointed out :) | [
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https://github.com/huggingface/datasets/pull/1778 | Narrative QA Manual | I've copied the same template as NarrativeQA now. Please let me know if this is fine. | Submitting the manual version of Narrative QA script which requires a manual download from the original repository | 16 | text: Narrative QA Manual
Submitting the manual version of Narrative QA script which requires a manual download from the original repository
I've copied the same template as NarrativeQA now. Please let me know if this is fine. | [
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https://github.com/huggingface/datasets/pull/1778 | Narrative QA Manual | > Awesome thank you !!
> This looks all good :)
>
> Just before we merge, I was wondering if you knew why the number of examples in the train set went from 1102 to 32747 in your last commit ? I can't see why the changes in the code would cause such a big difference
Ok the change was the way I presented the data.
In my previous code, I presented a story with a list of questions-answers related to the story per sample. So the total 1102 was the number of stories (not questions) in the train set.
In the case of `NarrativeQA`, the code presented each sample data with one single question. So the story gets replicated as many times based on number of questions per story. I felt this was not really memory efficient so I had coded the way I did earlier.
But since this would be inconsistent as you pointed out, I modified my code to suit the `NarrativeQA` approach. Hope it's clear now :) | Submitting the manual version of Narrative QA script which requires a manual download from the original repository | 172 | text: Narrative QA Manual
Submitting the manual version of Narrative QA script which requires a manual download from the original repository
> Awesome thank you !!
> This looks all good :)
>
> Just before we merge, I was wondering if you knew why the number of examples in the train set went from 1102 to 32747 in your last commit ? I can't see why the changes in the code would cause such a big difference
Ok the change was the way I presented the data.
In my previous code, I presented a story with a list of questions-answers related to the story per sample. So the total 1102 was the number of stories (not questions) in the train set.
In the case of `NarrativeQA`, the code presented each sample data with one single question. So the story gets replicated as many times based on number of questions per story. I felt this was not really memory efficient so I had coded the way I did earlier.
But since this would be inconsistent as you pointed out, I modified my code to suit the `NarrativeQA` approach. Hope it's clear now :) | [
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https://github.com/huggingface/datasets/pull/1778 | Narrative QA Manual | Thanks for your time and helping me with all this :) Really appreciate the hardwork you guys do. | Submitting the manual version of Narrative QA script which requires a manual download from the original repository | 18 | text: Narrative QA Manual
Submitting the manual version of Narrative QA script which requires a manual download from the original repository
Thanks for your time and helping me with all this :) Really appreciate the hardwork you guys do. | [
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https://github.com/huggingface/datasets/pull/1767 | Add Librispeech ASR | > Awesome thank you !
>
> The dummy data are quite big but it was expected given that the raw files are flac files.
> Given that the script doesn't even read the flac files I think we can remove them. Or maybe use empty flac files (see [here](https://hydrogenaud.io/index.php?topic=118685.0) for example). What do you think ?
>
> We'll find a better solution to be able to have bigger dummy_data (max 1MB instead of a few KB, maybe using git LFS.
Hmm, I already made the dummy data as small as possible (a single flac filie per split only). I'd like to keep them at least to have complete dummy data and don't think 500KB for all datasets together is a problem (the long-range summarization datasets are similarly heavy). The moment we allow dummy data to be loaded directly for testing, we need the flac files IMO.
But I agree that longterm, we need a better solution for the dummy data (maybe stop hosting it on github to not make the repo too heavy) | This PR adds the librispeech asr dataset: https://www.tensorflow.org/datasets/catalog/librispeech
There are 2 configs: "clean" and "other" whereas there are two "train" datasets for "clean", hence the name "train.100" and "train.360".
As suggested by @lhoestq, due to the enormous size of the dataset in `.arrow` format, the speech files are not directly prepared to a float32-array, but instead just the path to the array file is stored. | 175 | text: Add Librispeech ASR
This PR adds the librispeech asr dataset: https://www.tensorflow.org/datasets/catalog/librispeech
There are 2 configs: "clean" and "other" whereas there are two "train" datasets for "clean", hence the name "train.100" and "train.360".
As suggested by @lhoestq, due to the enormous size of the dataset in `.arrow` format, the speech files are not directly prepared to a float32-array, but instead just the path to the array file is stored.
> Awesome thank you !
>
> The dummy data are quite big but it was expected given that the raw files are flac files.
> Given that the script doesn't even read the flac files I think we can remove them. Or maybe use empty flac files (see [here](https://hydrogenaud.io/index.php?topic=118685.0) for example). What do you think ?
>
> We'll find a better solution to be able to have bigger dummy_data (max 1MB instead of a few KB, maybe using git LFS.
Hmm, I already made the dummy data as small as possible (a single flac filie per split only). I'd like to keep them at least to have complete dummy data and don't think 500KB for all datasets together is a problem (the long-range summarization datasets are similarly heavy). The moment we allow dummy data to be loaded directly for testing, we need the flac files IMO.
But I agree that longterm, we need a better solution for the dummy data (maybe stop hosting it on github to not make the repo too heavy) | [
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https://github.com/huggingface/datasets/pull/1761 | Add SILICONE benchmark | Thank you for your constructive feedback! I now know how to best format future datasets that our team plans to publish in the near future :) | My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication.
This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
| 26 | text: Add SILICONE benchmark
My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication.
This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
Thank you for your constructive feedback! I now know how to best format future datasets that our team plans to publish in the near future :) | [
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https://github.com/huggingface/datasets/pull/1761 | Add SILICONE benchmark | Hi @lhoestq ! One last question. Our research team would like to distribute a link to this dataset amongst the spoken dialogue research community but the dataset does not show in the dropdown menu at huggingface.co. Is there anything else we must do in order to find the dataset there ?
Once the dataset does show in the dropdown menu, how can I affiliate it with the Telecom Paris organization that I already created at the website ? | My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication.
This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
| 78 | text: Add SILICONE benchmark
My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication.
This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
Hi @lhoestq ! One last question. Our research team would like to distribute a link to this dataset amongst the spoken dialogue research community but the dataset does not show in the dropdown menu at huggingface.co. Is there anything else we must do in order to find the dataset there ?
Once the dataset does show in the dropdown menu, how can I affiliate it with the Telecom Paris organization that I already created at the website ? | [
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https://github.com/huggingface/datasets/pull/1761 | Add SILICONE benchmark | The files are not located in the right place in the repo. Let me move them | My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication.
This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
| 16 | text: Add SILICONE benchmark
My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication.
This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
The files are not located in the right place in the repo. Let me move them | [
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https://github.com/huggingface/datasets/pull/1761 | Add SILICONE benchmark | I just merged the change @eusip, now the dataset page is available at the url:
https://huggingface.co/datasets/silicone | My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication.
This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
| 16 | text: Add SILICONE benchmark
My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication.
This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
I just merged the change @eusip, now the dataset page is available at the url:
https://huggingface.co/datasets/silicone | [
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https://github.com/huggingface/datasets/pull/1752 | COMET metric citation | I think its better to create a new branch with this fix. I forgot I was still using the old branch. | In my last pull request to add COMET metric, the citations where not following the usual "format". Because of that they where not correctly displayed on the website:
<img width="814" alt="Screenshot 2021-01-20 at 09 48 44" src="https://user-images.githubusercontent.com/17256847/105158000-686efb80-5b05-11eb-8bb0-9c85fdac2938.png">
This pull request is only intended to fix that. | 21 | text: COMET metric citation
In my last pull request to add COMET metric, the citations where not following the usual "format". Because of that they where not correctly displayed on the website:
<img width="814" alt="Screenshot 2021-01-20 at 09 48 44" src="https://user-images.githubusercontent.com/17256847/105158000-686efb80-5b05-11eb-8bb0-9c85fdac2938.png">
This pull request is only intended to fix that.
