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620,908,786 | https://api.github.com/repos/huggingface/datasets/issues/167 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/167/events | [] | null | 2020-05-19T16:17:12Z | [] | https://github.com/huggingface/datasets/pull/167 | CONTRIBUTOR | null | false | null | [] | [Tests] refactor tests | {
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```python
if "/" not in dataset_name:
logging.info("Skip {} because it is a canonical dataset")
return
```
To run a `aws` test, one should now run the following command:
```python
pytest -s tests/test_dataset_common.py::AWSDatasetTest::test_builder_class_wmt14
```
The same `local` test, can be run with:
```python
pytest -s tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_wmt14
``` | {
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} | MDU6SXNzdWU2MjA4NTAyMTg= | null | 2020-05-19T10:08:46Z | https://api.github.com/repos/huggingface/datasets/issues/166/comments | Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.
Also, we could maybe have a clear indication of which method modify in-place and which methods return/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think? | {
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} | MDU6SXNzdWU2MjA3NTgyMjE= | null | 2020-05-19T07:50:57Z | https://api.github.com/repos/huggingface/datasets/issues/165/comments | Can I recommend the following:
For ANLI, use https://github.com/facebookresearch/anli. As that paper says, "Our dataset is not
to be confused with abductive NLI (Bhagavatula et al., 2019), which calls itself αNLI, or ART.".
Indeed, the paper cited under what is currently called anli says in the abstract "We introduce a challenge dataset, ART".
The current naming will confuse people :) | {
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} | MDU6SXNzdWU2MjA1NDAyNTA= | null | 2020-05-18T22:18:21Z | https://api.github.com/repos/huggingface/datasets/issues/164/comments | Hi guys,
In order to cover multilingual support a little step could be adding standard Datasets used for Spanish NER and POS tasks.
I can provide it in raw and preprocessed formats. | {
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} | MDU6SXNzdWU2MjA1MzQzMDc= | null | 2020-05-18T22:05:26Z | https://api.github.com/repos/huggingface/datasets/issues/163/comments | I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https://www.aclweb.org/anthology/P19-1487/), and v1.11 has noted [annotation](https://github.com/salesforce/cos-e/issues/2) [issues](https://arxiv.org/pdf/2004.14546.pdf). | {
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620,513,554 | https://api.github.com/repos/huggingface/datasets/issues/162 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/162/events | [] | null | 2020-05-18T21:36:21Z | [] | https://github.com/huggingface/datasets/pull/162 | MEMBER | null | false | null | [] | fix prev files hash in map | {
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} | 2020-05-18T21:20:51Z | https://api.github.com/repos/huggingface/datasets/issues/162/comments | Fix the `.map` issue in #160.
This makes sure it takes the previous files when computing the hash. | {
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620,487,535 | https://api.github.com/repos/huggingface/datasets/issues/161 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/161/events | [
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} | MDU6SXNzdWU2MjA0ODc1MzU= | null | 2020-05-18T20:31:30Z | https://api.github.com/repos/huggingface/datasets/issues/161/comments | Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp/utils/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. | {
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} | MDU6SXNzdWU2MjA0NDgyMzY= | null | 2020-05-18T19:22:03Z | https://api.github.com/repos/huggingface/datasets/issues/160/comments | hello,
I am working on a program that uses the `nlp` library with the `SST2` dataset.
The rough outline of the program is:
```
import nlp as nlp_datasets
...
parser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')
...
dataset = nlp_datasets.load_dataset(*args.dataset)
...
# Create feature vocabs
vocabs = create_vocabs(dataset.values(), vectorizers)
...
# Create a function to vectorize based on vectorizers and vocabs:
print('TS', train_set.num_rows)
print('VS', valid_set.num_rows)
print('ES', test_set.num_rows)
# factory method to create a `convert_to_features` function based on vocabs
convert_to_features = create_featurizer(vectorizers, vocabs)
train_set = train_set.map(convert_to_features, batched=True)
train_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)
valid_set = valid_set.map(convert_to_features, batched=True)
valid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)
test_set = test_set.map(convert_to_features, batched=True)
test_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)
print('TS', train_set.num_rows)
print('VS', valid_set.num_rows)
print('ES', test_set.num_rows)
```
Im not sure if Im using it incorrectly, but the results are not what I expect. Namely, the `.map()` seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:
```
TS 67349
VS 872
ES 1821
TS 67349
VS 67349
ES 67349
```
The behavior changes if I turn off the caching but then the results fail:
```
train_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)
...
valid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)
...
test_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)
```
Now I get the right set of features back...
