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https://api.github.com/repos/huggingface/datasets/issues/483 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/483/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/483/comments | https://api.github.com/repos/huggingface/datasets/issues/483/events | https://github.com/huggingface/datasets/issues/483 | 675,080,694 | MDU6SXNzdWU2NzUwODA2OTQ= | 483 | rotten tomatoes movie review dataset taken down | {
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"found a mirror: https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz",
"fixed in #484 ",
"Closing this one. Thanks again @jxmorris12 for taking care of this :)"
] | 1,596,813,121,000 | 1,599,557,794,000 | 1,599,557,793,000 | CONTRIBUTOR | null | null | null | In an interesting twist of events, the individual who created the movie review seems to have left Cornell, and their webpage has been removed, along with the movie review dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz). It's not downloadable anymore. | {
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"This comes from an overflow in pyarrow's array.\r\nIt is stuck inside the loop that reduces the batch size to avoid the overflow.\r\nI'll take a look",
"I created a PR to fix the issue.\r\nIt was due to an overflow check that handled badly an empty list.\r\n\r\nYou can try the changes by using \r\n```\r\n!pip in... | 1,596,788,615,000 | 1,597,241,626,000 | 1,597,190,115,000 | NONE | null | null | null | Hi Huggingface Team!
Thank you guys once again for this amazing repo.
I have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb)
However, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 / 0.17.0 / 1.0.0 also have the same frozen process.
Reproducible code can be found on [this colab notebook ](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.
----------------------------------------
**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)
I also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ? | {
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"Not sure why the AWS test is failing - perhaps I made too many concurrent CI builds 😢. Can someone please rerun the CI to check the error is not on my end?",
"I pushed an improved docstring and the unit tests now pass, which suggests the previous failure on AWS was simply a timeout error. \r\n\r\nFor some reaso... | 1,596,744,129,000 | 1,597,911,368,000 | 1,597,911,368,000 | MEMBER | null | false | {
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} | ## Description
This PR applies utf-8 encoding for all instances of `with open(...) as f` to all Python files in `datasets/`. As suggested by @thomwolf in #468 , we use regular expressions and the following function
```python
def apply_encoding_on_file_open(filepath: str):
"""Apply UTF-8 encoding for all instances where a non-binary file is opened."""
with open(filepath, 'r', encoding='utf-8') as input_file:
regexp = re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)")
input_text = input_file.read()
match = regexp.search(input_text)
if match:
output = regexp.sub(lambda m: m.group()[:-1]+', encoding="utf-8")', input_text)
with open(filepath, 'w', encoding='utf-8') as output_file:
output_file.write(output)
```
to perform the replacement.
Note:
1. I excluded all _**binary files**_ from the search since it's possible some objects are opened for which the encoding doesn't make sense. Please correct me if I'm wrong and I'll tweak the regexp accordingly
2. There were two edge cases where the regexp failed (e.g. two `open` instances on a single line), but I decided to just fix these manually in the interest of time.
3. I only applied the replacement to files in `datasets/`. Let me know if this should be extended to other places like `metrics/`
4. I have implemented a unit test that should catch missing encodings in future CI runs
Closes #468 and possibly #347 | {
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https://api.github.com/repos/huggingface/datasets/issues/480 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/480/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/480/comments | https://api.github.com/repos/huggingface/datasets/issues/480/events | https://github.com/huggingface/datasets/pull/480 | 674,245,959 | MDExOlB1bGxSZXF1ZXN0NDYzOTcwNjQ2 | 480 | Column indexing hotfix | {
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"Looks good to me as well but we'll want to add a test indeed.\r\nYou can add one if you have time @TevenLeScao.\r\nOtherwise, we'll do it when we are back with Quentin. ",
"I fixed it in #494 "
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} | As observed for example in #469 , currently `__getitem__` does not convert the data to the dataset format when indexing by column. This is a hotfix that imitates functional 0.3.0. code. In the future it'd probably be nice to have a test there. | {
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https://api.github.com/repos/huggingface/datasets/issues/479 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/479/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/479/comments | https://api.github.com/repos/huggingface/datasets/issues/479/events | https://github.com/huggingface/datasets/pull/479 | 673,905,407 | MDExOlB1bGxSZXF1ZXN0NDYzNjkxMjA0 | 479 | add METEOR metric | {
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"Really nice !\r\nThanks for adding this one.\r\n\r\nI noticed that there are some '-' that are left in the description in the middle of some workds. It migh come from copy-pasting the pdf paper. ex: `im-provement`. Could you fix that please ?",
"@lhoestq \r\nLinebreaks have been removed! Note that there are stil... | 1,596,669,180,000 | 1,597,844,349,000 | 1,597,844,349,000 | CONTRIBUTOR | null | false | {
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} | Added the METEOR metric. Can be used like this:
```python
import nlp
meteor = nlp.load_metric('metrics/meteor')
meteor.compute(["some string", "some string"], ["some string", "some similar string"])
# {'meteor': 0.6411637931034483}
meteor.add("some string", "some string")
meteor.add('some string", "some similar string")
meteor.compute()
# {'meteor': 0.6411637931034483}
```
Uses [NLTK's implementation](https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score), [(source)](https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py) | {
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"Nevermind, I restarted my python session and it worked fine...\r\n\r\n---\r\n\r\nI had an authentification error, and I authenticated from another terminal. After that, no more error but it was not working. Restarting the sessions makes it work :)"
] | 1,596,589,712,000 | 1,596,590,497,000 | 1,596,590,496,000 | NONE | null | null | null | Previously, I was writing TFRecords manually to GCP bucket with : `with tf.io.TFRecordWriter('gs://my_bucket/x.tfrecord')`
Since `0.4.0` is out with the `export()` function, I tried it. But it seems TFRecords cannot be directly written to GCP bucket.
`dataset.export('local.tfrecord')` works fine,
but `dataset.export('gs://my_bucket/x.tfrecord')` does not work.
There is no error message, I just can't find the file on my bucket...
---
Looking at the code, `nlp` is using `tf.data.experimental.TFRecordWriter`, while I was using `tf.io.TFRecordWriter`.
**What's the difference between those 2 ? How can I write TFRecords files directly to GCP bucket ?**
@jarednielsen @lhoestq | {
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"Thanks for reporting this issue\r\n\r\nThere was a bug where numpy arrays would get returned instead of tensorflow tensors.\r\nThis is fixed on master.\r\n\r\nI tried to re-run the colab and encountered this error instead:\r\n\r\n```\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no at... | 1,596,583,095,000 | 1,627,970,535,000 | 1,627,970,535,000 | NONE | null | null | null | with nlp 0.4.0, the TensorFlow example in Overview.ipynb throws the following exceptions:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-48907f2ad433> in <module>
----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
<ipython-input-5-48907f2ad433> in <dictcomp>(.0)
----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
AttributeError: 'numpy.ndarray' object has no attribute 'to_tensor' | {
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https://api.github.com/repos/huggingface/datasets/issues/476 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/476/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/476/comments | https://api.github.com/repos/huggingface/datasets/issues/476/events | https://github.com/huggingface/datasets/pull/476 | 672,991,854 | MDExOlB1bGxSZXF1ZXN0NDYyOTMyMTgx | 476 | CheckList | {
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"> Also, a little out of my depth there, but would there be a way to have the default pip install checklist command not require mysql and mariadb to be installed? Feels like that might be a source of confusion for users.\r\n\r\nI removed the pattern dependency, mysql is not a requirement anymore. I'm not sure where... | 1,596,565,925,000 | 1,657,120,786,000 | null | NONE | null | false | {
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- Added checklists as datasets. I can't run `test_load_real_dataset` (see #474), but I can load the datasets successfully as shown in the example notebook
- Added a checklist wrapper | {
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"Cool thanks, I made those changes. LMK if you think it's ready for merge.",
"Ok to merge for me"
] | 1,596,555,149,000 | 1,597,698,848,000 | 1,597,698,847,000 | CONTRIBUTOR | null | false | {
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} | A few misc. bugs and QOL improvements that I've come across in using the library. Let me know if you don't like any of them and I can adjust/remove them.