I think its better to create a new branch with this fix. I forgot I was still using the old branch. | [
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https://github.com/huggingface/datasets/pull/1749 | Added metadata and correct splits for swda. | @lhoestq thank you for your comments! I went ahead and fixed the code 😃. Please let me know if I missed anything. | Switchboard Dialog Act Corpus
I made some changes following @bhavitvyamalik recommendation in #1678:
* Contains all metadata.
* Used official implementation from the [/swda](https://github.com/cgpotts/swda) repo.
* Add official train and test splits used in [Stolcke et al. (2000)](https://web.stanford.edu/~jurafsky/ws97) and validation split used in [Probabilistic-RNN-DA-Classifier](https://github.com/NathanDuran/Probabilistic-RNN-DA-Classifier). | 22 | text: Added metadata and correct splits for swda.
Switchboard Dialog Act Corpus
I made some changes following @bhavitvyamalik recommendation in #1678:
* Contains all metadata.
* Used official implementation from the [/swda](https://github.com/cgpotts/swda) repo.
* Add official train and test splits used in [Stolcke et al. (2000)](https://web.stanford.edu/~jurafsky/ws97) and validation split used in [Probabilistic-RNN-DA-Classifier](https://github.com/NathanDuran/Probabilistic-RNN-DA-Classifier).
@lhoestq thank you for your comments! I went ahead and fixed the code 😃. Please let me know if I missed anything. | [
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https://github.com/huggingface/datasets/pull/1744 | Add missing "brief" entries to reuters | @lhoestq I ran `make style` but CI code quality still failing and I don't have access to logs | This brings the number of examples for ModApte to match the stated `Training set (9,603 docs)...Test Set (3,299 docs)` | 18 | text: Add missing "brief" entries to reuters
This brings the number of examples for ModApte to match the stated `Training set (9,603 docs)...Test Set (3,299 docs)`
@lhoestq I ran `make style` but CI code quality still failing and I don't have access to logs | [
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https://github.com/huggingface/datasets/pull/1744 | Add missing "brief" entries to reuters | It's also likely that due to the previous placement of the field initialization, much of the data about topics etc was simply wrong and carried over from previous entries. Model scores seem to improve significantly with this PR. | This brings the number of examples for ModApte to match the stated `Training set (9,603 docs)...Test Set (3,299 docs)` | 38 | text: Add missing "brief" entries to reuters
This brings the number of examples for ModApte to match the stated `Training set (9,603 docs)...Test Set (3,299 docs)`
It's also likely that due to the previous placement of the field initialization, much of the data about topics etc was simply wrong and carried over from previous entries. Model scores seem to improve significantly with this PR. | [
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https://github.com/huggingface/datasets/pull/1742 | Add GLUE Compat (compatible with transformers<3.5.0) | Maybe it would be simpler to just overwrite the order of the label classes of the `glue` dataset ?
```python
mnli = load_dataset("glue", "mnli", label_classes=["contradiction", "entailment", "neutral"])
``` | Link to our discussion on Slack (HF internal)
https://huggingface.slack.com/archives/C014N4749J9/p1609668119337400
The next step is to add a compatible option in the new `run_glue.py`
I duplicated `glue` and made the following changes:
1. Change the name to `glue_compat`.
2. Change the label assignments for MNLI and AX. | 28 | text: Add GLUE Compat (compatible with transformers<3.5.0)
Link to our discussion on Slack (HF internal)
https://huggingface.slack.com/archives/C014N4749J9/p1609668119337400
The next step is to add a compatible option in the new `run_glue.py`
I duplicated `glue` and made the following changes:
1. Change the name to `glue_compat`.
2. Change the label assignments for MNLI and AX.
Maybe it would be simpler to just overwrite the order of the label classes of the `glue` dataset ?
```python
mnli = load_dataset("glue", "mnli", label_classes=["contradiction", "entailment", "neutral"])
``` | [
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https://github.com/huggingface/datasets/pull/1739 | fixes and improvements for the WebNLG loader | The dataset card is fantastic!
Looks good to me! Did you check that this still passes the slow tests with the existing dummy data? | - fixes test sets loading in v3.0
- adds additional fields for v3.0_ru
- adds info to the WebNLG data card | 24 | text: fixes and improvements for the WebNLG loader
- fixes test sets loading in v3.0
- adds additional fields for v3.0_ru
- adds info to the WebNLG data card
The dataset card is fantastic!
Looks good to me! Did you check that this still passes the slow tests with the existing dummy data? | [
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https://github.com/huggingface/datasets/pull/1739 | fixes and improvements for the WebNLG loader | Yes, I ran and passed all the tests specified in [this guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata), including the slow ones. | - fixes test sets loading in v3.0
- adds additional fields for v3.0_ru
- adds info to the WebNLG data card | 16 | text: fixes and improvements for the WebNLG loader
- fixes test sets loading in v3.0
- adds additional fields for v3.0_ru
- adds info to the WebNLG data card
Yes, I ran and passed all the tests specified in [this guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata), including the slow ones. | [
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https://github.com/huggingface/datasets/pull/1738 | Conda support | Nice thanks :)
Note that in `datasets` the tags are simply the version without the `v`. For example `1.2.1`. | Will push a new version on anaconda cloud every time a tag starting with `v` is pushed (like `v1.2.2`).
Will appear here: https://anaconda.org/huggingface/datasets
Depends on `conda-forge` for now, so the following is required for installation:
```
conda install -c huggingface -c conda-forge datasets
``` | 19 | text: Conda support
Will push a new version on anaconda cloud every time a tag starting with `v` is pushed (like `v1.2.2`).
Will appear here: https://anaconda.org/huggingface/datasets
Depends on `conda-forge` for now, so the following is required for installation:
```
conda install -c huggingface -c conda-forge datasets
```
Nice thanks :)
Note that in `datasets` the tags are simply the version without the `v`. For example `1.2.1`. | [
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https://github.com/huggingface/datasets/pull/1726 | Offline loading | It's maybe a bit annoying to add but could we maybe have as well a version of the local data loading scripts in the package?
The `text`, `json`, `csv`. Thinking about people like in #1725 who are expecting to be able to work with local data without downloading anything.
Maybe we can add them to package_data or something? | As discussed in #824 it would be cool to make the library work in offline mode.
Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError.
This is because `prepare_module` fetches online for the latest version of the module.
To make it work in offline mode one suggestion was to reload the latest local version of the module.
I implemented that and I also raise a warning saying that the module that is loaded is the latest local version.
```python
logger.warning(
f"Using the latest cached version of the module from {cached_module_path} since it "
f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})."
)
```
I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing.
I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet. | 58 | text: Offline loading
As discussed in #824 it would be cool to make the library work in offline mode.
Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError.
This is because `prepare_module` fetches online for the latest version of the module.
To make it work in offline mode one suggestion was to reload the latest local version of the module.
I implemented that and I also raise a warning saying that the module that is loaded is the latest local version.
```python
logger.warning(
f"Using the latest cached version of the module from {cached_module_path} since it "
f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})."
)
```
I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing.
I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet.
It's maybe a bit annoying to add but could we maybe have as well a version of the local data loading scripts in the package?
