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https://api.github.com/repos/huggingface/datasets/issues/comments/627883414
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| 2020-05-13T10:02:13
| 2020-05-13T10:02:13
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awesome - so no manual downloaded needed here?
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| 2020-05-13T10:03:06
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Yes exactly. It works like a standard download
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| 2020-05-13T12:30:35
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Looks good to me. I have not really looked too much into the Beam Datasets yet though - so I think you can merge whenever you think is good for Beam datasets :-)
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| 2020-05-13T21:28:57
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@jplu - I changed this weird archieve - iter method to something simpler. It's only one file to download anyways so I don't see the point of using weird iter methods...It's a huge file though :D 30 million lines of text. Took me quite some time to download :D
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| 2020-05-13T23:08:59
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@thomwolf - can you check and merge if ok?
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| 2020-05-13T23:15:28
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Great! LGTM :-)
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| 2020-05-13T23:20:38
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> Ok, really clean!
> I like the logic (not a huge fan of using `_asdict_inner` but it makes sense).
> I think it's a nice improvement!
>
> How should we update the files in the repo? Run a big job on a server or on somebody's computer who has most of the datasets already downloaded?
Maybe we can split the updates among us...IMO most datasets run very quickly.
I think I've downloaded 50 datasets and 80% are loaded in <5min, 15% in <1h and then `wmt` which is still downloading (since 12h).
I deleted my cache because the `wmt` downloads require quite a lot of space, so I only have parts of the `wmt` datasets on my computer.
@mariamabarham I guess you have downloaded most of the datasets no?
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| 2020-05-14T09:50:54
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LGTM :-) Some small suggestions to shorten the code a bit :-)
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| 2020-05-14T10:26:36
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Can you put the kwargs as normal kwargs instead of a dict? (And add them to the kwargs description As well)
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| 2020-05-14T10:38:49
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@thom Is-it what you meant?
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| 2020-05-14T11:14:29
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Yes and there is a dynamically generated doc string in the metric script KWARGS DESCRIPTION
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| 2020-05-14T11:17:41
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Should that rather be in an organization scope, @thomwolf @patrickvonplaten ?
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| 2020-05-14T11:43:13
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> Should that rather be in an organization scope, @thomwolf @patrickvonplaten ?
I'm a bit indifferent - both would be fine for me!
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| 2020-05-14T11:43:41
| 2020-05-14T11:43:41
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@jplu - if creating the dummy_data is too tedious, I can do it as well :-)
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| 2020-05-14T11:50:31
| 2020-05-14T11:50:31
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There is dummy_data here, no ?
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| 2020-05-14T11:54:20
| 2020-05-14T11:54:20
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Yeah I think naming it webis/tl_dr would be best @jplu if that works for you
|
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| 2020-05-14T11:59:59
| 2020-05-14T11:59:59
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No problem at all!! On it^^
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| 2020-05-14T12:02:34
| 2020-05-14T12:04:30
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> There is dummy_data here, no ?
Some paths were wrong - the structure is really confusing and the error messages don't really help either - I have to think about how to make this easier to understand!
Hope it was ok that I fiddled with your PR !
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| 2020-05-14T12:04:04
| 2020-05-14T12:04:29
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> Some paths were wrong - the structure is really confusing and the error message don't really help either - I have to think about how to make this easier to understand!
Oh ok! I haven't noticed that sorry :(
> Hope it was ok that I fiddled with your PR !
Of course it was ok :)
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| 2020-05-14T12:37:53
| 2020-05-14T12:37:53
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@julien-c Looks like what you have in mind?
```python
import nlp
nlp.load_dataset("datasets/webis", "tl_dr")
#Output: Downloading and preparing dataset webis/tl_dr (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/jplu/.cache/huggingface/datasets/webis/tl_dr/1.0.0...
```
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mariamabarham
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| 2020-05-14T13:48:45
| 2020-05-14T13:48:45
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> Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset.
>
> The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`.