1. Printing datasets without a description field throws an error when formatting the `single_line_description`. This fixes that, and also adds some formatting to the repr to make it slightly more readable.
```
>>> print(list_datasets()[0])
nlp.ObjectInfo(
id='aeslc',
description='A collection of email messages of employees in the Enron Corporation.There are two features: - email_body: email body text. - subject_line: email subject text.',
files=[nlp.S3Object('aeslc.py'), nlp.S3Object('dataset_infos.json'), nlp.S3Object('dummy/1.0.0/dummy_data-zip-extracted/dummy_data/AESLC-master/enron_subject_line/dev/allen-p_inbox_29.subject'), nlp.S3Object('dummy/1.0.0/dummy_data-zip-extracted/dummy_data/AESLC-master/enron_subject_line/test/allen-p_inbox_24.subject'), nlp.S3Object('dummy/1.0.0/dummy_data-zip-extracted/dummy_data/AESLC-master/enron_subject_line/train/allen-p_inbox_20.subject'), nlp.S3Object('dummy/1.0.0/dummy_data.zip'), nlp.S3Object('urls_checksums/checksums.txt')]
)
```
2. Add id-only option to `list_datasets` and `list_metrics` to allow the user to easily print out just the names of the datasets & metrics. I often found myself annoyed that this took so many strokes to do.
```python
[dataset.id for dataset in list_datasets()] # before
list_datasets(id_only=True) # after
```
3. Fix null-seed randomization caching. When using `train_test_split` and `shuffle`, the computation was being cached even without a seed or generator being passed. The result was that calling `.shuffle` more than once on the same dataset didn't do anything without passing a distinct seed or generator. Likewise with `train_test_split`.
4. Indexing by iterables of bool. I added support for passing an iterable of type bool to `_getitem` as a numpy/pandas-like indexing method. Let me know if you think it's redundant with `filter` (I know it's not optimal memory-wise), but I think it's nice to have as a lightweight alternative to do simple things without having to create a copy of the entire dataset, e.g.
```python
dataset[dataset['label'] == 0] # numpy-like bool indexing to look at instances with labels of 0
```
5. Add an `input_column` argument to `map` and `filter`, which allows you to filter/map on a particular column rather than passing the whole dict to the function. Also adds `fn_kwargs` to be passed to the function. I think these together make mapping much cleaner in many cases such as mono-column tokenization:
```python
# before
dataset = dataset.map(lambda batch: tokenizer(batch["text"])
# after
dataset = dataset.map(tokenizer, input_column="text")
dataset = dataset.map(tokenizer, input_column="text", fn_kwargs={"truncation": True, "padding": True})
``` | {
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"The `data_dir` parameter has been removed. Now the error is `ValueError: Config name is missing`\r\n\r\nAs mentioned in #470 I think we can have one test with the first config of BUILDER_CONFIGS, and another test that runs all of the configs in BUILDER_CONFIGS",
"This was fixed in #527 \r\n\r\nClosing this one, ... | 1,596,498,396,000 | 1,599,490,393,000 | 1,599,490,393,000 | NONE | null | null | null | It a dataset has custom `BUILDER_CONFIGS` with non-keyword arguments (or keyword arguments with non default values), the config is not loaded during the test and causes an error.
I think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https://github.com/huggingface/nlp/blob/master/tests/test_dataset_common.py#L200)). This causes [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L201) to always be false because `config_kwargs` is not `None`. [This line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L222) will be run instead, which doesn't use `BUILDER_CONFIGS`.
For an example, you can try running the test for lince:
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lince`
which yields
> E TypeError: __init__() missing 3 required positional arguments: 'colnames', 'classes', and 'label_column' | {
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"This PR was already approved by @lhoestq in #456 . This one just make style to remove some typos"
] | 1,596,453,302,000 | 1,596,453,730,000 | 1,596,453,729,000 | CONTRIBUTOR | null | false | {
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"Ok I tried to add the dummy dataset (I actually modified the dummy_data command to generate them for me because it was too painful to do that manually).\r\n\r\nThe dummy_data test seems to work:\r\n```bash\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_iwslt2017\... | 1,596,448,359,000 | 1,599,482,010,000 | 1,599,482,010,000 | CONTRIBUTOR | null | false | {
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} | Created a [IWSLT 2017](https://sites.google.com/site/iwsltevaluation2017/TED-tasks) dataset script for the *multilingual data*.
```
Bilingual data: {Arabic, German, French, Japanese, Korean, Chinese} <-> English
Multilingual data: German, English, Italian, Dutch, Romanian. (Any pair)
```
I'm unsure how to handle bilingual vs multilingual. Given `nlp` architecture a Config option seems to be the way to go, however, it might be a bit confusing to have different language pairs with different option. Using just language pairs is not viable as English to German exists in both.
Any opinion on how that should be done ?
EDIT: I decided to just omit de-en from multilingual as it's only a subset of the bilingual one. That way only language pairs exist.
EDIT : Could be interesting for #438 | {
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"Hi ! Did you try to set the output format to pytorch ? (or tensorflow if you're using tensorflow)\r\nIt can be done with `dataset.set_format(\"torch\", columns=columns)` (or \"tensorflow\").\r\n\r\nNote that for pytorch, string columns can't be converted to `torch.Tensor`, so you have to specify in `columns=` the... | 1,596,440,909,000 | 1,603,357,466,000 | null | NONE | null | null | null | I trying to build multi label text classifier model using Transformers lib.
I'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error
File "C:\***\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
I'm using pyarrow 1.0.0. And I have simple custom data set with Text and Integer Label.
Ex: Data
Text , Label #Column Header
I'm facing an Network issue, 1
I forgot my password, 2
Error StackTrace:
File "C:\**\transformers\trainer.py", line 492, in train
for step, inputs in enumerate(epoch_iterator):
File "C:\**\tqdm\std.py", line 1104, in __iter__
for obj in iterable:
File "C:\**\torch\utils\data\dataloader.py", line 345, in __next__
data = self._next_data()
File "C:\**\torch\utils\data\dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\nlp\arrow_dataset.py", line 414, in __getitem__
output_all_columns=self._output_all_columns,
File "C:\**\nlp\arrow_dataset.py", line 403, in _getitem
outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns
File "C:\**\nlp\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
| {
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https://api.github.com/repos/huggingface/datasets/issues/468 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/468/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/468/comments | https://api.github.com/repos/huggingface/datasets/issues/468/events | https://github.com/huggingface/datasets/issues/468 | 671,622,441 | MDU6SXNzdWU2NzE2MjI0NDE= | 468 | UnicodeDecodeError while loading PAN-X task of XTREME dataset | {
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"Indeed. Solution 1 is the simplest.\r\n\r\nThis is actually a recurring problem.\r\nI think we should scan all the datasets with regexpr to fix the use of `open()` without encodings.\r\nAnd probably add a test in the CI to forbid using this in the future.",
"I'm happy to tackle the broader problem - will open a ... | 1,596,377,110,000 | 1,597,911,368,000 | 1,597,911,368,000 | MEMBER | null | null | null | Hi 🤗 team!
