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https://api.github.com/repos/huggingface/datasets/issues/186
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| 623,595,180
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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
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NONE
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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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| 623,172,484
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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
|
2020-05-22 14:06:34+00:00
|
CONTRIBUTOR
| null | null | null | null |
### 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
|
2020-05-28 08:31:18+00:00
|
2020-05-28 08:31:18+00:00
|
CONTRIBUTOR
| null | null | null | null |
**`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. πππππππ
|
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[Bug] labels of glue/ax are all -1
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2020-05-22 08:43:36+00:00
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2020-05-22 22:14:05+00:00
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2020-05-22 22:14:05+00:00
|
CONTRIBUTOR
| null | null |
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```
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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|
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 | null | null |
Updated the URL for Newsroom download so it's more robust to future changes.
|
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| 622,634,420
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MDU6SXNzdWU2MjI2MzQ0MjA=
| 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
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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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| 622,556,861
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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
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2020-05-22 16:35:14+00:00
|
MEMBER
| null | null | null | null |
powered by https://github.com/sourcerer-io/hall-of-fame
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| 622,525,410
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MDU6SXNzdWU2MjI1MjU0MTA=
| 179
|
[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
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2020-05-22 13:31:07+00:00
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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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MDExOlB1bGxSZXF1ZXN0NDIwOTMyMDI5
| 178
|
[Manual data] improve error message for manual data in general
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2020-05-20 18:10:45+00:00
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2020-05-20 18:18:52+00:00
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2020-05-20 18:18:50+00:00
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CONTRIBUTOR
| null | null | null | null |
`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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CONTRIBUTOR
| null | null | null | null |
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[Tests] Refactor MockDownloadManager
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2020-05-20 17:07:36+00:00
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2020-05-20 18:17:19+00:00
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2020-05-20 18:17:18+00:00
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CONTRIBUTOR
| null | null | null | null |
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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|
[Manual data dir] Error message: nlp.load_dataset('xsum') -> TypeError
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2020-05-20 17:00:32+00:00
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2020-05-20 18:18:50+00:00
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CONTRIBUTOR
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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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Rm extracted test dirs
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2020-05-20 13:30:48+00:00
|
2020-05-22 16:34:36+00:00
|
2020-05-22 16:34:35+00:00
|
MEMBER
| null | null | null | null |
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 |
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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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| 621,199,128
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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
|
2020-05-22 13:36:48+00:00
|
MEMBER
| null | null | null | null |
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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| 621,119,747
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MDExOlB1bGxSZXF1ZXN0NDIwMjMwMDIx
| 170
|
Rename anli dataset
|
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2020-05-19 16:26:57+00:00
|
2020-05-20 12:23:09+00:00
|
2020-05-20 12:23:08+00:00
|
MEMBER
| null | null | null | null |
What we have now as the `anli` dataset is actually the Ξ±NLI 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 | null | null |
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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MDU6SXNzdWU2MjA5NTk4MTk=
| 168
|
Loading 'wikitext' dataset fails
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[] | null | 6
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2020-05-19 13:04:29+00:00
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2020-05-26 21:46:52+00:00
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2020-05-26 21:46:52+00:00
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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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| 167
|
[Tests] refactor tests
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[] | null | 1
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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 | null | null |
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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MDU6SXNzdWU2MjA4NTAyMTg=
| 166
|
Add a method to shuffle a dataset
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[] | null | 4
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2020-05-19 10:08:46+00:00
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2020-06-23 15:07:33+00:00
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2020-06-23 15:07:32+00:00
|
MEMBER
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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
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2020-05-20 12:23:07+00:00
|
2020-05-20 12:23:07+00:00
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NONE
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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 Ξ±NLI, 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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| 620,540,250
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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
|
2020-05-25 16:28:45+00:00
|
CONTRIBUTOR
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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
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NONE
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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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MDExOlB1bGxSZXF1ZXN0NDE5NzQ4Mzky
| 162
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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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2020-05-18 21:36:20+00:00
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MEMBER
| null | null | null | null |
Fix the `.map` issue in #160.
This makes sure it takes the previous files when computing the hash.
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MDU6SXNzdWU2MjA0ODc1MzU=
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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
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CONTRIBUTOR
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Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp/utils/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.
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MDU6SXNzdWU2MjA0NDgyMzY=
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caching in map causes same result to be returned for train, validation and test
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] | null | 7
|
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 |
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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%|ββββββββββ| 68/68 [00:00<00:00, 92.78it/s]
100%|ββββββββββ| 1/1 [00:00<00:00, 75.47it/s]
0%| | 0/2 [00:00<?, ?it/s]TS 67349
VS 872
ES 1821
100%|ββββββββββ| 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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[] | null | 1
|
2020-05-18 18:35:31+00:00
|
2020-05-18 18:37:08+00:00
|
2020-05-18 18:37:07+00:00
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NONE
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https://github.com/huggingface/datasets/pull/158
| 620,396,658
|
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 | null | null |
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
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|
I'm trying to load datasets from nlp but there seems to have error saying
"TypeError: list_() takes exactly one argument (2 given)"
gist can be found here
https://gist.github.com/saahiluppal/c4b878f330b10b9ab9762bc0776c0a6a
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| 620,263,687
|
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
|
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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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MDExOlB1bGxSZXF1ZXN0NDE5Mzg1ODM0
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|
Include more links in README, fix typos
|
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2020-05-18 09:47:08+00:00
|
2020-05-28 08:31:57+00:00
|
2020-05-28 08:31:57+00:00
|
CONTRIBUTOR
| null | null | null | null |
Include more links and fix typos in README
|
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MDExOlB1bGxSZXF1ZXN0NDE5Mzc4Mzgw
| 154
|
add Ubuntu Dialogs Corpus datasets
|
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2020-05-18 09:34:48+00:00
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2020-05-18 10:12:28+00:00
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2020-05-18 10:12:27+00:00
|
CONTRIBUTOR
| null | null | null | null |
This PR adds the Ubuntu Dialog Corpus datasets version 2.0.
