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https://api.github.com/repos/huggingface/datasets/issues/145
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MDExOlB1bGxSZXF1ZXN0NDE4OTcxMjg0
| 145
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[AWS Tests] Follow-up PR from #144
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2020-05-16 13:54:23+00:00
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I forgot to add this line in PR #145 .
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[AWS tests] AWS test should not run for canonical datasets
|
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2020-05-16 13:39:30+00:00
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2020-05-16 13:44:34+00:00
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2020-05-16 13:44:33+00:00
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CONTRIBUTOR
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AWS tests should in general not run for canonical datasets. Only local tests will run in this case. This way a PR is able to pass when adding a new dataset.
This PR changes to logic to the following:
1) All datasets that are present in `nlp/datasets` are tested only locally. This way when one adds a canonical dataset, the PR includes his dataset in the tests.
2) All datasets that are only present on AWS, such as `webis/tl_dr` atm are tested only on AWS.
I think the testing structure might need a bigger refactoring and better documentation very soon.
Merging for now to unblock new PRs @thomwolf @mariamabarham .
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MDU6SXNzdWU2MTk0NTc2NDE=
| 143
|
ArrowTypeError in squad metrics
|
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"default": false,
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2020-05-16 12:06:37+00:00
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2020-05-22 13:38:52+00:00
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2020-05-22 13:36:48+00:00
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CONTRIBUTOR
| null | null | null | null |
`squad_metric.compute` is giving following error
```
ArrowTypeError: Could not convert [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}] with type list: was not a dict, tuple, or recognized null value for conversion to struct type
```
This is how my predictions and references look like
```
predictions[0]
# {'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}
```
```
references[0]
# {'answers': [{'text': 'Denver Broncos'},
{'text': 'Denver Broncos'},
{'text': 'Denver Broncos'}],
'id': '56be4db0acb8001400a502ec'}
```
These are structured as per the `squad_metric.compute` help string.
|
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| 142
|
[WMT] Add all wmt
|
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2020-05-16 11:28:46+00:00
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2020-05-17 12:18:21+00:00
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2020-05-17 12:18:20+00:00
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CONTRIBUTOR
| null | null | 0
|
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This PR adds all wmt datasets scripts. At the moment the script is **not** functional for the language pairs "cs-en", "ru-en", "hi-en" because apparently it takes up to a week to get the manual data for these datasets: see http://ufal.mff.cuni.cz/czeng.
The datasets are fully functional though for the "big" language pairs "de-en" and "fr-en".
Overall I think the scripts are very messy and might need a big refactoring at some point.
For now I think there are good to merge (most dataset configs can be used). I will add "cs", "ru" and "hi" when the manual data is available.
|
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| 141
|
[Clean up] remove bogus folder
|
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2020-05-16 11:13:42+00:00
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2020-05-16 13:24:27+00:00
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2020-05-16 13:24:26+00:00
|
CONTRIBUTOR
| null | null | 0
|
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@mariamabarham - I think you accidentally placed it there.
|
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MDExOlB1bGxSZXF1ZXN0NDE4OTUxMzg4
| 140
|
[Tests] run local tests as default
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2020-05-16 10:56:06+00:00
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2020-05-16 13:21:44+00:00
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2020-05-16 13:21:43+00:00
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CONTRIBUTOR
| null | null | 0
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This PR also enables local tests by default
I think it's safer for now to enable both local and aws tests for every commit. The problem currently is that when we do a PR to add a dataset, the dataset is not yet on AWS on therefore not tested on the PR itself. Thus the PR will always be green even if the datasets are not correct. This PR aims at fixing this.
## Suggestion on how to commit to the repo from now on:
Now since the repo is "online", I think we should adopt a couple of best practices:
1) - No direct committing to the repo anymore. Every change should be opened in a PR and be well documented so that we can find it later
2) - Every PR has to be reviewed by at least x people (I guess @thomwolf you should decide here) because we now have to be much more careful when doing changes to the API for backward compatibility, etc...
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| 139
|
Add GermEval 2014 NER dataset
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2020-05-15 23:42:09+00:00
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2020-05-16 13:56:37+00:00
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2020-05-16 13:56:22+00:00
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CONTRIBUTOR
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Hi,
this PR adds the GermEval 2014 NER dataset 😃
> The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation [1] with the following properties:
> - The data was sampled from German Wikipedia and News Corpora as a collection of citations.
> - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens.
> - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]].
Dataset will be downloaded from the [official GermEval 2014 website](https://sites.google.com/site/germeval2014ner/data).
## Dataset format
Here's an example of the dataset format from the original dataset:
```tsv
# http://de.wikipedia.org/wiki/Manfred_Korfmann [2009-10-17]
1 Aufgrund O O
2 seiner O O
3 Initiative O O
4 fand O O
5 2001/2002 O O
6 in O O
7 Stuttgart B-LOC O
8 , O O
9 Braunschweig B-LOC O
10 und O O
11 Bonn B-LOC O
12 eine O O
13 große O O
14 und O O
15 publizistisch O O
16 vielbeachtete O O
17 Troia-Ausstellung B-LOCpart O
18 statt O O
19 , O O
20 „ O O
21 Troia B-OTH B-LOC
22 - I-OTH O
23 Traum I-OTH O
24 und I-OTH O
25 Wirklichkeit I-OTH O
26 “ O O
27 . O O
```
The sentence is encoded as one token per line (tab separated columns.
The first column contains either a `#`, which signals the source the sentence is cited from and the date it was retrieved, or the token number within the sentence.
The second column contains the token.
Column three and four contain the named entity (in IOB2 scheme).
Outer spans are encoded in the third column, embedded/nested spans in the fourth column.
## Features
I decided to keep most information from the dataset. That means the so called "source" information (where the sentences come from + date information) is also returned for each sentence in the feature vector.
For each sentence in the dataset, one feature vector (`nlp.Features` definition) will be returned:
| Feature | Example | Description
| ---- | ---- | -----------------
| `id` | `0` | Number (id) of current sentence
| `source` | `http://de.wikipedia.org/wiki/Manfred_Korfmann [2009-10-17]` | URL and retrieval date as string
| `tokens` | `["Schwartau", "sagte", ":"]` | List of tokens (strings) for a sentence
| `labels` | `["B-PER", "O", "O"]` | List of labels (outer span)
| `nested-labels` | `["O", "O", "O"]` | List of labels for nested span
## Example
The following command downloads the dataset from the official GermEval 2014 page and pre-processed it:
```bash
python nlp-cli test datasets/germeval_14 --all_configs
```
It then outputs the number for training, development and testset. The training set consists of 24,000 sentences, the development set of 2,200 and the test of 5,100 sentences.
Now it can be imported and used with `nlp`:
```python
import nlp
germeval = nlp.load_dataset("./datasets/germeval_14/germeval_14.py")
assert len(germeval["train"]) == 24000
# Show first sentence of training set:
germeval["train"][0]
```
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MDU6SXNzdWU2MTkyMjUxOTE=
| 138
|
Consider renaming to nld
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2020-05-15 20:23:27+00:00
|
2022-09-16 05:18:22+00:00
|
2020-09-28 00:08:10+00:00
|
NONE
| null | null | null | null |
Hey :)
Just making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.
The issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.
If you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.
If people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.
I don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.
I suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.
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MDExOlB1bGxSZXF1ZXN0NDE4NzgxNzI4
| 136
|
Update README.md
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2020-05-15 20:01:07+00:00
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2020-05-17 12:17:28+00:00
|
2020-05-17 12:17:28+00:00
|
NONE
| null | null | 0
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small typo
|
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|
Fix print statement in READ.md
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2020-05-15 19:52:23+00:00
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2020-05-17 12:14:06+00:00
|
2020-05-17 12:14:05+00:00
|
CONTRIBUTOR
| null | null | 0
|
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print statement was throwing generator object instead of printing names of available datasets/metrics
|
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| 619,112,641
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| 134
|
Update README.md
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2020-05-15 16:56:14+00:00
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2020-05-28 08:21:49+00:00
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2020-05-28 08:21:49+00:00
|
NONE
| null | null | 0
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| 133
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[Question] Using/adding a local dataset
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2020-05-15 16:26:06+00:00
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2020-07-23 16:44:09+00:00
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2020-07-23 16:44:09+00:00
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NONE
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Users may want to either create/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.
It appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.
A notebook/example script demonstrating this would be very helpful.
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[Feature Request] Add the OpenWebText dataset
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2020-05-15 15:57:29+00:00
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2020-10-07 14:22:48+00:00
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2020-10-07 14:22:48+00:00
|
MEMBER
| null | null | null | null |
The OpenWebText dataset is an open clone of OpenAI's WebText dataset. It can be used to train ELECTRA as is specified in the [README](https://www.github.com/google-research/electra).
More information and the download link are available [here](https://skylion007.github.io/OpenWebTextCorpus/).
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[Feature request] Add Toronto BookCorpus dataset
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2020-05-15 15:50:44+00:00
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2020-06-28 21:27:31+00:00
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2020-06-28 21:27:31+00:00
|
CONTRIBUTOR
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I know the copyright/distribution of this one is complex, but it would be great to have! That, combined with the existing `wikitext`, would provide a complete dataset for pretraining models like BERT.
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Loading GLUE dataset loads CoLA by default
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[] | null | 3
|
2020-05-15 14:55:50+00:00
|
2020-05-27 22:08:15+00:00
|
2020-05-27 22:08:15+00:00
|
NONE
| null | null | null | null |
If I run:
```python
dataset = nlp.load_dataset('glue')
```
The resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:
```python
metric = nlp.load_metric("glue")
```
which throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets?
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MDU6SXNzdWU2MTg5OTc3MjU=
| 129
|
[Feature request] Add Google Natural Question dataset
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|
2020-05-15 14:14:20+00:00
|
2020-07-23 13:21:29+00:00
|
2020-07-23 13:21:29+00:00
|
NONE
| null | null | null | null |
Would be great to have https://github.com/google-research-datasets/natural-questions as an alternative to SQuAD.
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MDU6SXNzdWU2MTg5NTExMTc=
| 128
|
Some error inside nlp.load_dataset()
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2020-05-15 13:01:29+00:00
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2020-05-15 13:10:40+00:00
|
2020-05-15 13:10:40+00:00
|
NONE
| null | null | null | null |
First of all, nice work!
I am going through [this overview notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb)
In simple step `dataset = nlp.load_dataset('squad', split='validation[:10%]')`
I get an error, which is connected with some inner code, I think:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-d848d3a99b8c> in <module>()
1 # Downloading and loading a dataset
2
----> 3 dataset = nlp.load_dataset('squad', split='validation[:10%]')
8 frames
/usr/local/lib/python3.6/dist-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)
515 download_mode=download_mode,
516 ignore_verifications=ignore_verifications,
--> 517 save_infos=save_infos,
518 )
519
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)
361 verify_infos = not save_infos and not ignore_verifications
362 self._download_and_prepare(
--> 363 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
364 )
365 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
414 try:
415 # Prepare split will record examples associated to the split
--> 416 self._prepare_split(split_generator, **prepare_split_kwargs)
417 except OSError:
418 raise OSError("Cannot find data file. " + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or ""))
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _prepare_split(self, split_generator)
585 fname = "{}-{}.arrow".format(self.name, split_generator.name)
586 fpath = os.path.join(self._cache_dir, fname)
--> 587 examples_type = self.info.features.type
588 writer = ArrowWriter(data_type=examples_type, path=fpath, writer_batch_size=self._writer_batch_size)
589
/usr/local/lib/python3.6/dist-packages/nlp/features.py in type(self)
460 @property
461 def type(self):
--> 462 return get_nested_type(self)
463
464 @classmethod
/usr/local/lib/python3.6/dist-packages/nlp/features.py in get_nested_type(schema)
370 # Nested structures: we allow dict, list/tuples, sequences
371 if isinstance(schema, dict):
--> 372 return pa.struct({key: get_nested_type(value) for key, value in schema.items()})
373 elif isinstance(schema, (list, tuple)):
374 assert len(schema) == 1, "We defining list feature, you should just provide one example of the inner type"
/usr/local/lib/python3.6/dist-packages/nlp/features.py in <dictcomp>(.0)
370 # Nested structures: we allow dict, list/tuples, sequences
371 if isinstance(schema, dict):
--> 372 return pa.struct({key: get_nested_type(value) for key, value in schema.items()})
373 elif isinstance(schema, (list, tuple)):
374 assert len(schema) == 1, "We defining list feature, you should just provide one example of the inner type"
/usr/local/lib/python3.6/dist-packages/nlp/features.py in get_nested_type(schema)
379 # We allow to reverse list of dict => dict of list for compatiblity with tfds
380 if isinstance(inner_type, pa.StructType):
--> 381 return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))
382 return pa.list_(inner_type, schema.length)
383
/usr/local/lib/python3.6/dist-packages/nlp/features.py in <genexpr>(.0)
379 # We allow to reverse list of dict => dict of list for compatiblity with tfds
380 if isinstance(inner_type, pa.StructType):
--> 381 return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))
382 return pa.list_(inner_type, schema.length)
383
TypeError: list_() takes exactly one argument (2 given)
```
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MDExOlB1bGxSZXF1ZXN0NDE4NTQ1MDcy
| 127
|
Update Overview.ipynb
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2020-05-15 11:46:48+00:00
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2020-05-15 11:47:27+00:00
|
2020-05-15 11:47:25+00:00
|
CONTRIBUTOR
| null | null | 0
|
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update notebook
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MDExOlB1bGxSZXF1ZXN0NDE4NTM1Mzc5
| 126
|
remove webis
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2020-05-15 11:25:20+00:00
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2020-05-15 11:31:24+00:00
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2020-05-15 11:30:26+00:00
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|
Remove webis from dataset folder.