```
TS 67349
VS 872
ES 1821
100%|██████████| 68/68 [00:00<00:00, 92.78it/s]
100%|██████████| 1/1 [00:00<00:00, 75.47it/s]
0%| | 0/2 [00:00<?, ?it/s]TS 67349
VS 872
ES 1821
100%|██████████| 2/2 [00:00<00:00, 77.19it/s]
```
but I think its losing track of the original training set:
```
Traceback (most recent call last):
File "/home/dpressel/dev/work/baseline/api-examples/layers-classify-hf-datasets.py", line 148, in <module>
for x in train_loader:
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
data = self._next_data()
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__
output_all_columns=self._output_all_columns,
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 294, in _getitem
outputs = self._unnest(self._data.slice(key, 1).to_pydict())
File "pyarrow/table.pxi", line 1211, in pyarrow.lib.Table.slice
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 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)
Process finished with exit code 1
```
The full-example program (minus the print stmts) is here:
https://github.com/dpressel/mead-baseline/pull/620/files
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620,420,700 | https://api.github.com/repos/huggingface/datasets/issues/159 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/159/events | [] | null | 2020-05-18T18:37:08Z | [] | https://github.com/huggingface/datasets/issues/159 | NONE | completed | null | null | [] | How can we add more datasets to nlp library? | {
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} | MDU6SXNzdWU2MjA0MjA3MDA= | null | 2020-05-18T18:35:31Z | https://api.github.com/repos/huggingface/datasets/issues/159/comments | {
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|
620,396,658 | https://api.github.com/repos/huggingface/datasets/issues/158 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/158/events | [] | null | 2020-06-11T07:49:15Z | [] | https://github.com/huggingface/datasets/pull/158 | CONTRIBUTOR | null | false | null | [] | add Toronto Books Corpus | {
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} | 2020-05-18T17:54:45Z | https://api.github.com/repos/huggingface/datasets/issues/158/comments | This PR adds the Toronto Books Corpus.
.
It on consider TMX and plain text files (Moses) defined in the table **Statistics and TMX/Moses Downloads** [here](http://opus.nlpl.eu/Books.php ) | {
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620,356,542 | https://api.github.com/repos/huggingface/datasets/issues/157 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/157/events | [] | null | 2020-06-05T08:08:58Z | [
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] | https://github.com/huggingface/datasets/issues/157 | NONE | completed | null | null | [] | nlp.load_dataset() gives "TypeError: list_() takes exactly one argument (2 given)" | {
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} | MDU6SXNzdWU2MjAzNTY1NDI= | null | 2020-05-18T16:46:38Z | https://api.github.com/repos/huggingface/datasets/issues/157/comments | I'm trying to load datasets from nlp but there seems to have error saying
"TypeError: list_() takes exactly one argument (2 given)"
gist can be found here
https://gist.github.com/saahiluppal/c4b878f330b10b9ab9762bc0776c0a6a | {
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620,263,687 | https://api.github.com/repos/huggingface/datasets/issues/156 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/156/events | [] | null | 2020-07-23T16:41:55Z | [
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] | https://github.com/huggingface/datasets/issues/156 | NONE | completed | null | null | [] | SyntaxError with WMT datasets | {
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} | MDU6SXNzdWU2MjAyNjM2ODc= | null | 2020-05-18T14:38:18Z | https://api.github.com/repos/huggingface/datasets/issues/156/comments | The following snippet produces a syntax error:
```
import nlp
dataset = nlp.load_dataset('wmt14')
