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MDU6SXNzdWU2MzE5ODUxMDg=
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SST-2 test labels are all -1
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2020-06-05 21:41:42+00:00
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2021-12-08 00:47:32+00:00
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2020-06-06 16:56:41+00:00
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CONTRIBUTOR
| null | null | null | null |
I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}
>>> list(l['label'] for l in glue['test'])
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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Add Allociné Dataset
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2020-06-05 19:19:26+00:00
|
2020-06-11 07:47:26+00:00
|
2020-06-11 07:47:26+00:00
|
CONTRIBUTOR
| null | null | 0
|
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This is a french binary sentiment classification dataset, which was used to train this model: https://huggingface.co/tblard/tf-allocine.
Basically, it's a french "IMDB" dataset, with more reviews.
More info on [this repo](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert).
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| 631,735,848
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MDExOlB1bGxSZXF1ZXN0NDI4NTY2MTEy
| 243
|
Specify utf-8 encoding for GLUE
|
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2020-06-05 16:33:00+00:00
|
2020-06-17 21:16:06+00:00
|
2020-06-08 08:42:01+00:00
|
CONTRIBUTOR
| null | null | 0
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|
#242
This makes the GLUE-MNLI dataset readable on my machine, not sure if it's a Windows-only bug.
|
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| 631,733,683
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MDU6SXNzdWU2MzE3MzM2ODM=
| 242
|
UnicodeDecodeError when downloading GLUE-MNLI
|
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2020-06-05 16:30:01+00:00
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2020-06-09 16:06:47+00:00
|
2020-06-08 08:45:03+00:00
|
CONTRIBUTOR
| null | null | null | null |
When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 try:
-> 1129 for obj in iterable:
1130 yield obj
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\glue.py in _generate_examples(self, data_file, split, mrpc_files)
529
--> 530 for n, row in enumerate(reader):
531 if is_cola_non_test:
~\Miniconda3\envs\nlp\lib\csv.py in __next__(self)
110 self.fieldnames
--> 111 row = next(self.reader)
112 self.line_num = self.reader.line_num
~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final)
22 def decode(self, input, final=False):
---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0]
24
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>
```
Anyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay.
|
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MDExOlB1bGxSZXF1ZXN0NDI4NTQwMDM0
| 241
|
Fix empty cache dir
|
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2020-06-05 15:45:22+00:00
|
2020-06-08 08:35:33+00:00
|
2020-06-08 08:35:31+00:00
|
MEMBER
| null | null | 0
|
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If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful.
So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future.
This should fix #239
|
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| 240
|
Deterministic dataset loading
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2020-06-05 09:03:26+00:00
|
2020-06-08 09:18:14+00:00
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2020-06-08 09:18:14+00:00
|
CONTRIBUTOR
| null | null | null | null |
When calling:
```python
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```
the resulting dataset is not deterministic over different google colabs.
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:
https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/datasets/trivia_qa/trivia_qa.py#L180
which seems to return an ordering of files that depends on the filesystem:
https://stackoverflow.com/questions/6773584/how-is-pythons-glob-glob-ordered
I think we should go through all the dataset scripts and make sure to have deterministic behavior.
A simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name.
What do you think @lhoestq?
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|
[Creating new dataset] Not found dataset_info.json
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|
2020-06-05 06:15:04+00:00
|
2020-06-07 13:01:04+00:00
|
2020-06-07 13:01:04+00:00
|
CONTRIBUTOR
| null | null | null | null |
Hi, I am trying to create Toronto Book Corpus. #131
I ran
`~/nlp % python nlp-cli test datasets/bookcorpus --save_infos --all_configs`
but this doesn't create `dataset_info.json` and try to use it
```
INFO:nlp.load:Checking datasets/bookcorpus/bookcorpus.py for additional imports.
INFO:filelock:Lock 139795325778640 acquired on datasets/bookcorpus/bookcorpus.py.lock
INFO:nlp.load:Found main folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus
INFO:nlp.load:Found specific version folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9
INFO:nlp.load:Found script file from datasets/bookcorpus/bookcorpus.py to /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.py
INFO:nlp.load:Couldn't find dataset infos file at datasets/bookcorpus/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.json
INFO:filelock:Lock 139795325778640 released on datasets/bookcorpus/bookcorpus.py.lock
INFO:nlp.builder:Overwrite dataset info from restored data version.
INFO:nlp.info:Loading Dataset info from /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0
Traceback (most recent call last):
File "nlp-cli", line 37, in <module>
service.run()
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/commands/test.py", line 78, in run
builders.append(builder_cls(name=config.name, data_dir=self._data_dir))
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 610, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 152, in __init__
self.info = DatasetInfo.from_directory(self._cache_dir)
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/info.py", line 157, in from_directory
with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), "r") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/dataset_info.json'
```
btw, `ls /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/` show me nothing is in the directory.
I have also pushed the script to my fork [bookcorpus.py](https://github.com/richardyy1188/nlp/blob/bookcorpusdev/datasets/bookcorpus/bookcorpus.py).
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| 631,260,143
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MDU6SXNzdWU2MzEyNjAxNDM=
| 238
|
[Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0.
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2020-06-05 02:14:47+00:00
|
2020-06-29 17:10:19+00:00
|
2020-06-29 17:10:19+00:00
|
NONE
| null | null | null | null |
When running BERT-Score, I'm meeting this warning :
> Warning: Empty candidate sentence; Setting recall to be 0.
Code :
```
import nlp
metric = nlp.load_metric("bertscore")
scores = metric.compute(["swag", "swags"], ["swags", "totally something different"], lang="en", device=0)
```
---
**What am I doing wrong / How can I hide this warning ?**
|
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MDU6SXNzdWU2MzExOTk5NDA=
| 237
|
Can't download MultiNLI
|
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[] | null | 3
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2020-06-04 23:05:21+00:00
|
2020-06-06 10:51:34+00:00
|
2020-06-06 10:51:34+00:00
|
CONTRIBUTOR
| null | null | null | null |
When I try to download MultiNLI with
```python
dataset = load_dataset('multi_nli')
```
I get this long error:
```python
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-13-3b11f6be4cb9> in <module>
1 # Load a dataset and print the first examples in the training set
2 # nli_dataset = nlp.load_dataset('multi_nli')
----> 3 dataset = load_dataset('multi_nli')
4 # nli_dataset = nlp.load_dataset('multi_nli', split='validation_matched[:10%]')
5 # print(nli_dataset['train'][0])
~\Miniconda3\envs\nlp\lib\site-packages\nlp\load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
514
515 # Download and prepare data
--> 516 builder_instance.download_and_prepare(
517 download_config=download_config,
518 download_mode=download_mode,
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
417 with utils.temporary_assignment(self, "_cache_dir", tmp_data_dir):
418 verify_infos = not save_infos and not ignore_verifications
--> 419 self._download_and_prepare(
420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
455 split_dict = SplitDict(dataset_name=self.name)
456 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 457 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
458 # Checksums verification
459 if verify_infos:
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\multi_nli\60774175381b9f3f1e6ae1028229e3cdb270d50379f45b9f2c01008f50f09e6b\multi_nli.py in _split_generators(self, dl_manager)
99 def _split_generators(self, dl_manager):
100
--> 101 downloaded_dir = dl_manager.download_and_extract(
102 "http://storage.googleapis.com/tfds-data/downloads/multi_nli/multinli_1.0.zip"
103 )
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in download_and_extract(self, url_or_urls)
214 extracted_path(s): `str`, extracted paths of given URL(s).
215 """
--> 216 return self.extract(self.download(url_or_urls))
217
218 def get_recorded_sizes_checksums(self):
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in extract(self, path_or_paths)
194 path_or_paths.