>
> The dataset can then be loaded via:
>
> ```python
> import nlp
> nlp.load_dataset("wikihow", data_dir="~/wikihow/manual_dir")
> ```
>
> I added/changed so that there are explicit error messages when using manually downloaded files.
wouldn't be nicer if we can have `manual_dir/wikihow`?
|
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patrickvonplaten
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User
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| 2020-05-14T13:54:29
| 2020-05-14T13:54:29
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> > Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset.
> > The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`.
> > The dataset can then be loaded via:
> > ```python
> > import nlp
> > nlp.load_dataset("wikihow", data_dir="~/wikihow/manual_dir")
> > ```
> >
> >
> > I added/changed so that there are explicit error messages when using manually downloaded files.
>
> wouldn't be nicer if we can have `manual_dir/wikihow`?
Sure, I mean the user can decide whatever he likes best :-) The path one puts in `data_dir` will be used as the path to the manual dir. `nlp.load_dataset("wikihow", data_dir="~/manual_dir/wikihow")` would work as well as any other path ;-)
|
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User
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| 2020-05-14T13:57:58
| 2020-05-14T13:57:58
|
Haha that cornell dialogue dataset - that ran for 3h on my computer as well. The `generate_examples` method in this script is one of the most inefficient code samples I've ever seen :D
|
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lhoestq
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| 2020-05-14T14:00:05
| 2020-05-14T14:00:05
|
Indeed it's been 3 hours already
```73111 examples [3:07:48, 2.40 examples/s]```
|
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User
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| 2020-05-14T14:12:57
| 2020-05-14T14:12:57
|
I might have a different version of `isort` than others. It seems like I'm always reordering the imports of others. But isn't really a problem...
|
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| 2020-05-14T14:26:51
| 2020-05-14T14:26:51
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Perfect! You can merge!
|
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| 628,750,935
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| 2020-05-14T16:36:56
| 2020-05-14T16:36:56
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Awesome that's great!
|
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| 2020-05-14T16:49:04
| 2020-05-14T16:49:04
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Nice - I think we can merge this. I will update the checksums for `wikihow` then as well
|
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| 2020-05-14T20:54:09
| 2020-05-14T20:54:09
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Merging this for now. Maybe we can see whether to rename it in a different PR @julien-c ?
|
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| 2020-05-15T00:19:51
| 2020-05-15T00:19:51
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Installing from source (instead of Pypi package) solved the problem.
|
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| 2020-05-15T00:33:57
| 2020-05-15T00:33:57
|
I could access the info by first accessing the different splits :
```python
import nlp
cnn_dm = nlp.load_dataset('cnn_dailymail')
print(cnn_dm['train'].info)
```
Information seems to be duplicated between the subsets :
```python
print(cnn_dm["train"].info == cnn_dm["test"].info == cnn_dm["validation"].info)
# True
```
Is it expected ?
|
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| 2020-05-15T02:41:22
| 2020-05-15T02:46:52
|
It's strange, after installing `nlp` on Colab, the `pyarrow` version seems fine from `pip` but not from python :
```python
import pyarrow
!pip show pyarrow
print("version = {}".format(pyarrow.__version__))
```
> Name: pyarrow
Version: 0.17.0
Summary: Python library for Apache Arrow
Home-page: https://arrow.apache.org/
Author: None
Author-email: None
License: Apache License, Version 2.0
Location: /usr/local/lib/python3.6/dist-packages
Requires: numpy
Required-by: nlp, feather-format
>
> version = 0.14.1
|
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| 2020-05-15T02:45:27
| 2020-05-15T05:11:22
|
Ok I just had to restart the runtime after installing `nlp`. After restarting, the version of `pyarrow` is fine.
|
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| 2020-05-15T07:02:37
| 2020-05-15T07:02:37
|
I didn't assign the output 🤦♂️
```python
dataset.map(test)
```
should be :
```python
dataset = dataset.map(test)
```
|
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| 2020-05-15T07:04:02
| 2020-05-15T07:04:02
|
Hi, you can't do that at the moment.
|
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| 2020-05-15T07:04:22
| 2020-05-15T07:04:22
|
That's the way!
|
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| 2020-05-15T07:42:01
| 2020-05-15T07:42:01
|
Can you share your data files or a minimally reproducible example?
|
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public
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| 2020-05-15T08:56:23
| 2020-05-15T08:56:23
|
Good point @Colanim ! What happens under the hood when running:
```python
import nlp
cnn_dm = nlp.load_dataset('cnn_dailymail')
```
is that for every split in `cnn_dailymail`, a different dataset object (which all holds the same info) is created. This has the advantages that the datasets are easily separable in a training setup.