Our first dataset script that only lives on AWS :-) https://s3.console.aws.amazon.com/s3/buckets/datasets.huggingface.co/nlp/datasets/webis/tl_dr/?region=us-east-1 @julien-c @jplu
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MDExOlB1bGxSZXF1ZXN0NDE4NTExNDE0
| 125
|
[Newsroom] add newsroom
|
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2020-05-15 10:34:34+00:00
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2020-05-15 10:37:07+00:00
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2020-05-15 10:37:02+00:00
|
CONTRIBUTOR
| null | null | 0
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I checked it with the data link of the mail you forwarded @thomwolf => works well!
|
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MDExOlB1bGxSZXF1ZXN0NDE4NTA3NDUx
| 124
|
Xsum, require manual download of some files
|
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2020-05-15 10:26:13+00:00
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2020-05-15 11:04:48+00:00
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2020-05-15 11:04:46+00:00
|
CONTRIBUTOR
| null | null | 0
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MDExOlB1bGxSZXF1ZXN0NDE4NDcxODU5
| 123
|
[Tests] Local => aws
|
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2020-05-15 09:12:25+00:00
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2020-05-15 10:06:12+00:00
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2020-05-15 10:03:26+00:00
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CONTRIBUTOR
| null | null | 0
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## Change default Test from local => aws
As a default we set` aws=True`, `Local=False`, `slow=False`
### 1. RUN_AWS=1 (default)
This runs 4 tests per dataset script.
a) Does the dataset script have a valid etag / Can it be reached on AWS?
b) Can we load its `builder_class`?
c) Can we load **all** dataset configs?
d) _Most importantly_: Can we load the dataset?
Important - we currently only test the first config of each dataset to reduce test time. Total test time is around 1min20s.
### 2. RUN_LOCAL=1 RUN_AWS=0
***This should be done when debugging dataset scripts of the ./datasets folder***
This only runs 1 test per dataset test, which is equivalent to aws d) - Can we load the dataset from the local `datasets` directory?
### 3. RUN_SLOW=1
We should set up to run these tests maybe 1 time per week ? @thomwolf
The `slow` tests include two more important tests.
e) Can we load the dataset with all possible configs? This test will probably fail at the moment because a lot of dummy data is missing. We should add the dummy data step by step to be sure that all configs work.
f) Test that the actual dataset can be loaded. This will take quite some time to run, but is important to make sure that the "real" data can be loaded. It will also test whether the dataset script has the correct checksums file which is currently not tested with `aws=True`. @lhoestq - is there an easy way to check cheaply whether the `dataset_info.json` is correct for each dataset script?
|
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| 122
|
Final cleanup of readme and metrics
|
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2020-05-15 09:00:52+00:00
|
2021-09-03 19:40:09+00:00
|
2020-05-15 09:02:22+00:00
|
MEMBER
| null | null | 0
|
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| 121
|
make style
|
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2020-05-15 08:23:36+00:00
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2020-05-15 08:25:39+00:00
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2020-05-15 08:25:38+00:00
|
CONTRIBUTOR
| null | null | 0
|
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MDU6SXNzdWU2MTg3Mzc3ODM=
| 120
|
🐛 `map` not working
|
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2020-05-15 06:43:08+00:00
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2020-05-15 07:02:38+00:00
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2020-05-15 07:02:38+00:00
|
NONE
| null | null | null | null |
I'm trying to run a basic example (mapping function to add a prefix).
[Here is the colab notebook I'm using.](https://colab.research.google.com/drive/1YH4JCAy0R1MMSc-k_Vlik_s1LEzP_t1h?usp=sharing)
```python
import nlp
dataset = nlp.load_dataset('squad', split='validation[:10%]')
def test(sample):
sample['title'] = "test prefix @@@ " + sample["title"]
return sample
print(dataset[0]['title'])
dataset.map(test)
print(dataset[0]['title'])
```
Output :
> Super_Bowl_50
Super_Bowl_50
Expected output :
> Super_Bowl_50
test prefix @@@ Super_Bowl_50
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| 119
|
🐛 Colab : type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
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2020-05-15 05:11:22+00:00
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2020-05-15 02:45:28+00:00
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NONE
| null | null | null | null |
I'm trying to load CNN/DM dataset on Colab.
[Colab notebook](https://colab.research.google.com/drive/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)
But I meet this error :
> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
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MDU6SXNzdWU2MTg2NDMwODg=
| 118
|
❓ How to apply a map to all subsets ?
|
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2020-05-15 01:58:52+00:00
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2020-05-15 07:05:49+00:00
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2020-05-15 07:04:25+00:00
|
NONE
| null | null | null | null |
I'm working with CNN/DM dataset, where I have 3 subsets : `train`, `test`, `validation`.
Should I apply my map function on the subsets one by one ?
```python
import nlp
cnn_dm = nlp.load_dataset('cnn_dailymail')
for corpus in ['train', 'test', 'validation']:
cnn_dm[corpus] = cnn_dm[corpus].map(my_func)
```
Or is there a better way to do this ?
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|
❓ How to remove specific rows of a dataset ?
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2020-05-15 01:25:06+00:00
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2022-07-15 08:36:44+00:00
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2020-05-15 07:04:32+00:00
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NONE
| null | null | null | null |
I saw on the [example notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb#scrollTo=efFhDWhlvSVC) how to remove a specific column :
```python
dataset.drop('id')
```
But I didn't find how to remove a specific row.
**For example, how can I remove all sample with `id` < 10 ?**
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|
🐛 Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323
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2020-05-28 23:43:07+00:00
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2020-05-28 23:43:07+00:00
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NONE
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I'm trying to use rouge metric.
I have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.
I tried :
```python
import nlp
rouge = nlp.load_metric('rouge')
with open("test.pred.tokenized") as p, open("test.gold.tokenized") as g:
for lp, lg in zip(p, g):
rouge.add(lp, lg)
```
But I meet following error :
> pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323
---
Full stack-trace :
```
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/metric.py", line 224, in add
self.writer.write_batch(batch)
File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/arrow_writer.py", line 148, in write_batch
pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)
File "pyarrow/table.pxi", line 1550, in pyarrow.lib.Table.from_pydict
File "pyarrow/table.pxi", line 1503, in pyarrow.lib.Table.from_arrays
File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table
File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323
```
(`nlp` installed from source)
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2020-05-15 00:29:47+00:00
|
2020-05-17 13:11:00+00:00
|
2020-05-17 13:11:00+00:00
|
NONE
| null | null | null | null |
I'm trying to access the information of CNN/DM dataset :
```python
cnn_dm = nlp.load_dataset('cnn_dailymail')
print(cnn_dm.info)
```
returns :
> AttributeError: 'dict' object has no attribute 'info'
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| 618,611,310
|
MDU6SXNzdWU2MTg2MTEzMTA=
| 114
|
Couldn't reach CNN/DM dataset
|
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[] | null | 1
|
2020-05-15 00:16:17+00:00
|
2020-05-15 00:19:52+00:00
|
2020-05-15 00:19:51+00:00
|
NONE
| null | null | null | null |
I can't get CNN / DailyMail dataset.