195 """
--> 196 return map_nested(
197 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
198 )
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)
168 return tuple(mapped)
169 # Singleton
--> 170 return function(data_struct)
171
172
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in <lambda>(path)
195 """
196 return map_nested(
--> 197 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
198 )
199
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
231 if is_zipfile(output_path):
232 with ZipFile(output_path, "r") as zip_file:
--> 233 zip_file.extractall(output_path_extracted)
234 zip_file.close()
235 elif tarfile.is_tarfile(output_path):
~\Miniconda3\envs\nlp\lib\zipfile.py in extractall(self, path, members, pwd)
1644
1645 for zipinfo in members:
-> 1646 self._extract_member(zipinfo, path, pwd)
1647
1648 @classmethod
~\Miniconda3\envs\nlp\lib\zipfile.py in _extract_member(self, member, targetpath, pwd)
1698
1699 with self.open(member, pwd=pwd) as source, \
-> 1700 open(targetpath, "wb") as target:
1701 shutil.copyfileobj(source, target)
1702
OSError: [Errno 22] Invalid argument: 'C:\\Users\\Python\\.cache\\huggingface\\datasets\\3e12413b8ec69f22dfcfd54a79d1ba9e7aac2e18e334bbb6b81cca64fd16bffc\\multinli_1.0\\Icon\r'
```
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MDExOlB1bGxSZXF1ZXN0NDI4MDUwNzI4
| 236
|
CompGuessWhat?! dataset
|
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[] | null | 9
|
2020-06-04 19:45:50+00:00
|
2020-06-11 09:43:42+00:00
|
2020-06-11 07:45:21+00:00
|
CONTRIBUTOR
| null | null | 0
|
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|
Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences?
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| 630,952,297
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MDExOlB1bGxSZXF1ZXN0NDI3OTM1MjQ0
| 235
|
Add experimental datasets
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2020-06-04 15:54:56+00:00
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2020-06-12 15:38:55+00:00
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2020-06-12 15:38:55+00:00
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MEMBER
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## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
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MDU6SXNzdWU2MzA1MzQ0Mjc=
| 234
|
Huggingface NLP, Uploading custom dataset
|
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2020-06-04 05:59:06+00:00
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2020-07-06 09:33:26+00:00
|
2020-07-06 09:33:26+00:00
|
NONE
| null | null | null | null |
Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you!
|
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| 630,432,132
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MDU6SXNzdWU2MzA0MzIxMzI=
| 233
|
Fail to download c4 english corpus
|
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[] | null | 5
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2020-06-04 01:06:38+00:00
|
2021-01-08 07:17:32+00:00
|
2020-06-08 09:16:59+00:00
|
NONE
| null | null | null | null |
i run following code to download c4 English corpus.
```
dataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'
, data_dir='/mypath')
```
and i met failure as follows
```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/adam/.cache/huggingface/datasets/c4/en/2.3.0...
Traceback (most recent call last):
File "download_corpus.py", line 38, in <module>
, data_dir='/home/adam/data/corpus/en/c4')
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset
save_infos=save_infos,
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 420, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 816, in _download_and_prepare
dl_manager, verify_infos=False, pipeline=pipeline,
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 457, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/datasets/c4/f545de9f63300d8d02a6795e2eb34e140c47e62a803f572ac5599e170ee66ecc/c4.py", line 175, in _split_generators
dl_manager.download_checksums(_CHECKSUMS_URL)
AttributeError: 'DownloadManager' object has no attribute 'download_checksums
```
can i get any advice?
|
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MDExOlB1bGxSZXF1ZXN0NDI3MjI5NDcy
| 232
|
Nlp cli fix endpoints
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2020-06-03 14:10:39+00:00
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2020-06-08 09:02:58+00:00
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2020-06-08 09:02:57+00:00
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MEMBER
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With this PR users will be able to upload their own datasets and metrics.
As mentioned in #181, I had to use the new endpoints and revert the use of dataclasses (just in case we have changes in the API in the future).
We now distinguish commands for datasets and commands for metrics:
```bash
nlp-cli upload_dataset <path/to/dataset>
nlp-cli upload_metric <path/to/metric>
nlp-cli s3_datasets {rm, ls}
nlp-cli s3_metrics {rm, ls}
```
Does it sound good to you @julien-c @thomwolf ?
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| 629,988,694
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MDExOlB1bGxSZXF1ZXN0NDI3MTk3MTcz
| 231
|
Add .download to MockDownloadManager
|
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2020-06-03 13:20:00+00:00
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2020-06-03 14:25:56+00:00
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2020-06-03 14:25:55+00:00
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MEMBER
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One method from the DownloadManager was missing and some users couldn't run the tests because of that.
@yjernite
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MDExOlB1bGxSZXF1ZXN0NDI3MTkzMTQ0
| 230
|
Don't force to install apache beam for wikipedia dataset
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2020-06-03 13:13:07+00:00
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2020-06-03 14:34:09+00:00
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2020-06-03 14:34:07+00:00
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As pointed out in #227, we shouldn't force users to install apache beam if the processed dataset can be downloaded. I moved the imports of some datasets to avoid this problem
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MDExOlB1bGxSZXF1ZXN0NDI3MTcxMzc5
| 229
|
Rename dataset_infos.json to dataset_info.json
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2020-06-03 12:31:44+00:00
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2020-06-03 12:52:54+00:00
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2020-06-03 12:48:33+00:00
|
NONE
| null | null | 0
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As the file required for the viewing in the live nlp viewer is named as dataset_info.json
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MDU6SXNzdWU2Mjk5NTI0MDI=
| 228
|
Not able to access the XNLI dataset
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2020-06-03 12:25:14+00:00
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2020-07-17 17:44:22+00:00
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2020-07-17 17:44:22+00:00
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NONE
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When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.
```
FileNotFoundError: [Errno 2] No such file or directory: '/home/sasha/.cache/huggingface/datasets/xnli/plain_text/1.0.0/dataset_info.json'
Traceback:
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp_viewer/run.py", line 86, in <module>
dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 591, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 575, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp_viewer/run.py", line 72, in get
builder_instance = builder_cls(name=conf)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/builder.py", line 610, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/builder.py", line 152, in __init__
self.info = DatasetInfo.from_directory(self._cache_dir)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/info.py", line 157, in from_directory
with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), "r") as f:
```
Is it possible to see if the dataset_info.json is correctly placed?
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MDU6SXNzdWU2Mjk4NDU3MDQ=
| 227
|
Should we still have to force to install apache_beam to download wikipedia ?
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2020-06-03 09:33:20+00:00
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2020-06-03 15:25:41+00:00
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2020-06-03 15:25:41+00:00
|
CONTRIBUTOR
| null | null | null | null |
Hi, first thanks to @lhoestq 's revolutionary work, I successfully downloaded processed wikipedia according to the doc. 😍😍😍
But at the first try, it tell me to install `apache_beam` and `mwparserfromhell`, which I thought wouldn't be used according to #204 , it was kind of confusing me at that time.
Maybe we should not force users to install these ? Or we just add them to`nlp`'s dependency ?
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MDExOlB1bGxSZXF1ZXN0NDI1OTA0MjEz
| 226
|
add BlendedSkillTalk dataset
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2020-06-01 10:54:45+00:00
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2020-06-03 14:37:23+00:00
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CONTRIBUTOR
| null | null | 0
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This PR add the BlendedSkillTalk dataset, which is used to fine tune the blenderbot.
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| 225
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[ROUGE] Different scores with `files2rouge`
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2020-06-01 00:50:36+00:00
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2020-06-03 15:27:18+00:00
|
2020-06-03 15:27:18+00:00
|
NONE
| null | null | null | null |
It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.
Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rouge1 0.33508031962733364
rouge2 0.14574333776191592
rougeL 0.2321187823256159
`files2rouge` :
>Running ROUGE...
===========================
1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)
1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)
1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)
===========================
1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)
1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)
1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)
===========================
1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)
1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)
1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)
---
When using longer predictions/gold, the difference is bigger.
**How can I reproduce same score as `files2rouge` ?**
@lhoestq
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[Feature Request/Help] BLEURT model -> PyTorch
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2020-05-30 18:30:40+00:00
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2023-08-26 17:38:48+00:00
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2021-01-04 09:53:32+00:00
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NONE
| null | null | null | null |
Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).
I had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried https://colab.research.google.com/drive/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!
Thank you muchly!
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MDU6SXNzdWU2Mjc2ODMzODY=
| 223
|
[Feature request] Add FLUE dataset
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2020-05-30 08:52:15+00:00
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2020-12-03 13:39:33+00:00
|
2020-12-03 13:39:33+00:00
|
NONE
| null | null | null | null |
Hi,
I think it would be interesting to add the FLUE dataset for francophones or anyone wishing to work on French.
In other requests, I read that you are already working on some datasets, and I was wondering if FLUE was planned.
If it is not the case, I can provide each of the cleaned FLUE datasets (in the form of a directly exploitable dataset rather than in the original xml formats which require additional processing, with the French part for cases where the dataset is based on a multilingual dataframe, etc.).