## Description of the problem
I'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset:
```
---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-5-1d61f439b843> in <module>
----> 1 dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
528 ignore_verifications = ignore_verifications or save_infos
529 # Download/copy dataset processing script
--> 530 module_path, hash = prepare_module(path, download_config=download_config, dataset=True)
531
532 # Get dataset builder class from the processing script
/usr/local/lib/python3.6/dist-packages/nlp/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)
265
266 # Download external imports if needed
--> 267 imports = get_imports(local_path)
268 local_imports = []
269 library_imports = []
/usr/local/lib/python3.6/dist-packages/nlp/load.py in get_imports(file_path)
156 lines = []
157 with open(file_path, mode="r") as f:
--> 158 lines.extend(f.readlines())
159
160 logger.info("Checking %s for additional imports.", file_path)
/usr/lib/python3.6/encodings/ascii.py in decode(self, input, final)
24 class IncrementalDecoder(codecs.IncrementalDecoder):
25 def decode(self, input, final=False):
---> 26 return codecs.ascii_decode(input, self.errors)[0]
27
28 class StreamWriter(Codec,codecs.StreamWriter):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)
```
## Steps to reproduce
Install from nlp's master branch
```python
pip install git+https://github.com/huggingface/nlp.git
```
then run
```python
from nlp import load_dataset
# AmazonPhotos.zip is located in data/
dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
```
## OS / platform details
- `nlp` version: latest from master
- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.4.0 (True)
- Tensorflow version (GPU?): 2.1.0 (True)
- Using GPU in script?: True
- Using distributed or parallel set-up in script?: False
## Proposed solution
Either change [line 762](https://github.com/huggingface/nlp/blob/7ada00b1d62f94eee22a7df38c6b01e3f27194b7/datasets/xtreme/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:
```
# old
with open(filepath) as f
# new
with open(filepath, encoding='utf-8') as f
```
or raise a warning that suggests setting the locale explicitly, e.g.
```python
import locale
locale.setlocale(locale.LC_ALL, 'C.UTF-8')
```
I have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix! | {
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https://api.github.com/repos/huggingface/datasets/issues/467 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/467/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/467/comments | https://api.github.com/repos/huggingface/datasets/issues/467/events | https://github.com/huggingface/datasets/pull/467 | 671,580,010 | MDExOlB1bGxSZXF1ZXN0NDYxNzgwMzUy | 467 | DOCS: Fix typo | {
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"Thanks!"
] | 1,596,358,777,000 | 1,596,376,347,000 | 1,596,359,934,000 | CONTRIBUTOR | null | false | {
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"The cast function is now called inside `features.encode_example`.\r\nI also added `encode_batch` that was missing.\r\n\r\nMoreover I used the cast function in `Dataset.map` to support torch/tensorflow tensors or numpy arrays inputs.\r\n\r\nThere are tests for tensors inputs in metrics and in .map",
"I think we c... | 1,596,279,825,000 | 1,597,677,300,000 | 1,597,677,299,000 | MEMBER | null | false | {
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} | - Disallow the use of positional arguments to avoid `predictions` vs `references` mistakes
- Allow to directly feed numpy/pytorch/tensorflow/pandas objects in metrics | {
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"One note on features inference:\r\n\r\nif an arrow type is `struct of items` where each item is a `list`, then we return a `dict` in which each item is a `Sequence`.\r\nIt means that we don't use the Sequence <-> dict swap when we infer features.\r\n\r\nIt's fine because the swap is generally used in dataset scrip... | 1,596,206,601,000 | 1,596,220,053,000 | 1,596,220,052,000 | MEMBER | null | false | {
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} | When applying a transform like `map`, some features were lost (and inferred features were used).
It was the case for ClassLabel, Translation, etc.
To fix that, I did some modifications in the `ArrowWriter`:
- added the `update_features` parameter. When it's `True`, then the features specified by the user (if any) can be updated with inferred features if their type don't match. `map` transform sets `update_features=True` when writing to cache file or buffer. Features won't change by default in `map`.
- added the `with_metadata` parameter. If `True`, the `features` (after update) will be written inside the metadata of the schema in this format:
```
{
"huggingface": {"features" : <serialized Features exactly like dataset_info.json>}
}
```
Then, once a dataset is instantiated without info/features, these metadata are used to set the features of the dataset. | {
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These methods are added to `Dataset` as well as `DatasetDict`.
Added tests for these new methods and add the methods to the doc.
Naming follows the new pattern with a trailing underscore indicating in-place methods. | {
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"I think the problem is related to `wiki_dpr` dataset which is making the circle CI failed as you can see:\r\n```\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/dummy_psgs_w100_no_embeddings\r... | 1,596,196,252,000 | 1,598,280,882,000 | 1,598,280,882,000 | CONTRIBUTOR | null | false | {
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} | New pull request that should correct the previous errors.
The load_real_data stills fails because it is looking for a default dataset URL that does not exists, this does not happen when loading the dataset with load_dataset | {
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"Thanks @TevenLeScao for finding this issue",
"Thanks @lhoestq for catching this ❤️"
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Therefore the next time you called map, it was loading an incomplete arrow file.
We had the same issue with select if there was a bad indice at one point.
To fix that I used temporary files that are renamed once everything is finished. | {
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} | This PR contains a few breaking changes so it's probably good to keep it for the next (major) release:
- rename the `flatten`, `drop` and `dictionary_encode_column` methods in `flatten_`, `drop_` and `dictionary_encode_column_` to indicate that these methods have in-place effects as discussed in #166. From now on we should keep the convention of having a trailing underscore for methods which have an in-place effet. I also adopt the conversion of not returning the (self) dataset for these methods. This is different than what PyTorch does for instance (`model.to()` is in-place but return the self model) but I feel like it's a safer approach in terms of UX.
- remove the `dataset.columns` property which returns a low-level Apache Arrow object and should not be used by users. Similarly, remove `dataset. nbytes` which we don't really want to expose in this bare-bone format.
- add a few more properties and methods to `DatasetDict` | {
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Also changed the function call to use named rather than positional arguments. | {
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Add tests to these for `Dataset` and `DatasetDict`.
Fix some bugs uncovered by the tests for `pandas` formating. | {
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"Sorry one nit: Could we use named arguments for the call to BLEURT?\r\n\r\ni.e. \r\n scores = self.scorer.score(references=references, candidates=predictions)\r\n\r\n(i.e. so it is less bug prone)\r\n",
"Following up on Ankur's comment---we are going to drop support for\npositional (not named) arguments i... | 1,596,046,112,000 | 1,596,203,774,000 | 1,596,203,774,000 | MEMBER | null | false | {
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} | This PR adds the BLEURT metric to the library.
The BLEURT `Metric` downloads a TF checkpoint corresponding to its `config_name` at creation (in the `_info` function). Default is set to `bleurt-base-128`.
Note that the default in the original package is `bleurt-tiny-128`, but they throw a warning and recommend using `bleurt-base-128` instead. I think it's safer to have our users have a functioning metric when they call the default behavior, we'll address discrepancies in the issues/discussions if it comes up.
