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https://github.com/huggingface/datasets/pull/1254 | Added WikiText-TL-39 | looks like this PR also includes changes about another dataset `covid_qa_deepset`
Could you create another branch and another PR that only includes the changes for the wikitext-tl-39 dataset ? | This PR adds the WikiText-TL-39 Filipino Language Modeling dataset.
Paper: https://arxiv.org/abs/1907.00409
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks | 29 | text: Added WikiText-TL-39
This PR adds the WikiText-TL-39 Filipino Language Modeling dataset.
Paper: https://arxiv.org/abs/1907.00409
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
looks like this PR also includes changes about another dataset `covid_qa_deepset`
Could you create another branch and another PR that only includes the changes for the wikitext-tl-39 dataset ? | [
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https://github.com/huggingface/datasets/pull/1249 | Add doc2dial dataset | It not always practical to use nested `Sequence`. If you have troubles with sequence you can use lists instead.
For example
```python
features=datasets.Features(
{
"dial_id": datasets.Value("string"),
"doc_id": datasets.Value("string"),
"domain": datasets.Value("string"),
"turns": [
{
"turn_id": datasets.Value("int32"),
"role": datasets.Value("string"),
"da": datasets.Value("string"),
"reference": [
{
"keys" : datasets.Value("string"),
"values": datasets.Value("string"),
}
],
"utterance": datasets.Value("string"),
}
],
}
),
```
this way `turns` will be a list of dict, and the "reference" key of `turns` will be a list of dict as well | ### Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset v0.9
Once complete this will add the [Doc2dial](https://doc2dial.github.io/data.html) dataset from the generic data sets list. | 79 | text: Add doc2dial dataset
### Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset v0.9
Once complete this will add the [Doc2dial](https://doc2dial.github.io/data.html) dataset from the generic data sets list.
It not always practical to use nested `Sequence`. If you have troubles with sequence you can use lists instead.
For example
```python
features=datasets.Features(
{
"dial_id": datasets.Value("string"),
"doc_id": datasets.Value("string"),
"domain": datasets.Value("string"),
"turns": [
{
"turn_id": datasets.Value("int32"),
"role": datasets.Value("string"),
"da": datasets.Value("string"),
"reference": [
{
"keys" : datasets.Value("string"),
"values": datasets.Value("string"),
}
],
"utterance": datasets.Value("string"),
}
],
}
),
```
this way `turns` will be a list of dict, and the "reference" key of `turns` will be a list of dict as well | [
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https://github.com/huggingface/datasets/pull/1249 | Add doc2dial dataset | No problem thanks for all your help getting this to the final stages! Added .gitignore, removed .lock and applied the changes you asked for. | ### Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset v0.9
Once complete this will add the [Doc2dial](https://doc2dial.github.io/data.html) dataset from the generic data sets list. | 24 | text: Add doc2dial dataset
### Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset v0.9
Once complete this will add the [Doc2dial](https://doc2dial.github.io/data.html) dataset from the generic data sets list.
No problem thanks for all your help getting this to the final stages! Added .gitignore, removed .lock and applied the changes you asked for. | [
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https://github.com/huggingface/datasets/pull/1247 | Adding indonlu dataset | looks like this PR includes changes about many files other than the ones for IndoNLU
Could you create another branch and another PR please ? | IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia. It contains 12 datasets. | 25 | text: Adding indonlu dataset
IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia. It contains 12 datasets.
looks like this PR includes changes about many files other than the ones for IndoNLU
Could you create another branch and another PR please ? | [
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https://github.com/huggingface/datasets/pull/1247 | Adding indonlu dataset | > looks like this PR includes changes about many files other than the ones for IndoNLU
> Could you create another branch and another PR please ?
Okay I'll make it | IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia. It contains 12 datasets. | 31 | text: Adding indonlu dataset
IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia. It contains 12 datasets.
> looks like this PR includes changes about many files other than the ones for IndoNLU
> Could you create another branch and another PR please ?
Okay I'll make it | [
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https://github.com/huggingface/datasets/pull/1242 | adding bprec | looks like this PR includes changes to many files other than the ones related to bprec
Can you create another branch and another PR please ? | 26 | text: adding bprec
looks like this PR includes changes to many files other than the ones related to bprec
Can you create another branch and another PR please ? | [
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|
https://github.com/huggingface/datasets/pull/1242 | adding bprec | > looks like this PR includes changes to many files other than the ones related to bprec
> Can you create another branch and another PR please ?
Yes, I realized I messed this one up, learning my way :) I'll close this one and open another hopefully clean PR :) Thanks! | 52 | text: adding bprec
> looks like this PR includes changes to many files other than the ones related to bprec
> Can you create another branch and another PR please ?
Yes, I realized I messed this one up, learning my way :) I'll close this one and open another hopefully clean PR :) Thanks! | [
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|
https://github.com/huggingface/datasets/pull/1240 | Multi Domain Sentiment Analysis Dataset (MDSA) | Also if you would use `xml.etree.ElementTree` to parse the XML it would be awesome, because right now you're using an external dependency `xmltodict ` | 24 | text: Multi Domain Sentiment Analysis Dataset (MDSA)
Also if you would use `xml.etree.ElementTree` to parse the XML it would be awesome, because right now you're using an external dependency `xmltodict ` | [
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|
https://github.com/huggingface/datasets/pull/1240 | Multi Domain Sentiment Analysis Dataset (MDSA) | > Also if you would use xml.etree.ElementTree to parse the XML it would be awesome, because right now you're using an external dependency xmltodict
Its pseudo xml so elementtree fails. xmltodict seems to be working quite good for this. do we have examples of pseudo xml datasets? | 47 | text: Multi Domain Sentiment Analysis Dataset (MDSA)
> Also if you would use xml.etree.ElementTree to parse the XML it would be awesome, because right now you're using an external dependency xmltodict
Its pseudo xml so elementtree fails. xmltodict seems to be working quite good for this. do we have examples of pseudo xml datasets? | [
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|
https://github.com/huggingface/datasets/pull/1240 | Multi Domain Sentiment Analysis Dataset (MDSA) | Can you add `xmltodict` to the test dependencies in setup.py please to fix the CI please ? | 17 | text: Multi Domain Sentiment Analysis Dataset (MDSA)
Can you add `xmltodict` to the test dependencies in setup.py please to fix the CI please ? | [
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|
https://github.com/huggingface/datasets/pull/1240 | Multi Domain Sentiment Analysis Dataset (MDSA) | Hi :) have you had a chance to fix the test dependency and apply `make style` ?
Feel fee to ping me when it's ready for a review | 28 | text: Multi Domain Sentiment Analysis Dataset (MDSA)
Hi :) have you had a chance to fix the test dependency and apply `make style` ?
Feel fee to ping me when it's ready for a review | [
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|
https://github.com/huggingface/datasets/pull/1235 | Wino bias | Closing this PR because of messed up history and opening another one after discussion with Quentin Lhoest.
| The PR will fail circleCi tests because of the requirement of manual loading of data. Fresh PR because of messed up history of the previous one. | 17 | text: Wino bias
The PR will fail circleCi tests because of the requirement of manual loading of data. Fresh PR because of messed up history of the previous one.
Closing this PR because of messed up history and opening another one after discussion with Quentin Lhoest.
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https://github.com/huggingface/datasets/pull/1233 | Add Curiosity Dialogs Dataset | @lhoestq I tried manually creating the dummy files. But unfortunately it was raising an error during testing the dummy data (regarding JSON parsing).
The JSONs are pretty big so I cannot actually open it without crashing the text editor.
Do you have any suggestions? | Add Facebook [Curiosity Dialogs](https://github.com/facebookresearch/curiosity) Dataset. | 44 | text: Add Curiosity Dialogs Dataset
Add Facebook [Curiosity Dialogs](https://github.com/facebookresearch/curiosity) Dataset.
@lhoestq I tried manually creating the dummy files. But unfortunately it was raising an error during testing the dummy data (regarding JSON parsing).
The JSONs are pretty big so I cannot actually open it without crashing the text editor.
Do you have any suggestions? | [
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https://github.com/huggingface/datasets/pull/1229 | Muchocine - Spanish movie reviews dataset | Hi @mapmeld !
have you had a chance to take a look at my suggestions ?
Feel free to ping me if you have questions or when you're ready for a review | 32 | text: Muchocine - Spanish movie reviews dataset
Hi @mapmeld !
have you had a chance to take a look at my suggestions ?