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MDU6SXNzdWU2Mjc1ODY2OTA=
| 222
|
Colab Notebook breaks when downloading the squad dataset
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2020-05-29 22:55:59+00:00
|
2020-06-04 00:21:05+00:00
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2020-06-04 00:21:05+00:00
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NONE
| null | null | null | null |
When I run the notebook in Colab
https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb
breaks when running this cell:
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MDExOlB1bGxSZXF1ZXN0NDI1MTI5OTc0
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|
Fix tests/test_dataset_common.py
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2020-05-29 14:12:15+00:00
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2020-06-01 12:20:42+00:00
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2020-05-29 15:02:23+00:00
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CONTRIBUTOR
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When I run the command `RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_arcd` while working on #220. I get the error ` unexpected keyword argument "'download_and_prepare_kwargs'"` at the level of `load_dataset`. Indeed, this [function](https://github.com/huggingface/nlp/blob/master/src/nlp/load.py#L441) no longer has the argument `download_and_prepare_kwargs` but rather `download_config`. So here I change the tests accordingly.
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MDExOlB1bGxSZXF1ZXN0NDI1MTEzMzEy
| 220
|
dataset_arcd
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2020-05-29 13:46:50+00:00
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2020-05-29 14:58:40+00:00
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2020-05-29 14:57:21+00:00
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CONTRIBUTOR
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Added Arabic Reading Comprehension Dataset (ARCD): https://arxiv.org/abs/1906.05394
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MDExOlB1bGxSZXF1ZXN0NDI1MDc2NjQx
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|
force mwparserfromhell as third party
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2020-05-29 12:33:17+00:00
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2020-05-29 13:30:13+00:00
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2020-05-29 13:30:12+00:00
|
MEMBER
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|
This should fix your env because you had `mwparserfromhell ` as a first party for `isort` @patrickvonplaten
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| 218
|
Add Natual Questions and C4 scripts
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2020-05-29 10:40:30+00:00
|
2020-05-29 12:31:01+00:00
|
2020-05-29 12:31:00+00:00
|
MEMBER
| null | null | 0
|
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Scripts are ready !
However they are not processed nor directly available from gcp yet.
|
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MDU6SXNzdWU2MjcxMjg0MDM=
| 217
|
Multi-task dataset mixing
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[] | null | 28
|
2020-05-29 09:22:26+00:00
|
2025-09-24 08:59:38+00:00
|
NaT
|
CONTRIBUTOR
| null | null | null | null |
It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).
The [T5 paper](https://arxiv.org/pdf/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:
- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size
- **Equal mixing** - sample uniformly from each task
- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.
Following this discussion https://github.com/huggingface/transformers/issues/4340 in [transformers](https://github.com/huggingface/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.
Some method for combining datasets could be implemented ,e.g.
```
dataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)
```
We would need a few additions:
- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?
- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.
It would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn.
I'm willing to write bits/most of this I just need some guidance on the interface and other library details so I can integrate it properly.
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MDU6SXNzdWU2MjY4OTY4OTA=
| 216
|
❓ How to get ROUGE-2 with the ROUGE metric ?
|
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2020-05-28 23:47:32+00:00
|
2020-06-01 00:04:35+00:00
|
2020-06-01 00:04:35+00:00
|
NONE
| null | null | null | null |
I'm trying to use ROUGE metric, but I don't know how to get the ROUGE-2 metric.
---
I compute scores with :
```python
import nlp
rouge = nlp.load_metric('rouge')
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
rouge.add([lp], [lg])
score = rouge.compute()
```
then : _(print only the F-score for readability)_
```python
for k, s in score.items():
print(k, s.mid.fmeasure)
```
It gives :
>rouge1 0.7915168355671788
rougeL 0.7915168355671788
---
**How can I get the ROUGE-2 score ?**
Also, it's seems weird that ROUGE-1 and ROUGE-L scores are the same. Did I made a mistake ?
@lhoestq
|
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| 215
|
NonMatchingSplitsSizesError when loading blog_authorship_corpus
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[] | null | 12
|
2020-05-28 22:55:19+00:00
|
2025-01-04 00:03:12+00:00
|
2022-02-10 13:05:45+00:00
|
NONE
| null | null | null | null |
Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`.
```
raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train',
num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'),
'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323,
dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation',
num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'),
'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766,
dataset_name='blog_authorship_corpus')}]
```
Upon checking it seems like there is a disparity between the information in `datasets/blog_authorship_corpus/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.
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MDExOlB1bGxSZXF1ZXN0NDI0NTk1NjIx
| 214
|
[arrow_dataset.py] add new filter function
|
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2020-05-28 16:21:40+00:00
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2020-05-29 11:43:29+00:00
|
2020-05-29 11:32:20+00:00
|
CONTRIBUTOR
| null | null | 0
|
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The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
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MDExOlB1bGxSZXF1ZXN0NDI0NTUxODE3
| 213
|
better message if missing beam options
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WDYT @yjernite ?
For example:
```python
dataset = nlp.load_dataset('wikipedia', '20200501.aa')
```
Raises:
```
MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20200501.aa', beam_runner='DirectRunner')`
```
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MDExOlB1bGxSZXF1ZXN0NDI0NTQ1NjAy
| 212
|
have 'add' and 'add_batch' for metrics
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This should fix #116
Previously the `.add` method of metrics expected a batch of examples.
Now `.add` expects one prediction/reference and `.add_batch` expects a batch.
I think it is more coherent with the way the ArrowWriter works.
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| 211
|
[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type
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2020-05-28 14:38:14+00:00
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2020-07-23 10:15:16+00:00
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2020-07-23 10:15:16+00:00
|
CONTRIBUTOR
| null | null | null | null |
Running the following code
```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, load_from_cache_file=False)
```
triggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.
On the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:
```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, remove_columns=["entity_pages"], load_from_cache_file=False)
```
. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?
**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly.
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MDExOlB1bGxSZXF1ZXN0NDI0NDgyNDgz
| 210
|
fix xnli metric kwargs description
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The text was wrong as noticed in #202
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| 209
|
Add a Google Drive exception for small files
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2020-05-28 10:40:17+00:00
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2020-05-28 15:15:04+00:00
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2020-05-28 15:15:04+00:00
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CONTRIBUTOR
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I tried to use the ``nlp`` library to load personnal datasets. I mainly copy-paste the code for ``multi-news`` dataset because my files are stored on Google Drive.
One of my dataset is small (< 25Mo) so it can be verified by Drive without asking the authorization to the user. This makes the download starts directly.
Currently the ``nlp`` raises a error: ``ConnectionError: Couldn't reach https://drive.google.com/uc?export=download&id=1DGnbUY9zwiThTdgUvVTSAvSVHoloCgun`` while the url is working. So I just add a new exception as you have already done for ``firebasestorage.googleapis.com`` :
```
elif (response.status_code == 400 and "firebasestorage.googleapis.com" in url) or (response.status_code == 405 and "drive.google.com" in url)
```
I make an example of the error that you can run on [](https://colab.research.google.com/drive/1ae_JJ9uvUt-9GBh0uGZhjbF5aXkl-BPv?usp=sharing)
I avoid the error by adding an exception but there is maybe a proper way to do it.
Many thanks :hugs:
Best,
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[Dummy data] insert config name instead of config
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2020-05-28 12:48:01+00:00
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2020-05-28 12:48:00+00:00
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CONTRIBUTOR
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Thanks @yjernite for letting me know. in the dummy data command the config name shuold be passed to the dataset builder and not the config itself.
Also, @lhoestq fixed small import bug introduced by beam command I think.
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MDU6SXNzdWU2MjU5MzIyMDA=
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|
Remove test set from NLP viewer
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2020-05-27 18:32:07+00:00
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2022-02-10 13:17:45+00:00
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2022-02-10 13:17:45+00:00
|
NONE
| null | null | null | null |
While the new [NLP viewer](https://huggingface.co/nlp/viewer/) is a great tool, I think it would be best to outright remove the option of looking at the test sets. At the very least, a warning should be displayed to users before showing the test set. Newcomers to the field might not be aware of best practices, and small things like this can help increase awareness.