In addition to the BLEURT file, `load.py` was changed so we can ask users to pip install the required packages from git when they have a `setup.py` but are not on PyPL
cc @ankparikh @tsellam | {
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"Hi ! Glad you managed to fix the version issue.\r\n\r\nThe command `\r\npython nlp-cli dummy_data datasets/guardian_authorship --save_infos --all_configs` is supposed to generate a json file `dataset_infos.json` next to your dataset script, but I can't see it in the PR.\r\nCan you make sure you have the json file ... | 1,595,989,437,000 | 1,597,936,197,000 | 1,597,936,076,000 | CONTRIBUTOR | null | false | {
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} | A new dataset: Guardian news articles for authorship attribution
**tests passed:**
python nlp-cli dummy_data datasets/guardian_authorship --save_infos --all_configs
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_guardian_authorship
**Tests failed:**
Real data: RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_guardian_authorship
output: __init__() missing 3 required positional arguments: 'train_folder', 'valid_folder', and 'tes...'
Remarks: This is the init function of my class. I am not sure why it passes in both my tests and with nlp-cli, but fails here. By the way, I ran this command with another 2 datasets and they failed:
* _glue - OSError: Cannot find data file.
*_newsgroup - FileNotFoundError: Local file datasets/newsgroup/dummy/18828_comp.graphics/3.0.0/dummy_data.zip doesn't exist
Thank you for letting us contribute to such a huge and important library!
EDIT:
I was able to fix the dummy_data issue. This dataset has around 14 configurations. I was testing with only 2, but their versions were not in a sequence, they were V1.0.0 and V.12.0.0. It seems that the testing code generates testes for all the versions from 0 to MAX, and was testing for versions (and dummy_data.zip files) that do not exist. I fixed that by changing the versions to 1 and 2.
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"I think this is the way to go but I’m afraid this might be a little slow. I was thinking that we could use a high quality very fast non crypto hash like xxhash for these stuff (hashing data files)",
"Yep good idea, I'll take a look",
"I tested the hashing speed [here](https://colab.research.google.com/drive/1h... | 1,595,953,851,000 | 1,596,031,043,000 | 1,596,031,042,000 | MEMBER | null | false | {
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} | The cache dir for csv/json/txt datasets was always the same. This is an issue because it should be different depending on the data files provided by the user.
To fix that, I added a line that use the hash of the data files provided by the user to define the cache dir.
This should fix #444 | {
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#353 | {
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"> Awesome !\r\n> Good job on parsing these files :O\r\n> \r\n> Do you think it would be hard to get the two other split configurations ?\r\n\r\nIt shouldn't be that hard, I think I can consider different config names for each split ",
"> > Awesome !\r\n> > Good job on parsing these files :O\r\n> > Do you think i... | 1,595,926,692,000 | 1,596,453,031,000 | 1,596,453,031,000 | CONTRIBUTOR | null | false | {
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} | This PR adds the `Reuters_21578` dataset https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html
#353
The datasets is a lit of `.sgm` files which are a bit different from xml file indeed `xml.etree` couldn't be used to read files. I consider them as text file (to avoid using external library) and read line by line (maybe there is a better way to do, happy to get your opinion on it)
In the Readme file 3 ways to split the dataset are given.:
- The Modified Lewis ("ModLewis") Split: train, test and unused-set
- The Modified Apte ("ModApte") Split : train, test and unused-set
- The Modified Hayes ("ModHayes") Split: train and test
Here I consider the last one as the readme file highlight that this split provides the ability to compare results with those of the 2 first splits.
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"Could you run `make style` to fix the code_quality fail ?\r\nYou'll need `black` and `isort` that you can install by doing `pip install -e .[quality]`",
"Thanks @lhoestq\r\nI fixed the styling",
"Thank you :)"
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Added a test to recognize import errors of all metrics | {
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"This issue was resolved by #447 "
] | 1,595,921,490,000 | 1,595,941,136,000 | 1,595,941,136,000 | CONTRIBUTOR | null | null | null | Latest Version 0.3.0
When loading the metric "sacrebleu" there is an import error due to the wrong path
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"Same here !",
"This is the only fix I could come up with without touching the repo's code.\r\n```python\r\nfrom nlp.builder import FORCE_REDOWNLOAD\r\ndataset = load_dataset('csv', data_file='./a.csv', download_mode=FORCE_REDOWNLOAD, version='0.0.1')\r\n```\r\nYou'll have to change the version each time you want... | 1,595,855,286,000 | 1,596,031,042,000 | 1,596,031,042,000 | NONE | null | null | null | I used load a file called 'a.csv' by
```
dataset = load_dataset('csv', data_file='./a.csv')
```
And after a while, I tried to load another csv called 'b.csv'
```
dataset = load_dataset('csv', data_file='./b.csv')
```
However, the new dataset seems to remain the old 'a.csv' and not loading new csv file.
Even worse, after I load a.csv, the load_dataset function keeps loading the 'a.csv' afterward.
Is this a cache problem?
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"This seems to be fixed in a non-released version. \r\n\r\nInstalling nlp from source\r\n```\r\ngit clone https://github.com/huggingface/nlp\r\ncd nlp\r\npip install .\r\n```\r\nsolves the issue. "
] | 1,595,852,017,000 | 1,595,855,111,000 | 1,595,855,111,000 | CONTRIBUTOR | null | null | null | Saving a formatted torch dataset to file using `torch.save()`. Loading the same file fails during unpickling:
```python
>>> import torch
>>> import nlp
>>> squad = nlp.load_dataset("squad.py", split="train")
>>> squad
Dataset(features: {'source_text': Value(dtype='string', id=None), 'target_text': Value(dtype='string', id=None)}, num_rows: 87599)
>>> squad = squad.map(create_features, batched=True)
>>> squad.set_format(type="torch", columns=["source_ids", "target_ids", "attention_mask"])
>>> torch.save(squad, "squad.pt")
>>> squad_pt = torch.load("squad.pt")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 593, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 773, in _legacy_load
result = unpickler.load()
File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/splits.py", line 493, in __setitem__
raise ValueError("Cannot add elem. Use .add() instead.")
ValueError: Cannot add elem. Use .add() instead.
```
where `create_features` is a function that tokenizes the data using `batch_encode_plus` and returns a Dict with `input_ids`, `target_ids` and `attention_mask`.
```python
def create_features(batch):
source_text_encoding = tokenizer.batch_encode_plus(
batch["source_text"],
max_length=max_source_length,
pad_to_max_length=True,
truncation=True)
target_text_encoding = tokenizer.batch_encode_plus(
batch["target_text"],
max_length=max_target_length,
pad_to_max_length=True,
truncation=True)
features = {
"source_ids": source_text_encoding["input_ids"],
"target_ids": target_text_encoding["input_ids"],
"attention_mask": source_text_encoding["attention_mask"]
}
return features
```
I found a similar issue in [issue 5267 in the huggingface/transformers repo](https://github.com/huggingface/transformers/issues/5267) which was solved by downgrading to `nlp==0.2.0`. That did not solve this problem, however. | {
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] | open | false | null | [] | null | [] | 1,595,847,598,000 | 1,598,282,000,000 | null | NONE | null | null | null | Hello! First of all, thanks for setting up this useful project!
I've just realised you provide the the [Glue Diagnostics Data](https://huggingface.co/nlp/viewer/?dataset=glue&config=ax) without labels, indicating in the `GlueConfig` that you've only a test set.