Also note that you can load e.g. only the `train` split of the dataset via:
```python
cnn_dm_train = nlp.load_dataset('cnn_dailymail', split="train")
print(cnn_dm_train.info)
```
I think we should make the `info` object slightly different when creating the dataset for each split - at the moment it contains for example the variable `splits` which should maybe be renamed to `split` and contain only one `SplitInfo` object ...
|
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lhoestq
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| 2020-05-15T09:46:07
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For each dataset, If there exist a `dataset_info.json`, then the command `nlp-cli test path/to/my/dataset --al_configs` is successful only if the `dataset_infos.json` is correct. The infos are correct if the size and checksums of the downloaded file are correct, and if the number of examples in each split are correct.
Note: the `test` command is supposed to test the script, that's why it runs the script even if the cached files already exist. Let me know if it's good to you.
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| 2020-05-15T10:03:14
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> For each dataset, If there exist a `dataset_info.json`, then the command `nlp-cli test path/to/my/dataset --al_configs` is successful only if the `dataset_infos.json` is correct. The infos are correct if the size and checksums of the downloaded file are correct, and if the number of examples in each split are correct.
>
> Note: the `test` command is supposed to test the script, that's why it runs the script even if the cached files already exist. Let me know if it's good to you.
Does it have to download the whole data to check if the checksums are correct? I guess so no?
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> > For each dataset, If there exist a `dataset_info.json`, then the command `nlp-cli test path/to/my/dataset --al_configs` is successful only if the `dataset_infos.json` is correct. The infos are correct if the size and checksums of the downloaded file are correct, and if the number of examples in each split are correct.
> > Note: the `test` command is supposed to test the script, that's why it runs the script even if the cached files already exist. Let me know if it's good to you.
>
> Does it have to download the whole data to check if the checksums are correct? I guess so no?
Yes it has to download them all (unless they were already downloaded in which case it just uses the cached downloaded files).
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| 2020-05-15T13:06:43
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Google colab has an old version of Apache Arrow built-in.
Be sure you execute the "pip install" cell and restart the notebook environment if the colab asks for it.
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polkaYK
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| 2020-05-15T13:10:40
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Thanks for reply, worked fine!
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| 2020-05-15T15:03:31
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As a follow-up to this: It looks like the actual GLUE task name is supplied as the `name` argument. Is there a way to check what `name`s/sub-datasets are available under a grouping like GLUE? That information doesn't seem to be readily available in info from `nlp.list_datasets()`.
Edit: I found the info under `Glue.BUILDER_CONFIGS`
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| 2020-05-15T17:27:18
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The tests for the Wikipedia dataset do not pass anymore with the error:
```
To be able to use this dataset, you need to install the following dependencies ['mwparserfromhell'] using 'pip install mwparserfromhell' for instance'
```
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igrinis
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User
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| 2020-05-15T20:50:54
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|
I would suggest `nlds`. NLP is a very general, broad and ambiguous term, the library is not about NLP (as in processing) per se, it is about accessing Natural Language related datasets. So the name should reflect its purpose.
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lhoestq
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| 2020-05-15T21:12:24
| 2020-05-15T21:12:24
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This was an issue on master. You can just rebase from master.
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| 2020-05-15T21:32:57
| 2020-05-15T21:32:57
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Perfect! Indeed, it worked^^ Thanks @lhoestq
|
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stefan-it
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| 2020-05-15T23:43:33
| 2020-05-15T23:43:50
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Had really fun playing around with this new library :heart:
|
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patrickvonplaten
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| 2020-05-16T11:12:00
| 2020-05-16T11:12:00
|
Currently the dummy_data tests are always green because in a PR the dataset is not yet synchronized with aws. This PR fixes this: https://github.com/huggingface/nlp/pull/140 .