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MDU6SXNzdWU2MjU4NDI5ODk=
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[Question] Combine 2 datasets which have the same columns
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2020-05-27 16:25:52+00:00
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2020-06-10 09:11:14+00:00
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2020-06-10 09:11:14+00:00
|
CONTRIBUTOR
| null | null | null | null |
Hi,
I am using ``nlp`` to load personal datasets. I created summarization datasets in multi-languages based on wikinews. I have one dataset for english and one for german (french is getting to be ready as well). I want to keep these datasets independent because they need different pre-processing (add different task-specific prefixes for T5 : *summarize:* for english and *zusammenfassen:* for german)
My issue is that I want to train T5 on the combined english and german datasets to see if it improves results. So I would like to combine 2 datasets (which have the same columns) to make one and train T5 on it. I was wondering if there is a proper way to do it? I assume that it can be done by combining all examples of each dataset but maybe you have a better solution.
Hoping this is clear enough,
Thanks a lot 😊
Best
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Better arrow dataset iter
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2020-05-27 16:20:21+00:00
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2020-05-27 16:39:58+00:00
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2020-05-27 16:39:56+00:00
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MEMBER
| null | null | 0
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I tried to play around with `tf.data.Dataset.from_generator` and I found out that the `__iter__` that we have for `nlp.arrow_dataset.Dataset` ignores the format that has been set (torch or tensorflow).
With these changes I should be able to come up with a `tf.data.Dataset` that uses lazy loading, as asked in #193.
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Add Dataflow support + Wikipedia + Wiki40b
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2020-05-27 12:32:49+00:00
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2020-05-28 08:10:35+00:00
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2020-05-28 08:10:34+00:00
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# Add Dataflow support + Wikipedia + Wiki40b
## Support datasets processing with Apache Beam
Some datasets are too big to be processed on a single machine, for example: wikipedia, wiki40b, etc. Apache Beam allows to process datasets on many execution engines like Dataflow, Spark, Flink, etc.
To process such datasets with Beam, I added a command to run beam pipelines `nlp-cli run_beam path/to/dataset/script`. Then I used it to process the english + french wikipedia, and the english of wiki40b.
The processed arrow files are on GCS and are the result of a Dataflow job.
I added a markdown documentation file in `docs` that explains how to use it properly.
## Load already processed datasets
Now that we have those datasets already processed, I made it possible to load datasets that are already processed. You can do `load_dataset('wikipedia', '20200501.en')` and it will download the processed files from the Hugging Face GCS directly into the user's cache and be ready to use !
The Wikipedia dataset was already asked in #187 and this PR should soon allow to add Natural Questions as asked in #129
## Other changes in the code
To make things work, I had to do a few adjustments:
- add a `ship_files_with_pipeline` method to the `DownloadManager`. This is because beam pipelines can be run in the cloud and therefore need to have access to your downloaded data. I used it in the wikipedia script:
```python
if not pipeline.is_local():
downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)
```
- add parquet to arrow conversion. This is because the output of beam pipelines are parquet files so we need to convert them to arrow and have the arrow files on GCS
- add a test script with a dummy beam dataset
- minor adjustments to allow read/write operations on remote files using `apache_beam.io.filesystems.FileSystems` if we want (it can be connected to gcp, s3, hdfs, etc...)
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| 625,515,488
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MDExOlB1bGxSZXF1ZXN0NDIzNzEyMTQ3
| 203
|
Raise an error if no config name for datasets like glue
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2020-05-27 09:03:58+00:00
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2020-05-27 16:40:39+00:00
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2020-05-27 16:40:38+00:00
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|
Some datasets like glue (see #130) and scientific_papers (see #197) have many configs.
For example for glue there are cola, sst2, mrpc etc.
Currently if a user does `load_dataset('glue')`, then Cola is loaded by default and it can be confusing. Instead, we should raise an error to let the user know that he has to pick one of the available configs (as proposed in #152). For example for glue, the message looks like:
```
ValueError: Config name is missing.
Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']
Example of usage:
`load_dataset('glue', 'cola')`
```
The error is raised if the config name is missing and if there are >=2 possible configs.
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MDU6SXNzdWU2MjU0OTM5ODM=
| 202
|
Mistaken `_KWARGS_DESCRIPTION` for XNLI metric
|
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2020-05-27 08:34:42+00:00
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2020-05-28 13:22:36+00:00
|
2020-05-28 13:22:36+00:00
|
NONE
| null | null | null | null |
Hi!
The [`_KWARGS_DESCRIPTION`](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/xnli/xnli.py#L45) for the XNLI metric uses `Args` and `Returns` text from [BLEU](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/bleu/bleu.py#L58) metric:
```
_KWARGS_DESCRIPTION = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: list of translations to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
'bleu': bleu score,
'precisions': geometric mean of n-gram precisions,
'brevity_penalty': brevity penalty,
'length_ratio': ratio of lengths,
'translation_length': translation_length,
'reference_length': reference_length
"""
```
But it should be something like:
```
_KWARGS_DESCRIPTION = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
'accuracy': accuracy
```
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| 625,235,430
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MDExOlB1bGxSZXF1ZXN0NDIzNDkzNTMw
| 201
|
Fix typo in README
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2020-05-26 22:18:21+00:00
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2020-05-26 23:40:31+00:00
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2020-05-26 23:00:56+00:00
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MDExOlB1bGxSZXF1ZXN0NDIzNDg2NTM0
| 200
|
[ArrowWriter] Set schema at first write example
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2020-05-26 21:59:48+00:00
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2020-05-27 09:07:54+00:00
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2020-05-27 09:07:53+00:00
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Right now if the schema was not specified when instantiating `ArrowWriter`, then it could be set with the first `write_table` for example (it calls `self._build_writer()` to do so).
I noticed that it was not done if the first example is added via `.write`, so I added it for coherence.
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MDExOlB1bGxSZXF1ZXN0NDIzNDc4ODIx
| 199
|
Fix GermEval 2014 dataset infos
|
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2020-05-26 21:41:44+00:00
|
2020-05-26 21:50:24+00:00
|
2020-05-26 21:50:24+00:00
|
CONTRIBUTOR
| null | null | 0
|
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Hi,
this PR just removes the `dataset_info.json` file and adds a newly generated `dataset_infos.json` file.
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MDU6SXNzdWU2MjUyMDA2Mjc=
| 198
|
Index outside of table length
|
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2020-05-26 21:09:40+00:00
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2020-05-26 22:43:49+00:00
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2020-05-26 22:43:49+00:00
|
NONE
| null | null | null | null |
The offset input box warns of numbers larger than a limit (like 2000) but then the errors start at a smaller value than that limit (like 1955).
> ValueError: Index (2000) outside of table length (2000).
> Traceback:
> File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
> exec(code, module.__dict__)
> File "/home/sasha/nlp_viewer/run.py", line 116, in <module>
> v = d[item][k]
> File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__
> output_all_columns=self._output_all_columns,
> File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 290, in _getitem
> raise ValueError(f"Index ({key}) outside of table length ({self._data.num_rows}).")
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MDU6SXNzdWU2MjQ5NjY5MDQ=
| 197
|
Scientific Papers only downloading Pubmed
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2020-05-26 15:18:47+00:00
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2020-05-28 08:19:28+00:00
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2020-05-28 08:19:28+00:00
|
NONE
| null | null | null | null |
Hi!
I have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following:
```
dataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.05k/5.05k [00:00<00:00, 2.66MB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.90k/4.90k [00:00<00:00, 2.42MB/s]
Downloading and preparing dataset scientific_papers/pubmed (download: 4.20 GiB, generated: 2.33 GiB, total: 6.53 GiB) to ./scientific_papers/pubmed/1.1.1...
Downloading: 3.62GB [00:40, 90.5MB/s]
Downloading: 880MB [00:08, 101MB/s]
Dataset scientific_papers downloaded and prepared to ./scientific_papers/pubmed/1.1.1. Subsequent calls will reuse this data.
```
only a pubmed folder is created. There doesn't seem to be something for arxiv. Are these two datasets merged? Or have I misunderstood something?
Thanks!