Yet, the data with labels is available, too (see also [here](https://gluebenchmark.com/diagnostics#introduction)):
https://www.dropbox.com/s/ju7d95ifb072q9f/diagnostic-full.tsv?dl=1
Have you considered incorporating it? | {
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"This one is ready for review now",
"I changed to using features only, instead of info.\r\nLet mw know if it sounds good to you now @thomwolf "
] | 1,595,843,401,000 | 1,596,113,477,000 | 1,596,113,476,000 | MEMBER | null | false | {
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If they don't match the data type, it raises a `ValueError`.
It's a draft PR because #440 needs to be merged first. | {
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https://api.github.com/repos/huggingface/datasets/issues/440 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/440/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/440/comments | https://api.github.com/repos/huggingface/datasets/issues/440/events | https://github.com/huggingface/datasets/pull/440 | 666,116,823 | MDExOlB1bGxSZXF1ZXN0NDU3MDE2MjQy | 440 | Fix user specified features in map | {
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The writer used to overwrite the user specified features with inferred features.
I also added tests to make sure it doesn't happen again. | {
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"`DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.\r\n\r\nRight now you can experiment with it by installing `transformers` from the master branch.\r\nYou can also check the docs of DPR [here](https://huggingface.co/transformers/master/model_doc/dpr.html).... | 1,595,823,917,000 | 1,603,849,584,000 | 1,603,849,584,000 | NONE | null | null | null | It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https://huggingface.co/nlp/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab. Is there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ? | {
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https://api.github.com/repos/huggingface/datasets/issues/438 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/438/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/438/comments | https://api.github.com/repos/huggingface/datasets/issues/438/events | https://github.com/huggingface/datasets/issues/438 | 665,865,490 | MDU6SXNzdWU2NjU4NjU0OTA= | 438 | New Datasets: IWSLT15+, ITTB | {
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"Thanks Sam, we now have a very detailed tutorial and template on how to add a new dataset to the library. It typically take 1-2 hours to add one. Do you want to give it a try ?\r\nThe tutorial on writing a new dataset loading script is here: https://huggingface.co/nlp/add_dataset.html\r\nAnd the part on how to sha... | 1,595,799,784,000 | 1,598,281,935,000 | null | CONTRIBUTOR | null | null | null | **Links:**
[iwslt](https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/datasets/iwslt.html)
Don't know if that link is up to date.
[ittb](http://www.cfilt.iitb.ac.in/iitb_parallel/)
**Motivation**: replicate mbart finetuning results (table below)
For future readers, we already have the following language pairs in the wmt namespaces:
```
wmt14: ['cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en']
wmt15: ['cs-en', 'de-en', 'fi-en', 'fr-en', 'ru-en']
wmt16: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en']
wmt17: ['cs-en', 'de-en', 'fi-en', 'lv-en', 'ru-en', 'tr-en', 'zh-en']
wmt18: ['cs-en', 'de-en', 'et-en', 'fi-en', 'kk-en', 'ru-en', 'tr-en', 'zh-en']
wmt19: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/437 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/437/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/437/comments | https://api.github.com/repos/huggingface/datasets/issues/437/events | https://github.com/huggingface/datasets/pull/437 | 665,597,176 | MDExOlB1bGxSZXF1ZXN0NDU2NjIzNjc3 | 437 | Fix XTREME PAN-X loading | {
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"There is an interesting design question here (cc @lhoestq).\r\n\r\nI guess the labels form a closed set so we could also use a [nlp.ClassLabel](https://huggingface.co/nlp/package_reference/main_classes.html#nlp.ClassLabel) instead of a string. The differences will be mainly that:\r\n- the labels are stored as inte... | 1,595,688,297,000 | 1,596,097,695,000 | 1,596,097,695,000 | MEMBER | null | false | {
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} | Hi 🤗
In response to the discussion in #425 @lewtun and I made some fixes to the repo. In the original XTREME implementation the PAN-X dataset for named entity recognition loaded each word/tag pair as a single row and the sentence relation was lost. With the fix each row contains the list of all words in a single sentence and their NER tags. This is also in agreement with the [NER example](https://github.com/huggingface/transformers/tree/master/examples/token-classification) in the transformers repo.
With the fix the output of the dataset should look as follows:
```python
>>> dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
>>> dataset['train'][0]
{'words': ['R.H.', 'Saunders', '(', 'St.', 'Lawrence', 'River', ')', '(', '968', 'MW', ')'],
'ner_tags': ['B-ORG', 'I-ORG', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O'],
'langs': ['en', 'en', 'en', 'en', 'en', 'en', 'en', 'en', 'en', 'en', 'en']}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/436 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/436/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/436/comments | https://api.github.com/repos/huggingface/datasets/issues/436/events | https://github.com/huggingface/datasets/issues/436 | 665,582,167 | MDU6SXNzdWU2NjU1ODIxNjc= | 436 | Google Colab - load_dataset - PyArrow exception | {
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"Indeed, we’ll make a new PyPi release next week to solve this. Cc @lhoestq ",
"+1! this is the reason our tests are failing at [TextAttack](https://github.com/QData/TextAttack) \r\n\r\n(Though it's worth noting if we fixed the version number of pyarrow to 0.16.0 that would fix our problem too. But in this case w... | 1,595,682,320,000 | 1,597,910,898,000 | 1,597,910,898,000 | NONE | null | null | null | With latest PyArrow 1.0.0 installed, I get the following exception . Restarting colab has the same issue
ImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.
The error goes only when I install version 0.16.0
i.e. !pip install pyarrow==0.16.0 | {
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"This was fixed in #434 \r\nWe'll do a release later this week to include this fix.\r\nThanks for reporting",
"I dont know if the fix was made but the problem is still present : \r\nInstaled with pip : NLP 0.3.0 // pyarrow 1.0.0 \r\nOS : archlinux with kernel zen 5.8.5",
"Yes it was fixed in `nlp>=0.4.0`\r\nYou... | 1,595,648,679,000 | 1,599,587,835,000 | 1,596,472,652,000 | NONE | null | null | null | The following PR raised ImportWarning at `pyarrow ==1.0.0` https://github.com/huggingface/nlp/pull/265/files | {
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"Great, thanks!"
] | 1,595,636,213,000 | 1,595,658,994,000 | 1,595,658,994,000 | CONTRIBUTOR | null | false | {
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"Hi @ArneBinder, we have a few \"generic\" datasets which are intended to load data files with a predefined format:\r\n- csv: https://github.com/huggingface/nlp/tree/master/datasets/csv\r\n- json: https://github.com/huggingface/nlp/tree/master/datasets/json\r\n- text: https://github.com/huggingface/nlp/tree/master/... | 1,595,611,657,000 | 1,596,190,997,000 | null | NONE | null | null | null | I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https://brat.nlplab.org/standoff.html), [dataset code](https://github.com/ArneBinder/nlp/blob/brat/datasets/brat/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to reuse formats and loading functionality for datasets with a common format?