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| 619,972,246
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MDU6SXNzdWU2MTk5NzIyNDY=
| 153
|
Meta-datasets (GLUE/XTREME/...) β Special care to attributions and citations
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open
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| null |
[] | null | 4
|
2020-05-18 07:24:22+00:00
|
2020-05-18 21:18:16+00:00
|
NaT
|
MEMBER
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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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MDExOlB1bGxSZXF1ZXN0NDE5MzA4OTE2
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|
Add GLUE config name check
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2020-05-18 07:23:43+00:00
|
2020-05-27 22:09:12+00:00
|
2020-05-27 22:09:12+00:00
|
CONTRIBUTOR
| null | null | null | null |
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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CONTRIBUTOR
| null | null | null | null |
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MDExOlB1bGxSZXF1ZXN0NDE5MTgyODU4
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|
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
|
CONTRIBUTOR
| null | null | null | null |
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 βso.. kktny in 30 mins?β - 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=
| 149
|
[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
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https://github.com/rkadlec/ubuntu-ranking-dataset-creator or http://dataset.cs.mcgill.ca/ubuntu-corpus-1.0/
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MDU6SXNzdWU2MTk1OTA1NTU=
| 148
|
_download_and_prepare() got an unexpected keyword argument 'verify_infos'
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2020-05-17 01:48:53+00:00
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2020-05-18 07:38:33+00:00
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2020-05-18 07:38:33+00:00
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# 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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MDU6SXNzdWU2MTk1ODE5MDc=
| 147
|
Error with sklearn train_test_split
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2020-06-18 16:23:23+00:00
|
2020-06-18 16:23:23+00:00
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NONE
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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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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 | null | null |
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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|
[AWS Tests] Follow-up PR from #144
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2020-05-16 13:53:46+00:00
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2020-05-16 13:54:23+00:00
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2020-05-16 13:54:22+00:00
|
CONTRIBUTOR
| null | null | null | null |
I forgot to add this line in PR #145 .
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|
[AWS tests] AWS test should not run for canonical datasets
|
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2020-05-16 13:39:30+00:00
|
2020-05-16 13:44:34+00:00
|
2020-05-16 13:44:33+00:00
|
CONTRIBUTOR
| null | null | null | null |
AWS tests should in general not run for canonical datasets. Only local tests will run in this case. This way a PR is able to pass when adding a new dataset.
This PR changes to logic to the following:
1) All datasets that are present in `nlp/datasets` are tested only locally. This way when one adds a canonical dataset, the PR includes his dataset in the tests.
2) All datasets that are only present on AWS, such as `webis/tl_dr` atm are tested only on AWS.
I think the testing structure might need a bigger refactoring and better documentation very soon.
Merging for now to unblock new PRs @thomwolf @mariamabarham .
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| 143
|
ArrowTypeError in squad metrics
|
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"default": false,
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2020-05-16 12:06:37+00:00
|
2020-05-22 13:38:52+00:00
|
2020-05-22 13:36:48+00:00
|
CONTRIBUTOR
| null | null |
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|
`squad_metric.compute` is giving following error
```
ArrowTypeError: Could not convert [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}] with type list: was not a dict, tuple, or recognized null value for conversion to struct type
```
This is how my predictions and references look like
```
predictions[0]
# {'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}
```
```
references[0]
# {'answers': [{'text': 'Denver Broncos'},
{'text': 'Denver Broncos'},
{'text': 'Denver Broncos'}],
'id': '56be4db0acb8001400a502ec'}
```
These are structured as per the `squad_metric.compute` help string.
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MDExOlB1bGxSZXF1ZXN0NDE4OTU0OTc1
| 142
|
[WMT] Add all wmt
|
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|
2020-05-16 11:28:46+00:00
|
2020-05-17 12:18:21+00:00
|
2020-05-17 12:18:20+00:00
|
CONTRIBUTOR
| null | null | null | null |
This PR adds all wmt datasets scripts. At the moment the script is **not** functional for the language pairs "cs-en", "ru-en", "hi-en" because apparently it takes up to a week to get the manual data for these datasets: see http://ufal.mff.cuni.cz/czeng.
The datasets are fully functional though for the "big" language pairs "de-en" and "fr-en".
Overall I think the scripts are very messy and might need a big refactoring at some point.