Could you test `json` locally or wait until the PR: https://github.com/huggingface/nlp/pull/140 is merged ? :-)
|
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| 2020-05-16T11:39:57
| 2020-05-16T11:39:57
|
You are right and I think those are usual best practice :) I'm 100% fine with this^^
|
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| 2020-05-16T11:40:43
| 2020-05-16T11:40:43
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Ok, I will wait #140 to be merged and then rebase :)
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| 2020-05-16T11:43:01
| 2020-05-16T11:43:01
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Same for the dataset_infos.json at the project root no ?
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| 2020-05-16T12:23:22
| 2020-05-16T12:23:22
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Sorry guys, I haven't noticed. Thank you for mentioning it.
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| 2020-05-16T12:56:47
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Chiming in to second everything @honnibal said, and to add that I think the current name is going to impact the discoverability of this library. People who are looking for "NLP Datasets" through a search engine are going to see a library called `nlp` and think it's too broad. People who are looking to do NLP in python are going to search "Python NLP" and end up here, confused that this is a collection of datasets.
The names of the other huggingface libraries work because they're the only game in town: there are not very many robust, distinct libraries for `tokenizers` or `transformers` in python, for example. But there are several options for NLP in python, and adding this as a possible search result for "python nlp" when datasets are likely not what someone is searching for adds noise and frustrates potential users.
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patrickvonplaten
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| 2020-05-16T13:21:37
| 2020-05-16T13:21:37
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Merging this for now to unblock other PRs.
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User
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| 2020-05-16T13:23:19
| 2020-05-16T13:23:19
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That's awesome - thanks @stefan-it :-)
Could you maybe rebase to master and check if all dummy data tests are fine. I should have included the local tests directly in the test suite so that all PRs are fully checked: #140 - sorry :D
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User
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| 2020-05-16T13:51:32
| 2020-05-16T13:51:32
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@patrickvonplaten Rebased it 😅
How can it test 🤔 I used:
```bash
RUN_SLOW=1 RUN_LOCAL=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_local_germeval_14
# and
RUN_SLOW=1 RUN_LOCAL=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_dataset_all_configs_local_germeval_14
```
and the tests still pass :)
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User
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| 2020-05-16T13:56:04
| 2020-05-16T13:56:04
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Perfect, if these tests pass that's great - I'll merge the PR then :-) Was it very difficult to create the dummy data structure?
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dennlinger
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User
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public
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| 2020-05-16T15:09:43
| 2020-05-16T15:09:43
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I'm also not sure whether the naming of `nlp` is the problem itself, as long as it comes with the appropriate identifier, so maybe something like `huggingface_nlp`? This is analogous to what @honnibal and spacy are doing for `spacy-transformers`. Of course, this is a "step back" from the recent changes/renaming of transformers, but may be some middle ground between a complete rebranding, and keeping it identifiable.
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richarddwang
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User
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| 2020-05-17T01:45:08
| 2020-05-17T01:45:08
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As far as I understand, `wikitext` is refer to `WikiText-103` and `WikiText-2` that created by researchers in Salesforce, and mostly used in traditional language modeling.
You might want to say `wikipedia`, a dump from wikimedia foundation.
Also I would like to have Toronto BookCorpus too ! Though it involves copyright problem...
|
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thomwolf
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| 2020-05-17T12:14:02
| 2020-05-17T12:14:02
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Indeed, thanks!
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thomwolf
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| 2020-05-17T12:17:28
| 2020-05-17T12:17:28
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Thanks, this was fixed with #135 :)
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User
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public
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| 2020-05-17T12:32:09
| 2020-05-17T12:32:09
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Indeed. Probably we will want to have a similar method directly in the library
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| 2020-05-17T12:54:28
| 2020-05-17T12:54:28
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Hi @zphang,
So you can just give the local path to a dataset script file and it should work.
Here is an example:
- you can download one of the scripts in the `datasets` folder of the present repo (or clone the repo)
- then you can load it with `load_dataset('PATH/TO/YOUR/LOCAL/SCRIPT.py')`
Does it make sense?