In my case, it took a bit of time to create the Brat dataset and I think others would appreciate to not have to think about that again. Also, I assume there are other formats (e.g. conll) that are widely used, so having this would really ease dataset onboarding and adoption of the library. | {
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https://api.github.com/repos/huggingface/datasets/issues/432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/432/comments | https://api.github.com/repos/huggingface/datasets/issues/432/events | https://github.com/huggingface/datasets/pull/432 | 665,234,340 | MDExOlB1bGxSZXF1ZXN0NDU2MzQxNDk3 | 432 | Fix handling of config files while loading datasets from multiple processes | {
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"Ok for this but I think we may want to use the general `filelock` method we are using at other places in the library instead of filecmp (in particular `filelock` take care of being an atomic operation which is safer for concurrent processes)",
"Ok I see.\r\nWhy not use filelock in this case then ?",
"I think w... | 1,595,603,457,000 | 1,596,301,902,000 | 1,596,097,528,000 | CONTRIBUTOR | null | false | {
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} | When loading shards on several processes, each process upon loading the dataset will overwrite dataset_infos.json in <package path>/datasets/<dataset name>/<hash>/dataset_infos.json. It does so every time, even when the target file already exists and is identical. Because multiple processes rewrite the same file in parallel, it creates a race condition when a process tries to load the file, often resulting in a JSON decoding exception because the file is only partially written.
This pull requests partially address this by comparing if the files are already identical before copying over the downloaded copy to the cached destination. There's still a race condition, but now it's less likely to occur if some basic precautions are taken by the library user, e.g., download all datasets to cache before spawning multiple processes. | {
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https://api.github.com/repos/huggingface/datasets/issues/431 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/431/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/431/comments | https://api.github.com/repos/huggingface/datasets/issues/431/events | https://github.com/huggingface/datasets/pull/431 | 665,044,416 | MDExOlB1bGxSZXF1ZXN0NDU2MTgyNDE2 | 431 | Specify split post processing + Add post processing resources downloading | {
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"I was using a hack in `wiki_dpr` to download the index from GCS even for the configurations without the embeddings.\r\nHowever as GCS is something internal, I changed the logic to add a download step for indexes directly in the dataset script, using the `DownloadManager`.\r\n\r\nThis change was directly linked to ... | 1,595,582,959,000 | 1,596,186,304,000 | 1,596,186,303,000 | MEMBER | null | false | {
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} | Previously if you tried to do
```python
from nlp import load_dataset
wiki = load_dataset("wiki_dpr", "psgs_w100_with_nq_embeddings", split="train[:100]", with_index=True)
```
Then you'd get an error `Index size should match Dataset size...`
This was because it was trying to use the full index (21M elements).
To fix that I made it so post processing resources can be named according to the split.
I'm going to add tests on post processing too.
Note that the CI will fail as I added a new argument in `_post_processing_resources`: the AWS version of wiki_dpr fails, and there's also an error telling that it is not synced (it'll be synced once it's merged):
```
=========================== short test summary info ============================
FAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr
FAILED tests/test_hf_gcp.py::TestDatasetSynced::test_script_synced_with_s3_wiki_dpr
```
EDIT: I did a change to ignore the script hash to locate the arrow files on GCS, so I removed the sync test. It was there just because of the hash logic for files on GCS | {
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https://api.github.com/repos/huggingface/datasets/issues/430 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/430/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/430/comments | https://api.github.com/repos/huggingface/datasets/issues/430/events | https://github.com/huggingface/datasets/pull/430 | 664,583,837 | MDExOlB1bGxSZXF1ZXN0NDU1ODAxOTI2 | 430 | add DatasetDict | {
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"I did the changes in the docstrings and I added a type check in each `DatasetDict` method to make sure all values are of type `Dataset`",
"Awesome, do you mind adding these in the doc as well?",
"I added it to the docs (processing + main classes)",
"I'm trying to follow along with the following about dataset... | 1,595,519,029,000 | 1,596,502,913,000 | 1,596,013,582,000 | MEMBER | null | false | {
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} | ## Add DatasetDict
### Overview
When you call `load_dataset` it can return a dictionary of datasets if there are several splits (train/test for example).
If you wanted to apply dataset transforms you had to iterate over each split and apply the transform.
Instead of returning a dict, it now returns a `nlp.DatasetDict` object which inherits from dict and contains the same data as before, except that now users can call dataset transforms directly from the output, and they'll be applied on each split.
Before:
```python
from nlp import load_dataset
squad = load_dataset("squad")
print(squad.keys())
# dict_keys(['train', 'validation'])
squad = {
split_name: dataset.map(my_func) for split_name, dataset in squad.items()
}
print(squad.keys())
# dict_keys(['train', 'validation'])
```
Now:
```python
from nlp import load_dataset
squad = load_dataset("squad")
print(squad.keys())
# dict_keys(['train', 'validation'])
squad = squad.map(my_func)
print(squad.keys())
# dict_keys(['train', 'validation'])
```
### Dataset transforms
`nlp.DatasetDict` implements the following dataset transforms:
- map
- filter
- sort
- shuffle
### Arguments
The arguments of the methods are the same except for split-specific arguments like `cache_file_name`.
For such arguments, the expected input is a dictionary `{split_name: argument_value}`
It concerns:
- `cache_file_name` in map, filter, sort, shuffle
- `seed` and `generator` in shuffle | {
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https://api.github.com/repos/huggingface/datasets/issues/429 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/429/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/429/comments | https://api.github.com/repos/huggingface/datasets/issues/429/events | https://github.com/huggingface/datasets/pull/429 | 664,412,137 | MDExOlB1bGxSZXF1ZXN0NDU1NjU2MDk5 | 429 | mlsum | {
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"Thanks @RachelKer for this PR.\r\n\r\nI think the dummy_data structure does not also match. In the `_split_generator` you have something like `os.path.join(downloaded_files[\"validation\"], lang+'_val.jsonl')` but in you dummy_data you have `os.path.join(downloaded_files[\"validation\"], lang+\"_val.zip\", lang+'... | 1,595,505,159,000 | 1,596,195,980,000 | 1,596,195,980,000 | CONTRIBUTOR | null | false | {
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} | Hello,
The tests for the load_real_data fail, as there is no default language subset to download it looks for a file that does not exist. This bug does not happen when using the load_dataset function, as it asks you to specify a language if you do not, so I submit this PR anyway. The dataset is avalaible on : https://gitlab.lip6.fr/scialom/mlsum_data | {
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https://api.github.com/repos/huggingface/datasets/issues/428 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/428/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/428/comments | https://api.github.com/repos/huggingface/datasets/issues/428/events | https://github.com/huggingface/datasets/pull/428 | 664,367,086 | MDExOlB1bGxSZXF1ZXN0NDU1NjE3Nzcy | 428 | fix concatenate_datasets | {
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} | `concatenate_datatsets` used to test that the different`nlp.Dataset.schema` match, but this attribute was removed in #423 | {
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} | ## Allow Sequence features for Beam Datasets + add Natural Questions
### The issue
The steps of beam datasets processing is the following:
- download the source files and send them in a remote storage (gcs)
- process the files using a beam runner (dataflow)
- save output in remote storage (gcs)
- convert output to arrow in remote storage (gcs)
However it wasn't possible to process `natural_questions` because apache beam's processing outputs parquet files, and it's not yet possible to read parquet files with list features.
### The proposed solution
To allow sequence features for beam I added a workaround that serializes the values using `json.dumps`, so that we end up with strings instead of the original features. Then when the arrow file is created, the serialized objects are transformed back to normal with `json.loads`. Not sure if there's a better way to do it.
### Natural Questions
I was able to process NQ with it, and so I added the json infos file in this PR too.
The processed arrow files are also stored in gcs.