Feel free to ping me if you have questions or when you're ready for a review | [
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|
https://github.com/huggingface/datasets/pull/1229 | Muchocine - Spanish movie reviews dataset | It's fine, you can just add the sections titles back and leave the content with `[More Information Needed]`
| 18 | text: Muchocine - Spanish movie reviews dataset
It's fine, you can just add the sections titles back and leave the content with `[More Information Needed]`
| [
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|
https://github.com/huggingface/datasets/pull/1226 | Add menyo_20k_mt dataset | looks like your PR includes changes about many other files than the ones for menyo 20k mt
Can you create another branch and another PR please ? | Add menyo_20k_mt dataset | 27 | text: Add menyo_20k_mt dataset
Add menyo_20k_mt dataset
looks like your PR includes changes about many other files than the ones for menyo 20k mt
Can you create another branch and another PR please ? | [
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https://github.com/huggingface/datasets/pull/1224 | adding conceptnet5 | Thank you. I'll make those changes. but I'm having problems trying to push my changes to my fork
| Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki | 18 | text: adding conceptnet5
Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki
Thank you. I'll make those changes. but I'm having problems trying to push my changes to my fork
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https://github.com/huggingface/datasets/pull/1224 | adding conceptnet5 | > Hi, I've removed the TODO, and added a README.md. How do I push these changes?
you can just commit and push your changes to the same branch as your first commit. | Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki | 32 | text: adding conceptnet5
Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki
> Hi, I've removed the TODO, and added a README.md. How do I push these changes?
you can just commit and push your changes to the same branch as your first commit. | [
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https://github.com/huggingface/datasets/pull/1224 | adding conceptnet5 | @ghomasHudson I've tried it but still getting code quality error. I've removed all blank lines, etc. required by flake8. Don't know what else to do | Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki | 25 | text: adding conceptnet5
Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki
@ghomasHudson I've tried it but still getting code quality error. I've removed all blank lines, etc. required by flake8. Don't know what else to do | [
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https://github.com/huggingface/datasets/pull/1224 | adding conceptnet5 | > @ghomasHudson I've tried it but still getting code quality error. I've removed all blank lines, etc. required by flake8. Don't know what else to do
Did you run `make style` before committing? When I run it, it fixes some things (e.g. Splitting line 96 which is currently too long). | Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki | 50 | text: adding conceptnet5
Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki
> @ghomasHudson I've tried it but still getting code quality error. I've removed all blank lines, etc. required by flake8. Don't know what else to do
Did you run `make style` before committing? When I run it, it fixes some things (e.g. Splitting line 96 which is currently too long). | [
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https://github.com/huggingface/datasets/pull/1224 | adding conceptnet5 | looks like your PR includes changes about many other files than the ones related to conceptnet5
could you create another branch and another PR please ? | Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki | 26 | text: adding conceptnet5
Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki
looks like your PR includes changes about many other files than the ones related to conceptnet5
could you create another branch and another PR please ? | [
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https://github.com/huggingface/datasets/pull/1224 | adding conceptnet5 | @lhoestq I'm not sure what I did wrong. What did I push that wasn't conceptnet5? How do I see this?
did this
make style
flake8 datasets
git add datasets/<your_dataset_name>
git commit
git fetch upstream
git rebase upstream/master
git pull
git push -u origin conceptnet5 | Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki | 44 | text: adding conceptnet5
Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki
@lhoestq I'm not sure what I did wrong. What did I push that wasn't conceptnet5? How do I see this?
did this
make style
flake8 datasets
git add datasets/<your_dataset_name>
git commit
git fetch upstream
git rebase upstream/master
git pull
git push -u origin conceptnet5 | [
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https://github.com/huggingface/datasets/pull/1222 | Add numeric fused head dataset | > Thanks for adding this @ghomasHudson!
> I added some comments for some of the fields.
>
> Also, I'm not sure about this since I haven't used the library yet, but maybe it's worth adding the identification and resolution as two separate datasets?
Thanks for replying @yanaiela - I hope this will make your dataset more accessible to a wider audience - I've added the changes to the model card you suggested.
In terms of the identification and resolution tasks, I've currently added them as separate `splits` in huggingface/datasets so you can load identification like this:
```
import datasets
dataset = datasets.load_dataset("numeric_fused_head", "identification")
print(dataset["train"][0])
>> {"tokens": ["The", "quick", "brown", "fox",....], "start_index": 11, "end_index": 12, "label": 0}
```
And resolution like this:
```
import datasets
dataset = datasets.load_dataset("numeric_fused_head", "resolution")
print(dataset["train"][0])
>> {"tokens": ["The", "quick", "brown", "fox",....], "head": ["AGE"], "anchors_indices": [12], ...}
``` | Adding the [NFH: Numeric Fused Head](https://nlp.biu.ac.il/~lazary/fh/) dataset.
Everything looks sensible and I've included both the identification and resolution tasks. I haven't personally used this dataset in my research so am unable to specify what the default configuration / supervised keys should be.
I've filled out the basic info on the model card to the best of my knowledge but it's a little tricky to understand exactly what the fields represent.
Dataset author: @yanaiela | 142 | text: Add numeric fused head dataset
Adding the [NFH: Numeric Fused Head](https://nlp.biu.ac.il/~lazary/fh/) dataset.
Everything looks sensible and I've included both the identification and resolution tasks. I haven't personally used this dataset in my research so am unable to specify what the default configuration / supervised keys should be.
I've filled out the basic info on the model card to the best of my knowledge but it's a little tricky to understand exactly what the fields represent.
Dataset author: @yanaiela
> Thanks for adding this @ghomasHudson!
> I added some comments for some of the fields.
>
> Also, I'm not sure about this since I haven't used the library yet, but maybe it's worth adding the identification and resolution as two separate datasets?
Thanks for replying @yanaiela - I hope this will make your dataset more accessible to a wider audience - I've added the changes to the model card you suggested.
In terms of the identification and resolution tasks, I've currently added them as separate `splits` in huggingface/datasets so you can load identification like this:
```
import datasets
dataset = datasets.load_dataset("numeric_fused_head", "identification")
print(dataset["train"][0])
>> {"tokens": ["The", "quick", "brown", "fox",....], "start_index": 11, "end_index": 12, "label": 0}
```
And resolution like this:
```
import datasets
dataset = datasets.load_dataset("numeric_fused_head", "resolution")
print(dataset["train"][0])
>> {"tokens": ["The", "quick", "brown", "fox",....], "head": ["AGE"], "anchors_indices": [12], ...}
``` | [
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https://github.com/huggingface/datasets/pull/1220 | add Korean HateSpeech dataset | It looks like you forgot to `make style` (I forget it a lot too 🤦 )
+ add dummy data | 20 | text: add Korean HateSpeech dataset
It looks like you forgot to `make style` (I forget it a lot too 🤦 )
+ add dummy data | [
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|
https://github.com/huggingface/datasets/pull/1220 | add Korean HateSpeech dataset | hi @cceyda 👋, thanks for the hint! it looks like i've run into some other errors though in `_split_generators` or `_generate_examples`. do you have any idea of what's wrong here? 😅 | 31 | text: add Korean HateSpeech dataset
hi @cceyda 👋, thanks for the hint! it looks like i've run into some other errors though in `_split_generators` or `_generate_examples`. do you have any idea of what's wrong here? 😅 | [
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|
https://github.com/huggingface/datasets/pull/1220 | add Korean HateSpeech dataset | I get the same errors on another pr too, so it probably has something to do with circleci, waiting on help. | 21 | text: add Korean HateSpeech dataset
I get the same errors on another pr too, so it probably has something to do with circleci, waiting on help. | [
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|
https://github.com/huggingface/datasets/pull/1218 | Add WMT20 MLQE 3 shared tasks | It should be ok now!
Sorry I wasn't attentive enough.
(tests are currently failing, I understand it's from other datasets) | 3 tasks for the WMT 20 MLQE shared tasks -> 3 different datasets
(I re-created #1137 because it was too messy).
Note that in L199 `task3.py`, I used `logging.warning` to print some missing data in the train set. | 20 | text: Add WMT20 MLQE 3 shared tasks
3 tasks for the WMT 20 MLQE shared tasks -> 3 different datasets
(I re-created #1137 because it was too messy).
Note that in L199 `task3.py`, I used `logging.warning` to print some missing data in the train set.
It should be ok now!
Sorry I wasn't attentive enough.
(tests are currently failing, I understand it's from other datasets) | [
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https://github.com/huggingface/datasets/pull/1216 | Add limit | My bad, didn't see this on the open dataset list. Closing this since it overlaps with PR#1256 | This PR adds [LiMiT](https://github.com/ilmgut/limit_dataset), a dataset for literal motion classification/extraction by [Manotas et al., 2020](https://www.aclweb.org/anthology/2020.findings-emnlp.88.pdf). | 17 | text: Add limit
This PR adds [LiMiT](https://github.com/ilmgut/limit_dataset), a dataset for literal motion classification/extraction by [Manotas et al., 2020](https://www.aclweb.org/anthology/2020.findings-emnlp.88.pdf).
My bad, didn't see this on the open dataset list. Closing this since it overlaps with PR#1256 | [
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https://github.com/huggingface/datasets/pull/1215 | Add irc disentanglement | looks like this PR includes changes about many files other than the ones for irc_disentanglement
Could you please create a new branch and create another PR please ? | added files for irc disentanglement dataset
was unable to test dummy data as a result of vpn/proxy issues | 28 | text: Add irc disentanglement
added files for irc disentanglement dataset
was unable to test dummy data as a result of vpn/proxy issues
looks like this PR includes changes about many files other than the ones for irc_disentanglement
Could you please create a new branch and create another PR please ? | [
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https://github.com/huggingface/datasets/pull/1213 | add taskmaster3 | (you were unlucky, my rule of thumb for reducing the dummy data is to check whether they're above 50KB and you're at 52KB ^^') | Adding Taskmaster-3 dataset
https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020.
The dataset structure almost same as original dataset with these two changes
1. In original dataset, each `apis` has a `args` filed which is a `dict` with variable keys, which represent the name and value of the args. Here converted that to a `list` of `dict` with keys `arg_name` and `arg_value`. For ex.
```python
args = {"name.movie": "Mulan", "name.theater": ": "Mountain AMC 16"}
```
becomes
```python
[
{
"arg_name": "name.movie",
"arg_value": "Mulan"
},
{
"arg_name": "name.theater",
"arg_value": "Mountain AMC 16"
}
]
```
2. Each `apis` has a `response` which is also a `dict` with variable keys representing response name/type and it's value. As above converted it to `list` of `dict` with keys `response_name` and `response_value`.
| 24 | text: add taskmaster3
Adding Taskmaster-3 dataset
https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020.