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| 2020-05-17T13:01:01
| 2020-05-17T13:01:01
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Yes so the first config is loaded by default when no `name` is supplied but for GLUE this should probably throw an error indeed.
We can probably just add an `__init__` at the top of the `class Glue(nlp.GeneratorBasedBuilder)` in the `glue.py` script which does this check:
```
class Glue(nlp.GeneratorBasedBuilder):
def __init__(self, *args, **kwargs):
assert 'name' in kwargs and kwargs[name] is not None, "Glue has to be called with a configuration name"
super(Glue, self).__init__(*args, **kwargs)
```
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| 2020-05-17T13:02:22
| 2020-05-17T13:02:22
|
Indeed, I think this one is almost ready cc @lhoestq
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| 2020-05-17T13:10:12
| 2020-05-17T13:35:31
|
Interesting, thanks for sharing your thoughts.
As we’ll move toward a first non-beta release, we will pool the community of contributors/users of the library for their opinions on a good final name (like when we renamed the beautifully (?) named `pytorch-pretrained-bert`)
In the meantime, using `from nlp import load_dataset, load_metric` should work 😉
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| 2020-05-17T17:47:04
| 2020-05-17T17:47:04
|
Same error for dataset 'wiki40b'
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astariul
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| 2020-05-17T22:21:47
| 2020-05-17T22:21:47
|
Sure, [here is a Colab notebook](https://colab.research.google.com/drive/1uiS89fnHMG7HV_cYxp3r-_LqJQvNNKs9?usp=sharing) reproducing the error.
> ArrowInvalid: Column 1 named references expected length 36 but got length 56
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public
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| 2020-05-18T07:24:33
| 2020-05-18T07:24:33
|
If tests are being added, any guidance on where to add tests would be helpful!
Tagging @thomwolf for review
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dseddah
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public
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| 2020-05-18T07:29:54
| 2020-05-18T07:29:54
|
As @yoavgo suggested, there should be the possibility to call a function like nlp.bib that outputs all bibtex ref from the datasets and models actually used and eventually nlp.bib.forreadme that would output the same info + versions numbers so they can be included in a readme.md file.
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| 2020-05-18T07:38:06
| 2020-05-18T07:38:06
|
Should be fixed on master :)
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| 2020-05-18T07:52:52
| 2020-05-18T07:53:22
|
Actually, double checking with @mariamabarham, we already have this feature I think.
It's like this currently:
```python
>>> from nlp import load_dataset
>>>
>>> dataset = load_dataset('glue', 'cola', split='train')
>>> print(dataset.info.citation)
@article{warstadt2018neural,
title={Neural Network Acceptability Judgments},
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
journal={arXiv preprint arXiv:1805.12471},
year={2018}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
Note that each GLUE dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
```
What do you think @dseddah?
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| 2020-05-18T08:49:07
| 2020-05-18T08:49:07
|
I'm doing the latest adjustments to make the processing of the dataset run on Dataflow
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mariamabarham
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User
|
public
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| 2020-05-18T10:17:55
| 2020-05-18T10:17:55
|
@AlphaMycelium the Ubuntu Dialogue Corpus [version 2]( https://github.com/rkadlec/ubuntu-ranking-dataset-creator) is added. Note that it requires a manual download by following the download instructions in the [repos]( https://github.com/rkadlec/ubuntu-ranking-dataset-creator).
Maybe we can close this issue for now?
|
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dseddah
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public
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| 2020-05-18T12:07:49
| 2020-05-18T12:07:49
|
Looks good but why would there be a difference between the ref in the source and the one to be printed?