It allows you to load NQ with
```python
from nlp import load_dataset
nq = load_dataset("natural_questions") # download the 90GB arrow files from gcs and return the dataset
```
### Tests
I added a test case to make sure it works as expected.
Note that the CI will fail because I am updating `natural_questions.py`: it's not synced with the script on S3. It will be synced as soon as this PR is merged.
```
=========================== short test summary info ============================
FAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_natural_questions/default
``` | {
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"Yes that's definitely something we plan to add ^^",
"Yes, that would be nice. We could take a look at what tensorflow `tf.data` does under the hood for instance.",
"So `tf.data.Dataset.map()` returns a `ParallelMapDataset` if `num_parallel_calls is not None` [link](https://github.com/tensorflow/tensorflow/blob... | 1,595,480,441,000 | 1,615,541,652,000 | 1,599,490,084,000 | NONE | null | null | null | It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process/thread/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together? | {
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https://api.github.com/repos/huggingface/datasets/issues/425 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/425/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/425/comments | https://api.github.com/repos/huggingface/datasets/issues/425/events | https://github.com/huggingface/datasets/issues/425 | 664,029,848 | MDU6SXNzdWU2NjQwMjk4NDg= | 425 | Correct data structure for PAN-X task in XTREME dataset? | {
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"Thanks for noticing ! This looks more reasonable indeed.\r\nFeel free to open a PR",
"Hi @lhoestq \r\nI made the proposed changes to the `xtreme.py` script. I noticed that I also need to change the schema in the `dataset_infos.json` file. More specifically the `\"features\"` part of the PAN-X.LANG dataset:\r\n\... | 1,595,449,760,000 | 1,596,375,034,000 | 1,596,375,034,000 | MEMBER | null | null | null | Hi 🤗 team!
## Description of the problem
Thanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:
```python
from nlp import load_dataset
# AmazonPhotos.zip is located in data/
dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
dataset_train = dataset['train']
```
However, I am not sure that `load_dataset()` is returning the correct data structure for NER.
Currently, every row in `dataset_train` is of the form
```python
{'word': str, 'ner_tag': str, 'lang': str}
```
but I think we actually want something like
```python
{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}
```
so that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.
Indeed, [this line](https://github.com/google-research/xtreme/blob/522434d1aece34131d997a97ce7e9242a51a688a/third_party/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.
## Proposed solution
Replace
```python
with open(filepath) as f:
data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for id_, row in enumerate(data):
if row:
lang, word = row[0].split(":")[0], row[0].split(":")[1]
tag = row[1]
yield id_, {"word": word, "ner_tag": tag, "lang": lang}
```
from [these lines](https://github.com/huggingface/nlp/blob/ce7d3a1d630b78fe27188d1706f3ea980e8eec43/datasets/xtreme/xtreme.py#L881-L887) of the `_generate_examples()` function with something like
```python
guid_index = 1
with open(filepath, encoding="utf-8") as f:
words = []
ner_tags = []
langs = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if words:
yield guid_index, {"words": words, "ner_tags": ner_tags, "langs": langs}
guid_index += 1
words = []
ner_tags = []
else:
# pan-x data is tab separated
splits = line.split("\t")
# strip out en: prefix
langs.append(splits[0][:2])
words.append(splits[0][3:])
if len(splits) > 1:
labels.append(splits[-1].replace("\n", ""))
else:
# examples have no label in test set
labels.append("O")
```
If you agree, me or @lvwerra would be happy to implement this and create a PR. | {
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#353 | {
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"I had to make `SplitDict` serializable to be able to copy `DatasetInfo` objects properly.\r\nSerialization was also asked in #389 ",
"One thing I forgot to say here, is that we also want to use the features arguments of `load_dataset` (which goes in the builder’s config) to override the default features of a dat... | 1,595,343,167,000 | 1,595,668,114,000 | 1,595,499,317,000 | MEMBER | null | false | {
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} | ## New logic for `nlp.Features` in datasets
Previously, it was confusing to have `features` and pyarrow's `schema` in `nlp.Dataset`.
However `features` is supposed to be the front-facing object to define the different fields of a dataset, while `schema` is only used to write arrow files.
Changes:
- Remove `schema` field in `nlp.Dataset`
- Make `features` the source of truth to read/write examples
- `features` can no longer be `None` in `nlp.Dataset`
- Update `features` after each dataset transform such as `nlp.Dataset.map`
Todo: change the tests to take these changes into account | {
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} | The preparation phase (after the download phase) crashed on windows because of charmap encoding not being able to decode certain characters. This change suggested in Issue #347 fixes it for the IMDB dataset. | {
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"What about the other PR #419 ?",
"Oh this is the PR where I ran make quality and make style and some previous files from master were changed",
"Oh right ! Let me fix the style myself if you don't mind"
] | 1,595,275,709,000 | 1,595,434,120,000 | 1,595,434,119,000 | CONTRIBUTOR | null | false | {
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} | Changes:
- added arrow schema to features conversion (it's going to be useful to fix #342 )
- make flatten handle deep features (useful for tfrecords conversion in #339 )
- add tests for flatten and features conversions
- the reader now returns the kwargs to instantiate a Dataset (fix circular dependencies) | {
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Signed-off-by: lordtt13 <thakurtanmay72@yahoo.com> | {
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https://api.github.com/repos/huggingface/datasets/issues/418 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/418/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/418/comments | https://api.github.com/repos/huggingface/datasets/issues/418/events | https://github.com/huggingface/datasets/issues/418 | 661,914,873 | MDU6SXNzdWU2NjE5MTQ4NzM= | 418 | Addition of google drive links to dl_manager | {
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"I think the problem is the way you wrote your urls. Try the following structure to see `https://drive.google.com/uc?export=download&id=your_file_id` . \r\n\r\n@lhoestq ",
"Oh sorry, I think `_get_drive_url` is doing that. \r\n\r\nHave you tried to use `dl_manager.download_and_extract(_get_drive_url(_TRAIN_URL)`... | 1,595,256,722,000 | 1,595,259,572,000 | 1,595,259,572,000 | CONTRIBUTOR | null | null | null | Hello there, I followed the template to create a download script of my own, which works fine for me, although I had to shun the dl_manager because it was downloading nothing from the drive links and instead use gdown.
This is the script for me:
```python
class EmoConfig(nlp.BuilderConfig):
"""BuilderConfig for SQUAD."""