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MDExOlB1bGxSZXF1ZXN0NDIzMjIwMjIw
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|
Check invalid config name
|
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2020-05-26 13:52:51+00:00
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2020-05-26 21:04:56+00:00
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2020-05-26 21:04:55+00:00
|
MEMBER
| null | null | 0
|
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As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
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MDExOlB1bGxSZXF1ZXN0NDIzMTg1NTAy
| 195
|
[Dummy data command] add new case to command
|
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2020-05-26 12:50:47+00:00
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2020-05-26 14:38:28+00:00
|
2020-05-26 14:38:27+00:00
|
CONTRIBUTOR
| null | null | 0
|
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|
Qanta: #194 introduces a case that was not noticed before. This change in code helps community users to have an easier time creating the dummy data.
|
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| 194
|
Add Dataset: Qanta
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2020-05-26 12:44:35+00:00
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2020-05-26 16:58:17+00:00
|
2020-05-26 13:16:20+00:00
|
CONTRIBUTOR
| null | null | 0
|
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Fixes dummy data for #169 @EntilZha
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| 624,655,558
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MDU6SXNzdWU2MjQ2NTU1NTg=
| 193
|
[Tensorflow] Use something else than `from_tensor_slices()`
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2020-05-26 07:19:14+00:00
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2020-10-27 15:28:11+00:00
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2020-10-27 15:28:11+00:00
|
NONE
| null | null | null | null |
In the example notebook, the TF Dataset is built using `from_tensor_slices()` :
```python
columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']
train_tf_dataset.set_format(type='tensorflow', columns=columns)
features = {x: train_tf_dataset[x] for x in columns[:3]}
labels = {"output_1": train_tf_dataset["start_positions"]}
labels["output_2"] = train_tf_dataset["end_positions"]
tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
```
But according to [official tensorflow documentation](https://www.tensorflow.org/guide/data#consuming_numpy_arrays), this will load the entire dataset to memory.
**This defeats one purpose of this library, which is lazy loading.**
Is there any other way to load the `nlp` dataset into TF dataset lazily ?
---
For example, is it possible to use [Arrow dataset](https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowDataset) ? If yes, is there any code example ?
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|
[Question] Create Apache Arrow dataset from raw text file
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2020-05-25 16:42:47+00:00
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2021-12-18 01:45:34+00:00
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2020-10-27 15:20:22+00:00
|
CONTRIBUTOR
| null | null | null | null |
Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as "Crime and punishment" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?
Is the worth of send it to you and add it to the NLP library?
Thanks, Manu
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MDExOlB1bGxSZXF1ZXN0NDIyODI3MDMy
| 191
|
[Squad es] add dataset_infos
|
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2020-05-25 16:35:52+00:00
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2020-05-25 16:39:59+00:00
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2020-05-25 16:39:58+00:00
|
CONTRIBUTOR
| null | null | 0
|
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@mariamabarham - was still about to upload this. Should have waited with my comment a bit more :D
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MDExOlB1bGxSZXF1ZXN0NDIyNjA4NzAw
| 190
|
add squad Spanish v1 and v2
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2020-05-25 08:08:40+00:00
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2020-05-25 16:28:46+00:00
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2020-05-25 16:28:45+00:00
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CONTRIBUTOR
| null | null | 0
|
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This PR add the Spanish Squad versions 1 and 2 datasets.
Fixes #164
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MDU6SXNzdWU2MjQwNDg4ODE=
| 189
|
[Question] BERT-style multiple choice formatting
|
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|
2020-05-25 05:11:05+00:00
|
2020-05-25 18:38:28+00:00
|
2020-05-25 18:38:28+00:00
|
NONE
| null | null | null | null |
Hello, I am wondering what the equivalent formatting of a dataset should be to allow for multiple-choice answering prediction, BERT-style. Previously, this was done by passing a list of `InputFeatures` to the dataloader instead of a list of `InputFeature`, where `InputFeatures` contained lists of length equal to the number of answer choices in the MCQ instead of single items. I'm a bit confused on what the output of my feature conversion function should be when using `dataset.map()` to ensure similar behavior.
Thanks!
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MDU6SXNzdWU2MjM4OTA0MzA=
| 188
|
When will the remaining math_dataset modules be added as dataset objects
|
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[] | null | 3
|
2020-05-24 15:46:52+00:00
|
2020-05-24 18:53:48+00:00
|
2020-05-24 18:53:48+00:00
|
NONE
| null | null | null | null |
Currently only the algebra_linear_1d is supported. Is there a timeline for making the other modules supported. If no timeline is established, how can I help?
|
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MDU6SXNzdWU2MjM2Mjc4MDA=
| 187
|
[Question] How to load wikipedia ? Beam runner ?
|
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2020-05-23 10:18:52+00:00
|
2020-05-25 00:12:02+00:00
|
2020-05-25 00:12:02+00:00
|
CONTRIBUTOR
| null | null | null | null |
When `nlp.load_dataset('wikipedia')`, I got
* `WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.`
* `AttributeError: 'NoneType' object has no attribute 'size'`
Could somebody tell me what should I do ?
# Env
On Colab,
```
git clone https://github.com/huggingface/nlp
cd nlp
pip install -q .
```
```
%pip install -q apache_beam mwparserfromhell
-> ERROR: pydrive 1.3.1 has requirement oauth2client>=4.0.0, but you'll have oauth2client 3.0.0 which is incompatible.
ERROR: google-api-python-client 1.7.12 has requirement httplib2<1dev,>=0.17.0, but you'll have httplib2 0.12.0 which is incompatible.
ERROR: chainer 6.5.0 has requirement typing-extensions<=3.6.6, but you'll have typing-extensions 3.7.4.2 which is incompatible.
```
```
pip install -q apache-beam[interactive]
ERROR: google-colab 1.0.0 has requirement ipython~=5.5.0, but you'll have ipython 5.10.0 which is incompatible.
```
# The whole message
```
WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.
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...
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
44 frames
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
/usr/local/lib/python3.6/dist-packages/apache_beam/io/iobase.py in process(self, element, init_result)
1081 writer.write(e)
-> 1082 return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]
1083
/usr/local/lib/python3.6/dist-packages/apache_beam/io/filebasedsink.py in close(self)
422 def close(self):
--> 423 self.sink.close(self.temp_handle)
424 return self.temp_shard_path
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in close(self, writer)
537 if len(self._buffer[0]) > 0:
--> 538 self._flush_buffer()
539 if self._record_batches_byte_size > 0:
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in _flush_buffer(self)
569 for b in x.buffers():
--> 570 size = size + b.size
571 self._record_batches_byte_size = self._record_batches_byte_size + size
AttributeError: 'NoneType' object has no attribute 'size'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-9-340aabccefff> in <module>()
----> 1 dset = nlp.load_dataset('wikipedia')
/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)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
/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)
370 verify_infos = not save_infos and not ignore_verifications
371 self._download_and_prepare(
--> 372 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
373 )
374 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
770 with beam.Pipeline(runner=beam_runner, options=beam_options,) as pipeline:
771 super(BeamBasedBuilder, self)._download_and_prepare(
--> 772 dl_manager, pipeline=pipeline, verify_infos=False
773 ) # TODO{beam} verify infos
774
/usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in __exit__(self, exc_type, exc_val, exc_tb)
501 def __exit__(self, exc_type, exc_val, exc_tb):
502 if not exc_type:
--> 503 self.run().wait_until_finish()
504
505 def visit(self, visitor):
/usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in run(self, test_runner_api)
481 return Pipeline.from_runner_api(
482 self.to_runner_api(use_fake_coders=True), self.runner,
--> 483 self._options).run(False)
484
485 if self._options.view_as(TypeOptions).runtime_type_check:
/usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in run(self, test_runner_api)
494 finally:
495 shutil.rmtree(tmpdir)
--> 496 return self.runner.run_pipeline(self, self._options)
497
498 def __enter__(self):
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/direct/direct_runner.py in run_pipeline(self, pipeline, options)
128 runner = BundleBasedDirectRunner()
129
--> 130 return runner.run_pipeline(pipeline, options)
131
132
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_pipeline(self, pipeline, options)
553
554 self._latest_run_result = self.run_via_runner_api(
--> 555 pipeline.to_runner_api(default_environment=self._default_environment))
556 return self._latest_run_result
557
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_via_runner_api(self, pipeline_proto)
563 # TODO(pabloem, BEAM-7514): Create a watermark manager (that has access to
564 # the teststream (if any), and all the stages).