The dataset structure almost same as original dataset with these two changes
1. In original dataset, each `apis` has a `args` filed which is a `dict` with variable keys, which represent the name and value of the args. Here converted that to a `list` of `dict` with keys `arg_name` and `arg_value`. For ex.
```python
args = {"name.movie": "Mulan", "name.theater": ": "Mountain AMC 16"}
```
becomes
```python
[
{
"arg_name": "name.movie",
"arg_value": "Mulan"
},
{
"arg_name": "name.theater",
"arg_value": "Mountain AMC 16"
}
]
```
2. Each `apis` has a `response` which is also a `dict` with variable keys representing response name/type and it's value. As above converted it to `list` of `dict` with keys `response_name` and `response_value`.
(you were unlucky, my rule of thumb for reducing the dummy data is to check whether they're above 50KB and you're at 52KB ^^') | [
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https://github.com/huggingface/datasets/pull/1213 | add taskmaster3 | > (you were unlucky, my rule of thumb for reducing the dummy data is to check whether they're above 50KB and you're at 52KB ^^')
Oops :(
Thanks for the suggestion, will reduce the size | Adding Taskmaster-3 dataset
https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020.
The dataset structure almost same as original dataset with these two changes
1. In original dataset, each `apis` has a `args` filed which is a `dict` with variable keys, which represent the name and value of the args. Here converted that to a `list` of `dict` with keys `arg_name` and `arg_value`. For ex.
```python
args = {"name.movie": "Mulan", "name.theater": ": "Mountain AMC 16"}
```
becomes
```python
[
{
"arg_name": "name.movie",
"arg_value": "Mulan"
},
{
"arg_name": "name.theater",
"arg_value": "Mountain AMC 16"
}
]
```
2. Each `apis` has a `response` which is also a `dict` with variable keys representing response name/type and it's value. As above converted it to `list` of `dict` with keys `response_name` and `response_value`.
| 35 | text: add taskmaster3
Adding Taskmaster-3 dataset
https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020.
The dataset structure almost same as original dataset with these two changes
1. In original dataset, each `apis` has a `args` filed which is a `dict` with variable keys, which represent the name and value of the args. Here converted that to a `list` of `dict` with keys `arg_name` and `arg_value`. For ex.
```python
args = {"name.movie": "Mulan", "name.theater": ": "Mountain AMC 16"}
```
becomes
```python
[
{
"arg_name": "name.movie",
"arg_value": "Mulan"
},
{
"arg_name": "name.theater",
"arg_value": "Mountain AMC 16"
}
]
```
2. Each `apis` has a `response` which is also a `dict` with variable keys representing response name/type and it's value. As above converted it to `list` of `dict` with keys `response_name` and `response_value`.
> (you were unlucky, my rule of thumb for reducing the dummy data is to check whether they're above 50KB and you're at 52KB ^^')
Oops :(
Thanks for the suggestion, will reduce the size | [
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https://github.com/huggingface/datasets/pull/1209 | [AfriBooms] Dataset exists already | It's so cool seeing all these datasets fly by and see how they are still of interest. I did my internship at the research group of Liesbeth Augustinus et al. They're a very kind group of people! | When trying to add "AfriBooms": https://docs.google.com/spreadsheets/d/12ShVow0M6RavnzbBEabm5j5dv12zBaf0y-niwEPPlo4/edit#gid=1386399609 I noticed that the dataset exists already as a config of Universal Dependencies (universal_dependencies.py). I checked and the data exactly matches so that the new data link does not give any new data.
This PR improves the config's description a bit by linking to the paper. | 37 | text: [AfriBooms] Dataset exists already
When trying to add "AfriBooms": https://docs.google.com/spreadsheets/d/12ShVow0M6RavnzbBEabm5j5dv12zBaf0y-niwEPPlo4/edit#gid=1386399609 I noticed that the dataset exists already as a config of Universal Dependencies (universal_dependencies.py). I checked and the data exactly matches so that the new data link does not give any new data.
This PR improves the config's description a bit by linking to the paper.
It's so cool seeing all these datasets fly by and see how they are still of interest. I did my internship at the research group of Liesbeth Augustinus et al. They're a very kind group of people! | [
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https://github.com/huggingface/datasets/pull/1206 | Adding Enriched WebNLG dataset | Nice :)
could you add the tags and also remove all the dummy data files that are not zipped ? The diff currently shows 800 files changes xD | This pull requests adds the `en` and `de` versions of the [Enriched WebNLG](https://github.com/ThiagoCF05/webnlg) dataset | 28 | text: Adding Enriched WebNLG dataset
This pull requests adds the `en` and `de` versions of the [Enriched WebNLG](https://github.com/ThiagoCF05/webnlg) dataset
Nice :)
could you add the tags and also remove all the dummy data files that are not zipped ? The diff currently shows 800 files changes xD | [
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https://github.com/huggingface/datasets/pull/1205 | add lst20 with manual download | The pytest suite doesn't allow manual downloads so we just make sure that the `datasets-cli test` command to run without errors instead | passed on local:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_lst20
```
Not sure how to test:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lst20
```
```
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
``` | 22 | text: add lst20 with manual download
passed on local:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_lst20
```
Not sure how to test:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lst20
```
```
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
```
The pytest suite doesn't allow manual downloads so we just make sure that the `datasets-cli test` command to run without errors instead | [
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https://github.com/huggingface/datasets/pull/1205 | add lst20 with manual download | @lhoestq Changes made. Thank you for the review. I've made some same mistakes for https://github.com/huggingface/datasets/pull/1253 too. Will fix them before review. | passed on local:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_lst20
```
Not sure how to test:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lst20
```
```
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
``` | 21 | text: add lst20 with manual download
passed on local:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_lst20
```
Not sure how to test:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lst20
```
```
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
```
@lhoestq Changes made. Thank you for the review. I've made some same mistakes for https://github.com/huggingface/datasets/pull/1253 too. Will fix them before review. | [
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] |
https://github.com/huggingface/datasets/pull/1203 | Add Neural Code Search Dataset | > Really good thanks !
>
> I left a few comments
Thanks, resolved them :) | 16 | text: Add Neural Code Search Dataset
> Really good thanks !
>
> I left a few comments
Thanks, resolved them :) | [
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|
https://github.com/huggingface/datasets/pull/1203 | Add Neural Code Search Dataset | looks like this PR includes changes about many other files than the ones for Code Search
can you create another branch and another PR please ? | 26 | text: Add Neural Code Search Dataset
looks like this PR includes changes about many other files than the ones for Code Search
can you create another branch and another PR please ? | [
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|
https://github.com/huggingface/datasets/pull/1203 | Add Neural Code Search Dataset | > looks like this PR includes changes about many other files than the ones for Code Search
>
> can you create another branch and another PR please ?
Okay sure | 31 | text: Add Neural Code Search Dataset
> looks like this PR includes changes about many other files than the ones for Code Search
>
> can you create another branch and another PR please ?
Okay sure | [
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|
https://github.com/huggingface/datasets/pull/1199 | Turkish NER dataset, script works fine, couldn't generate dummy data | the .DUMP file looks like a txt with one example per line so adding `--match_text_files *.DUMP --n_lines 50` to the dummy generation command might work . | I've written the script (Turkish_NER.py) that includes dataset. The dataset is a zip inside another zip, and it's extracted as .DUMP file. However, after preprocessing I only get .arrow file. After I ran the script with no error messages, I get .arrow file of dataset, LICENSE and dataset_info.json. | 26 | text: Turkish NER dataset, script works fine, couldn't generate dummy data
I've written the script (Turkish_NER.py) that includes dataset. The dataset is a zip inside another zip, and it's extracted as .DUMP file. However, after preprocessing I only get .arrow file. After I ran the script with no error messages, I get .arrow file of dataset, LICENSE and dataset_info.json.
the .DUMP file looks like a txt with one example per line so adding `--match_text_files *.DUMP --n_lines 50` to the dummy generation command might work . | [
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https://github.com/huggingface/datasets/pull/1196 | Add IWSLT'15 English-Vietnamese machine translation Data | Thanks ! feel free to ping me once you've added the tags in the dataset card :) | Preprocessed Dataset from IWSLT'15 English-Vietnamese machine translation: English-Vietnamese.
from https://nlp.stanford.edu/projects/nmt/data/iwslt15.en-vi/ | 17 | text: Add IWSLT'15 English-Vietnamese machine translation Data
Preprocessed Dataset from IWSLT'15 English-Vietnamese machine translation: English-Vietnamese.
from https://nlp.stanford.edu/projects/nmt/data/iwslt15.en-vi/
Thanks ! feel free to ping me once you've added the tags in the dataset card :) | [
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https://github.com/huggingface/datasets/pull/1195 | addition of py_ast | Hi @reshinthadithyan !
As mentioned on the Slack, it would be better in this case to parse the file lines into the following feature structure:
```python
"ast": datasets.Sequence(
{
"type": datasets.Value("string"),
"value": datasets.Value("string"),
"children": datasets.Sequence(datasets.Value("int32")),
},
)
```
Here are a few more things to fix before we can move forward:
- the class name needs to be the CamelCase equivalent of the script name, so here it will have to be `PyAst`
- the `README.md` needs to have the tags at the top
- The homepage/info list at the top should be in the same format as the template (added a suggestion)
- You should add the dataset tags and field description to the README as described here: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card
Good luck, let us know if you need any help! | The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository)
,keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files | 130 | text: addition of py_ast
The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository)
,keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files
Hi @reshinthadithyan !