For now I think there are good to merge (most dataset configs can be used). I will add "cs", "ru" and "hi" when the manual data is available.
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MDExOlB1bGxSZXF1ZXN0NDE4OTUzMzQw
| 141
|
[Clean up] remove bogus folder
|
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2020-05-16 11:13:42+00:00
|
2020-05-16 13:24:27+00:00
|
2020-05-16 13:24:26+00:00
|
CONTRIBUTOR
| null | null | null | null |
@mariamabarham - I think you accidentally placed it there.
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MDExOlB1bGxSZXF1ZXN0NDE4OTUxMzg4
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|
[Tests] run local tests as default
|
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2020-05-16 10:56:06+00:00
|
2020-05-16 13:21:44+00:00
|
2020-05-16 13:21:43+00:00
|
CONTRIBUTOR
| null | null | null | null |
This PR also enables local tests by default
I think it's safer for now to enable both local and aws tests for every commit. The problem currently is that when we do a PR to add a dataset, the dataset is not yet on AWS on therefore not tested on the PR itself. Thus the PR will always be green even if the datasets are not correct. This PR aims at fixing this.
## Suggestion on how to commit to the repo from now on:
Now since the repo is "online", I think we should adopt a couple of best practices:
1) - No direct committing to the repo anymore. Every change should be opened in a PR and be well documented so that we can find it later
2) - Every PR has to be reviewed by at least x people (I guess @thomwolf you should decide here) because we now have to be much more careful when doing changes to the API for backward compatibility, etc...
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| 139
|
Add GermEval 2014 NER dataset
|
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2020-05-15 23:42:09+00:00
|
2020-05-16 13:56:37+00:00
|
2020-05-16 13:56:22+00:00
|
CONTRIBUTOR
| null | null | null | null |
Hi,
this PR adds the GermEval 2014 NER dataset π
> The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation [1] with the following properties:
> - The data was sampled from German Wikipedia and News Corpora as a collection of citations.
> - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens.
> - The NER annotation uses the NoSta-D guidelines, which extend the TΓΌbingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]].
Dataset will be downloaded from the [official GermEval 2014 website](https://sites.google.com/site/germeval2014ner/data).
## Dataset format
Here's an example of the dataset format from the original dataset:
```tsv
# http://de.wikipedia.org/wiki/Manfred_Korfmann [2009-10-17]
1 Aufgrund O O
2 seiner O O
3 Initiative O O
4 fand O O
5 2001/2002 O O
6 in O O
7 Stuttgart B-LOC O
8 , O O
9 Braunschweig B-LOC O
10 und O O
11 Bonn B-LOC O
12 eine O O
13 groΓe O O
14 und O O
15 publizistisch O O
16 vielbeachtete O O
17 Troia-Ausstellung B-LOCpart O
18 statt O O
19 , O O
20 β O O
21 Troia B-OTH B-LOC
22 - I-OTH O
23 Traum I-OTH O
24 und I-OTH O
25 Wirklichkeit I-OTH O
26 β O O
27 . O O
```
The sentence is encoded as one token per line (tab separated columns.
The first column contains either a `#`, which signals the source the sentence is cited from and the date it was retrieved, or the token number within the sentence.
The second column contains the token.
Column three and four contain the named entity (in IOB2 scheme).
Outer spans are encoded in the third column, embedded/nested spans in the fourth column.
## Features
I decided to keep most information from the dataset. That means the so called "source" information (where the sentences come from + date information) is also returned for each sentence in the feature vector.
For each sentence in the dataset, one feature vector (`nlp.Features` definition) will be returned:
| Feature | Example | Description
| ---- | ---- | -----------------
| `id` | `0` | Number (id) of current sentence
| `source` | `http://de.wikipedia.org/wiki/Manfred_Korfmann [2009-10-17]` | URL and retrieval date as string
| `tokens` | `["Schwartau", "sagte", ":"]` | List of tokens (strings) for a sentence
| `labels` | `["B-PER", "O", "O"]` | List of labels (outer span)
| `nested-labels` | `["O", "O", "O"]` | List of labels for nested span
## Example
The following command downloads the dataset from the official GermEval 2014 page and pre-processed it:
```bash
python nlp-cli test datasets/germeval_14 --all_configs
```
It then outputs the number for training, development and testset. The training set consists of 24,000 sentences, the development set of 2,200 and the test of 5,100 sentences.
Now it can be imported and used with `nlp`:
```python
import nlp
germeval = nlp.load_dataset("./datasets/germeval_14/germeval_14.py")
assert len(germeval["train"]) == 24000
# Show first sentence of training set:
germeval["train"][0]
```
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MDU6SXNzdWU2MTkyMjUxOTE=
| 138
|
Consider renaming to nld
|
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2020-05-15 20:23:27+00:00
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2022-09-16 05:18:22+00:00
|
2020-09-28 00:08:10+00:00
|
NONE
| null | null |
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Hey :)
Just making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.
The issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.
If you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.
If people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.
I don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.
I suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.