print(dataset['train'][0])
```
```
Traceback (most recent call last):
File "/home/tom/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-8-3206959998b9>", line 3, in <module>
dataset = nlp.load_dataset('wmt14')
File "/home/tom/.local/lib/python3.6/site-packages/nlp/load.py", line 505, in load_dataset
builder_cls = import_main_class(module_path, dataset=True)
File "/home/tom/.local/lib/python3.6/site-packages/nlp/load.py", line 56, in import_main_class
module = importlib.import_module(module_path)
File "/usr/lib/python3.6/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 994, in _gcd_import
File "<frozen importlib._bootstrap>", line 971, in _find_and_load
File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 678, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/tom/.local/lib/python3.6/site-packages/nlp/datasets/wmt14/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2/wmt14.py", line 21, in <module>
from .wmt_utils import Wmt, WmtConfig
File "/home/tom/.local/lib/python3.6/site-packages/nlp/datasets/wmt14/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2/wmt_utils.py", line 659
<<<<<<< HEAD
^
SyntaxError: invalid syntax
```
Python version:
`3.6.9 (default, Apr 18 2020, 01:56:04) [GCC 8.4.0]`
Running on Ubuntu 18.04, via a Jupyter notebook | {
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Let's take GLUE as an example:
The configuration has the citation for each dataset included (e.g. [here](https://github.com/huggingface/nlp/blob/master/datasets/glue/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded. | {
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619,971,900 | https://api.github.com/repos/huggingface/datasets/issues/152 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/152/events | [] | null | 2020-05-27T22:09:12Z | [] | https://github.com/huggingface/datasets/pull/152 | CONTRIBUTOR | null | false | null | [] | Add GLUE config name check | {
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} | 2020-05-18T07:23:43Z | https://api.github.com/repos/huggingface/datasets/issues/152/comments | Fixes #130 by adding a name check to the Glue class | {
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619,809,645 | https://api.github.com/repos/huggingface/datasets/issues/150 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/150/events | [] | null | 2020-05-26T20:37:59Z | [] | https://github.com/huggingface/datasets/pull/150 | CONTRIBUTOR | null | false | null | [] | Add WNUT 17 NER dataset | {
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} | 2020-05-17T22:19:04Z | https://api.github.com/repos/huggingface/datasets/issues/150/comments | Hi,
this PR adds the WNUT 17 dataset to `nlp`.
> Emerging and Rare entity recognition
> This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text.
>
> The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.
More information about the dataset can be found on the [shared task page](https://noisy-text.github.io/2017/emerging-rare-entities.html).
Dataset is taken is taken from their [GitHub repository](https://github.com/leondz/emerging_entities_17), because the data provided in this repository contains minor fixes in the dataset format.
## Usage
Then the WNUT 17 dataset can be used in `nlp` like this:
```python
import nlp
wnut_17 = nlp.load_dataset("./datasets/wnut_17/wnut_17.py")
print(wnut_17)
```
This outputs:
```txt
'train': Dataset(schema: {'id': 'string', 'tokens': 'list<item: string>', 'labels': 'list<item: string>'}, num_rows: 3394)
'validation': Dataset(schema: {'id': 'string', 'tokens': 'list<item: string>', 'labels': 'list<item: string>'}, num_rows: 1009)
'test': Dataset(schema: {'id': 'string', 'tokens': 'list<item: string>', 'labels': 'list<item: string>'}, num_rows: 1287)
```
Number are identical with the ones in [this paper](https://www.ijcai.org/Proceedings/2019/0702.pdf) and are the same as using the `dataset` reader in Flair.