The `text`, `json`, `csv`. Thinking about people like in #1725 who are expecting to be able to work with local data without downloading anything.
Maybe we can add them to package_data or something? | [
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https://github.com/huggingface/datasets/pull/1726 | Offline loading | Alright now `csv`, `json`, `text` and `pandas` are "packaged datasets", i.e. they're part of the `datasets` package, which makes them available in offline mode without any change in terms of API:
```python
from datasets import load_dataset
d = load_dataset("csv", data_files=["path/to/data.csv"])
```
Instead of loading the dataset script from the module cache, it's loaded from inside the `datasets` package.
I updated the test to still be able to fetch the dummy data files for those datasets from `datasets/{text|csv|pandas|json}/dummy` in the repo. | As discussed in #824 it would be cool to make the library work in offline mode.
Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError.
This is because `prepare_module` fetches online for the latest version of the module.
To make it work in offline mode one suggestion was to reload the latest local version of the module.
I implemented that and I also raise a warning saying that the module that is loaded is the latest local version.
```python
logger.warning(
f"Using the latest cached version of the module from {cached_module_path} since it "
f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})."
)
```
I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing.
I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet. | 80 | text: Offline loading
As discussed in #824 it would be cool to make the library work in offline mode.
Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError.
This is because `prepare_module` fetches online for the latest version of the module.
To make it work in offline mode one suggestion was to reload the latest local version of the module.
I implemented that and I also raise a warning saying that the module that is loaded is the latest local version.
```python
logger.warning(
f"Using the latest cached version of the module from {cached_module_path} since it "
f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})."
)
```
I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing.
I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet.
Alright now `csv`, `json`, `text` and `pandas` are "packaged datasets", i.e. they're part of the `datasets` package, which makes them available in offline mode without any change in terms of API:
```python
from datasets import load_dataset
d = load_dataset("csv", data_files=["path/to/data.csv"])
```
Instead of loading the dataset script from the module cache, it's loaded from inside the `datasets` package.
I updated the test to still be able to fetch the dummy data files for those datasets from `datasets/{text|csv|pandas|json}/dummy` in the repo. | [
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https://github.com/huggingface/datasets/pull/1726 | Offline loading | LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning? | As discussed in #824 it would be cool to make the library work in offline mode.
Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError.
This is because `prepare_module` fetches online for the latest version of the module.
To make it work in offline mode one suggestion was to reload the latest local version of the module.
I implemented that and I also raise a warning saying that the module that is loaded is the latest local version.
```python
logger.warning(
f"Using the latest cached version of the module from {cached_module_path} since it "
f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})."
)
```
I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing.
I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet. | 26 | text: Offline loading
As discussed in #824 it would be cool to make the library work in offline mode.
Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError.
This is because `prepare_module` fetches online for the latest version of the module.
To make it work in offline mode one suggestion was to reload the latest local version of the module.
I implemented that and I also raise a warning saying that the module that is loaded is the latest local version.
```python
logger.warning(
f"Using the latest cached version of the module from {cached_module_path} since it "
f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})."
)
```
I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing.
I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet.
LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning? | [
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https://github.com/huggingface/datasets/pull/1726 | Offline loading | > LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning?
Yep good idea. I added the date in the warning. For example `(last modified on Mon Nov 30 11:01:56 2020)` | As discussed in #824 it would be cool to make the library work in offline mode.
Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError.
This is because `prepare_module` fetches online for the latest version of the module.
To make it work in offline mode one suggestion was to reload the latest local version of the module.
I implemented that and I also raise a warning saying that the module that is loaded is the latest local version.
```python
logger.warning(
f"Using the latest cached version of the module from {cached_module_path} since it "
f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})."
)
```
I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing.
I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet. | 47 | text: Offline loading
As discussed in #824 it would be cool to make the library work in offline mode.
Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError.
This is because `prepare_module` fetches online for the latest version of the module.
To make it work in offline mode one suggestion was to reload the latest local version of the module.
I implemented that and I also raise a warning saying that the module that is loaded is the latest local version.
```python
logger.warning(
f"Using the latest cached version of the module from {cached_module_path} since it "
f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})."
)
```
I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing.
I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet.
> LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning?
Yep good idea. I added the date in the warning. For example `(last modified on Mon Nov 30 11:01:56 2020)` | [
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] |
https://github.com/huggingface/datasets/pull/1723 | ADD S3 support for downloading and uploading processed datasets | I created the documentation for `FileSystem Integration for cloud storage` with loading and saving datasets to/from a filesystem with an example of using `datasets.filesystem.S3Filesystem`. I added a note on the `Saving a processed dataset on disk and reload` saying that it is also possible to use other filesystems and cloud storages such as S3 with a link to the newly created documentation page from me.
I Attach a screenshot of it here.
| # What does this PR do?
This PR adds the functionality to load and save `datasets` from and to s3.
You can save `datasets` with either `Dataset.save_to_disk()` or `DatasetDict.save_to_disk`.
You can load `datasets` with either `load_from_disk` or `Dataset.load_from_disk()`, `DatasetDict.load_from_disk()`.
Loading `csv` or `json` datasets from s3 is not implemented.
To save/load datasets to s3 you either need to provide an `aws_profile`, which is set up on your machine, per default it uses the `default` profile or you have to pass an `aws_access_key_id` and `aws_secret_access_key`.
The implementation was done with the `fsspec` and `boto3`.
### Example `aws_profile` :
<details>
```python
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
```
</details>
### Example `aws_access_key_id` and `aws_secret_access_key` :
<details>
```python
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk",
aws_access_key_id="fake_access_key",
aws_secret_access_key="fake_secret_key"
)
load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk",
aws_access_key_id="fake_access_key",
aws_secret_access_key="fake_secret_key"
)
```
</details>
If you want to load a dataset from a public s3 bucket you can pass `anon=True`
### Example `anon=True` :
<details>
```python
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
load_from_disk("s3://moto-mock-s3-bucketdatasets/sdk",anon=True)
```
</details>
### Full Example
```python
import datasets
dataset = datasets.load_dataset("imdb")
print(f"DatasetDict contains {len(dataset)} datasets")
print(f"train Dataset has the size of: {len(dataset['train'])}")
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
remote_dataset = datasets.load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
print(f"DatasetDict contains {len(remote_dataset)} datasets")
print(f"train Dataset has the size of: {len(remote_dataset['train'])}")
```
Related to #878
I would also adjust the documentation after the code would be reviewed, as long as I leave the PR in "draft" status. Something that we can consider is renaming the functions and changing the `_disk` maybe to `_filesystem`
| 73 | text: ADD S3 support for downloading and uploading processed datasets
# What does this PR do?
This PR adds the functionality to load and save `datasets` from and to s3.
You can save `datasets` with either `Dataset.save_to_disk()` or `DatasetDict.save_to_disk`.
You can load `datasets` with either `load_from_disk` or `Dataset.load_from_disk()`, `DatasetDict.load_from_disk()`.
Loading `csv` or `json` datasets from s3 is not implemented.
To save/load datasets to s3 you either need to provide an `aws_profile`, which is set up on your machine, per default it uses the `default` profile or you have to pass an `aws_access_key_id` and `aws_secret_access_key`.
The implementation was done with the `fsspec` and `boto3`.