As mentioned on the Slack, it would be better in this case to parse the file lines into the following feature structure:
```python
"ast": datasets.Sequence(
{
"type": datasets.Value("string"),
"value": datasets.Value("string"),
"children": datasets.Sequence(datasets.Value("int32")),
},
)
```
Here are a few more things to fix before we can move forward:
- the class name needs to be the CamelCase equivalent of the script name, so here it will have to be `PyAst`
- the `README.md` needs to have the tags at the top
- The homepage/info list at the top should be in the same format as the template (added a suggestion)
- You should add the dataset tags and field description to the README as described here: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card
Good luck, let us know if you need any help! | [
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https://github.com/huggingface/datasets/pull/1195 | addition of py_ast | Hello @yjernite, changes have been made as we talked. Hope this would suffice. Thanks. Feel free to point out any room to improvement. | The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository)
,keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files | 23 | text: addition of py_ast
The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository)
,keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files
Hello @yjernite, changes have been made as we talked. Hope this would suffice. Thanks. Feel free to point out any room to improvement. | [
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https://github.com/huggingface/datasets/pull/1195 | addition of py_ast | Good progress! Here's what still needs to be done:
- first, you need to rebase to master for the tests to pass :)
- the information in your `Data Fields` paragraph should go into `Data Instances`. Data fields should describe the fields one by one, as in e.g. https://github.com/huggingface/datasets/tree/master/datasets/eli5#data-fields
- you still need to add the YAML tags obtained with the tagging app
Should be good to go after that! | The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository)
,keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files | 70 | text: addition of py_ast
The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository)
,keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files
Good progress! Here's what still needs to be done:
- first, you need to rebase to master for the tests to pass :)
- the information in your `Data Fields` paragraph should go into `Data Instances`. Data fields should describe the fields one by one, as in e.g. https://github.com/huggingface/datasets/tree/master/datasets/eli5#data-fields
- you still need to add the YAML tags obtained with the tagging app
Should be good to go after that! | [
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https://github.com/huggingface/datasets/pull/1195 | addition of py_ast | Looks like this PR includes changes about many other files than the ones for py_ast
Could you create another branch and another PR please ? | The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository)
,keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files | 25 | text: addition of py_ast
The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository)
,keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files
Looks like this PR includes changes about many other files than the ones for py_ast
Could you create another branch and another PR please ? | [
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https://github.com/huggingface/datasets/pull/1191 | Added Translator Human Parity Data For a Chinese-English news transla… | > Can you run `make style` to format the code and fix the CI please ?
I ran `make style` before this PR and just a few minutes ago. No changes to the code. Not sure why the CI is failing. | …tion system from Open dataset list for Dataset sprint, Microsoft Datasets tab. | 41 | text: Added Translator Human Parity Data For a Chinese-English news transla…
…tion system from Open dataset list for Dataset sprint, Microsoft Datasets tab.
> Can you run `make style` to format the code and fix the CI please ?
I ran `make style` before this PR and just a few minutes ago. No changes to the code. Not sure why the CI is failing. | [
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https://github.com/huggingface/datasets/pull/1191 | Added Translator Human Parity Data For a Chinese-English news transla… | Also, I attempted to see if I can get the source Chinese sentences from `wmt17` dataset. But this call `data = load_dataset('wmt17', "zh-en")` failed with this error: `FileNotFoundError: Couldn't find file at https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz`. I think it should be possible and fairly straightforward to get the pairing source sentences from it. I just can not test it right now. | …tion system from Open dataset list for Dataset sprint, Microsoft Datasets tab. | 58 | text: Added Translator Human Parity Data For a Chinese-English news transla…
…tion system from Open dataset list for Dataset sprint, Microsoft Datasets tab.
Also, I attempted to see if I can get the source Chinese sentences from `wmt17` dataset. But this call `data = load_dataset('wmt17', "zh-en")` failed with this error: `FileNotFoundError: Couldn't find file at https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz`. I think it should be possible and fairly straightforward to get the pairing source sentences from it. I just can not test it right now. | [
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https://github.com/huggingface/datasets/pull/1190 | Add Fake News Detection in Filipino dataset | Hi! I'm the author of this paper (surprised to see our datasets have been added already).
That paper link only leads to the conference index, here's a link to the actual paper: https://www.aclweb.org/anthology/2020.lrec-1.316/
Would it be fine if I also edited your gsheet entry to reflect this change? | This PR adds the Fake News Filipino Dataset, a low-resource fake news detection corpora in Filipino. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
Link to the paper: http://www.lrec-conf.org/proceedings/lrec2020/index.html
Link to the dataset/repo: https://github.com/jcblaisecruz02/Tagalog-fake-news | 48 | text: Add Fake News Detection in Filipino dataset
This PR adds the Fake News Filipino Dataset, a low-resource fake news detection corpora in Filipino. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
Link to the paper: http://www.lrec-conf.org/proceedings/lrec2020/index.html
Link to the dataset/repo: https://github.com/jcblaisecruz02/Tagalog-fake-news
Hi! I'm the author of this paper (surprised to see our datasets have been added already).
That paper link only leads to the conference index, here's a link to the actual paper: https://www.aclweb.org/anthology/2020.lrec-1.316/
Would it be fine if I also edited your gsheet entry to reflect this change? | [
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https://github.com/huggingface/datasets/pull/1190 | Add Fake News Detection in Filipino dataset | Hi Jan, please go ahead and update. I see you are also in the sprint slack channel. Let me know if what else needs updating. Thanks.
| This PR adds the Fake News Filipino Dataset, a low-resource fake news detection corpora in Filipino. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
Link to the paper: http://www.lrec-conf.org/proceedings/lrec2020/index.html
Link to the dataset/repo: https://github.com/jcblaisecruz02/Tagalog-fake-news | 26 | text: Add Fake News Detection in Filipino dataset
This PR adds the Fake News Filipino Dataset, a low-resource fake news detection corpora in Filipino. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
Link to the paper: http://www.lrec-conf.org/proceedings/lrec2020/index.html
Link to the dataset/repo: https://github.com/jcblaisecruz02/Tagalog-fake-news
Hi Jan, please go ahead and update. I see you are also in the sprint slack channel. Let me know if what else needs updating. Thanks.
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https://github.com/huggingface/datasets/pull/1188 | adding hind_encorp dataset | you can add the `--match_text_file "*.plaintext"` flag when generating the dummy data
also it looks like the PR is empty, is this expected ? | adding Hindi_Encorp05 dataset | 24 | text: adding hind_encorp dataset
adding Hindi_Encorp05 dataset
you can add the `--match_text_file "*.plaintext"` flag when generating the dummy data
also it looks like the PR is empty, is this expected ? | [
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https://github.com/huggingface/datasets/pull/1188 | adding hind_encorp dataset | yes it is expected because I made all my changes in PR #1186 then I again run code and open PR #1188 to see if this time test passes or not only so there is no code change from #1186 to #1188
i tried --match_text_file "*.plaintext" this time it is also not generating dummy data don't know why | adding Hindi_Encorp05 dataset | 58 | text: adding hind_encorp dataset
adding Hindi_Encorp05 dataset
yes it is expected because I made all my changes in PR #1186 then I again run code and open PR #1188 to see if this time test passes or not only so there is no code change from #1186 to #1188
i tried --match_text_file "*.plaintext" this time it is also not generating dummy data don't know why | [
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https://github.com/huggingface/datasets/pull/1188 | adding hind_encorp dataset | well this PR includes no code change at all, can you make sure you added your changes in this one ? | adding Hindi_Encorp05 dataset | 21 | text: adding hind_encorp dataset
adding Hindi_Encorp05 dataset
well this PR includes no code change at all, can you make sure you added your changes in this one ? | [
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https://github.com/huggingface/datasets/pull/1188 | adding hind_encorp dataset | feel free to ping me when you have added the files so I can take a look and help you with the dummy data | adding Hindi_Encorp05 dataset | 24 | text: adding hind_encorp dataset
adding Hindi_Encorp05 dataset
feel free to ping me when you have added the files so I can take a look and help you with the dummy data | [
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https://github.com/huggingface/datasets/pull/1188 | adding hind_encorp dataset | actually all my changes are visible in #1186 but don't know how to show same changes here | adding Hindi_Encorp05 dataset | 17 | text: adding hind_encorp dataset
adding Hindi_Encorp05 dataset
actually all my changes are visible in #1186 but don't know how to show same changes here | [
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https://github.com/huggingface/datasets/pull/1188 | adding hind_encorp dataset | these are a the which i did in #1186 and same in #1188
these same codes are in both #1186 and #1188 so because it is already present from PR #1186 because of that it is showing zeor code change in #1188 because it is already present from #1186 how i can show or highlight those changes
| adding Hindi_Encorp05 dataset | 68 | text: adding hind_encorp dataset
adding Hindi_Encorp05 dataset
these are a the which i did in #1186 and same in #1188
these same codes are in both #1186 and #1188 so because it is already present from PR #1186 because of that it is showing zeor code change in #1188 because it is already present from #1186 how i can show or highlight those changes
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https://github.com/huggingface/datasets/pull/1188 | adding hind_encorp dataset | This PR tries to merge the master branch of you fork into this repo, however I can't find changes with your files inside your master branch.
Maybe you can fork again the repo and try to create another PR ? | adding Hindi_Encorp05 dataset | 40 | text: adding hind_encorp dataset
adding Hindi_Encorp05 dataset
This PR tries to merge the master branch of you fork into this repo, however I can't find changes with your files inside your master branch.