## Features
The following feature format is used to represent a sentence in the WNUT 17 dataset:
| Feature | Example | Description
| ---- | ---- | -----------------
| `id` | `0` | Number (id) of current sentence
| `tokens` | `["AHFA", "extends", "deadline"]` | List of tokens (strings) for a sentence
| `labels` | `["B-group", "O", "O"]` | List of labels (outer span)
The following labels are used in WNUT 17:
```txt
O
B-corporation
I-corporation
B-location
I-location
B-product
I-product
B-person
I-person
B-group
I-group
B-creative-work
I-creative-work
``` | {
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619,735,739 | https://api.github.com/repos/huggingface/datasets/issues/149 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/149/events | [
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} | MDU6SXNzdWU2MTk3MzU3Mzk= | null | 2020-05-17T15:42:39Z | https://api.github.com/repos/huggingface/datasets/issues/149/comments | https://github.com/rkadlec/ubuntu-ranking-dataset-creator or http://dataset.cs.mcgill.ca/ubuntu-corpus-1.0/ | {
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In Colab,
```
%pip install -q nlp
%pip install -q apache_beam mwparserfromhell
dataset = nlp.load_dataset('wikipedia')
```
get
```
Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wikipedia/20200501.aa/1.0.0...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-52471d2a0088> in <module>()
----> 1 dataset = nlp.load_dataset('wikipedia')
1 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
TypeError: _download_and_prepare() got an unexpected keyword argument 'verify_infos'
``` | {
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} | MDU6SXNzdWU2MTk1ODE5MDc= | null | 2020-05-17T00:28:24Z | https://api.github.com/repos/huggingface/datasets/issues/147/comments | It would be nice if we could use sklearn `train_test_split` to quickly generate subsets from the dataset objects returned by `nlp.load_dataset`. At the moment the code:
```python
data = nlp.load_dataset('imdb', cache_dir=data_cache)
f_half, s_half = train_test_split(data['train'], test_size=0.5, random_state=seed)
```
throws:
```
ValueError: Can only get row(s) (int or slice) or columns (string).
```
It's not a big deal, since there are other ways to split the data, but it would be a cool thing to have. | {
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Here is an example of how to use it.
```sh
import nlp
bertscore = nlp.load_metric('metrics/bertscore') # or simply nlp.load_metric('bertscore') after this is added to huggingface's s3 bucket
predictions = ['example', 'fruit']
references = [['this is an example.', 'this is one example.'], ['apple']]
results = bertscore.compute(predictions, references, lang='en')
print(results)
``` | {
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} | 2020-05-16T13:39:30Z | https://api.github.com/repos/huggingface/datasets/issues/144/comments | 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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```
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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} | 2020-05-16T11:28:46Z | https://api.github.com/repos/huggingface/datasets/issues/142/comments | 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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619,443,613 | https://api.github.com/repos/huggingface/datasets/issues/140 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/140/events | [] | null | 2020-05-16T13:21:44Z | [] | https://github.com/huggingface/datasets/pull/140 | CONTRIBUTOR | null | false | null | [] | [Tests] run local tests as default | {
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} | 2020-05-16T10:56:06Z | https://api.github.com/repos/huggingface/datasets/issues/140/comments | 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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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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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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619,211,018 | https://api.github.com/repos/huggingface/datasets/issues/136 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/136/events | [] | null | 2020-05-17T12:17:28Z | [] | https://github.com/huggingface/datasets/pull/136 | NONE | null | false | null | [] | Update README.md | {
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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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619,077,851 | https://api.github.com/repos/huggingface/datasets/issues/132 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/132/events | [
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] | null | 2020-10-07T14:22:48Z | [] | https://github.com/huggingface/datasets/issues/132 | MEMBER | completed | null | null | [] | [Feature Request] Add the OpenWebText dataset | {
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} | MDU6SXNzdWU2MTkwNzc4NTE= | null | 2020-05-15T15:57:29Z | https://api.github.com/repos/huggingface/datasets/issues/132/comments | 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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} | MDU6SXNzdWU2MTkwNzM3MzE= | null | 2020-05-15T15:50:44Z | https://api.github.com/repos/huggingface/datasets/issues/131/comments | 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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] | null | 2020-05-27T22:08:15Z | [] | https://github.com/huggingface/datasets/issues/130 | NONE | completed | null | null | [] | Loading GLUE dataset loads CoLA by default | {