|
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MDEyOklzc3VlQ29tbWVudDYzMDMwNTI0MQ==
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julien-c
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public
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| 2020-05-18T16:45:15
| 2020-05-18T16:45:15
|
I feel like we are conflating two distinct subjects here:
1. @honnibal's point is that using `nlp` as a package name might break existing code and bring developer usability issues in the future
2. @pmbaumgartner's point is that the `nlp` package name is too broad and shouldn't be used by a package that exposes only datasets and metrics
(let me know if I mischaracterize your point)
I'll chime in to say that the first point is a bit silly IMO. As Python developers due to the limitations of the import system we already have to share:
- a single flat namespace for packages
- which also conflicts with local modules i.e. local files
If we add the constraint that this flat namespace also be shared with variable names this gets untractable pretty fast :)
I also think all Python software developers/ML engineers/scientists are capable of at least a subset of:
- importing only the methods that they need like @thomwolf suggested
- aliasing their import
- renaming a local variable
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| 2020-05-18T18:37:07
| 2020-05-18T18:37:07
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Found it. https://github.com/huggingface/nlp/tree/master/datasets
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| 2020-05-18T21:00:31
| 2020-05-18T21:00:31
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usually you can replace `download` in your dataset script with `download_and_prepare()` - could you share the code for your dataset here? :-)
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| 2020-05-18T21:13:37
| 2020-05-18T21:13:37
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Jeez - don't know what happened there :D Should be fixed now!
Thanks a lot for reporting this @tomhosking !
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public
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| 2020-05-18T21:15:11
| 2020-05-18T21:15:11
|
You can just run:
`val = nlp.load_dataset('squad')`
if you want to have just the validation script you can also do:
`val = nlp.load_dataset('squad', split="validation")`
|
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| 2020-05-18T21:16:05
| 2020-05-18T21:16:05
|
If you want to load a local dataset, make sure you include a `./` before the folder name.
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thomwolf
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public
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| 2020-05-18T21:18:16
| 2020-05-18T21:18:16
|
Yes, I think we should remove this warning @mariamabarham.
It's probably a relic of tfds which didn't have the same way to access citations.
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| 630,441,107
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| 2020-05-18T21:19:52
| 2020-05-18T21:19:52
|
Hi @dpressel,
thanks for posting your issue! Can you maybe add a complete code snippet that we can copy paste to reproduce the error? For example, I'm not sure where the variable `train_set` comes from in your code and it seems like you are loading multiple datasets at once?
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| 2020-05-18T21:23:12
| 2020-05-18T21:23:12
|
Awesome!
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dpressel
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| 2020-05-18T21:23:15
| 2020-05-18T21:23:15
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Hi, the full example was listed in the PR above, but here is the exact link:
https://github.com/dpressel/mead-baseline/blob/3c1aa3ca062cb23f303ca98ac40b6652b37ee971/api-examples/layers-classify-hf-datasets.py
The problem is coming from
```
if cache_file_name is None:
# we create a unique hash from the function, current dataset file and the mapping args
cache_kwargs = {
"with_indices": with_indices,
"batched": batched,
"batch_size": batch_size,
"remove_columns": remove_columns,
"keep_in_memory": keep_in_memory,
"load_from_cache_file": load_from_cache_file,
"cache_file_name": cache_file_name,
"writer_batch_size": writer_batch_size,
"arrow_schema": arrow_schema,
"disable_nullable": disable_nullable,
}
cache_file_name = self._get_cache_file_path(function, cache_kwargs)
```
The cached value is always the same, but I was able to change that by just renaming the function each time which seems to fix the issue.
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| 2020-05-18T21:24:35
| 2020-05-18T21:24:35
|
Ok, I think @lhoestq has already found a solution :-) Maybe you can chime in @lhoestq
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| 2020-05-18T21:27:55
| 2020-05-18T21:27:55
|
This fixed my issue (I think)
https://github.com/dpressel/mead-baseline/commit/48aa8ecde4b307bd3e7dde5fe71e43a1d4769ee1
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| 2020-05-18T21:29:21
| 2020-05-18T21:29:21
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> Ok, I think @lhoestq has already found a solution :-) Maybe you can chime in @lhoestq
Oh, awesome! I see the PR, Ill check it out
|
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lhoestq
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| 2020-05-18T21:34:01
| 2020-05-18T21:34:01
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The PR should prevent the cache from losing track of the of the dataset type (based on the location of its data). Not sure about your second problem though (cache off).
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| 630,447,242
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EntilZha
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| 2020-05-18T21:34:01
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I have an initial version here: https://github.com/EntilZha/nlp/tree/master/datasets/qanta Thats pretty close to what I'll do as a PR, but still want to do some more sanity checks/tests (just got tests passing).