Maybe you can fork again the repo and try to create another PR ? | [
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https://github.com/huggingface/datasets/pull/1188 | adding hind_encorp dataset | @lhoestq i opened a new pr #1438 but this time it fails many circl ci tests | adding Hindi_Encorp05 dataset | 16 | text: adding hind_encorp dataset
adding Hindi_Encorp05 dataset
@lhoestq i opened a new pr #1438 but this time it fails many circl ci tests | [
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https://github.com/huggingface/datasets/pull/1186 | all test passed | looks like this PR includes changes to 5000 files
could you create a new branch and a new PR ? | need help creating dummy data | 20 | text: all test passed
need help creating dummy data
looks like this PR includes changes to 5000 files
could you create a new branch and a new PR ? | [
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https://github.com/huggingface/datasets/pull/1184 | Add Adversarial SQuAD dataset | the CI error was just a connection error due to all the activity on the repo this week ^^'
I re-ran it so it should be good now | # Adversarial SQuAD
Adding the Adversarial [SQuAD](https://github.com/robinjia/adversarial-squad) dataset as part of the sprint 🎉
This dataset adds adversarial sentences to a subset of the SQuAD dataset's dev examples. How to get the original squad example id is explained in readme->Data Instances. The whole data is intended for use in evaluation. (Which could of course be also used for training if one wants). So there is no classical train/val/test split, but a split based on the number of adversaries added.
There are 2 splits of this dataset:
- AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way.
- AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way.
(The AddAny and AddCommon datasets mentioned in the paper are dynamically generated based on model's output distribution thus are not included here)
The failing test look like some unrelated timeout thing, will probably clear if rerun.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could) | 28 | text: Add Adversarial SQuAD dataset
# Adversarial SQuAD
Adding the Adversarial [SQuAD](https://github.com/robinjia/adversarial-squad) dataset as part of the sprint 🎉
This dataset adds adversarial sentences to a subset of the SQuAD dataset's dev examples. How to get the original squad example id is explained in readme->Data Instances. The whole data is intended for use in evaluation. (Which could of course be also used for training if one wants). So there is no classical train/val/test split, but a split based on the number of adversaries added.
There are 2 splits of this dataset:
- AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way.
- AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way.
(The AddAny and AddCommon datasets mentioned in the paper are dynamically generated based on model's output distribution thus are not included here)
The failing test look like some unrelated timeout thing, will probably clear if rerun.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
the CI error was just a connection error due to all the activity on the repo this week ^^'
I re-ran it so it should be good now | [
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https://github.com/huggingface/datasets/pull/1184 | Add Adversarial SQuAD dataset | I hadn't realized the problem with the dummies since it had passed without errors.
Suggestion: maybe we can show the user a warning based on the generated dummy size. | # Adversarial SQuAD
Adding the Adversarial [SQuAD](https://github.com/robinjia/adversarial-squad) dataset as part of the sprint 🎉
This dataset adds adversarial sentences to a subset of the SQuAD dataset's dev examples. How to get the original squad example id is explained in readme->Data Instances. The whole data is intended for use in evaluation. (Which could of course be also used for training if one wants). So there is no classical train/val/test split, but a split based on the number of adversaries added.
There are 2 splits of this dataset:
- AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way.
- AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way.
(The AddAny and AddCommon datasets mentioned in the paper are dynamically generated based on model's output distribution thus are not included here)
The failing test look like some unrelated timeout thing, will probably clear if rerun.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could) | 29 | text: Add Adversarial SQuAD dataset
# Adversarial SQuAD
Adding the Adversarial [SQuAD](https://github.com/robinjia/adversarial-squad) dataset as part of the sprint 🎉
This dataset adds adversarial sentences to a subset of the SQuAD dataset's dev examples. How to get the original squad example id is explained in readme->Data Instances. The whole data is intended for use in evaluation. (Which could of course be also used for training if one wants). So there is no classical train/val/test split, but a split based on the number of adversaries added.
There are 2 splits of this dataset:
- AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way.
- AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way.
(The AddAny and AddCommon datasets mentioned in the paper are dynamically generated based on model's output distribution thus are not included here)
The failing test look like some unrelated timeout thing, will probably clear if rerun.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
I hadn't realized the problem with the dummies since it had passed without errors.
Suggestion: maybe we can show the user a warning based on the generated dummy size. | [
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https://github.com/huggingface/datasets/pull/1184 | Add Adversarial SQuAD dataset | Thanks for changing to configs ! Looks all good now :)
Before we merge, can you re-lighten the dummy data please if you don't mind ? The idea is to have them weigh only a few KB (currently it's 50KB each). Feel free to remove any unnecessary files or chunk of text | # Adversarial SQuAD
Adding the Adversarial [SQuAD](https://github.com/robinjia/adversarial-squad) dataset as part of the sprint 🎉
This dataset adds adversarial sentences to a subset of the SQuAD dataset's dev examples. How to get the original squad example id is explained in readme->Data Instances. The whole data is intended for use in evaluation. (Which could of course be also used for training if one wants). So there is no classical train/val/test split, but a split based on the number of adversaries added.
There are 2 splits of this dataset:
- AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way.
- AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way.
(The AddAny and AddCommon datasets mentioned in the paper are dynamically generated based on model's output distribution thus are not included here)
The failing test look like some unrelated timeout thing, will probably clear if rerun.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could) | 52 | text: Add Adversarial SQuAD dataset
# Adversarial SQuAD
Adding the Adversarial [SQuAD](https://github.com/robinjia/adversarial-squad) dataset as part of the sprint 🎉
This dataset adds adversarial sentences to a subset of the SQuAD dataset's dev examples. How to get the original squad example id is explained in readme->Data Instances. The whole data is intended for use in evaluation. (Which could of course be also used for training if one wants). So there is no classical train/val/test split, but a split based on the number of adversaries added.
There are 2 splits of this dataset:
- AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way.
- AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way.
(The AddAny and AddCommon datasets mentioned in the paper are dynamically generated based on model's output distribution thus are not included here)
The failing test look like some unrelated timeout thing, will probably clear if rerun.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
Thanks for changing to configs ! Looks all good now :)
Before we merge, can you re-lighten the dummy data please if you don't mind ? The idea is to have them weigh only a few KB (currently it's 50KB each). Feel free to remove any unnecessary files or chunk of text | [
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https://github.com/huggingface/datasets/pull/1181 | added emotions detection in arabic dataset | Hi @abdulelahsm did you manage to fix your issue ?
Feel free to ping me if you have questions or if you're ready for a review | Dataset for Emotions detection in Arabic text
more info: https://github.com/AmrMehasseb/Emotional-Tone | 26 | text: added emotions detection in arabic dataset
Dataset for Emotions detection in Arabic text
more info: https://github.com/AmrMehasseb/Emotional-Tone
Hi @abdulelahsm did you manage to fix your issue ?
Feel free to ping me if you have questions or if you're ready for a review | [
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https://github.com/huggingface/datasets/pull/1180 | Add KorQuAD v2 Dataset | looks like this PR also includes the changes for the V1
Could you only include the files of the V2 ? | # The Korean Question Answering Dataset v2
Adding the [KorQuAD](https://korquad.github.io/) v2 dataset as part of the sprint 🎉
This dataset is very similar to SQuAD and is an extension of [squad_kor_v1](https://github.com/huggingface/datasets/pull/1178) which is why I added it as `squad_kor_v2`.
- Crowd generated questions and answer (1-answer per question) for Wikipedia articles. Differently from V1 it includes the html structure and markup, which makes it a different enough dataset. (doesn't share ids between v1 and v2 either)
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
Edit: 🤦 looks like squad_kor_v1 commit sneaked in here too | 21 | text: Add KorQuAD v2 Dataset
# The Korean Question Answering Dataset v2
Adding the [KorQuAD](https://korquad.github.io/) v2 dataset as part of the sprint 🎉
This dataset is very similar to SQuAD and is an extension of [squad_kor_v1](https://github.com/huggingface/datasets/pull/1178) which is why I added it as `squad_kor_v2`.
- Crowd generated questions and answer (1-answer per question) for Wikipedia articles. Differently from V1 it includes the html structure and markup, which makes it a different enough dataset. (doesn't share ids between v1 and v2 either)
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
Edit: 🤦 looks like squad_kor_v1 commit sneaked in here too
looks like this PR also includes the changes for the V1
Could you only include the files of the V2 ? | [
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https://github.com/huggingface/datasets/pull/1180 | Add KorQuAD v2 Dataset | hmm I have made the dummy data lighter retested on local and it passed not sure why it fails here? | # The Korean Question Answering Dataset v2
Adding the [KorQuAD](https://korquad.github.io/) v2 dataset as part of the sprint 🎉
This dataset is very similar to SQuAD and is an extension of [squad_kor_v1](https://github.com/huggingface/datasets/pull/1178) which is why I added it as `squad_kor_v2`.
- Crowd generated questions and answer (1-answer per question) for Wikipedia articles. Differently from V1 it includes the html structure and markup, which makes it a different enough dataset. (doesn't share ids between v1 and v2 either)
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
Edit: 🤦 looks like squad_kor_v1 commit sneaked in here too | 20 | text: Add KorQuAD v2 Dataset
# The Korean Question Answering Dataset v2
Adding the [KorQuAD](https://korquad.github.io/) v2 dataset as part of the sprint 🎉
This dataset is very similar to SQuAD and is an extension of [squad_kor_v1](https://github.com/huggingface/datasets/pull/1178) which is why I added it as `squad_kor_v2`.