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} | MDU6SXNzdWU2MTkwMzU0NDA= | null | 2020-05-15T14:55:50Z | https://api.github.com/repos/huggingface/datasets/issues/130/comments | 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= | null | 2020-05-15T14:14:20Z | https://api.github.com/repos/huggingface/datasets/issues/129/comments | Would be great to have https://github.com/google-research-datasets/natural-questions as an alternative to SQuAD. | {
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618,951,117 | https://api.github.com/repos/huggingface/datasets/issues/128 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/128/events | [] | null | 2020-05-15T13:10:40Z | [] | https://github.com/huggingface/datasets/issues/128 | NONE | completed | null | null | [] | Some error inside nlp.load_dataset() | {
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} | MDU6SXNzdWU2MTg5NTExMTc= | null | 2020-05-15T13:01:29Z | https://api.github.com/repos/huggingface/datasets/issues/128/comments | 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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} | 2020-05-15T11:25:20Z | https://api.github.com/repos/huggingface/datasets/issues/126/comments | 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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} | 2020-05-15T10:34:34Z | https://api.github.com/repos/huggingface/datasets/issues/125/comments | I checked it with the data link of the mail you forwarded @thomwolf => works well! | {
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} | 2020-05-15T09:12:25Z | https://api.github.com/repos/huggingface/datasets/issues/123/comments | ## 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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} | MDU6SXNzdWU2MTg3Mzc3ODM= | null | 2020-05-15T06:43:08Z | https://api.github.com/repos/huggingface/datasets/issues/120/comments | 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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} | MDU6SXNzdWU2MTg2NTIxNDU= | null | 2020-05-15T02:27:26Z | https://api.github.com/repos/huggingface/datasets/issues/119/comments | 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= | null | 2020-05-15T01:58:52Z | https://api.github.com/repos/huggingface/datasets/issues/118/comments | 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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618,632,573 | https://api.github.com/repos/huggingface/datasets/issues/117 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/117/events | [] | null | 2022-07-15T08:36:44Z | [] | https://github.com/huggingface/datasets/issues/117 | NONE | completed | null | null | [] | ❓ How to remove specific rows of a dataset ? | {
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} | MDU6SXNzdWU2MTg2MzI1NzM= | null | 2020-05-15T01:25:06Z | https://api.github.com/repos/huggingface/datasets/issues/117/comments | 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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} | MDU6SXNzdWU2MTg2MjgyNjQ= | null | 2020-05-15T01:12:06Z | https://api.github.com/repos/huggingface/datasets/issues/116/comments | 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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} | MDU6SXNzdWU2MTg2MTU4NTU= | null | 2020-05-15T00:29:47Z | https://api.github.com/repos/huggingface/datasets/issues/115/comments | 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 | https://api.github.com/repos/huggingface/datasets/issues/114 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/114/events | [] | null | 2020-05-15T00:19:52Z | [] | https://github.com/huggingface/datasets/issues/114 | NONE | completed | null | null | [] | Couldn't reach CNN/DM dataset | {
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} | MDU6SXNzdWU2MTg2MTEzMTA= | null | 2020-05-15T00:16:17Z | https://api.github.com/repos/huggingface/datasets/issues/114/comments | 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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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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618,233,637 | https://api.github.com/repos/huggingface/datasets/issues/103 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/103/events | [] | null | 2020-05-14T14:27:41Z | [] | https://github.com/huggingface/datasets/pull/103 | CONTRIBUTOR | null | false | null | [] | [Manual downloads] add logic proposal for manual downloads and add wikihow | {
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} | 2020-05-14T13:30:36Z | https://api.github.com/repos/huggingface/datasets/issues/103/comments | 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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} | 2020-05-14T13:27:26Z | https://api.github.com/repos/huggingface/datasets/issues/102/comments | 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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} | 2020-05-14T10:25:02Z | https://api.github.com/repos/huggingface/datasets/issues/101/comments | - Everything worked fine @mariamabarham. Made my computer nearly crash, but all seems to be working :-) | {
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618,081,602 | https://api.github.com/repos/huggingface/datasets/issues/100 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/100/events | [] | null | 2020-05-14T23:21:35Z | [] | https://github.com/huggingface/datasets/pull/100 | CONTRIBUTOR | null | false | null | [] | Add per type scores in seqeval metric | {
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} | 2020-05-14T09:37:52Z | https://api.github.com/repos/huggingface/datasets/issues/100/comments | 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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} | 2020-05-14T06:22:18Z | https://api.github.com/repos/huggingface/datasets/issues/98/comments | Add the Webid TL:DR dataset. | {