```python
import nlp
assert "cnn_dailymail" in [dataset.id for dataset in nlp.list_datasets()]
cnn_dm = nlp.load_dataset('cnn_dailymail')
```
[Colab notebook](https://colab.research.google.com/drive/1zQ3bYAVzm1h0mw0yWPqKAg_4EUlSx5Ex?usp=sharing)
gives following error :
```
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/cnn_dailymail/cnn_dailymail.py
```
|
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MDExOlB1bGxSZXF1ZXN0NDE4MjkxNjIx
| 113
|
Adding docstrings and some doc
|
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|
2020-05-14 23:14:41+00:00
|
2020-05-14 23:22:45+00:00
|
2020-05-14 23:22:44+00:00
|
MEMBER
| null | null | 0
|
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|
Some doc
|
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| 112
|
Qa4mre - add dataset
|
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2020-05-14 22:17:51+00:00
|
2020-05-15 09:16:43+00:00
|
2020-05-15 09:16:42+00:00
|
CONTRIBUTOR
| null | null | 0
|
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Added dummy data test only for the first config. Will do the rest later.
I had to do add some minor hacks to an important function to make it work.
There might be a cleaner way to handle it - can you take a look @thomwolf ?
|
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|
[Clean-up] remove under construction datastes
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2020-05-14 20:52:13+00:00
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2020-05-14 20:52:23+00:00
|
2020-05-14 20:52:22+00:00
|
CONTRIBUTOR
| null | null | 0
|
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MDExOlB1bGxSZXF1ZXN0NDE4MjMzODIy
| 110
|
fix reddit tifu dummy data
|
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2020-05-14 20:37:37+00:00
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2020-05-14 20:40:14+00:00
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2020-05-14 20:40:13+00:00
|
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| 618,508,359
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MDExOlB1bGxSZXF1ZXN0NDE4MjI0MDYw
| 109
|
[Reclor] fix reclor
|
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2020-05-14 20:16:26+00:00
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2020-05-14 20:19:09+00:00
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2020-05-14 20:19:08+00:00
|
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- That's probably one me. Could have made the manual data test more flexible. @mariamabarham
|
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| 618,386,394
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MDExOlB1bGxSZXF1ZXN0NDE4MTIzMzc3
| 108
|
convert can use manual dir as second argument
|
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2020-05-14 16:52:32+00:00
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2020-05-14 16:52:43+00:00
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2020-05-14 16:52:42+00:00
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@mariamabarham
|
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MDExOlB1bGxSZXF1ZXN0NDE4MTEyNzcx
| 107
|
add writer_batch_size to GeneratorBasedBuilder
|
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2020-05-14 16:35:39+00:00
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2020-05-14 16:50:30+00:00
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2020-05-14 16:50:29+00:00
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You can now specify `writer_batch_size` in the builder arguments or directly in `load_dataset`
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MDExOlB1bGxSZXF1ZXN0NDE4MTAzMjM3
| 106
|
Add data dir test command
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|
[New structure on AWS] Adapt paths
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2020-05-14 15:55:57+00:00
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2020-05-14 15:56:28+00:00
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2020-05-14 15:56:27+00:00
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CONTRIBUTOR
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Some small changes so that we have the correct paths. @julien-c
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| 104
|
Add trivia_q
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2020-05-14 14:27:19+00:00
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2020-07-12 05:34:20+00:00
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2020-05-14 20:23:32+00:00
|
CONTRIBUTOR
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Currently tested only for one config to pass tests. Needs to add more dummy data later.
|
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MDExOlB1bGxSZXF1ZXN0NDE3OTk5MDIy
| 103
|
[Manual downloads] add logic proposal for manual downloads and add wikihow
|
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2020-05-14 13:30:36+00:00
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2020-05-14 14:27:41+00:00
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2020-05-14 14:27:40+00:00
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CONTRIBUTOR
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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.
|
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MDExOlB1bGxSZXF1ZXN0NDE3OTk3MDQz
| 102
|
Run save infos
|
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2020-05-14 13:27:26+00:00
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2020-05-14 15:43:04+00:00
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2020-05-14 15:43:03+00:00
|
MEMBER
| null | null | 0
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I replaced the old checksum file with the new `dataset_infos.json` by running the script on almost all the datasets we have. The only one that is still running on my side is the cornell dialog
|
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MDExOlB1bGxSZXF1ZXN0NDE3ODk5OTQ2
| 101
|
[Reddit] add reddit
|
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2020-05-14 10:25:02+00:00
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2020-05-14 10:27:25+00:00
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2020-05-14 10:27:24+00:00
|
CONTRIBUTOR
| null | null | 0
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- Everything worked fine @mariamabarham. Made my computer nearly crash, but all seems to be working :-)
|
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MDExOlB1bGxSZXF1ZXN0NDE3ODc1MjE2
| 100
|
Add per type scores in seqeval metric
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2020-05-14 09:37:52+00:00
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2020-05-14 23:21:35+00:00
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2020-05-14 23:21:34+00:00
|
CONTRIBUTOR
| null | null | 0
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This PR add a bit more detail in the seqeval metric. Now the usage and output are:
```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
met.compute(predictions, references)
#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.8}
```
It is also possible to compute scores for non IOB notations, POS tagging for example hasn't this kind of notation. Add `suffix` parameter:
```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
predictions = [['O', 'O', 'MISC', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
met.compute(predictions, references, metrics_kwargs={"suffix": True})
#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.9}
```
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MDExOlB1bGxSZXF1ZXN0NDE3ODMxNjky
| 99
|
[Cmrc 2018] fix cmrc2018
|
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2020-05-14 08:49:42+00:00
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2020-05-14 08:49:41+00:00
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MDExOlB1bGxSZXF1ZXN0NDE3Nzc3NDcy
| 98
|
Webis tl-dr
|
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2020-05-14 06:22:18+00:00
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2020-09-03 10:00:21+00:00
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2020-05-14 20:54:16+00:00
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Add the Webid TL:DR dataset.
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[Csv] add tests for csv dataset script
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2020-05-13 23:23:16+00:00
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2020-05-13 23:23:15+00:00
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Adds dummy data tests for csv.
|
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MDExOlB1bGxSZXF1ZXN0NDE3NjAwMjY4
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lm1b
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2020-05-13 20:38:44+00:00
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2020-05-14 14:13:30+00:00
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2020-05-14 14:13:29+00:00
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Add lm1b dataset.
|
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| 95
|
Replace checksums files by Dataset infos json
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2020-05-13 19:36:16+00:00
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2020-05-14 08:58:43+00:00
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2020-05-14 08:58:42+00:00
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MEMBER
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### Better verifications when loading a dataset
I replaced the `urls_checksums` directory that used to contain `checksums.txt` and `cached_sizes.txt`, by a single file `dataset_infos.json`. It's just a dict `config_name` -> `DatasetInfo`.
It simplifies and improves how verifications of checksums and splits sizes are done, as they're all stored in `DatasetInfo` (one per config). Also, having already access to `DatasetInfo` enables to check disk space before running `download_and_prepare` for a given config.
The dataset infos json file is user readable, you can take a look at the squad one that I generated in this PR.