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MDExOlB1bGxSZXF1ZXN0NDE4NzgxNzI4
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|
Update README.md
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2020-05-15 20:01:07+00:00
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2020-05-17 12:17:28+00:00
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2020-05-17 12:17:28+00:00
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NONE
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small typo
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|
Fix print statement in READ.md
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2020-05-15 19:52:23+00:00
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2020-05-17 12:14:06+00:00
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2020-05-17 12:14:05+00:00
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CONTRIBUTOR
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print statement was throwing generator object instead of printing names of available datasets/metrics
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|
Update README.md
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2020-05-15 16:56:14+00:00
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2020-05-28 08:21:49+00:00
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2020-05-28 08:21:49+00:00
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NONE
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| 133
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[Question] Using/adding a local dataset
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2020-05-15 16:26:06+00:00
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2020-07-23 16:44:09+00:00
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2020-07-23 16:44:09+00:00
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NONE
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Users may want to either create/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.
It appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.
A notebook/example script demonstrating this would be very helpful.
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[Feature Request] Add the OpenWebText dataset
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2020-05-15 15:57:29+00:00
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2020-10-07 14:22:48+00:00
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2020-10-07 14:22:48+00:00
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MEMBER
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The OpenWebText dataset is an open clone of OpenAI's WebText dataset. It can be used to train ELECTRA as is specified in the [README](https://www.github.com/google-research/electra).
More information and the download link are available [here](https://skylion007.github.io/OpenWebTextCorpus/).
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MDU6SXNzdWU2MTkwNzM3MzE=
| 131
|
[Feature request] Add Toronto BookCorpus dataset
|
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2020-05-15 15:50:44+00:00
|
2020-06-28 21:27:31+00:00
|
2020-06-28 21:27:31+00:00
|
CONTRIBUTOR
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I know the copyright/distribution of this one is complex, but it would be great to have! That, combined with the existing `wikitext`, would provide a complete dataset for pretraining models like BERT.
|
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| 619,035,440
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MDU6SXNzdWU2MTkwMzU0NDA=
| 130
|
Loading GLUE dataset loads CoLA by default
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2020-05-15 14:55:50+00:00
|
2020-05-27 22:08:15+00:00
|
2020-05-27 22:08:15+00:00
|
NONE
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If I run:
```python
dataset = nlp.load_dataset('glue')
```
The resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:
```python
metric = nlp.load_metric("glue")
```
which throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets?
|
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MDU6SXNzdWU2MTg5OTc3MjU=
| 129
|
[Feature request] Add Google Natural Question dataset
|
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2020-05-15 14:14:20+00:00
|
2020-07-23 13:21:29+00:00
|
2020-07-23 13:21:29+00:00
|
NONE
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Would be great to have https://github.com/google-research-datasets/natural-questions as an alternative to SQuAD.
|
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MDU6SXNzdWU2MTg5NTExMTc=
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|
Some error inside nlp.load_dataset()
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2020-05-15 13:01:29+00:00
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2020-05-15 13:10:40+00:00
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2020-05-15 13:10:40+00:00
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First of all, nice work!
I am going through [this overview notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb)
In simple step `dataset = nlp.load_dataset('squad', split='validation[:10%]')`
I get an error, which is connected with some inner code, I think:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-d848d3a99b8c> in <module>()
1 # Downloading and loading a dataset
2
----> 3 dataset = nlp.load_dataset('squad', split='validation[:10%]')
8 frames
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
515 download_mode=download_mode,
516 ignore_verifications=ignore_verifications,
--> 517 save_infos=save_infos,
518 )
519
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)
361 verify_infos = not save_infos and not ignore_verifications
362 self._download_and_prepare(
--> 363 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
364 )
365 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
414 try:
415 # Prepare split will record examples associated to the split
--> 416 self._prepare_split(split_generator, **prepare_split_kwargs)
417 except OSError:
418 raise OSError("Cannot find data file. " + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or ""))
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _prepare_split(self, split_generator)
585 fname = "{}-{}.arrow".format(self.name, split_generator.name)
586 fpath = os.path.join(self._cache_dir, fname)
--> 587 examples_type = self.info.features.type
588 writer = ArrowWriter(data_type=examples_type, path=fpath, writer_batch_size=self._writer_batch_size)
589
/usr/local/lib/python3.6/dist-packages/nlp/features.py in type(self)
460 @property
461 def type(self):
--> 462 return get_nested_type(self)
463
464 @classmethod
/usr/local/lib/python3.6/dist-packages/nlp/features.py in get_nested_type(schema)
370 # Nested structures: we allow dict, list/tuples, sequences
371 if isinstance(schema, dict):
--> 372 return pa.struct({key: get_nested_type(value) for key, value in schema.items()})
373 elif isinstance(schema, (list, tuple)):
374 assert len(schema) == 1, "We defining list feature, you should just provide one example of the inner type"
/usr/local/lib/python3.6/dist-packages/nlp/features.py in <dictcomp>(.0)
370 # Nested structures: we allow dict, list/tuples, sequences
371 if isinstance(schema, dict):
--> 372 return pa.struct({key: get_nested_type(value) for key, value in schema.items()})
373 elif isinstance(schema, (list, tuple)):
374 assert len(schema) == 1, "We defining list feature, you should just provide one example of the inner type"
/usr/local/lib/python3.6/dist-packages/nlp/features.py in get_nested_type(schema)
379 # We allow to reverse list of dict => dict of list for compatiblity with tfds
380 if isinstance(inner_type, pa.StructType):
--> 381 return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))
382 return pa.list_(inner_type, schema.length)
383
/usr/local/lib/python3.6/dist-packages/nlp/features.py in <genexpr>(.0)
379 # We allow to reverse list of dict => dict of list for compatiblity with tfds
380 if isinstance(inner_type, pa.StructType):
--> 381 return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))
382 return pa.list_(inner_type, schema.length)
383
TypeError: list_() takes exactly one argument (2 given)
```
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MDExOlB1bGxSZXF1ZXN0NDE4NTQ1MDcy
| 127
|
Update Overview.ipynb
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2020-05-15 11:46:48+00:00
|
2020-05-15 11:47:27+00:00
|
2020-05-15 11:47:25+00:00
|
CONTRIBUTOR
| null | null | null | null |
update notebook
|
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| 618,897,499
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MDExOlB1bGxSZXF1ZXN0NDE4NTM1Mzc5
| 126
|
remove webis
|
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2020-05-15 11:25:20+00:00
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2020-05-15 11:31:24+00:00
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2020-05-15 11:30:26+00:00
|
CONTRIBUTOR
| null | null | null | null |
Remove webis from dataset folder.