- Crowd generated questions and answer (1-answer per question) for Wikipedia articles. Differently from V1 it includes the html structure and markup, which makes it a different enough dataset. (doesn't share ids between v1 and v2 either)
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
Edit: 🤦 looks like squad_kor_v1 commit sneaked in here too
hmm I have made the dummy data lighter retested on local and it passed not sure why it fails here? | [
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https://github.com/huggingface/datasets/pull/1176 | Add OpenPI Dataset | Hi @Bharat123rox ! It looks like some of the dummy data is broken or missing. Did you auto-generate it? Does the local test pass for you? | Add the OpenPI Dataset by AI2 (AllenAI) | 26 | text: Add OpenPI Dataset
Add the OpenPI Dataset by AI2 (AllenAI)
Hi @Bharat123rox ! It looks like some of the dummy data is broken or missing. Did you auto-generate it? Does the local test pass for you? | [
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https://github.com/huggingface/datasets/pull/1176 | Add OpenPI Dataset | @yjernite requesting you to have a look as to why the tests are failing only on Windows, there seems to be a backslash error somewhere, could it be the result of `os.path.join` and what should be the fix for this? | Add the OpenPI Dataset by AI2 (AllenAI) | 40 | text: Add OpenPI Dataset
Add the OpenPI Dataset by AI2 (AllenAI)
@yjernite requesting you to have a look as to why the tests are failing only on Windows, there seems to be a backslash error somewhere, could it be the result of `os.path.join` and what should be the fix for this? | [
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https://github.com/huggingface/datasets/pull/1176 | Add OpenPI Dataset | This is the `black` output locally:
```
(datasets_env) datasets (openpi) > black --check --line-length 119 --target-version py36 datasets/openpi/
All done! ✨ 🍰 ✨
1 file would be left unchanged.
``` | Add the OpenPI Dataset by AI2 (AllenAI) | 30 | text: Add OpenPI Dataset
Add the OpenPI Dataset by AI2 (AllenAI)
This is the `black` output locally:
```
(datasets_env) datasets (openpi) > black --check --line-length 119 --target-version py36 datasets/openpi/
All done! ✨ 🍰 ✨
1 file would be left unchanged.
``` | [
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https://github.com/huggingface/datasets/pull/1176 | Add OpenPI Dataset | Can you check your version of black (should be `20.8b1`) and run `make style again`? (And don't forget to rebase before pushing ;) )
The other test was a time-out error so should be good on the next commit | Add the OpenPI Dataset by AI2 (AllenAI) | 39 | text: Add OpenPI Dataset
Add the OpenPI Dataset by AI2 (AllenAI)
Can you check your version of black (should be `20.8b1`) and run `make style again`? (And don't forget to rebase before pushing ;) )
The other test was a time-out error so should be good on the next commit | [
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https://github.com/huggingface/datasets/pull/1176 | Add OpenPI Dataset | Hi @Bharat123rox did you manage to join the different config into one using the IDs ?
Feel free to ping me when you're ready for the next review :) | Add the OpenPI Dataset by AI2 (AllenAI) | 29 | text: Add OpenPI Dataset
Add the OpenPI Dataset by AI2 (AllenAI)
Hi @Bharat123rox did you manage to join the different config into one using the IDs ?
Feel free to ping me when you're ready for the next review :) | [
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https://github.com/huggingface/datasets/pull/1176 | Add OpenPI Dataset | > Hi @Bharat123rox did you manage to join the different config into one using the IDs ?
>
> Feel free to ping me when you're ready for the next review :)
Not yet @lhoestq still working on this! Meanwhile please review #1507 where I added the SelQA dataset :) | Add the OpenPI Dataset by AI2 (AllenAI) | 50 | text: Add OpenPI Dataset
Add the OpenPI Dataset by AI2 (AllenAI)
> Hi @Bharat123rox did you manage to join the different config into one using the IDs ?
>
> Feel free to ping me when you're ready for the next review :)
Not yet @lhoestq still working on this! Meanwhile please review #1507 where I added the SelQA dataset :) | [
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https://github.com/huggingface/datasets/pull/1176 | Add OpenPI Dataset | Apologies for the very late response. Here is the openpi dataset file with a single file per partition after merging `id_answers, answers.jsonl, question.jsonl , question_metadata.jsonl`
https://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl | Add the OpenPI Dataset by AI2 (AllenAI) | 26 | text: Add OpenPI Dataset
Add the OpenPI Dataset by AI2 (AllenAI)
Apologies for the very late response. Here is the openpi dataset file with a single file per partition after merging `id_answers, answers.jsonl, question.jsonl , question_metadata.jsonl`
https://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl | [
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https://github.com/huggingface/datasets/pull/1176 | Add OpenPI Dataset | Hi @Bharat123rox , when you get a chance, please feel free to use the dataset from the repo ( [Link](https://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl) ) . Please let me know if any file is missing! Thank you | Add the OpenPI Dataset by AI2 (AllenAI) | 33 | text: Add OpenPI Dataset
Add the OpenPI Dataset by AI2 (AllenAI)
Hi @Bharat123rox , when you get a chance, please feel free to use the dataset from the repo ( [Link](https://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl) ) . Please let me know if any file is missing! Thank you | [
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https://github.com/huggingface/datasets/pull/1174 | Add Universal Morphologies | Sorry for the delay, changed the default language to "ady" (first alphabetical) and only downloading the relevant files for each config (dataset_infos is till 918KB though) | Adding unimorph universal morphology annotations for 110 languages, pfew!!!
one lemma per row with all possible forms and annotations
https://unimorph.github.io/ | 26 | text: Add Universal Morphologies
Adding unimorph universal morphology annotations for 110 languages, pfew!!!
one lemma per row with all possible forms and annotations
https://unimorph.github.io/
Sorry for the delay, changed the default language to "ady" (first alphabetical) and only downloading the relevant files for each config (dataset_infos is till 918KB though) | [
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https://github.com/huggingface/datasets/pull/1174 | Add Universal Morphologies | Thanks for merging it ! Looks all good
Looks like I didn't reply to your last message, sorry about that.
Feel free to ping me when this happens :) | Adding unimorph universal morphology annotations for 110 languages, pfew!!!
one lemma per row with all possible forms and annotations
https://unimorph.github.io/ | 29 | text: Add Universal Morphologies
Adding unimorph universal morphology annotations for 110 languages, pfew!!!
one lemma per row with all possible forms and annotations
https://unimorph.github.io/
Thanks for merging it ! Looks all good
Looks like I didn't reply to your last message, sorry about that.
Feel free to ping me when this happens :) | [
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https://github.com/huggingface/datasets/pull/1173 | add wikipedia biography dataset | Does anyone know why am I getting this "Some checks were not successful" message? For the _code_quality_ one, I have successfully run the flake8 command. | My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests. | 25 | text: add wikipedia biography dataset
My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests.
Does anyone know why am I getting this "Some checks were not successful" message? For the _code_quality_ one, I have successfully run the flake8 command. | [
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https://github.com/huggingface/datasets/pull/1173 | add wikipedia biography dataset | Ok, I need to update the README.md, but don't know if that will fix the errors | My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests. | 16 | text: add wikipedia biography dataset
My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests.
Ok, I need to update the README.md, but don't know if that will fix the errors | [
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https://github.com/huggingface/datasets/pull/1173 | add wikipedia biography dataset | Hi @ACR0S , thanks for adding the dataset!
It looks like `black` is throwing the code quality error: you need to run `make style` with the latest version of `black` (`black --version` should return 20.8b1)
We also added a requirement to specify encodings when using the python `open` function (line 163 in the current version of your script)
Finally, you will need to add the tags and field descriptions to the README as described here https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card
Let us know if you have any further questions! | My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests. | 85 | text: add wikipedia biography dataset
My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests.
Hi @ACR0S , thanks for adding the dataset!
It looks like `black` is throwing the code quality error: you need to run `make style` with the latest version of `black` (`black --version` should return 20.8b1)
We also added a requirement to specify encodings when using the python `open` function (line 163 in the current version of your script)
Finally, you will need to add the tags and field descriptions to the README as described here https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card
Let us know if you have any further questions! | [
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https://github.com/huggingface/datasets/pull/1173 | add wikipedia biography dataset | Also, please leave the full template of the readme with the `[More Information Needed]` paragraphs: you don't have to fill them out now but it will make it easier for us to go back to later :) | My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests. | 37 | text: add wikipedia biography dataset
My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests.
Also, please leave the full template of the readme with the `[More Information Needed]` paragraphs: you don't have to fill them out now but it will make it easier for us to go back to later :) | [
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https://github.com/huggingface/datasets/pull/1173 | add wikipedia biography dataset | Thank you for your help, @yjernite! I have updated everything (finally run the _make style_, added the tags, the ecoding to the _open_ function and put back the empty fields in the README). Hope it works now! :) | My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests. | 38 | text: add wikipedia biography dataset
My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests.