### Renaming
According to these changes, I did some renaming:
`save_checksums` -> `save_infos`
`ignore_checksums` -> `ignore_verifications`
for example, when you are creating a dataset you have to run
```nlp-cli test path/to/my/dataset --save_infos --all_configs```
instead of
```nlp-cli test path/to/my/dataset --save_checksums --all_configs```
### And now, the fun part
We'll have to rerun the `nlp-cli test ... --save_infos --all_configs` for all the datasets
-----------------
feedback appreciated !
|
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MDExOlB1bGxSZXF1ZXN0NDE3NDYyMTIw
| 94
|
Librispeech
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2020-05-13 16:04:14+00:00
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2020-05-13 21:29:03+00:00
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2020-05-13 21:29:02+00:00
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CONTRIBUTOR
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Add librispeech dataset and remove some useless content.
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| 93
|
Cleanup notebooks and various fixes
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2020-05-13 14:58:58+00:00
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2020-05-13 15:01:48+00:00
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2020-05-13 15:01:47+00:00
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Fixes on dataset (more flexible) metrics (fix) and general clean ups
|
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|
[WIP] add wmt14
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2020-05-13 10:42:03+00:00
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2020-05-16 11:17:38+00:00
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2020-05-16 11:17:37+00:00
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CONTRIBUTOR
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WMT14 takes forever to download :-/
- WMT is the first dataset that uses an abstract class IMO, so I had to modify the `load_dataset_module` a bit.
|
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| 91
|
[Paracrawl] add paracrawl
|
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2020-05-13 10:39:00+00:00
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2020-05-13 10:40:15+00:00
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2020-05-13 10:40:14+00:00
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CONTRIBUTOR
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- Huge dataset - took ~1h to download
- Also this PR reformats all dataset scripts and adds `datasets` to `make style`
|
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|
Add download gg drive
|
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2020-05-13 09:56:02+00:00
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2020-05-13 12:46:28+00:00
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2020-05-13 10:05:31+00:00
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We can now add datasets that download from google drive
|
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|
Add list and inspect methods - cleanup hf_api
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2020-05-13 14:05:00+00:00
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Add a bunch of methods to easily list and inspect the processing scripts up-loaded on S3:
```python
nlp.list_datasets()
nlp.list_metrics()
# Copy and prepare the scripts at `local_path` for easy inspection/modification.
nlp.inspect_dataset(path, local_path)
# Copy and prepare the scripts at `local_path` for easy inspection/modification.
nlp.inspect_metric(path, local_path)
```
Also clean up the `HfAPI` to use `dataclasses` for better user-experience
|
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MDExOlB1bGxSZXF1ZXN0NDE3MjI5ODQw
| 88
|
Add wiki40b
|
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2020-05-13 09:16:01+00:00
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2020-05-13 12:31:55+00:00
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2020-05-13 12:31:54+00:00
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This one is a beam dataset that downloads files using tensorflow.
I tested it on a small config and it works fine
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MDExOlB1bGxSZXF1ZXN0NDE3MjE1NzA0
| 87
|
Add Flores
|
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2020-05-13 08:51:29+00:00
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2020-05-13 09:23:34+00:00
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2020-05-13 09:23:33+00:00
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CONTRIBUTOR
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Beautiful language for sure!
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MDExOlB1bGxSZXF1ZXN0NDE3MjEwNzY2
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|
[Load => load_dataset] change naming
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2020-05-13 08:43:00+00:00
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2020-05-13 08:50:58+00:00
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2020-05-13 08:50:57+00:00
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Rename leftovers @thomwolf
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Add boolq
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2020-05-13 08:32:27+00:00
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2020-05-13 09:09:39+00:00
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2020-05-13 09:09:38+00:00
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I just added the dummy data for this dataset.
This one was uses `tf.io.gfile.copy` to download the data but I added the support for custom download in the mock_download_manager. I also had to add a `tensorflow` dependency for tests.
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[TedHrLr] add left dummy data
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2020-05-13 08:27:20+00:00
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2020-05-13 08:29:22+00:00
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2020-05-13 08:29:21+00:00
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New datasets
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2020-05-12 18:22:47+00:00
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2020-05-12 18:22:45+00:00
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MDExOlB1bGxSZXF1ZXN0NDE2ODQ1Njc5
| 82
|
[Datasets] add ted_hrlr
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2020-05-12 16:46:50+00:00
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2020-05-13 07:52:54+00:00
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2020-05-13 07:52:53+00:00
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@thomwolf - After looking at `xnli` I think it's better to leave the translation features and add a `translation` key to make them work in our framework.
The result looks like this:
you can see that each split has a `translation` key which value is the nlp.features.Translation object.
That's a simple change. If it's ok for you, I will add dummy data for the other configs and treat the other translation scripts in the same way.
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MDExOlB1bGxSZXF1ZXN0NDE2ODM1NzE1
| 81
|
add tests
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2020-05-12 16:28:19+00:00
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2020-05-13 07:43:57+00:00
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2020-05-13 07:43:56+00:00
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MEMBER
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Tests for py_utils functions and for the BaseReader used to read from arrow and parquet.
I also removed unused utils functions.
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MDExOlB1bGxSZXF1ZXN0NDE2ODMwNjk3
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|
Add nbytes + nexamples check
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2020-05-12 16:18:43+00:00
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2020-05-13 07:52:34+00:00
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### Save size and number of examples
Now when you do `save_checksums`, it also create `cached_sizes.txt` right next to the checksum file.
This new file stores the bytes sizes and the number of examples of each split that has been prepared and stored in the cache. Example:
```
# Cached sizes: <full_config_name> <num_bytes> <num_examples>
hansards/house/1.0.0/test 22906629 122290
hansards/house/1.0.0/train 191459584 947969
hansards/senate/1.0.0/test 5711686 25553
hansards/senate/1.0.0/train 40324278 182135
```
### Check processing output
If there is a `caches_sizes.txt`, then each time we run `download_and_prepare` it will make sure that the sizes match. You can set `ignore_checksums=True` if you don't want that to happen.
### Fill Dataset Info
All the split infos and the checksums are now stored correctly in DatasetInfo after `download_and_prepare`
### Check space on disk before running `download_and_prepare`
Check if the space is lower than the sum of the sizes of the files in `checksums.txt` and `cached_files.txt`. This is not ideal though as it considers the files for all configs.
TODO:
A better way to do it would be to have save the `DatasetInfo` instead of the `checksums.txt` and `cached_sizes.txt`, in order to have one file per dataset config (and therefore consider only the sizes of the files for one config and not all of them). It can also be the occasion to factorize all the `download_and_prepare` verifications. Maybe next PR ?