Our first dataset script that only lives on AWS :-) https://s3.console.aws.amazon.com/s3/buckets/datasets.huggingface.co/nlp/datasets/webis/tl_dr/?region=us-east-1 @julien-c @jplu
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|
[Newsroom] add newsroom
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2020-05-15 10:34:34+00:00
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2020-05-15 10:37:07+00:00
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2020-05-15 10:37:02+00:00
|
CONTRIBUTOR
| null | null | null | null |
I checked it with the data link of the mail you forwarded @thomwolf => works well!
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MDExOlB1bGxSZXF1ZXN0NDE4NTA3NDUx
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|
Xsum, require manual download of some files
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2020-05-15 10:26:13+00:00
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2020-05-15 11:04:48+00:00
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2020-05-15 11:04:46+00:00
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CONTRIBUTOR
| null | null | null | null |
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MDExOlB1bGxSZXF1ZXN0NDE4NDcxODU5
| 123
|
[Tests] Local => aws
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2020-05-15 09:12:25+00:00
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2020-05-15 10:06:12+00:00
|
2020-05-15 10:03:26+00:00
|
CONTRIBUTOR
| null | null | null | null |
## Change default Test from local => aws
As a default we set` aws=True`, `Local=False`, `slow=False`
### 1. RUN_AWS=1 (default)
This runs 4 tests per dataset script.
a) Does the dataset script have a valid etag / Can it be reached on AWS?
b) Can we load its `builder_class`?
c) Can we load **all** dataset configs?
d) _Most importantly_: Can we load the dataset?
Important - we currently only test the first config of each dataset to reduce test time. Total test time is around 1min20s.
### 2. RUN_LOCAL=1 RUN_AWS=0
***This should be done when debugging dataset scripts of the ./datasets folder***
This only runs 1 test per dataset test, which is equivalent to aws d) - Can we load the dataset from the local `datasets` directory?
### 3. RUN_SLOW=1
We should set up to run these tests maybe 1 time per week ? @thomwolf
The `slow` tests include two more important tests.
e) Can we load the dataset with all possible configs? This test will probably fail at the moment because a lot of dummy data is missing. We should add the dummy data step by step to be sure that all configs work.
f) Test that the actual dataset can be loaded. This will take quite some time to run, but is important to make sure that the "real" data can be loaded. It will also test whether the dataset script has the correct checksums file which is currently not tested with `aws=True`. @lhoestq - is there an easy way to check cheaply whether the `dataset_info.json` is correct for each dataset script?
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MDExOlB1bGxSZXF1ZXN0NDE4NDY2Mzc3
| 122
|
Final cleanup of readme and metrics
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2020-05-15 09:00:52+00:00
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2021-09-03 19:40:09+00:00
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2020-05-15 09:02:22+00:00
|
MEMBER
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| 618,790,040
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| 121
|
make style
|
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2020-05-15 08:23:36+00:00
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2020-05-15 08:25:39+00:00
|
2020-05-15 08:25:38+00:00
|
CONTRIBUTOR
| null | null | null | null |
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| 120
|
π `map` not working
|
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2020-05-15 06:43:08+00:00
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2020-05-15 07:02:38+00:00
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2020-05-15 07:02:38+00:00
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NONE
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I'm trying to run a basic example (mapping function to add a prefix).
[Here is the colab notebook I'm using.](https://colab.research.google.com/drive/1YH4JCAy0R1MMSc-k_Vlik_s1LEzP_t1h?usp=sharing)
```python
import nlp
dataset = nlp.load_dataset('squad', split='validation[:10%]')
def test(sample):
sample['title'] = "test prefix @@@ " + sample["title"]
return sample
print(dataset[0]['title'])
dataset.map(test)
print(dataset[0]['title'])
```
Output :
> Super_Bowl_50
Super_Bowl_50
Expected output :
> Super_Bowl_50
test prefix @@@ Super_Bowl_50
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| 119
|
π Colab : type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
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2020-05-15 02:27:26+00:00
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2020-05-15 05:11:22+00:00
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2020-05-15 02:45:28+00:00
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NONE
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I'm trying to load CNN/DM dataset on Colab.