I figured out how to get all tests passing by adding a download command and some finagling with the data zip https://github.com/EntilZha/nlp/blob/master/tests/utils.py#L127
|
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https://api.github.com/repos/huggingface/datasets/issues/162
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dpressel
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| 2020-05-18T21:34:54
| 2020-05-18T21:34:54
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Hi, yes, this seems to fix #160 -- I cloned the branch locally and verified
|
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dpressel
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| 2020-05-18T21:36:03
| 2020-05-18T21:36:03
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Yes, with caching on, it seems to work without the function renaming hack, I mentioned this also in the PR. Thanks!
|
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| 2020-05-18T21:36:12
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Perfect then :)
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patrickvonplaten
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| 2020-05-18T21:56:24
| 2020-05-18T21:56:24
|
I'm quite positive that you can just replace the `dl_manager.download()` statements here: https://github.com/EntilZha/nlp/blob/4d46443b65f1f756921db8275594e6af008a1de7/datasets/qanta/qanta.py#L194 with `dl_manager.download_and_extract()` even though you don't extract anything. I would prefer to avoid adding more functions to the MockDataLoadManager and keep it as simple as possible (It's already to complex now IMO).
Could you check if you can replace the `download()` function?
|
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EntilZha
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| 2020-05-18T23:49:51
| 2020-05-18T23:49:51
|
I might be doing something wrong, but swapping those two gives this error:
```
> with open(path) as f:
E IsADirectoryError: [Errno 21] Is a directory: 'datasets/qanta/dummy/mode=first,char_skip=25/2018.4.18/dummy_data-zip-extracted/dummy_data'
src/nlp/datasets/qanta/3d965403133687b819905ead4b69af7bcee365865279b2f797c79f809b4490c3/qanta.py:280: IsADirectoryError
During handling of the above exception, another exception occurred:
```
So it seems like the directory name is getting passed. Is this not functioning as expected, or is there some caching happening maybe? I deleted the dummy files and re-ran the import script with no changes. I'm digging a bit in with a debugger, but no clear reason yet
|
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| 630,495,203
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EntilZha
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| 2020-05-18T23:56:58
| 2020-05-18T23:56:58
|
From what I can tell here: https://github.com/huggingface/nlp/blob/master/tests/utils.py#L115
1. `data_url` is the correct http link
2. `path_to_dummy_data` is a directory, which is causing the issue
That path comes from `download_dummy_data`, which I think assumes that the data comes from the zip file, but isn't aware of individual files. So it seems like it data manager needs to be aware if the url its getting is for a file or a zip/directory, and pass this information along. This might happen in `download_dummy_data`, but probably better to happen in `download_and_extract`? Maybe a simple check to see if `os.path.basename` returns the dummy data zip filename, if not then join paths with the basename of the url?
|
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patrickvonplaten
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public
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| 2020-05-19T09:04:40
| 2020-05-19T09:05:41
|
I think the dataset script works correctly. Just the dummy data structure seems to be wrong. I will soon add more commands that should make the create of the dummy data easier.
I'd recommend that you won't concentrate too much on the dummy data.
If you manage to load the dataset correctly via:
```python
# use local path to qanta
nlp.load_dataset("./datasets/qanta")
```
then feel free to open a PR and we will look into the dummy data problem together :-)
Also please make sure that the Version is in the format 1.0.0 (three numbers separated by two points) - not a date.
|
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| 630,702,264
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tomhosking
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public
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| 2020-05-19T09:26:29
| 2020-05-19T09:26:29
|
Hi @patrickvonplaten!
I'm now getting the below error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-28-3206959998b9> in <module>
1 import nlp
2
----> 3 dataset = nlp.load_dataset('wmt14')
4 print(dataset['train'][0])
~/.local/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
507 # Instantiate the dataset builder
508 builder_instance = builder_cls(
--> 509 cache_dir=cache_dir, name=name, version=version, data_dir=data_dir, data_files=data_files, **config_kwargs,
510 )
511
TypeError: Can't instantiate abstract class Wmt with abstract methods _subsets
```
|
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