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MDExOlB1bGxSZXF1ZXN0NDE2ODI5NzMy
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[Convert] add new pattern
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2020-05-12 16:16:51+00:00
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2020-05-12 16:17:10+00:00
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2020-05-12 16:17:09+00:00
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CONTRIBUTOR
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[Tests] skip beam dataset tests for now
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2020-05-12 16:00:58+00:00
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2020-05-12 16:16:24+00:00
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2020-05-12 16:16:22+00:00
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CONTRIBUTOR
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For now we will skip tests for Beam Datasets
|
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MDExOlB1bGxSZXF1ZXN0NDE2NzQwMjAz
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|
New datasets
|
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2020-05-12 13:51:59+00:00
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2020-05-12 14:02:16+00:00
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2020-05-12 14:02:15+00:00
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pin flake 8
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2020-05-12 11:25:29+00:00
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2020-05-12 11:27:35+00:00
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2020-05-12 11:27:34+00:00
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Flake 8's new version does not like our format. Pinning the version for now.
|
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|
WIP adding metrics
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2020-05-12 09:52:00+00:00
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2020-05-13 07:44:12+00:00
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2020-05-13 07:44:10+00:00
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Adding the following metrics as identified by @mariamabarham:
1. BLEU: BiLingual Evaluation Understudy: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py, https://github.com/chakki-works/sumeval/blob/master/sumeval/metrics/bleu.py (multilingual)
2. GLEU: Google-BLEU: https://github.com/cnap/gec-ranking/blob/master/scripts/compute_gleu
3. Sacrebleu: https://pypi.org/project/sacrebleu/1.4.8/ (pypi package), https://github.com/mjpost/sacrebleu (github implementation)
4. ROUGE: Recall-Oriented Understudy for Gisting Evaluation: https://github.com/google-research/google-research/tree/master/rouge, https://github.com/chakki-works/sumeval/blob/master/sumeval/metrics/rouge.py (multilingual)
5. Seqeval: https://github.com/chakki-works/seqeval (github implementation), https://pypi.org/project/seqeval/0.0.12/ (pypi package)
6. Coval: coreference evaluation package for the CoNLL and ARRAU datasets https://github.com/ns-moosavi/coval
7. SQuAD v1 evaluation script
8. SQuAD V2 evaluation script: https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/
9. GLUE
10. XNLI
Not now:
1. Perplexity: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/perplexity.py
2. Spearman: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/spearman_correlation.py
3. F1_measure: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/f1_measure.py
4. Pearson_corelation: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/pearson_correlation.py
5. AUC: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/auc.py
6. Entropy: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/entropy.py
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MDExOlB1bGxSZXF1ZXN0NDE2NjA3MDcy
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|
fix overflow check
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2020-05-12 10:04:39+00:00
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I did some tests and unfortunately the test
```
pa_array.nbytes > MAX_BATCH_BYTES
```
doesn't work. Indeed for a StructArray, `nbytes` can be less 2GB even if there is an overflow (it loops...).
I don't think we can do a proper overflow test for the limit of 2GB...
For now I replaced it with a sanity check on the first element.
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|
JSON script
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2020-05-12 07:11:22+00:00
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2020-05-18 06:50:37+00:00
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2020-05-18 06:50:36+00:00
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CONTRIBUTOR
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Add a JSONS script to read JSON datasets from files.
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| 72
|
[README dummy data tests] README to better understand how the dummy data structure works
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2020-05-11 22:19:03+00:00
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2020-05-11 22:26:03+00:00
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2020-05-11 22:26:01+00:00
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CONTRIBUTOR
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In this PR a README.md is added to tests to shine more light on how the dummy data structure works. I try to explain the different possible cases. IMO the best way to understand the logic is to checkout the dummy data structure of the different datasets I mention in the README.md since those are the "edge cases".
@mariamabarham @thomwolf @lhoestq @jplu - I'd be happy to checkout the dummy data structure and get some feedback on possible improvements.
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Fix arrow writer for big datasets using writer_batch_size
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2020-05-11 14:45:36+00:00
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2020-05-11 20:09:47+00:00
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2020-05-11 20:00:38+00:00
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MEMBER
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This PR fixes Yacine's bug.
According to [this](https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations), it is not recommended to have pyarrow arrays bigger than 2Go.
Therefore I set a default batch size of 100 000 examples per batch. In general it shouldn't exceed 2Go. If it does, I reduce the batch_size on the fly, and I notify the user with a warning.
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| 70
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adding RACE, QASC, Super_glue and Tiny_shakespear datasets
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2020-05-11 08:07:49+00:00
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2020-05-12 13:21:52+00:00
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2020-05-12 13:21:51+00:00
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fix cache dir in builder tests
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2020-05-10 18:39:21+00:00
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2020-05-11 07:19:30+00:00
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minor fix
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[CSV] re-add csv
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2020-05-08 17:38:29+00:00
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2020-05-08 17:40:46+00:00
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Re-adding csv under the datasets under construction to keep circle ci happy - will have to see how to include it in the tests.
@lhoestq noticed that I accidently deleted it in https://github.com/huggingface/nlp/pull/63#discussion_r422263729.
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| 67
|
[Tests] Test files locally
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2020-05-08 15:02:43+00:00
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2020-05-08 19:50:47+00:00
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2020-05-08 15:17:00+00:00
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This PR adds a `aws` and a `local` decorator to the tests so that tests now run on the local datasets.
By default, the `aws` is deactivated and `local` is activated and `slow` is deactivated, so that only 1 test per dataset runs on circle ci.
**When local is activated all folders in `./datasets` are tested.**
**Important** When adding a dataset, we should no longer upload it to AWS. The steps are:
1. Open a PR
2. Add a dataset as described in `datasets/README.md`
3. If all tests pass, push to master
Currently we have 49 functional datasets in our code base.
We have 6 datasets "under-construction" that don't pass the tests - so I put them in a folder "datasets_under_construction" - it would be nice to open a PR to fix them and put them in the `datasets` folder.
**Important** when running tests locally, the datasets are cached so to rerun them delete your local cache via:
`rm -r ~/.cache/huggingface/datasets/*`
@thomwolf @mariamabarham @lhoestq
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[Datasets] ReadME
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fix math dataset and xcopa
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- fixes math dataset and xcopa, uploaded both of the to S3
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[Datasets] Make master ready for datasets adding
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Add all relevant files so that datasets can now be added on master
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[Dataset scripts] add all datasets scripts
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2020-05-08 10:50:15+00:00
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2020-05-08 17:39:22+00:00
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2020-05-08 11:34:00+00:00
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As mentioned, we can have the canonical datasets in the master. For now I also want to include all the data as present on S3 to make the synchronization easier when uploading new datastes.
@mariamabarham @lhoestq @thomwolf - what do you think?
If this is ok for you, I can sync up the master with the `add_dataset` branch: https://github.com/huggingface/nlp/pull/37 so that master is up to date.
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[Cached Path] Better error message
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2020-05-08 09:45:47+00:00
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2020-05-08 09:45:47+00:00
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CONTRIBUTOR
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IMO returning `None` in this function only leads to confusion and is never helpful.
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MDExOlB1bGxSZXF1ZXN0NDE1MTI3MTU4
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|
[Load] rename setup_module to prepare_module
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rename setup_module to prepare_module due to issues with pytests `setup_module` function.
See: PR #59.