Thank you for your help, @yjernite! I have updated everything (finally run the _make style_, added the tags, the ecoding to the _open_ function and put back the empty fields in the README). Hope it works now! :) | [
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https://github.com/huggingface/datasets/pull/1165 | Add ar rest reviews | Copy-pasted from the Slack discussion:
the annotation and language creators should be found , not unknown
the example should go under the "Data Instances" paragraph, not "Data fields"
can you remove the abstract from the citation and add it to the dataset description? More people will see that | added restaurants reviews in Arabic for sentiment analysis tasks | 48 | text: Add ar rest reviews
added restaurants reviews in Arabic for sentiment analysis tasks
Copy-pasted from the Slack discussion:
the annotation and language creators should be found , not unknown
the example should go under the "Data Instances" paragraph, not "Data fields"
can you remove the abstract from the citation and add it to the dataset description? More people will see that | [
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https://github.com/huggingface/datasets/pull/1165 | Add ar rest reviews | You can ignores these errors
```
=========================== short test summary info ===========================
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ajgt_twitter_ar
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_chr_en
FAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_ajgt_twitter_ar
FAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_chr_en
FAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_great_code
```
they're fixed on master | added restaurants reviews in Arabic for sentiment analysis tasks | 27 | text: Add ar rest reviews
added restaurants reviews in Arabic for sentiment analysis tasks
You can ignores these errors
```
=========================== short test summary info ===========================
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ajgt_twitter_ar
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_chr_en
FAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_ajgt_twitter_ar
FAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_chr_en
FAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_great_code
```
they're fixed on master | [
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https://github.com/huggingface/datasets/pull/1165 | Add ar rest reviews | Feel free to ping me for the final review once you managed to change to ClassLabel :) | added restaurants reviews in Arabic for sentiment analysis tasks | 17 | text: Add ar rest reviews
added restaurants reviews in Arabic for sentiment analysis tasks
Feel free to ping me for the final review once you managed to change to ClassLabel :) | [
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https://github.com/huggingface/datasets/pull/1165 | Add ar rest reviews | Hey @lhoestq I was able to fix it !! I think the same errors appeared on circleCI and now it's hopefully ready to be merged? | added restaurants reviews in Arabic for sentiment analysis tasks | 25 | text: Add ar rest reviews
added restaurants reviews in Arabic for sentiment analysis tasks
Hey @lhoestq I was able to fix it !! I think the same errors appeared on circleCI and now it's hopefully ready to be merged? | [
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https://github.com/huggingface/datasets/pull/1164 | Add DaNe dataset | Thanks, this looks great!
For the code quality test, it looks like `flake8` is throwing the error, so you can tun `flake8 datasets` locally and fix the errors it points out until it passes | 34 | text: Add DaNe dataset
Thanks, this looks great!
For the code quality test, it looks like `flake8` is throwing the error, so you can tun `flake8 datasets` locally and fix the errors it points out until it passes | [
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|
https://github.com/huggingface/datasets/pull/1160 | adding TabFact dataset | FYI you guys are on GitHub's homepage 😍
<img width="1589" alt="Screenshot 2020-12-09 at 12 34 28" src="https://user-images.githubusercontent.com/326577/101624883-a0ecc700-39e8-11eb-8a97-11af0d036536.png">
| Adding TabFact: A Large-scale Dataset for Table-based Fact Verification.
https://github.com/wenhuchen/Table-Fact-Checking
- The tables are stored as individual csv files, so need to download 16,573 🤯 csv files. As a result the `datasets_infos.json` file is huge (6.62 MB).
- Original dataset has nested structure where, where table is one example and each table has multiple statements,
flattening the structure here so that each statement is one example. | 17 | text: adding TabFact dataset
Adding TabFact: A Large-scale Dataset for Table-based Fact Verification.
https://github.com/wenhuchen/Table-Fact-Checking
- The tables are stored as individual csv files, so need to download 16,573 🤯 csv files. As a result the `datasets_infos.json` file is huge (6.62 MB).
- Original dataset has nested structure where, where table is one example and each table has multiple statements,
flattening the structure here so that each statement is one example.
FYI you guys are on GitHub's homepage 😍
<img width="1589" alt="Screenshot 2020-12-09 at 12 34 28" src="https://user-images.githubusercontent.com/326577/101624883-a0ecc700-39e8-11eb-8a97-11af0d036536.png">
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https://github.com/huggingface/datasets/pull/1158 | Add BBC Hindi NLI Dataset | Hi @avinsit123 !
Did you manage to rename the dataset and apply the suggestion I mentioned for the data fields ?
Feel free to ping me when you're ready for a review :) | # Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : "https://www.aclweb.org/anthology/2020.aacl-main.71"
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
| 33 | text: Add BBC Hindi NLI Dataset
# Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : "https://www.aclweb.org/anthology/2020.aacl-main.71"
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
Hi @avinsit123 !
Did you manage to rename the dataset and apply the suggestion I mentioned for the data fields ?
Feel free to ping me when you're ready for a review :) | [
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https://github.com/huggingface/datasets/pull/1158 | Add BBC Hindi NLI Dataset | Hi @avinsit123 ! Have you had a chance to take a look at my suggestions ?
Let me know if you have questions or if I can help | # Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : "https://www.aclweb.org/anthology/2020.aacl-main.71"
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
| 28 | text: Add BBC Hindi NLI Dataset
# Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : "https://www.aclweb.org/anthology/2020.aacl-main.71"
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
Hi @avinsit123 ! Have you had a chance to take a look at my suggestions ?
Let me know if you have questions or if I can help | [
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https://github.com/huggingface/datasets/pull/1158 | Add BBC Hindi NLI Dataset | @lhoestq I have fixed the code to resolve all your comments. Pls do check. I also don't seem to know why the CI tests are failing as I ran all the tests in CONTRIBUTING.md on my local pc and they passed. | # Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : "https://www.aclweb.org/anthology/2020.aacl-main.71"
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
| 41 | text: Add BBC Hindi NLI Dataset
# Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : "https://www.aclweb.org/anthology/2020.aacl-main.71"
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
@lhoestq I have fixed the code to resolve all your comments. Pls do check. I also don't seem to know why the CI tests are failing as I ran all the tests in CONTRIBUTING.md on my local pc and they passed. | [
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https://github.com/huggingface/datasets/pull/1158 | Add BBC Hindi NLI Dataset | @lhoestq thanks for ur patient review :) . I also wish to add similar 3 more NLI hindi datasets. Hope to do within this week. | # Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : "https://www.aclweb.org/anthology/2020.aacl-main.71"
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
| 25 | text: Add BBC Hindi NLI Dataset
# Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : "https://www.aclweb.org/anthology/2020.aacl-main.71"
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
@lhoestq thanks for ur patient review :) . I also wish to add similar 3 more NLI hindi datasets. Hope to do within this week. | [
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https://github.com/huggingface/datasets/pull/1155 | Add BSD | Glad to have more Japanese data! Couple of comments:
- the abbreviation might confuse some people as there is also an OPUS BSD corpus, would you mind renaming it as `bsd_ja_en`?
- `flake8` is throwing some errors, you can run it locally (`flake8 datasets`) and fix what it tells you until it's happy :)
- We're not using `os.path.join` for URLs as it's unstable across systems (introduces backslashes on Windows). Can you write the URLs explicitly instead?
Thanks! | This PR adds BSD, the Japanese-English business dialogue corpus by
[Rikters et al., 2020](https://www.aclweb.org/anthology/D19-5204.pdf). | 78 | text: Add BSD
This PR adds BSD, the Japanese-English business dialogue corpus by
[Rikters et al., 2020](https://www.aclweb.org/anthology/D19-5204.pdf).
Glad to have more Japanese data! Couple of comments:
- the abbreviation might confuse some people as there is also an OPUS BSD corpus, would you mind renaming it as `bsd_ja_en`?
- `flake8` is throwing some errors, you can run it locally (`flake8 datasets`) and fix what it tells you until it's happy :)
- We're not using `os.path.join` for URLs as it's unstable across systems (introduces backslashes on Windows). Can you write the URLs explicitly instead?
Thanks! | [
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https://github.com/huggingface/datasets/pull/1152 | hindi discourse analysis dataset commit | That's a great dataset to have! We need a couple more things to be good to go:
- you should `make style` and `flake8 datasets` before pushing to make the code quality check happy :)
- the dataset will need some dummy data which you should be able to auto-generate and test locally: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata
- there's some good information in your current README, but we need the format to follow the template [here](https://github.com/huggingface/datasets/blob/master/templates/README.md) and to have YAML tags at the top, as described in the guide: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card
LEt us know if you need any help! | 95 | text: hindi discourse analysis dataset commit
That's a great dataset to have! We need a couple more things to be good to go:
- you should `make style` and `flake8 datasets` before pushing to make the code quality check happy :)
- the dataset will need some dummy data which you should be able to auto-generate and test locally: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata
- there's some good information in your current README, but we need the format to follow the template [here](https://github.com/huggingface/datasets/blob/master/templates/README.md) and to have YAML tags at the top, as described in the guide: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card
LEt us know if you need any help! | [
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|
https://github.com/huggingface/datasets/pull/1152 | hindi discourse analysis dataset commit | Hi @yjernite
I was successfully able to generate the dataset_info.json file using the command
python datasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs
But unfortunately, could not generate the dummy data
While running the command
python datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate
I got an error as
ValueError: Couldn't parse columns ['0', '1', '2', '3', '4', ......, '9982']. Maybe specify which json field must be used to read the data with --json_field <my_field>.
The thing is the dataset I am trying to upload is of the format
{
'0': {'Story_no': 15, 'Sentence': ' गाँठ से साढ़े तीन रुपये लग गये, जो अब पेट में जाकर खनकते भी नहीं! जो तेरी करनी मालिक! ” “इसमें मालिक की क्या करनी है? ”', 'Discourse Mode': 'Dialogue'},
'1': {'Story_no': story_no, 'Sentence': sentence, 'Discourse Mode': discourse_mode},
.......,
'9982': {'Story_no': story_no, 'Sentence': sentence, 'Discourse Mode': discourse_mode}
}
Can you please suggest any errors I am making in the _generate_examples method?
Thanks! | 150 | text: hindi discourse analysis dataset commit
Hi @yjernite
I was successfully able to generate the dataset_info.json file using the command
python datasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs
But unfortunately, could not generate the dummy data
While running the command
python datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate
I got an error as
ValueError: Couldn't parse columns ['0', '1', '2', '3', '4', ......, '9982']. Maybe specify which json field must be used to read the data with --json_field <my_field>.