### Example `aws_profile` :
<details>
```python
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
```
</details>
### Example `aws_access_key_id` and `aws_secret_access_key` :
<details>
```python
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk",
aws_access_key_id="fake_access_key",
aws_secret_access_key="fake_secret_key"
)
load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk",
aws_access_key_id="fake_access_key",
aws_secret_access_key="fake_secret_key"
)
```
</details>
If you want to load a dataset from a public s3 bucket you can pass `anon=True`
### Example `anon=True` :
<details>
```python
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
load_from_disk("s3://moto-mock-s3-bucketdatasets/sdk",anon=True)
```
</details>
### Full Example
```python
import datasets
dataset = datasets.load_dataset("imdb")
print(f"DatasetDict contains {len(dataset)} datasets")
print(f"train Dataset has the size of: {len(dataset['train'])}")
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
remote_dataset = datasets.load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
print(f"DatasetDict contains {len(remote_dataset)} datasets")
print(f"train Dataset has the size of: {len(remote_dataset['train'])}")
```
Related to #878
I would also adjust the documentation after the code would be reviewed, as long as I leave the PR in "draft" status. Something that we can consider is renaming the functions and changing the `_disk` maybe to `_filesystem`
I created the documentation for `FileSystem Integration for cloud storage` with loading and saving datasets to/from a filesystem with an example of using `datasets.filesystem.S3Filesystem`. I added a note on the `Saving a processed dataset on disk and reload` saying that it is also possible to use other filesystems and cloud storages such as S3 with a link to the newly created documentation page from me.
I Attach a screenshot of it here.
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https://github.com/huggingface/datasets/pull/1722 | Added unfiltered versions of the Wiki-Auto training data for the GEM simplification task. | The current version of Wiki-Auto dataset contains a filtered version of the aligned dataset. The commit adds unfiltered versions of the data that can be useful the GEM task participants. | 30 | text: Added unfiltered versions of the Wiki-Auto training data for the GEM simplification task.
The current version of Wiki-Auto dataset contains a filtered version of the aligned dataset. The commit adds unfiltered versions of the data that can be useful the GEM task participants. | [
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|
https://github.com/huggingface/datasets/pull/1721 | [Scientific papers] Mirror datasets zip | > Nice !
>
> Could you try to reduce the size of the dummy_data.zip files ? they're quite big (300KB)
Yes, I think it might make sense to enhance the tool a tiny bit to prevent this automatically | Datasets were uploading to https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip and https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip respectively to escape google drive quota and enable faster download. | 39 | text: [Scientific papers] Mirror datasets zip
Datasets were uploading to https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip and https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip respectively to escape google drive quota and enable faster download.
> Nice !
>
> Could you try to reduce the size of the dummy_data.zip files ? they're quite big (300KB)
Yes, I think it might make sense to enhance the tool a tiny bit to prevent this automatically | [
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https://github.com/huggingface/datasets/pull/1721 | [Scientific papers] Mirror datasets zip | That's the lightest I can make it...it's long-range summarization so a single sample has ~11000 tokens. | Datasets were uploading to https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip and https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip respectively to escape google drive quota and enable faster download. | 16 | text: [Scientific papers] Mirror datasets zip
Datasets were uploading to https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip and https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip respectively to escape google drive quota and enable faster download.
That's the lightest I can make it...it's long-range summarization so a single sample has ~11000 tokens. | [
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | Quick question, @lhoestq. In this specific dataset, two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. However, I have not found an easy way to implement that. Using splits or configs does not seem appropriate.
| NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 63 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
Quick question, @lhoestq. In this specific dataset, two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. However, I have not found an easy way to implement that. Using splits or configs does not seem appropriate.
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | About the `GPE_LOC` and `GPE_ORG`. The original NorNE paper in which they published the dataset, does an evaluation on three different NER tag sets, one considering `GPE_LOC` and `GPE_ORG` as they are, another changing them to be just `GPE`, and another one by changing it to become `LOC` and `ORG`. The called these sets, `norne-full`, `norne-7`, and `norne-9`. What I would like is to provide a way for the user of this dataset to get `norne-7` and `norne-9` without having to duplicate the code. | NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 84 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
About the `GPE_LOC` and `GPE_ORG`. The original NorNE paper in which they published the dataset, does an evaluation on three different NER tag sets, one considering `GPE_LOC` and `GPE_ORG` as they are, another changing them to be just `GPE`, and another one by changing it to become `LOC` and `ORG`. The called these sets, `norne-full`, `norne-7`, and `norne-9`. What I would like is to provide a way for the user of this dataset to get `norne-7` and `norne-9` without having to duplicate the code. | [
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | Ok I see !
I guess you can have three configurations `norne-full`, `norne-7` and `norne-9`.
Each config can have different feature types. You can simply check for the `self.config.name` in the `_info(self)` method and pick the right ClassLabel names accordingly. And then in `_generate_examples` as well you can check for `self.config.name` to know how to process the labels to yield either GPE_LOC/GPE_ORG, GPE or LOC/ORG | NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 65 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
Ok I see !
I guess you can have three configurations `norne-full`, `norne-7` and `norne-9`.
Each config can have different feature types. You can simply check for the `self.config.name` in the `_info(self)` method and pick the right ClassLabel names accordingly. And then in `_generate_examples` as well you can check for `self.config.name` to know how to process the labels to yield either GPE_LOC/GPE_ORG, GPE or LOC/ORG | [
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | But I'm already using the configurations for the different language
varieties. So you propose having something like `bokmaal`, `bokmaal-7`,
etc? Would there be a different way? If not, I'd be fine the corpus as it
is until we come up with a solution. Thanks in any case.
--
Sent using a cell-phone, so sorry for the typos and wrong auto-corrections.
On Tue, Jan 19, 2021, 4:56 PM Quentin Lhoest <notifications@github.com>
wrote:
> Ok I see !
> I guess you can have three configurations norne-full, norne-7 and norne-9.
> Each config can have different feature types. You can simply check for the
> self.config.name in the _info(self) method and pick the right ClassLabel
> names accordingly. And then in _generate_examples as well you can check
> for self.config.name to know how to process the labels to yield either
> GPE_LOC/GPE_ORG, GPE or LOC/ORG
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/pull/1720#issuecomment-762936612>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AABKLYOWNDBD76WZPJHFCWLS2WTTHANCNFSM4V6GSUQA>
> .
>
| NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 176 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
But I'm already using the configurations for the different language
varieties. So you propose having something like `bokmaal`, `bokmaal-7`,
etc? Would there be a different way? If not, I'd be fine the corpus as it
is until we come up with a solution. Thanks in any case.
--
Sent using a cell-phone, so sorry for the typos and wrong auto-corrections.
On Tue, Jan 19, 2021, 4:56 PM Quentin Lhoest <notifications@github.com>
wrote:
> Ok I see !
> I guess you can have three configurations norne-full, norne-7 and norne-9.
> Each config can have different feature types. You can simply check for the
> self.config.name in the _info(self) method and pick the right ClassLabel
> names accordingly. And then in _generate_examples as well you can check
> for self.config.name to know how to process the labels to yield either
> GPE_LOC/GPE_ORG, GPE or LOC/ORG
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/pull/1720#issuecomment-762936612>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AABKLYOWNDBD76WZPJHFCWLS2WTTHANCNFSM4V6GSUQA>
> .
>
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | The first option about having configurations like `bokmaal-7`, `bokmaal-9` etc. would definitely work.
A second option would be to add a parameter `ner_tags_set` to `NorneConfig` and then one could load them with
```python
bokmaal_full = load_dataset("norne", "bokmaal", ner_tags_set="norne-full")
```
for example.