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MDExOlB1bGxSZXF1ZXN0NDE0OTQyNjEy
| 60
|
Update to simplify some datasets conversion
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This PR updates the encoding of `Values` like `integers`, `boolean` and `float` to use python casting and avoid having to cast in the dataset scripts, as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r420176626
We could also change (not included in this PR yet):
- `supervized_keys` to make them a NamedTuple instead of a dataclass, and
- handle specifically the `Translation` features.
as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r421740236
@patrickvonplaten @mariamabarham tell me if you want these two last changes as well.
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| 59
|
Fix tests
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@patrickvonplaten I've broken a bit the tests with #25 while simplifying and re-organizing the `load.py` and `download_manager.py` scripts.
I'm trying to fix them here but I have a weird error, do you think you can have a look?
```bash
(datasets) MacBook-Pro-de-Thomas:datasets thomwolf$ python -m pytest -sv ./tests/test_dataset_common.py::DatasetTest::test_builder_class_snli
============================================================================= test session starts =============================================================================
platform darwin -- Python 3.7.7, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 -- /Users/thomwolf/miniconda2/envs/datasets/bin/python
cachedir: .pytest_cache
rootdir: /Users/thomwolf/Documents/GitHub/datasets
plugins: xdist-1.31.0, forked-1.1.3
collected 1 item
tests/test_dataset_common.py::DatasetTest::test_builder_class_snli ERROR
=================================================================================== ERRORS ====================================================================================
____________________________________________________________ ERROR at setup of DatasetTest.test_builder_class_snli ____________________________________________________________
file_path = <module 'tests.test_dataset_common' from '/Users/thomwolf/Documents/GitHub/datasets/tests/test_dataset_common.py'>
download_config = DownloadConfig(cache_dir=None, force_download=False, resume_download=False, local_files_only=False, proxies=None, user_agent=None, extract_compressed_file=True, force_extract=True)
download_kwargs = {}
def setup_module(file_path: str, download_config: Optional[DownloadConfig] = None, **download_kwargs,) -> DatasetBuilder:
r"""
Download/extract/cache a dataset to add to the lib from a path or url which can be:
- a path to a local directory containing the dataset processing python script
- an url to a S3 directory with a dataset processing python script
Dataset codes are cached inside the lib to allow easy import (avoid ugly sys.path tweaks)
and using cloudpickle (among other things).
Return: tuple of
the unique id associated to the dataset
the local path to the dataset
"""
if download_config is None:
download_config = DownloadConfig(**download_kwargs)
download_config.extract_compressed_file = True
download_config.force_extract = True
> name = list(filter(lambda x: x, file_path.split("/")))[-1] + ".py"
E AttributeError: module 'tests.test_dataset_common' has no attribute 'split'
src/nlp/load.py:169: AttributeError
============================================================================== warnings summary ===============================================================================
/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15
/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
import imp
-- Docs: https://docs.pytest.org/en/latest/warnings.html
=========================================================================== short test summary info ===========================================================================
ERROR tests/test_dataset_common.py::DatasetTest::test_builder_class_snli - AttributeError: module 'tests.test_dataset_common' has no attribute 'split'
========================================================================= 1 warning, 1 error in 3.63s =========================================================================
```
|
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Aborted PR - Fix tests
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@patrickvonplaten I've broken a bit the tests with #25 while simplifying and re-organizing the `load.py` and `download_manager.py` scripts.
I'm trying to fix them here but I have a weird error, do you think you can have a look?
```bash
(datasets) MacBook-Pro-de-Thomas:datasets thomwolf$ python -m pytest -sv ./tests/test_dataset_common.py::DatasetTest::test_builder_class_snli
============================================================================= test session starts =============================================================================
platform darwin -- Python 3.7.7, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 -- /Users/thomwolf/miniconda2/envs/datasets/bin/python
cachedir: .pytest_cache
rootdir: /Users/thomwolf/Documents/GitHub/datasets
plugins: xdist-1.31.0, forked-1.1.3
collected 1 item
tests/test_dataset_common.py::DatasetTest::test_builder_class_snli ERROR
=================================================================================== ERRORS ====================================================================================
____________________________________________________________ ERROR at setup of DatasetTest.test_builder_class_snli ____________________________________________________________
file_path = <module 'tests.test_dataset_common' from '/Users/thomwolf/Documents/GitHub/datasets/tests/test_dataset_common.py'>
download_config = DownloadConfig(cache_dir=None, force_download=False, resume_download=False, local_files_only=False, proxies=None, user_agent=None, extract_compressed_file=True, force_extract=True)
download_kwargs = {}
def setup_module(file_path: str, download_config: Optional[DownloadConfig] = None, **download_kwargs,) -> DatasetBuilder:
r"""
Download/extract/cache a dataset to add to the lib from a path or url which can be:
- a path to a local directory containing the dataset processing python script
- an url to a S3 directory with a dataset processing python script
Dataset codes are cached inside the lib to allow easy import (avoid ugly sys.path tweaks)
and using cloudpickle (among other things).
Return: tuple of
the unique id associated to the dataset
the local path to the dataset
"""
if download_config is None:
download_config = DownloadConfig(**download_kwargs)
download_config.extract_compressed_file = True
download_config.force_extract = True
> name = list(filter(lambda x: x, file_path.split("/")))[-1] + ".py"
E AttributeError: module 'tests.test_dataset_common' has no attribute 'split'
src/nlp/load.py:169: AttributeError
============================================================================== warnings summary ===============================================================================
/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15
/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
import imp
-- Docs: https://docs.pytest.org/en/latest/warnings.html
=========================================================================== short test summary info ===========================================================================
ERROR tests/test_dataset_common.py::DatasetTest::test_builder_class_snli - AttributeError: module 'tests.test_dataset_common' has no attribute 'split'
========================================================================= 1 warning, 1 error in 3.63s =========================================================================
```
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MDExOlB1bGxSZXF1ZXN0NDE0ODUzMDM5
| 57
|
Better cached path
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2020-05-07 18:36:00+00:00
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2020-05-08 13:20:30+00:00
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2020-05-08 13:20:28+00:00
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### Changes:
- The `cached_path` no longer returns None if the file is missing/the url doesn't work. Instead, it can raise `FileNotFoundError` (missing file), `ConnectionError` (no cache and unreachable url) or `ValueError` (parsing error)
- Fix requests to firebase API that doesn't handle HEAD requests...
- Allow custom download in datasets script: it allows to use `tf.io.gfile.copy` for example, to download from google storage. I added an example: the `boolq` script
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MDExOlB1bGxSZXF1ZXN0NDE0ODMyODY4
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|
[Dataset] Tester add mock function
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2020-05-07 17:51:37+00:00
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2020-05-07 17:52:51+00:00
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2020-05-07 17:52:50+00:00
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CONTRIBUTOR
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need to add an empty `extract()` function to make `hansard` dataset test work.
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MDExOlB1bGxSZXF1ZXN0NDE0NjE0MjE1
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Beam datasets
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2020-05-07 11:04:32+00:00
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2020-05-11 07:20:02+00:00
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# Beam datasets
## Intro
Beam Datasets are using beam pipelines for preprocessing (basically lots of `.map` over objects called PCollections).
