Questions about XSUM 
We should try to regenerate the data using the official script.
But iirc that's what we used in the first place, so not sure why it didn't match in the first place.

I'll let you know when the dataset is updated Thanks, looking forward to hearing your update on this thread. 

This is a blocking issue for us; would appreciate any progress on this front. We can also help with the fix, if you deem it appropriately.  I just started the generation on my side, I'll let you know how it goes :)  Hmm after a first run I'm still missing 136668/226711 urls.
I'll relaunch it tomorrow to try to get the remaining ones. Update: I'm missing 36/226711 urls but I haven't managed to download them yet Thanks! That sounds like a reasonable number!  So I managed to download them all but when parsing only 226,181/226,711 worked.
Not sure if it's worth digging and debugging parsing at this point :/  Maybe @sshleifer can help, I think he's already played with xsum at one point Thanks @lhoestq
It would be great to improve coverage, but IDs are the really crucial part for us. We'd really appreciate an update to the dataset with IDs either way! I gave up at an even earlier point. The dataset I use has 204,017 train examples. @lhoestq @sshleifer like @jbragg said earlier, the main issue for us is that the current XSUM dataset (in your package) does not have IDs suggested by the original dataset ([here is the file](https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json).) Would appreciate if you update the XSUM dataset to include the instance IDs. 

The missing instances is also a problem, but likely not worth pursuing given its relatively small scale.  >So I managed to download them all but when parsing only 226,181/226,711 worked.

@lhoestq any chance we could update the HF-hosted dataset with the IDs in your new version? Happy to help if there's something I can do. Well I couldn't parse what I downloaded.
Unfortunately I think I won't be able to take a look at it this week.
I can try to send you what I got if you want to give it a shot @jbragg 
Otherwise feel free to re-run the xsum download script, maybe you'll be luckier than me
Hi there ✋ 

I'm looking into your `xsum` dataset and I have several questions on that. 
So here is how I loaded the data: 
```
>>> data = datasets.load_dataset('xsum', version='1.0.1')
>>> data['train']
Dataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)
>>> data['test']
Dataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)
```

The first issue is, the instance counts don’t match what I see on [the dataset's website](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)
```
 … training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.
```
Any thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https://github.com/huggingface/datasets/pull/289  (reviewed by @patrickvonplaten) 

Another issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https://github.com/EdinburghNLP/XSum/blob/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. 

CC @jbragg 