What do you think ? | NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 46 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
The first option about having configurations like `bokmaal-7`, `bokmaal-9` etc. would definitely work.
A second option would be to add a parameter `ner_tags_set` to `NorneConfig` and then one could load them with
```python
bokmaal_full = load_dataset("norne", "bokmaal", ner_tags_set="norne-full")
```
for example.
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | Hi @versae have you had a chance to consider one of the two options for the config ?
I think both are ok but I have a small preference for the first one since it's simpler to implement.
Feel free to ping me if you have questions or if I can help :) | NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 53 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
Hi @versae have you had a chance to consider one of the two options for the config ?
I think both are ok but I have a small preference for the first one since it's simpler to implement.
Feel free to ping me if you have questions or if I can help :) | [
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | Hi @lhoestq. Agree, option 1 seems easier to implement. Just haven't had bandwidth to get to it yet. Hopefully starting next week I'll be able to update the PR. | NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 29 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
Hi @lhoestq. Agree, option 1 seems easier to implement. Just haven't had bandwidth to get to it yet. Hopefully starting next week I'll be able to update the PR. | [
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | Hi @versae ! Did you manage to add the configurations ? Let me know if we can help you on this | NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 21 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
Hi @versae ! Did you manage to add the configurations ? Let me know if we can help you on this | [
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | Hi @lhoestq, I do actually have to code ready, just need to generate the dummy data for it. | NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 18 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
Hi @lhoestq, I do actually have to code ready, just need to generate the dummy data for it. | [
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https://github.com/huggingface/datasets/pull/1720 | Adding the NorNE dataset for NER | One thing I don't know how to do is to make `_info(self)` return the different NER tags in its `DatasetInfo` object depending on the specific config. | NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | 26 | text: Adding the NorNE dataset for NER
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
One thing I don't know how to do is to make `_info(self)` return the different NER tags in its `DatasetInfo` object depending on the specific config. | [
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https://github.com/huggingface/datasets/pull/1714 | Adding adversarialQA dataset | Oh that's a really cool one, we'll review/merge it soon!
In the meantime, do you have any specific positive/negative feedback on the process of adding a datasets Max?
Did you follow the instruction in the [detailed step-by-step](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)? | Adding the adversarialQA dataset (https://adversarialqa.github.io/) from Beat the AI (https://arxiv.org/abs/2002.00293) | 37 | text: Adding adversarialQA dataset
Adding the adversarialQA dataset (https://adversarialqa.github.io/) from Beat the AI (https://arxiv.org/abs/2002.00293)
Oh that's a really cool one, we'll review/merge it soon!
In the meantime, do you have any specific positive/negative feedback on the process of adding a datasets Max?
Did you follow the instruction in the [detailed step-by-step](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)? | [
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https://github.com/huggingface/datasets/pull/1714 | Adding adversarialQA dataset | Thanks Thom, been a while, hope all is well!
Yes, I followed the step by step instructions and found them pretty straightforward. The only things I wasn't sure of were what should go into the YAML tags field for the dataset card, and whether there was a list of options somewhere (maybe akin to the metrics?) of the possible supported tasks. I found the rest very intuitive and the automated metadata and dummy data generation very handy. Thanks! | Adding the adversarialQA dataset (https://adversarialqa.github.io/) from Beat the AI (https://arxiv.org/abs/2002.00293) | 78 | text: Adding adversarialQA dataset
Adding the adversarialQA dataset (https://adversarialqa.github.io/) from Beat the AI (https://arxiv.org/abs/2002.00293)
Thanks Thom, been a while, hope all is well!
Yes, I followed the step by step instructions and found them pretty straightforward. The only things I wasn't sure of were what should go into the YAML tags field for the dataset card, and whether there was a list of options somewhere (maybe akin to the metrics?) of the possible supported tasks. I found the rest very intuitive and the automated metadata and dummy data generation very handy. Thanks! | [
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https://github.com/huggingface/datasets/pull/1714 | Adding adversarialQA dataset | @maxbartolo cool addition!
For the YAML tag, you should use the tagging app we provide to choose from a drop-down menu:
https://github.com/huggingface/datasets-tagging
The process is described toward the end of the [step-by-step guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card), do you have any suggestions for making it easier to find?
Otherwise, the dataset card is really cool, thanks for making it so complete!
| Adding the adversarialQA dataset (https://adversarialqa.github.io/) from Beat the AI (https://arxiv.org/abs/2002.00293) | 57 | text: Adding adversarialQA dataset
Adding the adversarialQA dataset (https://adversarialqa.github.io/) from Beat the AI (https://arxiv.org/abs/2002.00293)
@maxbartolo cool addition!
For the YAML tag, you should use the tagging app we provide to choose from a drop-down menu:
https://github.com/huggingface/datasets-tagging
The process is described toward the end of the [step-by-step guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card), do you have any suggestions for making it easier to find?
Otherwise, the dataset card is really cool, thanks for making it so complete!
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https://github.com/huggingface/datasets/pull/1714 | Adding adversarialQA dataset | @yjernite
Thanks, YAML tags added. I think my main issue was with the flow of the [step-by-step guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). For example, the [card creator](https://huggingface.co/datasets/card-creator/) is introduced in Step 4, right after creating an empty directory for your dataset. The first field it requires are the YAML tags, which (at least for me) was the last step of the process.
I'd suggest having the guide structured in the same order as the creation process. For me it was something like:
- Step 1: Preparing your env
- Step 2: Write the loading/processing code
- Step 3: Automatically generate dummy data and `dataset_infos.json`
- Step 4: Tag the dataset
- Step 5: Write the dataset card using the [card creator](https://huggingface.co/datasets/card-creator/)
- Step 6: Open a Pull Request on the main HuggingFace repo and share your work!!
Thanks again! | Adding the adversarialQA dataset (https://adversarialqa.github.io/) from Beat the AI (https://arxiv.org/abs/2002.00293) | 135 | text: Adding adversarialQA dataset
Adding the adversarialQA dataset (https://adversarialqa.github.io/) from Beat the AI (https://arxiv.org/abs/2002.00293)
@yjernite
Thanks, YAML tags added. I think my main issue was with the flow of the [step-by-step guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). For example, the [card creator](https://huggingface.co/datasets/card-creator/) is introduced in Step 4, right after creating an empty directory for your dataset. The first field it requires are the YAML tags, which (at least for me) was the last step of the process.
I'd suggest having the guide structured in the same order as the creation process. For me it was something like:
- Step 1: Preparing your env
- Step 2: Write the loading/processing code
- Step 3: Automatically generate dummy data and `dataset_infos.json`
- Step 4: Tag the dataset
- Step 5: Write the dataset card using the [card creator](https://huggingface.co/datasets/card-creator/)
- Step 6: Open a Pull Request on the main HuggingFace repo and share your work!!
Thanks again! | [
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https://github.com/huggingface/datasets/pull/1712 | Silicone | Hi @eusip,
> When should we expect to see our dataset appear in the search dropdown at huggingface.co?
when this PR is merged. | My collaborators and I within the Affective Computing team at Telecom Paris would like to push our spoken dialogue dataset for publication. | 23 | text: Silicone
My collaborators and I within the Affective Computing team at Telecom Paris would like to push our spoken dialogue dataset for publication.
Hi @eusip,
> When should we expect to see our dataset appear in the search dropdown at huggingface.co?
when this PR is merged. | [
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https://github.com/huggingface/datasets/pull/1712 | Silicone | I've implemented all the changes requested by @lhoestq but I made the mistake of trying to change the remote branch name.