The advantage of apache beam is that you can choose which type of runner you want to use to preprocess your data. The main runners are:
- the `DirectRunner` to run the pipeline locally (default). However I encountered memory issues for big datasets (like the french or english wikipedia). Small dataset work fine
- Google Dataflow. I didn't play with it.
- Spark or Flink, two well known data processing frameworks. I tried to use the Spark/Flink local runners provided by apache beam for python and wasn't able to make them work properly though...
## From tfds beam datasets to our own beam datasets
Tensorflow datasets used beam and a complicated pipeline to shard the TFRecords files.
To allow users to download beam datasets and not having to preprocess them, they also allow to download the already preprocessed datasets from their google storage (the beam pipeline doesn't run in that case).
On our side, we replace TFRecords by something else. Arrow or Parquet do the job but I chose Parquet as: 1) there is a builtin apache beam parquet writer that is quite convenient, and 2) reading parquet from the pyarrow library is also simple and effective (there is a mmap option !)
Moreover we don't shard datasets in many many files like tfds (they were doing probably doing that mainly because of the limit of 2Gb per TFRecord file). Therefore we have a simpler pipeline that saves each split into one parquet file. We also removed the utilities to use their google storage (for now maybe ? we'll have to discuss it).
## Main changes
- Added a BeamWriter to save the output of beam pipelines into parquet files and fill dataset infos
- Create a ParquetReader and refactor a bit the arrow_reader.py
\> **With this, we can now try to add beam datasets from tfds**
I already added the wikipedia one, and I will also try to add the Wiki40b dataset
## Test the wikipedia script
You can download and run the beam pipeline for wikipedia (using the `DirectRunner` by default) like this:
```
>>> import nlp
>>> nlp.load("datasets/nlp/wikipedia", dataset_config="20200501.frr")
```
This wikipedia dataset (lang: frr, North Frisian) is a small one (~10Mb), but feel free to try bigger ones (and fill 20Gb of swap memory if you try the english one lol)
## Next
Should we allow to download preprocessed datasets from the tfds google storage ?
Should we try to optimize the beam pipelines to run locally without memory issues ?
Should we try other data processing frameworks for big datasets, like spark ?
## About this PR
It should be merged after #25
-----------------
I'd be happy to have your feedback and your ideas to improve the processing of big datasets like wikipedia :)
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[Tests] Improved Error message for dummy folder structure
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2020-05-06 18:11:48+00:00
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2020-05-06 18:13:00+00:00
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2020-05-06 18:12:59+00:00
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Improved Error message
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[Features] Typo in generate_from_dict
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2020-05-06 16:05:23+00:00
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2020-05-07 15:28:46+00:00
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2020-05-07 15:28:45+00:00
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CONTRIBUTOR
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Change `isinstance` test in features when generating features from dict.
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MDExOlB1bGxSZXF1ZXN0NDE0MTEyMDAy
| 52
|
allow dummy folder structure to handle dict of lists
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`esnli.py` needs that extension of the dummy data testing.
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[Testing] Improved testing structure
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2020-05-07 22:07:19+00:00
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This PR refactors the test design a bit and puts the mock download manager in the `utils` files as it is just a test helper class.
as @mariamabarham pointed out, creating a dummy folder structure can be quite hard to grasp.
This PR tries to change that to some extent.
It follows the following logic for the `dummy` folder structure now:
1.) The data bulider has no config -> the `dummy` folder structure is:
`dummy/<version>/dummy_data.zip`
2) The data builder has >= 1 configs -> the `dummy` folder structure is:
`dummy/<config_name_1>/<version>/dummy_data.zip`
`dummy/<config_name_2>/<version>/dummy_data.zip`
Now, the difficult part is how to create the `dummy_data.zip` file. There are two cases:
A) The `data_urs` parameter inserted into the `download_and_extract` fn is a **string**:
-> the `dummy_data.zip` file zips the folder:
`dummy_data/<relative_path_of_folder_structure_of_url>`
B) The `data_urs` parameter inserted into the `download_and_extract` fn is a **dict**:
-> the `dummy_data.zip` file zips the folder:
`dummy_data/<relative_path_of_folder_structure_of_url_behind _key_1>`
`dummy_data/<relative_path_of_folder_structure_of_url_behind _key_2>`
By relative folder structure I mean `url_path.split('./')[-1]`. As an example the dataset **xquad** by deepmind has the following url path behind the key `de`: `https://github.com/deepmind/xquad/blob/master/xquad.de.json`
-> This means that the relative url path should be `xquad.de.json`.
@mariamabarham B) is a change from how is was before and I think is makes more sense.
While before the `dummy_data.zip` file for xquad with config `de` looked like:
`dummy_data/de` it would now look like `dummy_data/xquad.de.json`. I think this is better and easier to understand.
Therefore there are currently 6 tests that would have to have changed their dummy folder structure, but which can easily be done (30min).
I also added a function: `print_dummy_data_folder_structure` that prints out the expected structures when testing which should be quite helpful.
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[Tests] test only for fast test as a default
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2020-05-05 12:59:22+00:00
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2020-05-05 13:02:18+00:00
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2020-05-05 13:02:16+00:00
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Test only for one config on circle ci to speed up testing. Add all config test as a slow test.
@mariamabarham @thomwolf
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fix flatten nested
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[Command Convert] remove tensorflow import
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2020-05-05 10:41:00+00:00
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2020-05-05 11:13:58+00:00
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2020-05-05 11:13:56+00:00
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Remove all tensorflow import statements.
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| 612,446,493
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MDExOlB1bGxSZXF1ZXN0NDEzMzg5MDc1
| 47
|
[PyArrow Feature] fix py arrow bool
|
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2020-05-05 08:56:28+00:00
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2020-05-05 10:40:28+00:00
|
2020-05-05 10:40:27+00:00
|
CONTRIBUTOR
| null | null | 0
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To me it seems that `bool` can only be accessed with `bool_` when looking at the pyarrow types: https://arrow.apache.org/docs/python/api/datatypes.html.
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| 612,398,190
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MDExOlB1bGxSZXF1ZXN0NDEzMzUxNTY0
| 46
|
[Features] Strip str key before dict look-up
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2020-05-05 07:31:45+00:00
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2020-05-05 08:37:45+00:00
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2020-05-05 08:37:44+00:00
|
CONTRIBUTOR
| null | null | 0
|
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The dataset `anli.py` currently fails because it tries to look up a key `1\n` in a dict that only has the key `1`. Added an if statement to strip key if it cannot be found in dict.
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| 612,386,583
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MDExOlB1bGxSZXF1ZXN0NDEzMzQzMjAy
| 45
|
[Load] Separate Module kwargs and builder kwargs.
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2020-05-05 07:09:54+00:00
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2022-10-04 09:32:11+00:00
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2020-05-08 09:51:22+00:00
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CONTRIBUTOR
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Kwargs for the `load_module` fn should be passed with `module_xxxx` to `builder_kwargs` of `load` fn.
This is a follow-up PR of: https://github.com/huggingface/nlp/pull/41
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