--> 565 return self.run_stages(stage_context, stages)
566
567 @contextlib.contextmanager
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_stages(self, stage_context, stages)
704 stage,
705 pcoll_buffers,
--> 706 stage_context.safe_coders)
707 metrics_by_stage[stage.name] = stage_results.process_bundle.metrics
708 monitoring_infos_by_stage[stage.name] = (
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in _run_stage(self, worker_handler_factory, pipeline_components, stage, pcoll_buffers, safe_coders)
1071 cache_token_generator=cache_token_generator)
1072
-> 1073 result, splits = bundle_manager.process_bundle(data_input, data_output)
1074
1075 def input_for(transform_id, input_id):
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)
2332
2333 with UnboundedThreadPoolExecutor() as executor:
-> 2334 for result, split_result in executor.map(execute, part_inputs):
2335
2336 split_result_list += split_result
/usr/lib/python3.6/concurrent/futures/_base.py in result_iterator()
584 # Careful not to keep a reference to the popped future
585 if timeout is None:
--> 586 yield fs.pop().result()
587 else:
588 yield fs.pop().result(end_time - time.monotonic())
/usr/lib/python3.6/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
/usr/lib/python3.6/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
/usr/local/lib/python3.6/dist-packages/apache_beam/utils/thread_pool_executor.py in run(self)
42 # If the future wasn't cancelled, then attempt to execute it.
43 try:
---> 44 self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))
45 except BaseException as exc:
46 # Even though Python 2 futures library has #set_exection(),
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in execute(part_map)
2329 self._registered,
2330 cache_token_generator=self._cache_token_generator)
-> 2331 return bundle_manager.process_bundle(part_map, expected_outputs)
2332
2333 with UnboundedThreadPoolExecutor() as executor:
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)
2243 process_bundle_descriptor_id=self._bundle_descriptor.id,
2244 cache_tokens=[next(self._cache_token_generator)]))
-> 2245 result_future = self._worker_handler.control_conn.push(process_bundle_req)
2246
2247 split_results = [] # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse]
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in push(self, request)
1557 self._uid_counter += 1
1558 request.instruction_id = 'control_%s' % self._uid_counter
-> 1559 response = self.worker.do_instruction(request)
1560 return ControlFuture(request.instruction_id, response)
1561
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/sdk_worker.py in do_instruction(self, request)
413 # E.g. if register is set, this will call self.register(request.register))
414 return getattr(self, request_type)(
--> 415 getattr(request, request_type), request.instruction_id)
416 else:
417 raise NotImplementedError
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/sdk_worker.py in process_bundle(self, request, instruction_id)
448 with self.maybe_profile(instruction_id):
449 delayed_applications, requests_finalization = (
--> 450 bundle_processor.process_bundle(instruction_id))
451 monitoring_infos = bundle_processor.monitoring_infos()
452 monitoring_infos.extend(self.state_cache_metrics_fn())
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/bundle_processor.py in process_bundle(self, instruction_id)
837 for data in data_channel.input_elements(instruction_id,
838 expected_transforms):
--> 839 input_op_by_transform_id[data.transform_id].process_encoded(data.data)
840
841 # Finish all operations.
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/bundle_processor.py in process_encoded(self, encoded_windowed_values)
214 decoded_value = self.windowed_coder_impl.decode_from_stream(
215 input_stream, True)
--> 216 self.output(decoded_value)
217
218 def try_split(self, fraction_of_remainder, total_buffer_size):
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
/usr/local/lib/python3.6/dist-packages/future/utils/__init__.py in raise_with_traceback(exc, traceback)
417 if traceback == Ellipsis:
418 _, _, traceback = sys.exc_info()
--> 419 raise exc.with_traceback(traceback)
420
421 else:
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
/usr/local/lib/python3.6/dist-packages/apache_beam/io/iobase.py in process(self, element, init_result)
1080 for e in bundle[1]: # values
1081 writer.write(e)
-> 1082 return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]
1083
1084
/usr/local/lib/python3.6/dist-packages/apache_beam/io/filebasedsink.py in close(self)
421
422 def close(self):
--> 423 self.sink.close(self.temp_handle)
424 return self.temp_shard_path
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in close(self, writer)
536 def close(self, writer):
537 if len(self._buffer[0]) > 0:
--> 538 self._flush_buffer()
539 if self._record_batches_byte_size > 0:
540 self._write_batches(writer)
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in _flush_buffer(self)
568 for x in arrays:
569 for b in x.buffers():
--> 570 size = size + b.size
571 self._record_batches_byte_size = self._record_batches_byte_size + size
AttributeError: 'NoneType' object has no attribute 'size' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
```
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MDU6SXNzdWU2MjM1OTUxODA=
| 186
|
Weird-ish: Not creating unique caches for different phases
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2020-05-23 06:40:58+00:00
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2020-05-23 20:22:18+00:00
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2020-05-23 20:22:17+00:00
|
NONE
| null | null | null | null |
Sample code:
```python
import nlp
dataset = nlp.load_dataset('boolq')
def func1(x):
return x
def func2(x):
return None
train_output = dataset["train"].map(func1)
valid_output = dataset["validation"].map(func1)
print()
print(len(train_output), len(valid_output))
# Output: 9427 9427
```
The map method in both cases seem to be pointing to the same cache, so the latter call based on the validation data will return the processed train data cache.
What's weird is that the following doesn't seem to be an issue:
```python
train_output = dataset["train"].map(func2)
valid_output = dataset["validation"].map(func2)
print()
print(len(train_output), len(valid_output))
# 9427 3270
```
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MDExOlB1bGxSZXF1ZXN0NDIxODkxNjY2
| 185
|
[Commands] In-detail instructions to create dummy data folder
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2020-05-22 12:26:25+00:00
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2020-05-22 14:06:35+00:00
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2020-05-22 14:06:34+00:00
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CONTRIBUTOR
| null | null | 0
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### Dummy data command
This PR adds a new command `python nlp-cli dummy_data <path_to_dataset_folder>` that gives in-detail instructions on how to add the dummy data files.
It would be great if you can try it out by moving the current dummy_data folder of any dataset in `./datasets` with `mv datasets/<dataset_script>/dummy_data datasets/<dataset_name>/dummy_data_copy` and running the command `python nlp-cli dummy_data ./datasets/<dataset_name>` to see if you like the instructions.
### CONTRIBUTING.md
Also the CONTRIBUTING.md is made cleaner including a new section on "How to add a dataset".
### Current PRs
It would be nice if we can try out if this command helps current PRs, *e.g.* #169 to add a dataset. I comment on those PRs.
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| 184
|
Use IndexError instead of ValueError when index out of range
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2020-05-22 10:43:42+00:00
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2020-05-28 08:31:18+00:00
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2020-05-28 08:31:18+00:00
|
CONTRIBUTOR
| null | null | 0
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**`default __iter__ needs IndexError`**.
When I want to create a wrapper of arrow dataset to adapt to fastai,
I don't know how to initialize it, so I didn't use inheritance but use object composition.
I wrote sth like this.
```
clas HF_dataset():
def __init__(self, arrow_dataset):
self.dset = arrow_dataset
def __getitem__(self, i):
return self.my_get_item(self.dset)
```
But `for sample in my_dataset:` gave me `ValueError(f"Index ({key}) outside of table length ({self._data.num_rows}).")` . This is because default `__iter__` will stop when it catched `IndexError`.
You can also see my [work](https://github.com/richardyy1188/Pretrain-MLM-and-finetune-on-GLUE-with-fastai/blob/master/GLUE_with_fastai.ipynb) that uses fastai2 to show/load batches from huggingface/nlp GLUE datasets
So I hope we can use `IndexError` instead to let other people who want to wrap it for any purpose won't be caught by this caveat.
BTW, I super appreciate your work, both transformers and nlp save my life. 💖💖💖💖💖💖💖
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| 183
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[Bug] labels of glue/ax are all -1
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2020-05-22 22:14:05+00:00
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2020-05-22 22:14:05+00:00
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CONTRIBUTOR
| null | null | null | null |
```
ax = nlp.load_dataset('glue', 'ax')
for i in range(30): print(ax['test'][i]['label'], end=', ')
```
```
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
```
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MDExOlB1bGxSZXF1ZXN0NDIxNDcxMjg4
| 182
|
Update newsroom.py
|
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2020-05-21 17:07:43+00:00
|
2020-05-22 16:38:23+00:00
|
2020-05-22 16:38:23+00:00
|
CONTRIBUTOR
| null | null | 0
|
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Updated the URL for Newsroom download so it's more robust to future changes.
|
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| 181
|
Cannot upload my own dataset
|
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2020-05-21 16:45:52+00:00
|
2020-06-18 22:14:42+00:00
|
2020-06-18 22:14:42+00:00
|
NONE
| null | null | null | null |
I look into `nlp-cli` and `user.py` to learn how to upload my own data.