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} | 2020-05-13T19:36:16Z | https://api.github.com/repos/huggingface/datasets/issues/95/comments | ### 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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- 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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- Also this PR reformats all dataset scripts and adds `datasets` to `make style` | {
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} | 2020-05-13T09:30:15Z | https://api.github.com/repos/huggingface/datasets/issues/89/comments | 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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} | 2020-05-13T09:16:01Z | https://api.github.com/repos/huggingface/datasets/issues/88/comments | 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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} | 2020-05-13T08:51:29Z | https://api.github.com/repos/huggingface/datasets/issues/87/comments | Beautiful language for sure! | {
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} | 2020-05-13T08:43:00Z | https://api.github.com/repos/huggingface/datasets/issues/86/comments | Rename leftovers @thomwolf | {
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} | 2020-05-13T08:32:27Z | https://api.github.com/repos/huggingface/datasets/issues/85/comments | 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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} | 2020-05-12T16:46:50Z | https://api.github.com/repos/huggingface/datasets/issues/82/comments | @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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I also removed unused utils functions. | {
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616,786,803 | https://api.github.com/repos/huggingface/datasets/issues/80 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/80/events | [] | null | 2020-05-13T07:52:34Z | [] | https://github.com/huggingface/datasets/pull/80 | MEMBER | null | false | null | [] | Add nbytes + nexamples check | {
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} | 2020-05-12T16:18:43Z | https://api.github.com/repos/huggingface/datasets/issues/80/comments | ### 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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616,785,613 | https://api.github.com/repos/huggingface/datasets/issues/79 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/79/events | [] | null | 2020-05-12T16:17:10Z | [] | https://github.com/huggingface/datasets/pull/79 | CONTRIBUTOR | null | false | null | [] | [Convert] add new pattern | {
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} | 2020-05-12T11:25:29Z | https://api.github.com/repos/huggingface/datasets/issues/76/comments | Flake 8's new version does not like our format. Pinning the version for now. | {
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616,520,163 | https://api.github.com/repos/huggingface/datasets/issues/75 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/75/events | [] | null | 2020-05-13T07:44:12Z | [] | https://github.com/huggingface/datasets/pull/75 | MEMBER | null | false | null | [] | WIP adding metrics | {
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} | 2020-05-12T09:52:00Z | https://api.github.com/repos/huggingface/datasets/issues/75/comments | 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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} | 2020-05-12T09:38:01Z | https://api.github.com/repos/huggingface/datasets/issues/74/comments | 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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} | 2020-05-12T07:11:22Z | https://api.github.com/repos/huggingface/datasets/issues/73/comments | Add a JSONS script to read JSON datasets from files. | {
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616,225,010 | https://api.github.com/repos/huggingface/datasets/issues/72 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/72/events | [] | null | 2020-05-11T22:26:03Z | [] | https://github.com/huggingface/datasets/pull/72 | CONTRIBUTOR | null | false | null | [] | [README dummy data tests] README to better understand how the dummy data structure works | {
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} | 2020-05-11T22:19:03Z | https://api.github.com/repos/huggingface/datasets/issues/72/comments | 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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} | 2020-05-11T14:45:36Z | https://api.github.com/repos/huggingface/datasets/issues/71/comments | 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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615,679,102 | https://api.github.com/repos/huggingface/datasets/issues/70 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/70/events | [] | null | 2020-05-12T13:21:52Z | [] | https://github.com/huggingface/datasets/pull/70 | CONTRIBUTOR | null | false | null | [] | adding RACE, QASC, Super_glue and Tiny_shakespear datasets | {
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} | 2020-05-10T18:39:21Z | https://api.github.com/repos/huggingface/datasets/issues/69/comments | minor fix | {
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} | 2020-05-08T17:38:29Z | https://api.github.com/repos/huggingface/datasets/issues/68/comments | 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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} | 2020-05-08T15:02:43Z | https://api.github.com/repos/huggingface/datasets/issues/67/comments | 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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