[Colab notebook](https://colab.research.google.com/drive/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)
But I meet this error :
> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
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| 118
|
β How to apply a map to all subsets ?
|
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2020-05-15 01:58:52+00:00
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2020-05-15 07:05:49+00:00
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2020-05-15 07:04:25+00:00
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NONE
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I'm working with CNN/DM dataset, where I have 3 subsets : `train`, `test`, `validation`.
Should I apply my map function on the subsets one by one ?
```python
import nlp
cnn_dm = nlp.load_dataset('cnn_dailymail')
for corpus in ['train', 'test', 'validation']:
cnn_dm[corpus] = cnn_dm[corpus].map(my_func)
```
Or is there a better way to do this ?
|
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| 117
|
β How to remove specific rows of a dataset ?
|
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2020-05-15 01:25:06+00:00
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2022-07-15 08:36:44+00:00
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2020-05-15 07:04:32+00:00
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NONE
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I saw on the [example notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb#scrollTo=efFhDWhlvSVC) how to remove a specific column :
```python
dataset.drop('id')
```
But I didn't find how to remove a specific row.
**For example, how can I remove all sample with `id` < 10 ?**
|
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| 116
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π Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323
|
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2020-05-15 01:12:06+00:00
|
2020-05-28 23:43:07+00:00
|
2020-05-28 23:43:07+00:00
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I'm trying to use rouge metric.
I have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.
I tried :
```python
import nlp
rouge = nlp.load_metric('rouge')
with open("test.pred.tokenized") as p, open("test.gold.tokenized") as g:
for lp, lg in zip(p, g):
rouge.add(lp, lg)
```
But I meet following error :
> pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323
---
Full stack-trace :
```
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/metric.py", line 224, in add
self.writer.write_batch(batch)
File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/arrow_writer.py", line 148, in write_batch
pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)
File "pyarrow/table.pxi", line 1550, in pyarrow.lib.Table.from_pydict
File "pyarrow/table.pxi", line 1503, in pyarrow.lib.Table.from_arrays
File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table
File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323
```
(`nlp` installed from source)
|
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MDU6SXNzdWU2MTg2MTU4NTU=
| 115
|
AttributeError: 'dict' object has no attribute 'info'
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|
2020-05-15 00:29:47+00:00
|
2020-05-17 13:11:00+00:00
|
2020-05-17 13:11:00+00:00
|
NONE
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I'm trying to access the information of CNN/DM dataset :
```python
cnn_dm = nlp.load_dataset('cnn_dailymail')
print(cnn_dm.info)
```
returns :
> AttributeError: 'dict' object has no attribute 'info'
|
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| 618,611,310
|
MDU6SXNzdWU2MTg2MTEzMTA=
| 114
|
Couldn't reach CNN/DM dataset
|
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2020-05-15 00:16:17+00:00
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2020-05-15 00:19:52+00:00
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2020-05-15 00:19:51+00:00
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I can't get CNN / DailyMail dataset.
```python
import nlp
assert "cnn_dailymail" in [dataset.id for dataset in nlp.list_datasets()]
cnn_dm = nlp.load_dataset('cnn_dailymail')
```
[Colab notebook](https://colab.research.google.com/drive/1zQ3bYAVzm1h0mw0yWPqKAg_4EUlSx5Ex?usp=sharing)
gives following error :
```
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/cnn_dailymail/cnn_dailymail.py
```
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| 618,590,562
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MDExOlB1bGxSZXF1ZXN0NDE4MjkxNjIx
| 113
|
Adding docstrings and some doc
|
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2020-05-14 23:14:41+00:00
|
2020-05-14 23:22:45+00:00
|
2020-05-14 23:22:44+00:00
|
MEMBER
| null | null | null | null |
Some doc
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| 618,569,195
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MDExOlB1bGxSZXF1ZXN0NDE4Mjc0MTU4
| 112
|
Qa4mre - add dataset
|
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2020-05-14 22:17:51+00:00
|
2020-05-15 09:16:43+00:00
|
2020-05-15 09:16:42+00:00
|
CONTRIBUTOR
| null | null | null | null |
Added dummy data test only for the first config. Will do the rest later.
I had to do add some minor hacks to an important function to make it work.