It is supposed to work like this
- Register to get username, password at huggingface.co
- `nlp-cli login` and type username, passworld
- I have a single file to upload at `./ttc/ttc_freq_extra.csv`
- `nlp-cli upload ttc/ttc_freq_extra.csv`
But I got this error.
```
2020-05-21 16:33:52.722464: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
About to upload file /content/ttc/ttc_freq_extra.csv to S3 under filename ttc/ttc_freq_extra.csv and namespace korakot
Proceed? [Y/n] y
Uploading... This might take a while if files are large
Traceback (most recent call last):
File "/usr/local/bin/nlp-cli", line 33, in <module>
service.run()
File "/usr/local/lib/python3.6/dist-packages/nlp/commands/user.py", line 234, in run
token=token, filename=filename, filepath=filepath, organization=self.args.organization
File "/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py", line 141, in presign_and_upload
urls = self.presign(token, filename=filename, organization=organization)
File "/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py", line 132, in presign
return PresignedUrl(**d)
TypeError: __init__() got an unexpected keyword argument 'cdn'
```
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MDExOlB1bGxSZXF1ZXN0NDIxMzk5Nzg2
| 180
|
Add hall of fame
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2020-05-21 14:53:48+00:00
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2020-05-22 16:35:16+00:00
|
2020-05-22 16:35:14+00:00
|
MEMBER
| null | null | 0
|
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powered by https://github.com/sourcerer-io/hall-of-fame
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MDU6SXNzdWU2MjI1MjU0MTA=
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|
[Feature request] separate split name and split instructions
|
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2020-05-21 14:10:51+00:00
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2020-05-22 13:31:08+00:00
|
2020-05-22 13:31:07+00:00
|
MEMBER
| null | null | null | null |
Currently, the name of an nlp.NamedSplit is parsed in arrow_reader.py and used as the instruction.
This makes it impossible to have several training sets, which can occur when:
- A dataset corresponds to a collection of sub-datasets
- A dataset was built in stages, adding new examples at each stage
Would it be possible to have two separate fields in the Split class, a name /instruction and a unique ID that is used as the key in the builder's split_dict ?
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[Manual data] improve error message for manual data in general
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`nlp.load("xsum")` now leads to the following error message:
I guess the manual download instructions for `xsum` can also be improved.
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Xsum manual download instruction
|
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[Tests] Refactor MockDownloadManager
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Clean mock download manager class.
The print function was not of much help I think.
We should think about adding a command that creates the dummy folder structure for the user.
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| 175
|
[Manual data dir] Error message: nlp.load_dataset('xsum') -> TypeError
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2020-05-20 18:18:50+00:00
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2020-05-20 18:18:50+00:00
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v 0.1.0 from pip
```python
import nlp
xsum = nlp.load_dataset('xsum')
```
Issue is `dl_manager.manual_dir`is `None`
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-42-8a32f066f3bd> in <module>
----> 1 xsum = nlp.load_dataset('xsum')
~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
515 download_mode=download_mode,
516 ignore_verifications=ignore_verifications,
--> 517 save_infos=save_infos,
518 )
519
~/miniconda3/envs/nb/lib/python3.7/site-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
~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
397 split_dict = SplitDict(dataset_name=self.name)
398 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 399 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
400 # Checksums verification
401 if verify_infos:
~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/datasets/xsum/5c5fca23aaaa469b7a1c6f095cf12f90d7ab99bcc0d86f689a74fd62634a1472/xsum.py in _split_generators(self, dl_manager)
102 with open(dl_path, "r") as json_file:
103 split_ids = json.load(json_file)
--> 104 downloaded_path = os.path.join(dl_manager.manual_dir, "xsum-extracts-from-downloads")
105 return [
106 nlp.SplitGenerator(
~/miniconda3/envs/nb/lib/python3.7/posixpath.py in join(a, *p)
78 will be discarded. An empty last part will result in a path that
79 ends with a separator."""
---> 80 a = os.fspath(a)
81 sep = _get_sep(a)
82 path = a
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
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nlp.load_dataset('xsum') -> TypeError
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MDExOlB1bGxSZXF1ZXN0NDIwNzUyNzQy
| 173
|
Rm extracted test dirs
|
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2020-05-20 13:30:48+00:00
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2020-05-22 16:34:36+00:00
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2020-05-22 16:34:35+00:00
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MEMBER
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All the dummy data used for tests were duplicated. For each dataset, we had one zip file but also its extracted directory. I removed all these directories
Furthermore instead of extracting next to the dummy_data.zip file, we extract in the temp `cached_dir` used for tests, so that all the extracted directories get removed after testing.
Finally there was a bug in the `mock_download_manager` that would let it create directories with invalid names, as in #172. I fixed that by encoding url arguments. I had to rename the dummy data for `scientific_papers` and `cnn_dailymail` (the aws tests don't pass for those 2 in this PR, but they will once aws will be synced, as the local ones do)
Let me know if it sounds good to you @patrickvonplaten . I'm still not entirely familiar with the mock downloader
|
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MDU6SXNzdWU2MjEzNzczODY=
| 172
|
Clone not working on Windows environment
|
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2020-05-20 00:45:14+00:00
|
2020-05-23 12:49:13+00:00
|
2020-05-23 11:27:52+00:00
|
CONTRIBUTOR
| null | null | null | null |
Cloning in a windows environment is not working because of use of special character '?' in folder name ..
Please consider changing the folder name ....
Reference to folder -
nlp/datasets/cnn_dailymail/dummy/3.0.0/3.0.0/dummy_data-zip-extracted/dummy_data/uc?export=download&id=0BwmD_VLjROrfM1BxdkxVaTY2bWs/dailymail/stories/
error log:
fatal: cannot create directory at 'datasets/cnn_dailymail/dummy/3.0.0/3.0.0/dummy_data-zip-extracted/dummy_data/uc?export=download&id=0BwmD_VLjROrfM1BxdkxVaTY2bWs': Invalid argument
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MDExOlB1bGxSZXF1ZXN0NDIwMjk0ODM0
| 171
|
fix squad metric format
|
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2020-05-19 18:37:36+00:00
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2020-05-22 13:36:50+00:00
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2020-05-22 13:36:48+00:00
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The format of the squad metric was wrong.
This should fix #143
I tested with
```python3
predictions = [
{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}
]
references = [
{'answers': [{'text': 'Denver Broncos'}], 'id': '56be4db0acb8001400a502ec'}
]
```
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MDExOlB1bGxSZXF1ZXN0NDIwMjMwMDIx
| 170
|
Rename anli dataset
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2020-05-19 16:26:57+00:00
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2020-05-20 12:23:09+00:00
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2020-05-20 12:23:08+00:00
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What we have now as the `anli` dataset is actually the αNLI dataset from the ART challenge dataset. This name is confusing because `anli` is also the name of adversarial NLI (see [https://github.com/facebookresearch/anli](https://github.com/facebookresearch/anli)).
I renamed the current `anli` dataset by `art`.
|
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| 169
|
Adding Qanta (Quizbowl) Dataset
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2020-05-19 16:03:01+00:00
|
2020-05-26 12:52:31+00:00
|
2020-05-26 12:52:31+00:00
|
CONTRIBUTOR
| null | null | 0
|
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This PR adds the qanta question answering datasets from [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) and [Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples](https://www.aclweb.org/anthology/Q19-1029/) (adversarial fold)
This partially continues a discussion around fixing dummy data from https://github.com/huggingface/nlp/issues/161
I ran the following code to double check that it works and did some sanity checks on the output. The majority of the code itself is from our `allennlp` version of the dataset reader.