Hopefully the changes are seen on your end as both branches `silicone` and `main` should be up-to-date. | My collaborators and I within the Affective Computing team at Telecom Paris would like to push our spoken dialogue dataset for publication. | 38 | text: Silicone
My collaborators and I within the Affective Computing team at Telecom Paris would like to push our spoken dialogue dataset for publication.
I've implemented all the changes requested by @lhoestq but I made the mistake of trying to change the remote branch name.
Hopefully the changes are seen on your end as both branches `silicone` and `main` should be up-to-date. | [
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https://github.com/huggingface/datasets/pull/1712 | Silicone | It looks like the PR includes changes about many other files than the ones for Silicone (+30,000 line changes)
Maybe you can try to create another branch and another PR ? | My collaborators and I within the Affective Computing team at Telecom Paris would like to push our spoken dialogue dataset for publication. | 31 | text: Silicone
My collaborators and I within the Affective Computing team at Telecom Paris would like to push our spoken dialogue dataset for publication.
It looks like the PR includes changes about many other files than the ones for Silicone (+30,000 line changes)
Maybe you can try to create another branch and another PR ? | [
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https://github.com/huggingface/datasets/pull/1712 | Silicone | > It looks like the PR includes changes about many other files than the ones for Silicone (+30,000 line changes)
>
> Maybe you can try to create another branch and another PR ?
Sure. I will make a new pull request. | My collaborators and I within the Affective Computing team at Telecom Paris would like to push our spoken dialogue dataset for publication. | 42 | text: Silicone
My collaborators and I within the Affective Computing team at Telecom Paris would like to push our spoken dialogue dataset for publication.
> It looks like the PR includes changes about many other files than the ones for Silicone (+30,000 line changes)
>
> Maybe you can try to create another branch and another PR ?
Sure. I will make a new pull request. | [
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https://github.com/huggingface/datasets/pull/1707 | Added generated READMEs for datasets that were missing one. | Looks like we need to trim the ones with too many configs, will look into it tomorrow! | This is it: we worked on a generator with Yacine @yjernite , and we generated dataset cards for all missing ones (161), with all the information we could gather from datasets repository, and using dummy_data to generate examples when possible.
Code is available here for the moment: https://github.com/madlag/datasets_readme_generator .
We will move it to a Hugging Face repository and to https://huggingface.co/datasets/card-creator/ later.
| 17 | text: Added generated READMEs for datasets that were missing one.
This is it: we worked on a generator with Yacine @yjernite , and we generated dataset cards for all missing ones (161), with all the information we could gather from datasets repository, and using dummy_data to generate examples when possible.
Code is available here for the moment: https://github.com/madlag/datasets_readme_generator .
We will move it to a Hugging Face repository and to https://huggingface.co/datasets/card-creator/ later.
Looks like we need to trim the ones with too many configs, will look into it tomorrow! | [
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https://github.com/huggingface/datasets/pull/1703 | Improvements regarding caching and fingerprinting | I few comments here for discussion:
- I'm not convinced yet the end user should really have to understand the difference between "caching" and 'fingerprinting", what do you think? I think fingerprinting should probably stay as an internal thing. Is there a case where we want cahing without fingerprinting or vice-versa?
- while I think the random fingerprint mechanism is smart, I have one question: when we disable caching or fingerprinting we also probably don't want the disk usage to grow so we should then try to keep only one cache file. Is it the case currently?
- the warning should be emitted only once per session if possible (we have a mechanism to do that in transformers, you should ask Lysandre/Sylvain)
| This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features | 122 | text: Improvements regarding caching and fingerprinting
This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features
I few comments here for discussion:
- I'm not convinced yet the end user should really have to understand the difference between "caching" and 'fingerprinting", what do you think? I think fingerprinting should probably stay as an internal thing. Is there a case where we want cahing without fingerprinting or vice-versa?
- while I think the random fingerprint mechanism is smart, I have one question: when we disable caching or fingerprinting we also probably don't want the disk usage to grow so we should then try to keep only one cache file. Is it the case currently?
- the warning should be emitted only once per session if possible (we have a mechanism to do that in transformers, you should ask Lysandre/Sylvain)
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https://github.com/huggingface/datasets/pull/1703 | Improvements regarding caching and fingerprinting | About your points:
- Yes I agree, I just wanted to bring the discussion on this point. Until now fingerprinting hasn't been blocking for user experience. I'll probably remove the enable/disable fingerprinting function to keep things simple from the user's perspective.
- Right now every time a not in-place transform (i.e. map, filter) is applied, a new cache file is created. It is the case even if caching is disabled since disabling it only means that the cache file won't be reloaded. Therefore you're right that it might end up filling the disk with files that won't be reused. I like the idea of keeping only one cache file. Currently all the cache files are kept on disk until the user clears the cache. To be able to keep only one, we need to know if a dataset that has been transformed is still loaded or not. For example
```python
# case 1 - keep both cache files (dataset1 and dataset2)
dataset2 = dataset1.map(...)
# case 2 - keep only the new cache file
dataset1 = dataset1.map(...)
```
In python it doesn't seem trivial to detect such changes. One thing that we can actually do on the other hand is store the cache files in a temporary directory that is cleared when the session closes. I think that's a good a simple solution for this problem.
- Yes good idea ! I don't like spam either :) | This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features | 237 | text: Improvements regarding caching and fingerprinting
This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features
About your points:
- Yes I agree, I just wanted to bring the discussion on this point. Until now fingerprinting hasn't been blocking for user experience. I'll probably remove the enable/disable fingerprinting function to keep things simple from the user's perspective.
- Right now every time a not in-place transform (i.e. map, filter) is applied, a new cache file is created. It is the case even if caching is disabled since disabling it only means that the cache file won't be reloaded. Therefore you're right that it might end up filling the disk with files that won't be reused. I like the idea of keeping only one cache file. Currently all the cache files are kept on disk until the user clears the cache. To be able to keep only one, we need to know if a dataset that has been transformed is still loaded or not. For example
```python
# case 1 - keep both cache files (dataset1 and dataset2)
dataset2 = dataset1.map(...)
# case 2 - keep only the new cache file
dataset1 = dataset1.map(...)
```
In python it doesn't seem trivial to detect such changes. One thing that we can actually do on the other hand is store the cache files in a temporary directory that is cleared when the session closes. I think that's a good a simple solution for this problem.
- Yes good idea ! I don't like spam either :) | [
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] |
https://github.com/huggingface/datasets/pull/1703 | Improvements regarding caching and fingerprinting | > * To be able to keep only one, we need to know if a dataset that has been transformed is still loaded or not. For example
>
> ```python
> # case 1 - keep both cache files (dataset1 and dataset2)
> dataset2 = dataset1.map(...)
> # case 2 - keep only the new cache file
> dataset1 = dataset1.map(...)
> ```
I see what you mean. It's a tricky question. One option would be that if caching is deactivated we have a single memory mapped file and have copy act as a copy by reference instead of a copy by value. We will then probably want a `copy()` or `deepcopy()` functionality. Maybe we should think a little bit about it though. | This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features | 123 | text: Improvements regarding caching and fingerprinting
This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features
> * To be able to keep only one, we need to know if a dataset that has been transformed is still loaded or not. For example
>
> ```python
> # case 1 - keep both cache files (dataset1 and dataset2)
> dataset2 = dataset1.map(...)
> # case 2 - keep only the new cache file
> dataset1 = dataset1.map(...)