There might be a cleaner way to handle it - can you take a look @thomwolf ?
|
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| 618,528,060
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MDExOlB1bGxSZXF1ZXN0NDE4MjQwMjMy
| 111
|
[Clean-up] remove under construction datastes
|
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2020-05-14 20:52:22+00:00
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CONTRIBUTOR
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| 618,520,325
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MDExOlB1bGxSZXF1ZXN0NDE4MjMzODIy
| 110
|
fix reddit tifu dummy data
|
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2020-05-14 20:40:14+00:00
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2020-05-14 20:40:13+00:00
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| 618,508,359
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MDExOlB1bGxSZXF1ZXN0NDE4MjI0MDYw
| 109
|
[Reclor] fix reclor
|
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2020-05-14 20:16:26+00:00
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2020-05-14 20:19:09+00:00
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2020-05-14 20:19:08+00:00
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CONTRIBUTOR
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- That's probably one me. Could have made the manual data test more flexible. @mariamabarham
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MDExOlB1bGxSZXF1ZXN0NDE4MTIzMzc3
| 108
|
convert can use manual dir as second argument
|
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2020-05-14 16:52:32+00:00
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2020-05-14 16:52:43+00:00
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2020-05-14 16:52:42+00:00
|
CONTRIBUTOR
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@mariamabarham
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MDExOlB1bGxSZXF1ZXN0NDE4MTEyNzcx
| 107
|
add writer_batch_size to GeneratorBasedBuilder
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2020-05-14 16:35:39+00:00
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2020-05-14 16:50:30+00:00
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2020-05-14 16:50:29+00:00
|
MEMBER
| null | null | null | null |
You can now specify `writer_batch_size` in the builder arguments or directly in `load_dataset`
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|
Add data dir test command
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2020-05-14 16:18:39+00:00
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2020-05-14 16:49:11+00:00
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2020-05-14 16:49:10+00:00
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MEMBER
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MDExOlB1bGxSZXF1ZXN0NDE4MDg5Njgz
| 105
|
[New structure on AWS] Adapt paths
|
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2020-05-14 15:55:57+00:00
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2020-05-14 15:56:28+00:00
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2020-05-14 15:56:27+00:00
|
CONTRIBUTOR
| null | null | null | null |
Some small changes so that we have the correct paths. @julien-c
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MDExOlB1bGxSZXF1ZXN0NDE4MDMzOTY0
| 104
|
Add trivia_q
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2020-05-14 14:27:19+00:00
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2020-07-12 05:34:20+00:00
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2020-05-14 20:23:32+00:00
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CONTRIBUTOR
| null | null | null | null |
Currently tested only for one config to pass tests. Needs to add more dummy data later.
|
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|
[Manual downloads] add logic proposal for manual downloads and add wikihow
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2020-05-14 13:30:36+00:00
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2020-05-14 14:27:41+00:00
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2020-05-14 14:27:40+00:00
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CONTRIBUTOR
| null | null | null | null |
Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset.
The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`.
The dataset can then be loaded via:
```python
import nlp
nlp.load_dataset("wikihow", data_dir="~/wikihow/manual_dir")
```
I added/changed so that there are explicit error messages when using manually downloaded files.
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| 102
|
Run save infos
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2020-05-14 13:27:26+00:00
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2020-05-14 15:43:04+00:00
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2020-05-14 15:43:03+00:00
|
MEMBER
| null | null | null | null |
I replaced the old checksum file with the new `dataset_infos.json` by running the script on almost all the datasets we have. The only one that is still running on my side is the cornell dialog
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| 101
|
[Reddit] add reddit
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2020-05-14 10:25:02+00:00
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2020-05-14 10:27:25+00:00
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2020-05-14 10:27:24+00:00
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CONTRIBUTOR
| null | null | null | null |
- Everything worked fine @mariamabarham. Made my computer nearly crash, but all seems to be working :-)
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MDExOlB1bGxSZXF1ZXN0NDE3ODc1MjE2
| 100
|
Add per type scores in seqeval metric
|
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2020-05-14 09:37:52+00:00
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2020-05-14 23:21:35+00:00
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2020-05-14 23:21:34+00:00
|
CONTRIBUTOR
| null | null | null | null |
This PR add a bit more detail in the seqeval metric. Now the usage and output are:
```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
met.compute(predictions, references)
#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.8}
```
It is also possible to compute scores for non IOB notations, POS tagging for example hasn't this kind of notation. Add `suffix` parameter:
```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
predictions = [['O', 'O', 'MISC', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
met.compute(predictions, references, metrics_kwargs={"suffix": True})
#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.9}
```
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MDExOlB1bGxSZXF1ZXN0NDE3ODMxNjky
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|
[Cmrc 2018] fix cmrc2018
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2020-05-14 08:22:03+00:00
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2020-05-14 08:49:42+00:00
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2020-05-14 08:49:41+00:00
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CONTRIBUTOR
| null | null | null | null |
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MDExOlB1bGxSZXF1ZXN0NDE3Nzc3NDcy
| 98
|
Webis tl-dr
|
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2020-05-14 06:22:18+00:00
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2020-09-03 10:00:21+00:00
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2020-05-14 20:54:16+00:00
|
CONTRIBUTOR
| null | null | null | null |
Add the Webid TL:DR dataset.
|
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|
[Csv] add tests for csv dataset script
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2020-05-13 23:06:11+00:00
|
2020-05-13 23:23:16+00:00
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2020-05-13 23:23:15+00:00
|
CONTRIBUTOR
| null | null | null | null |
Adds dummy data tests for csv.
|
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lm1b
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2020-05-13 20:38:44+00:00
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2020-05-14 14:13:30+00:00
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2020-05-14 14:13:29+00:00
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CONTRIBUTOR
| null | null | null | null |
Add lm1b dataset.
|
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|
Replace checksums files by Dataset infos json
|
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[] | null | 2
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2020-05-13 19:36:16+00:00
|
2020-05-14 08:58:43+00:00
|
2020-05-14 08:58:42+00:00
|
MEMBER
| null | null | null | null |
### Better verifications when loading a dataset
I replaced the `urls_checksums` directory that used to contain `checksums.txt` and `cached_sizes.txt`, by a single file `dataset_infos.json`. It's just a dict `config_name` -> `DatasetInfo`.