The thing is the dataset I am trying to upload is of the format
{
'0': {'Story_no': 15, 'Sentence': ' गाँठ से साढ़े तीन रुपये लग गये, जो अब पेट में जाकर खनकते भी नहीं! जो तेरी करनी मालिक! ” “इसमें मालिक की क्या करनी है? ”', 'Discourse Mode': 'Dialogue'},
'1': {'Story_no': story_no, 'Sentence': sentence, 'Discourse Mode': discourse_mode},
.......,
'9982': {'Story_no': story_no, 'Sentence': sentence, 'Discourse Mode': discourse_mode}
}
Can you please suggest any errors I am making in the _generate_examples method?
Thanks! | [
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|
https://github.com/huggingface/datasets/pull/1152 | hindi discourse analysis dataset commit | The dummy data generator doesn't support this kind of json format yet.
Can you create the dummy data manually please ? You can get the instructions by running the
```
datasets-cli dummy_data ./datasets/dataset_name
```
command. | 35 | text: hindi discourse analysis dataset commit
The dummy data generator doesn't support this kind of json format yet.
Can you create the dummy data manually please ? You can get the instructions by running the
```
datasets-cli dummy_data ./datasets/dataset_name
```
command. | [
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|
https://github.com/huggingface/datasets/pull/1152 | hindi discourse analysis dataset commit | Hi, I created the dummy data manually but the tests are still failing it seems.
Can you suggest the format of JSON which is supported by dummy data generator?
I will have to modify my _generate_examples method accordingly.
Please advice on the same.
Thanks much.
| 45 | text: hindi discourse analysis dataset commit
Hi, I created the dummy data manually but the tests are still failing it seems.
Can you suggest the format of JSON which is supported by dummy data generator?
I will have to modify my _generate_examples method accordingly.
Please advice on the same.
Thanks much.
| [
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|
https://github.com/huggingface/datasets/pull/1152 | hindi discourse analysis dataset commit | Can you run `make style` to format the code for the CI please ?
Also about the dummy data, here is how to generate them:
We need a dummy_data.zip file in ./datasets/hindiDiscourse/dummy/1.0.0 (or replace hindiDiscourse by hindi_discourse since we have to rename the folder anyway)
To create the zip file, first go in this directory and create a folder named dummy_data.
Then inside the dummy_data folder create a file `discourse_dataset.json` and fill it with something like 5 examples.
Finally zip the dummy_data folder to end up with the dummy_data.zip file
Once it's done you can check if the dummy data test passes with
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_hindi_discourse
```
If it passes you can then remove the dummy_data folder to keep only the dummy_data.zip file | 124 | text: hindi discourse analysis dataset commit
Can you run `make style` to format the code for the CI please ?
Also about the dummy data, here is how to generate them:
We need a dummy_data.zip file in ./datasets/hindiDiscourse/dummy/1.0.0 (or replace hindiDiscourse by hindi_discourse since we have to rename the folder anyway)
To create the zip file, first go in this directory and create a folder named dummy_data.
Then inside the dummy_data folder create a file `discourse_dataset.json` and fill it with something like 5 examples.
Finally zip the dummy_data folder to end up with the dummy_data.zip file
Once it's done you can check if the dummy data test passes with
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_hindi_discourse
```
If it passes you can then remove the dummy_data folder to keep only the dummy_data.zip file | [
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|
https://github.com/huggingface/datasets/pull/1152 | hindi discourse analysis dataset commit | Hi @duttahritwik did you manage to make the dummy data ?
Feel free to ping me if you have questions or if we can help | 25 | text: hindi discourse analysis dataset commit
Hi @duttahritwik did you manage to make the dummy data ?
Feel free to ping me if you have questions or if we can help | [
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https://github.com/huggingface/datasets/pull/1152 | hindi discourse analysis dataset commit | The error `tests/test_file_utils.py::TempSeedTest::test_tensorflow` just appeared because of tensorflow's update.
Once it's fixed on master we'll be free to merge this one | 21 | text: hindi discourse analysis dataset commit
The error `tests/test_file_utils.py::TempSeedTest::test_tensorflow` just appeared because of tensorflow's update.
Once it's fixed on master we'll be free to merge this one | [
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|
https://github.com/huggingface/datasets/pull/1145 | Add Species-800 | thanks @lhoestq ! I probably need to do the same change in the `SplitGenerator`s (lines 107, 110 and 113). I'll open a new PR for that | 26 | text: Add Species-800
thanks @lhoestq ! I probably need to do the same change in the `SplitGenerator`s (lines 107, 110 and 113). I'll open a new PR for that | [
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|
https://github.com/huggingface/datasets/pull/1144 | Add JFLEG | Hi @j-chim ! You're right it does feel redundant: your option works better, but I'd even suggest having the references in a Sequence feature, which you can declare as:
```
features=datasets.Features(
{
"sentence": datasets.Value("string"),
"corrections": datasets.Sequence(datasets.Value("string")),
}
),
```
To create the dummy data, you just need to tell the generator which files it should use, which you can do with:
`python datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate --match_text_files "train*,dev*,test*"`
| This PR adds [JFLEG ](https://www.aclweb.org/anthology/E17-2037/), an English grammatical error correction benchmark.
The tests were successful on real data, although it would be great if I can get some guidance on the **dummy data**. Basically, **for each source sentence there are 4 possible gold standard target sentences**. The original dataset comprise files in a flat structure, labelled by split then by source/target (e.g., dev.src, dev.ref0, ..., dev.ref3). Not sure what is the best way of adding this.
I imagine I can treat each distinct source-target pair as its own split? But having so many copies of the source sentence feels redundant, and it would make it less convenient to end-users who might want to access multiple gold standard targets simultaneously. | 68 | text: Add JFLEG
This PR adds [JFLEG ](https://www.aclweb.org/anthology/E17-2037/), an English grammatical error correction benchmark.
The tests were successful on real data, although it would be great if I can get some guidance on the **dummy data**. Basically, **for each source sentence there are 4 possible gold standard target sentences**. The original dataset comprise files in a flat structure, labelled by split then by source/target (e.g., dev.src, dev.ref0, ..., dev.ref3). Not sure what is the best way of adding this.
I imagine I can treat each distinct source-target pair as its own split? But having so many copies of the source sentence feels redundant, and it would make it less convenient to end-users who might want to access multiple gold standard targets simultaneously.
Hi @j-chim ! You're right it does feel redundant: your option works better, but I'd even suggest having the references in a Sequence feature, which you can declare as:
```
features=datasets.Features(
{
"sentence": datasets.Value("string"),
"corrections": datasets.Sequence(datasets.Value("string")),
}
),
```
To create the dummy data, you just need to tell the generator which files it should use, which you can do with:
`python datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate --match_text_files "train*,dev*,test*"`
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https://github.com/huggingface/datasets/pull/1139 | Add ReFreSD dataset | Cool dataset! Replying in-line:
> This PR adds the **ReFreSD dataset**.
> The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.
>
> Need feedback on:
>
> * I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.
you can use `--match_text_files` in the dummy data generation:
`python datasets-cli dummy_data datasets/refresd --auto_generate --match_text_files "REFreSD_rationale"`
> * The feature names.
>
> * I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.
It would actually be even better to use the `Translation` feature here to replace best:
`"sentence_pair": datasets.Translation(languages=['en', 'fr']),`
Then during `_generate_examples` this filed should look like"
`{"sentence_pair": {"fr": french, "en": english}}`
> * There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.
Looks good!
> * The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.
Having the feature declared as `"rationale_en": datasets.Sequence(datasets.Value("int32"))` should work
>
> Thanks in advance
Hope that helps you out! Don't forget to `make style`, rebase from master, and run all the tests before pushing again! You will also need to add a `README.md` as described in the guide:
https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card | This PR adds the **ReFreSD dataset**.
The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.
Need feedback on:
- I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.
- The feature names.
- I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.
- There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.
- The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.
Thanks in advance | 275 | text: Add ReFreSD dataset
This PR adds the **ReFreSD dataset**.
The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.
Need feedback on:
- I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.
- The feature names.
- I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.
- There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.
- The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.
Thanks in advance
Cool dataset! Replying in-line:
> This PR adds the **ReFreSD dataset**.
> The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.
>
> Need feedback on:
>
> * I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.
you can use `--match_text_files` in the dummy data generation:
`python datasets-cli dummy_data datasets/refresd --auto_generate --match_text_files "REFreSD_rationale"`
> * The feature names.
>
> * I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.
It would actually be even better to use the `Translation` feature here to replace best:
`"sentence_pair": datasets.Translation(languages=['en', 'fr']),`
Then during `_generate_examples` this filed should look like"
`{"sentence_pair": {"fr": french, "en": english}}`
> * There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.
Looks good!
> * The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.
Having the feature declared as `"rationale_en": datasets.Sequence(datasets.Value("int32"))` should work
>
> Thanks in advance
Hope that helps you out! Don't forget to `make style`, rebase from master, and run all the tests before pushing again! You will also need to add a `README.md` as described in the guide:
https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card | [
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https://github.com/huggingface/datasets/pull/1139 | Add ReFreSD dataset | Thanks a lot for the answer, that does help a lot !
I opened a PR for a License in the original repo so I was waiting for that for the model card. If there is no news on Monday, I'll add it without License. | This PR adds the **ReFreSD dataset**.
The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.
Need feedback on:
- I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.
- The feature names.
- I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.
- There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.
- The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.
Thanks in advance | 45 | text: Add ReFreSD dataset
This PR adds the **ReFreSD dataset**.
The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.
Need feedback on:
- I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.
- The feature names.
- I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.
- There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.
- The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.
Thanks in advance
Thanks a lot for the answer, that does help a lot !
I opened a PR for a License in the original repo so I was waiting for that for the model card. If there is no news on Monday, I'll add it without License. | [
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