> ```
I see what you mean. It's a tricky question. One option would be that if caching is deactivated we have a single memory mapped file and have copy act as a copy by reference instead of a copy by value. We will then probably want a `copy()` or `deepcopy()` functionality. Maybe we should think a little bit about it though. | [
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] |
https://github.com/huggingface/datasets/pull/1703 | Improvements regarding caching and fingerprinting | - I like the idea of using a temporary directory per session!
- If the default behavior when caching is disabled is to re-use the same file, I'm a little worried about people making mistakes and having to re-download and process from scratch.
- So we already have a keyword argument for `dataset1 = dataset1.map(..., in_place=True)`? | This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features | 56 | text: Improvements regarding caching and fingerprinting
This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features
- I like the idea of using a temporary directory per session!
- If the default behavior when caching is disabled is to re-use the same file, I'm a little worried about people making mistakes and having to re-download and process from scratch.
- So we already have a keyword argument for `dataset1 = dataset1.map(..., in_place=True)`? | [
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https://github.com/huggingface/datasets/pull/1703 | Improvements regarding caching and fingerprinting | > * If the default behavior when caching is disabled is to re-use the same file, I'm a little worried about people making mistakes and having to re-download and process from scratch.
We should distinguish between the caching from load_dataset (base dataset cache files) and the caching after dataset transforms such as map or filter (transformed dataset cache files). When disabling caching only the second type (for map and filter) doesn't reload from cache files.
Therefore nothing is re-downloaded. To re-download the dataset entirely the argument `download_mode="force_redownload"` must be used in `load_dataset`.
Do we have to think more about the naming to make things less confusing in your opinion ?
> * So we already have a keyword argument for `dataset1 = dataset1.map(..., in_place=True)`?
There's no such `in_place` parameter in map, what do you mean exactly ? | This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features | 137 | text: Improvements regarding caching and fingerprinting
This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features
> * If the default behavior when caching is disabled is to re-use the same file, I'm a little worried about people making mistakes and having to re-download and process from scratch.
We should distinguish between the caching from load_dataset (base dataset cache files) and the caching after dataset transforms such as map or filter (transformed dataset cache files). When disabling caching only the second type (for map and filter) doesn't reload from cache files.
Therefore nothing is re-downloaded. To re-download the dataset entirely the argument `download_mode="force_redownload"` must be used in `load_dataset`.
Do we have to think more about the naming to make things less confusing in your opinion ?
> * So we already have a keyword argument for `dataset1 = dataset1.map(..., in_place=True)`?
There's no such `in_place` parameter in map, what do you mean exactly ? | [
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https://github.com/huggingface/datasets/pull/1703 | Improvements regarding caching and fingerprinting | I updated the PR:
- I removed the enable/disable fingerprinting function
- if caching is disabled arrow files are written in a temporary directory that is deleted when session closes
- the warning that is showed when hashing a transform fails is only showed once
- I added the `set_caching_enabled` function to the docs and explained the caching mechanism and its relation with fingerprinting
I would love to have some feedback :) | This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features | 72 | text: Improvements regarding caching and fingerprinting
This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features
I updated the PR:
- I removed the enable/disable fingerprinting function
- if caching is disabled arrow files are written in a temporary directory that is deleted when session closes
- the warning that is showed when hashing a transform fails is only showed once
- I added the `set_caching_enabled` function to the docs and explained the caching mechanism and its relation with fingerprinting
I would love to have some feedback :) | [
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https://github.com/huggingface/datasets/pull/1703 | Improvements regarding caching and fingerprinting | > > * So we already have a keyword argument for `dataset1 = dataset1.map(..., in_place=True)`?
>
> There's no such `in_place` parameter in map, what do you mean exactly ?
Sorry, that wasn't clear at all. I was responding to your previous comment about case 1 / case 2. I don't think the behavior should depend on the command, but we could have:
```
# case 1 - keep both cache files (dataset1 and dataset2)
dataset2 = dataset1.map(...)
# case 2 - keep only the new cache file
dataset1 = dataset1.map(..., in_place=True)
```
Case 1 returns a new reference using the new cache file, case 2 returns the same reference | This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features | 110 | text: Improvements regarding caching and fingerprinting
This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features
> > * So we already have a keyword argument for `dataset1 = dataset1.map(..., in_place=True)`?
>
> There's no such `in_place` parameter in map, what do you mean exactly ?
Sorry, that wasn't clear at all. I was responding to your previous comment about case 1 / case 2. I don't think the behavior should depend on the command, but we could have:
```
# case 1 - keep both cache files (dataset1 and dataset2)
dataset2 = dataset1.map(...)
# case 2 - keep only the new cache file
dataset1 = dataset1.map(..., in_place=True)
```
Case 1 returns a new reference using the new cache file, case 2 returns the same reference | [
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https://github.com/huggingface/datasets/pull/1703 | Improvements regarding caching and fingerprinting | > Sorry, that wasn't clear at all. I was responding to your previous comment about case 1 / case 2. I don't think the behavior should depend on the command, but we could have:
>
> ```
> # case 1 - keep both cache files (dataset1 and dataset2)
> dataset2 = dataset1.map(...)
> # case 2 - keep only the new cache file
> dataset1 = dataset1.map(..., in_place=True)
> ```
>
> Case 1 returns a new reference using the new cache file, case 2 returns the same reference
Ok I see !
`in_place` is a parameter that is used in general to designate a transform so I would name that differently (maybe `overwrite` or something like that).
Not sure if it's possible to update an already existing arrow file that is memory-mapped, let me check real quick.
Also it's possible to call `dataset2.cleanup_cache_files()` to delete the other cache files if we create a new one after the transform. Or even to get the cache file with `dataset1.cache_files` and let the user remove them by hand.
EDIT: updating an arrow file in place is not part of the current API of pyarrow, so we would have to make new files.
| This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features | 201 | text: Improvements regarding caching and fingerprinting
This PR adds these features:
- Enable/disable caching
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
It is equivalent to setting `load_from_cache` to `False` in dataset transforms.
```python
from datasets import set_caching_enabled
set_caching_enabled(False)
```
- Allow unpicklable functions in `map`
If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised.
```python
logger.warning(
f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
"Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
"If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything."
)
```
and also (open to discussion, EDIT: actually NOT included):
- Enable/disable fingerprinting
Fingerprinting allows to have one deterministic fingerprint per dataset state.
A dataset fingerprint is updated after each transform.
Re-running the same transforms on a dataset in a different session results in the same fingerprint.
Disabling the fingerprinting mechanism makes all the fingerprints random.
Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different.
Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed.
Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching.
```python
from datasets import set_fingerprinting_enabled
set_fingerprinting_enabled(False)
```
Other details:
- I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints.
- I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`.
Todo: tests for set_fingerprinting_enabled + documentation for all the above features
> Sorry, that wasn't clear at all. I was responding to your previous comment about case 1 / case 2. I don't think the behavior should depend on the command, but we could have:
>
> ```
> # case 1 - keep both cache files (dataset1 and dataset2)
> dataset2 = dataset1.map(...)
> # case 2 - keep only the new cache file
> dataset1 = dataset1.map(..., in_place=True)
> ```
>
> Case 1 returns a new reference using the new cache file, case 2 returns the same reference
Ok I see !
`in_place` is a parameter that is used in general to designate a transform so I would name that differently (maybe `overwrite` or something like that).
Not sure if it's possible to update an already existing arrow file that is memory-mapped, let me check real quick.