It simplifies and improves how verifications of checksums and splits sizes are done, as they're all stored in `DatasetInfo` (one per config). Also, having already access to `DatasetInfo` enables to check disk space before running `download_and_prepare` for a given config.
The dataset infos json file is user readable, you can take a look at the squad one that I generated in this PR.
### Renaming
According to these changes, I did some renaming:
`save_checksums` -> `save_infos`
`ignore_checksums` -> `ignore_verifications`
for example, when you are creating a dataset you have to run
```nlp-cli test path/to/my/dataset --save_infos --all_configs```
instead of
```nlp-cli test path/to/my/dataset --save_checksums --all_configs```
### And now, the fun part
We'll have to rerun the `nlp-cli test ... --save_infos --all_configs` for all the datasets
-----------------
feedback appreciated !
|
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MDExOlB1bGxSZXF1ZXN0NDE3NDYyMTIw
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|
Librispeech
|
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2020-05-13 16:04:14+00:00
|
2020-05-13 21:29:03+00:00
|
2020-05-13 21:29:02+00:00
|
CONTRIBUTOR
| null | null | null | null |
Add librispeech dataset and remove some useless content.
|
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|
Cleanup notebooks and various fixes
|
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2020-05-13 14:58:58+00:00
|
2020-05-13 15:01:48+00:00
|
2020-05-13 15:01:47+00:00
|
MEMBER
| null | null | null | null |
Fixes on dataset (more flexible) metrics (fix) and general clean ups
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MDExOlB1bGxSZXF1ZXN0NDE3Mjc1ODky
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|
[WIP] add wmt14
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2020-05-13 10:42:03+00:00
|
2020-05-16 11:17:38+00:00
|
2020-05-16 11:17:37+00:00
|
CONTRIBUTOR
| null | null | null | null |
WMT14 takes forever to download :-/
- WMT is the first dataset that uses an abstract class IMO, so I had to modify the `load_dataset_module` a bit.
|
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MDExOlB1bGxSZXF1ZXN0NDE3Mjc0MjA0
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[Paracrawl] add paracrawl
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2020-05-13 10:39:00+00:00
|
2020-05-13 10:40:15+00:00
|
2020-05-13 10:40:14+00:00
|
CONTRIBUTOR
| null | null | null | null |
- Huge dataset - took ~1h to download
- Also this PR reformats all dataset scripts and adds `datasets` to `make style`
|
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Add download gg drive
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2020-05-13 09:56:02+00:00
|
2020-05-13 12:46:28+00:00
|
2020-05-13 10:05:31+00:00
|
MEMBER
| null | null | null | null |
We can now add datasets that download from google drive
|
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| 617,295,069
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MDExOlB1bGxSZXF1ZXN0NDE3MjM4MjU4
| 89
|
Add list and inspect methods - cleanup hf_api
|
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2020-05-13 09:30:15+00:00
|
2020-05-13 14:05:00+00:00
|
2020-05-13 09:33:10+00:00
|
MEMBER
| null | null | null | null |
Add a bunch of methods to easily list and inspect the processing scripts up-loaded on S3:
```python
nlp.list_datasets()
nlp.list_metrics()
# Copy and prepare the scripts at `local_path` for easy inspection/modification.
nlp.inspect_dataset(path, local_path)
# Copy and prepare the scripts at `local_path` for easy inspection/modification.
nlp.inspect_metric(path, local_path)
```
Also clean up the `HfAPI` to use `dataclasses` for better user-experience
|
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MDExOlB1bGxSZXF1ZXN0NDE3MjI5ODQw
| 88
|
Add wiki40b
|
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closed
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|
2020-05-13 09:16:01+00:00
|
2020-05-13 12:31:55+00:00
|
2020-05-13 12:31:54+00:00
|
MEMBER
| null | null | null | null |
This one is a beam dataset that downloads files using tensorflow.
I tested it on a small config and it works fine
|
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MDExOlB1bGxSZXF1ZXN0NDE3MjE1NzA0
| 87
|
Add Flores
|
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2020-05-13 08:51:29+00:00
|
2020-05-13 09:23:34+00:00
|
2020-05-13 09:23:33+00:00
|
CONTRIBUTOR
| null | null | null | null |
Beautiful language for sure!
|
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MDExOlB1bGxSZXF1ZXN0NDE3MjEwNzY2
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|
[Load => load_dataset] change naming
|
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2020-05-13 08:43:00+00:00
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2020-05-13 08:50:58+00:00
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2020-05-13 08:50:57+00:00
|
CONTRIBUTOR
| null | null | null | null |
Rename leftovers @thomwolf
|
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