```python
import nlp
# Default is full question
data = nlp.load_dataset('./datasets/qanta')
# Four configs
# Primarily useful for training
data = nlp.load_dataset('./datasets/qanta', 'mode=sentences,char_skip=25')
# Primarily used in evaluation
data = nlp.load_dataset('./datasets/qanta', 'mode=first,char_skip=25')
data = nlp.load_dataset('./datasets/qanta', 'mode=full,char_skip=25')
# Primarily useful in evaluation and "live" play
data = nlp.load_dataset('./datasets/qanta', 'mode=runs,char_skip=25')
```
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| 620,959,819
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MDU6SXNzdWU2MjA5NTk4MTk=
| 168
|
Loading 'wikitext' dataset fails
|
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2020-05-19 13:04:29+00:00
|
2020-05-26 21:46:52+00:00
|
2020-05-26 21:46:52+00:00
|
NONE
| null | null | null | null |
Loading the 'wikitext' dataset fails with Attribute error:
Code to reproduce (From example notebook):
import nlp
wikitext_dataset = nlp.load_dataset('wikitext')
Error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-17-d5d9df94b13c> in <module>()
11
12 # Load a dataset and print the first examples in the training set
---> 13 wikitext_dataset = nlp.load_dataset('wikitext')
14 print(wikitext_dataset['train'][0])
6 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)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
/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)
363 verify_infos = not save_infos and not ignore_verifications
364 self._download_and_prepare(
--> 365 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
366 )
367 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
416 try:
417 # Prepare split will record examples associated to the split
--> 418 self._prepare_split(split_generator, **prepare_split_kwargs)
419 except OSError:
420 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)
594 example = self.info.features.encode_example(record)
595 writer.write(example)
--> 596 num_examples, num_bytes = writer.finalize()
597
598 assert num_examples == num_examples, f"Expected to write {split_info.num_examples} but wrote {num_examples}"
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in finalize(self, close_stream)
173 def finalize(self, close_stream=True):
174 if self.pa_writer is not None:
--> 175 self.write_on_file()
176 self.pa_writer.close()
177 if close_stream:
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_on_file(self)
124 else:
125 # All good
--> 126 self._write_array_on_file(pa_array)
127 self.current_rows = []
128
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in _write_array_on_file(self, pa_array)
93 def _write_array_on_file(self, pa_array):
94 """Write a PyArrow Array"""
---> 95 pa_batch = pa.RecordBatch.from_struct_array(pa_array)
96 self._num_bytes += pa_array.nbytes
97 self.pa_writer.write_batch(pa_batch)
AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
|
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MDExOlB1bGxSZXF1ZXN0NDIwMDY0NDMw
| 167
|
[Tests] refactor tests
|
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2020-05-19 11:43:32+00:00
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2020-05-19 16:17:12+00:00
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2020-05-19 16:17:10+00:00
|
CONTRIBUTOR
| null | null | 0
|
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This PR separates AWS and Local tests to remove these ugly statements in the script:
```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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| 166
|
Add a method to shuffle a dataset
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2020-05-19 10:08:46+00:00
|
2020-06-23 15:07:33+00:00
|
2020-06-23 15:07:32+00:00
|
MEMBER
| null | null | null | null |
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=
| 165
|
ANLI
|
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|
2020-05-19 07:50:57+00:00
|
2020-05-20 12:23:07+00:00
|
2020-05-20 12:23:07+00:00
|
NONE
| null | null | null | null |
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=
| 164
|
Add Spanish POR and NER Datasets
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2020-05-18 22:18:21+00:00
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2020-05-25 16:28:45+00:00
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2020-05-25 16:28:45+00:00
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CONTRIBUTOR
| null | null | null | null |
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=
| 163
|
[Feature request] Add cos-e v1.0
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2020-05-18 22:05:26+00:00
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2020-06-16 23:15:25+00:00
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2020-06-16 18:52:06+00:00
|
NONE
| null | null | null | null |
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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|
fix prev files hash in map
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2020-05-18 21:20:51+00:00
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2020-05-18 21:36:21+00:00
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|
MEMBER
| null | null | 0
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Fix the `.map` issue in #160.
This makes sure it takes the previous files when computing the hash.
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Discussion on version identifier & MockDataLoaderManager for test data
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2020-05-18 20:31:30+00:00
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2020-05-24 18:10:03+00:00
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NaT
|
CONTRIBUTOR
| null | null | null | null |
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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caching in map causes same result to be returned for train, validation and test
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|
2020-05-18 19:22:03+00:00
|
2020-05-18 21:36:20+00:00
|
2020-05-18 21:36:20+00:00
|
NONE
| null | null | null | null |
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
|
MDU6SXNzdWU2MjA0MjA3MDA=
| 159
|
How can we add more datasets to nlp library?
|
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|
2020-05-18 18:35:31+00:00
|
2020-05-18 18:37:08+00:00
|
2020-05-18 18:37:07+00:00
|
NONE
| null | null | null | null |
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| 620,396,658
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MDExOlB1bGxSZXF1ZXN0NDE5NjUyNTQy
| 158
|
add Toronto Books Corpus
|
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|
2020-05-18 17:54:45+00:00
|
2020-06-11 07:49:15+00:00
|
2020-05-19 07:34:56+00:00
|
CONTRIBUTOR
| null | null | 0
|
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|
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
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MDU6SXNzdWU2MjAzNTY1NDI=
| 157
|
nlp.load_dataset() gives "TypeError: list_() takes exactly one argument (2 given)"
|
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|
2020-05-18 16:46:38+00:00
|
2020-06-05 08:08:58+00:00
|
2020-06-05 08:08:58+00:00
|
NONE
| null | null | null | null |
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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MDU6SXNzdWU2MjAyNjM2ODc=
| 156
|
SyntaxError with WMT datasets
|
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2020-05-18 14:38:18+00:00
|
2020-07-23 16:41:55+00:00
|
2020-07-23 16:41:55+00:00
|
NONE
| null | null | null | null |
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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Include more links in README, fix typos
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CONTRIBUTOR
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|
add Ubuntu Dialogs Corpus datasets
|
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CONTRIBUTOR
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This PR adds the Ubuntu Dialog Corpus datasets version 2.0.
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Meta-datasets (GLUE/XTREME/...) – Special care to attributions and citations
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open
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| null |
[] | null | 4
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2020-05-18 07:24:22+00:00
|
2020-05-18 21:18:16+00:00
|
NaT
|
MEMBER
| null | null | null | null |
Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation/specific homepage/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.
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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|
Add GLUE config name check
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2020-05-18 07:23:43+00:00
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2020-05-27 22:09:12+00:00
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2020-05-27 22:09:12+00:00
|
CONTRIBUTOR
| null | null | 0
|
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Fixes #130 by adding a name check to the Glue class
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MDExOlB1bGxSZXF1ZXN0NDE5MzA2MTYz
| 151
|
Fix JSON tests.
|
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2020-05-18 07:17:38+00:00
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2020-05-18 07:21:52+00:00
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2020-05-18 07:21:51+00:00
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MDExOlB1bGxSZXF1ZXN0NDE5MTgyODU4
| 150
|
Add WNUT 17 NER dataset
|
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2020-05-17 22:19:04+00:00
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2020-05-26 20:37:59+00:00
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2020-05-26 20:37:59+00:00
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CONTRIBUTOR
| null | null | 0
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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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MDU6SXNzdWU2MTk3MzU3Mzk=
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|
[Feature request] Add Ubuntu Dialogue Corpus dataset
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2020-05-17 15:42:39+00:00
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2020-05-18 17:01:46+00:00
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2020-05-18 17:01:46+00:00
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NONE
| null | null | null | null |
https://github.com/rkadlec/ubuntu-ranking-dataset-creator or http://dataset.cs.mcgill.ca/ubuntu-corpus-1.0/
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_download_and_prepare() got an unexpected keyword argument 'verify_infos'
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2020-05-17 01:48:53+00:00
|
2020-05-18 07:38:33+00:00
|
2020-05-18 07:38:33+00:00
|
CONTRIBUTOR
| null | null | null | null |
# Reproduce
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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|
Error with sklearn train_test_split
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2020-05-17 00:28:24+00:00
|
2020-06-18 16:23:23+00:00
|
2020-06-18 16:23:23+00:00
|
NONE
| null | null | null | null |
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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https://github.com/huggingface/datasets/pull/146
| 619,564,653
|
MDExOlB1bGxSZXF1ZXN0NDE5MDI5MjUx
| 146
|
Add BERTScore to metrics
|
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2020-05-16 22:09:39+00:00
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2020-05-17 22:22:10+00:00
|
2020-05-17 22:22:09+00:00
|
CONTRIBUTOR
| null | null | 0
|
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This PR adds [BERTScore](https://arxiv.org/abs/1904.09675) to metrics.
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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