Also it's possible to call `dataset2.cleanup_cache_files()` to delete the other cache files if we create a new one after the transform. Or even to get the cache file with `dataset1.cache_files` and let the user remove them by hand.
EDIT: updating an arrow file in place is not part of the current API of pyarrow, so we would have to make new files.
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https://github.com/huggingface/datasets/pull/1698 | Update Coached Conv Pref DatasetCard | Really cool!
Can you add some task tags for `dialogue-modeling` (under `sequence-modeling`) and `parsing` (under `structured-prediction`)? | Update Coached Conversation Preferance DatasetCard | 16 | text: Update Coached Conv Pref DatasetCard
Update Coached Conversation Preferance DatasetCard
Really cool!
Can you add some task tags for `dialogue-modeling` (under `sequence-modeling`) and `parsing` (under `structured-prediction`)? | [
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] |
https://github.com/huggingface/datasets/pull/1695 | fix ner_tag bugs in thainer | > Thanks :)
>
> Apparently the dummy_data.zip got removed. Is this expected ?
> Also can you remove the `data-pos.conll` file that you added ?
Not expected. I forgot to remove the `dummy_data` folder used to create `dummy_data.zip`.
Changed to only `dummy_data.zip`. | fix bug that results in `ner_tag` always equal to 'O'. | 43 | text: fix ner_tag bugs in thainer
fix bug that results in `ner_tag` always equal to 'O'.
> Thanks :)
>
> Apparently the dummy_data.zip got removed. Is this expected ?
> Also can you remove the `data-pos.conll` file that you added ?
Not expected. I forgot to remove the `dummy_data` folder used to create `dummy_data.zip`.
Changed to only `dummy_data.zip`. | [
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https://github.com/huggingface/datasets/pull/1694 | Add OSCAR | Hi @lhoestq, on the OSCAR dataset, the document boundaries are defined by an empty line. Are there any chances to keep this empty line or explicitly group the sentences of a document? I'm asking for this 'cause I need to know if some sentences belong to the same document on my current OSCAR dataset usage. | Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data | 55 | text: Add OSCAR
Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data
Hi @lhoestq, on the OSCAR dataset, the document boundaries are defined by an empty line. Are there any chances to keep this empty line or explicitly group the sentences of a document? I'm asking for this 'cause I need to know if some sentences belong to the same document on my current OSCAR dataset usage. | [
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https://github.com/huggingface/datasets/pull/1694 | Add OSCAR | Indeed currently it yields one example per line and ignore the empty lines.
Maybe the best is to group them by paragraph then, and yield one example when an empty line is found.
What do you think ? | Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data | 38 | text: Add OSCAR
Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data
Indeed currently it yields one example per line and ignore the empty lines.
Maybe the best is to group them by paragraph then, and yield one example when an empty line is found.
What do you think ? | [
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https://github.com/huggingface/datasets/pull/1694 | Add OSCAR | I think to group them is the best choice indeed, I actually did this on [brwac](https://github.com/huggingface/datasets/tree/master/datasets/brwac) dataset too, it's another huge textual dataset. | Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data | 23 | text: Add OSCAR
Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data
I think to group them is the best choice indeed, I actually did this on [brwac](https://github.com/huggingface/datasets/tree/master/datasets/brwac) dataset too, it's another huge textual dataset. | [
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] |
https://github.com/huggingface/datasets/pull/1694 | Add OSCAR | Ok I just launched the computation of the dataset_infos.json again by grouping lines in paragraphs.
The new _generate_examples is
```python
def _generate_examples(self, filepaths):
"""This function returns the examples in the raw (text) form."""
id_ = 0
current_lines = []
for filepath in filepaths:
logging.info("generating examples from = %s", filepath)
with gzip.open(filepath, "rt") as f:
for line in f:
if len(line.strip()) > 0:
current_lines.append(line)
else:
feature = id_, {"id": id_, "text": "".join(current_lines)}
yield feature
id_ += 1
current_lines = []
# last paragraph
if current_lines:
feature = id_, {"id": id_, "text": "".join(current_lines)}
yield feature
``` | Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data | 94 | text: Add OSCAR
Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data
Ok I just launched the computation of the dataset_infos.json again by grouping lines in paragraphs.
The new _generate_examples is
```python
def _generate_examples(self, filepaths):
"""This function returns the examples in the raw (text) form."""
id_ = 0
current_lines = []
for filepath in filepaths:
logging.info("generating examples from = %s", filepath)
with gzip.open(filepath, "rt") as f:
for line in f:
if len(line.strip()) > 0:
current_lines.append(line)
else:
feature = id_, {"id": id_, "text": "".join(current_lines)}
yield feature
id_ += 1
current_lines = []
# last paragraph
if current_lines:
feature = id_, {"id": id_, "text": "".join(current_lines)}
yield feature
``` | [
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https://github.com/huggingface/datasets/pull/1694 | Add OSCAR | Is there any chance to also keep the sentences raw (without the `"".join()`)?. This is useful if you wanna train models where one of the tasks you perform is document sentence permutation... that's my case :) | Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data | 36 | text: Add OSCAR
Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data
Is there any chance to also keep the sentences raw (without the `"".join()`)?. This is useful if you wanna train models where one of the tasks you perform is document sentence permutation... that's my case :) | [
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https://github.com/huggingface/datasets/pull/1694 | Add OSCAR | They are raw in the sense that nothing is changed from the raw file for each paragraph.
You can split sentences on new lines `\n` for example.
The first example for the unshuffled deduplicated english is going to be
> Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visit to Malawi. Chief Napoleon conveyed the desperate need for a program to intervene and care for the orphans and vulnerable children (OVC) in Malawi, and John committed to help.
> Established in honor of John & Lindy’s son, Christopher Blanchard, this particular program is very dear to the Blanchard family. Dana Blanchard, or Mama Dana as she is more commonly referred to at Mtendere, lived on site during the initial development, and she returns each summer to spend the season with her Malawian family. The heart of the program is to be His hands and feet by caring for the children at Mtendere, and meeting their spiritual, physical, academic, and emotional needs.
> [...]
> 100X Development Foundation, Inc. is registered 503 (c)(3) nonprofit organization. Donations are deductable to the full extent allowable under IRS regulations. | Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data | 197 | text: Add OSCAR
Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data
They are raw in the sense that nothing is changed from the raw file for each paragraph.
You can split sentences on new lines `\n` for example.
The first example for the unshuffled deduplicated english is going to be
> Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visit to Malawi. Chief Napoleon conveyed the desperate need for a program to intervene and care for the orphans and vulnerable children (OVC) in Malawi, and John committed to help.
> Established in honor of John & Lindy’s son, Christopher Blanchard, this particular program is very dear to the Blanchard family. Dana Blanchard, or Mama Dana as she is more commonly referred to at Mtendere, lived on site during the initial development, and she returns each summer to spend the season with her Malawian family. The heart of the program is to be His hands and feet by caring for the children at Mtendere, and meeting their spiritual, physical, academic, and emotional needs.
> [...]
> 100X Development Foundation, Inc. is registered 503 (c)(3) nonprofit organization. Donations are deductable to the full extent allowable under IRS regulations. | [
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https://github.com/huggingface/datasets/pull/1694 | Add OSCAR | I thought the line reader would omit the `\n` character. I can easily split the sentences as you suggested. Thanks @lhoestq! 😃 | Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data | 22 | text: Add OSCAR
Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data
I thought the line reader would omit the `\n` character. I can easily split the sentences as you suggested. Thanks @lhoestq! 😃 | [
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