def __init__(self, **kwargs):
"""BuilderConfig for EmoContext.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(EmoConfig, self).__init__(**kwargs)
_TEST_URL = "https://drive.google.com/file/d/1Hn5ytHSSoGOC4sjm3wYy0Dh0oY_oXBbb/view?usp=sharing"
_TRAIN_URL = "https://drive.google.com/file/d/12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X/view?usp=sharing"
class EmoDataset(nlp.GeneratorBasedBuilder):
""" SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Version 1.0.0 """
VERSION = nlp.Version("1.0.0")
force = False
def _info(self):
return nlp.DatasetInfo(
description=_DESCRIPTION,
features=nlp.Features(
{
"text": nlp.Value("string"),
"label": nlp.features.ClassLabel(names=["others", "happy", "sad", "angry"]),
}
),
supervised_keys=None,
homepage="https://www.aclweb.org/anthology/S19-2005/",
citation=_CITATION,
)
def _get_drive_url(self, url):
base_url = 'https://drive.google.com/uc?id='
split_url = url.split('/')
return base_url + split_url[5]
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
if(not os.path.exists("emo-train.json") or self.force):
gdown.download(self._get_drive_url(_TRAIN_URL), "emo-train.json", quiet = True)
if(not os.path.exists("emo-test.json") or self.force):
gdown.download(self._get_drive_url(_TEST_URL), "emo-test.json", quiet = True)
return [
nlp.SplitGenerator(
name=nlp.Split.TRAIN,
gen_kwargs={
"filepath": "emo-train.json",
"split": "train",
},
),
nlp.SplitGenerator(
name=nlp.Split.TEST,
gen_kwargs={"filepath": "emo-test.json", "split": "test"},
),
]
def _generate_examples(self, filepath, split):
""" Yields examples. """
with open(filepath, 'rb') as f:
data = json.load(f)
for id_, text, label in zip(data["text"].keys(), data["text"].values(), data["Label"].values()):
yield id_, {
"text": text,
"label": label,
}
```
Can someone help me in adding gdrive links to be used with default dl_manager or adding gdown as another dl_manager, because I'd like to add this dataset to nlp's official database. | {
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https://api.github.com/repos/huggingface/datasets/issues/417 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/417/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/417/comments | https://api.github.com/repos/huggingface/datasets/issues/417/events | https://github.com/huggingface/datasets/pull/417 | 661,804,054 | MDExOlB1bGxSZXF1ZXN0NDUzNDMyODE5 | 417 | Fix docstrins multiple metrics instances | {
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} | We change the docstrings of `nlp.Metric.compute`, `nlp.Metric.add` and `nlp.Metric.add_batch` depending on which metric is instantiated. However we had issues when instantiating multiple metrics (docstrings were duplicated).
This should fix #304 | {
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https://api.github.com/repos/huggingface/datasets/issues/416 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/416/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/416/comments | https://api.github.com/repos/huggingface/datasets/issues/416/events | https://github.com/huggingface/datasets/pull/416 | 661,635,393 | MDExOlB1bGxSZXF1ZXN0NDUzMjg1NTM4 | 416 | Fix xtreme panx directory | {
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"great, I think I did not download the data the way you do, but yours is more reasonable."
] | 1,595,239,757,000 | 1,595,319,346,000 | 1,595,319,344,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/415 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/415/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/415/comments | https://api.github.com/repos/huggingface/datasets/issues/415/events | https://github.com/huggingface/datasets/issues/415 | 660,687,076 | MDU6SXNzdWU2NjA2ODcwNzY= | 415 | Something is wrong with WMT 19 kk-en dataset | {
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"name": "dataset bug",
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] | open | false | null | [] | null | [] | 1,595,146,731,000 | 1,595,238,866,000 | null | NONE | null | null | null | The translation in the `train` set does not look right:
```
>>>import nlp
>>>from nlp import load_dataset
>>>dataset = load_dataset('wmt19', 'kk-en')
>>>dataset["train"]["translation"][0]
{'kk': 'Trumpian Uncertainty', 'en': 'Трамптық белгісіздік'}
>>>dataset["validation"]["translation"][0]
{'kk': 'Ақша-несие саясатының сценарийін қайта жазсақ', 'en': 'Rewriting the Monetary-Policy Script'}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/414 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/414/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/414/comments | https://api.github.com/repos/huggingface/datasets/issues/414/events | https://github.com/huggingface/datasets/issues/414 | 660,654,013 | MDU6SXNzdWU2NjA2NTQwMTM= | 414 | from_dict delete? | {
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"`from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\nRight now if you want to use `from_dict` you have to install the package from the master branch\r\n```\r\npip install git+https://github.com/... | 1,595,142,516,000 | 1,595,298,077,000 | 1,595,298,077,000 | NONE | null | null | null | AttributeError: type object 'Dataset' has no attribute 'from_dict' | {
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https://api.github.com/repos/huggingface/datasets/issues/413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/413/comments | https://api.github.com/repos/huggingface/datasets/issues/413/events | https://github.com/huggingface/datasets/issues/413 | 660,063,655 | MDU6SXNzdWU2NjAwNjM2NTU= | 413 | Is there a way to download only NQ dev? | {
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"Unfortunately it's not possible to download only the dev set of NQ.\r\n\r\nI think we could add a way to download only the test set by adding a custom configuration to the processing script though.",
"Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially al... | 1,595,068,103,000 | 1,644,573,021,000 | 1,644,573,021,000 | NONE | null | null | null | Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)?
As we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data.
I tried
```
dataset = nlp.load_dataset('natural_questions', split="validation", beam_runner="DirectRunner")
```
But this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits / slicing options only available after downloading?
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/412/comments | https://api.github.com/repos/huggingface/datasets/issues/412/events | https://github.com/huggingface/datasets/issues/412 | 660,047,139 | MDU6SXNzdWU2NjAwNDcxMzk= | 412 | Unable to load XTREME dataset from disk | {
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"Hi @lewtun, you have to provide the full path to the downloaded file for example `/home/lewtum/..`",
"I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !",
"Thanks for the rapid fix @lhoestq!"
] | 1,595,066,100,000 | 1,595,319,344,000 | 1,595,319,344,000 | MEMBER | null | null | null | Hi 🤗 team!
## Description of the problem
Following the [docs](https://huggingface.co/nlp/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https://github.com/google-research/xtreme) benchmark.
I have manually downloaded the `AmazonPhotos.zip` file from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset.
As far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path:
```
# path where load_dataset is looking for fr.tar.gz
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/
# path where it actually exists
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/panx_dataset/
```
## Steps to reproduce the problem
1. Manually download the XTREME benchmark from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1)
2. Run the following code snippet
```python
from nlp import load_dataset
# AmazonPhotos.zip is in the root of the folder
dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
```
3. Here is the stack trace
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-4-26786bb5fa93> in <module>
----> 1 dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
/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)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
/usr/local/lib/python3.6/dist-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)
430 verify_infos = not save_infos and not ignore_verifications
431 self._download_and_prepare(
--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
433 )
434 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
464 split_dict = SplitDict(dataset_name=self.name)
465 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 466 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
467 # Checksums verification
468 if verify_infos:
/usr/local/lib/python3.6/dist-packages/nlp/datasets/xtreme/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746/xtreme.py in _split_generators(self, dl_manager)
725 panx_dl_dir = dl_manager.extract(panx_path)
726 lang = self.config.name.split(".")[1]
--> 727 lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + ".tar.gz"))
728 return [
729 nlp.SplitGenerator(
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in extract(self, path_or_paths)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)
170 return tuple(mapped)
171 # Singleton
--> 172 return function(data_struct)
173
174
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in <lambda>(path)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
203 elif urlparse(url_or_filename).scheme == "":
204 # File, but it doesn't exist.
--> 205 raise FileNotFoundError("Local file {} doesn't exist".format(url_or_filename))
206 else:
207 # Something unknown
FileNotFoundError: Local file /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/fr.tar.gz doesn't exist
```
## OS and hardware
```
- `nlp` version: 0.3.0
- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.4.0 (True)
- Tensorflow version (GPU?): 2.1.0 (True)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
``` | {
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dataset homepage: https://homes.cs.washington.edu/~msap/social-bias-frames/ | {
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#353 | {
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"It was fixed in 2ddd18d139d3047c9c3abe96e1e7d05bb360132c.\r\nCould you pull the latest changes from master @morganmcg1 ?",
"Thanks @lhoestq, works fine now!"
] | 1,594,982,188,000 | 1,595,342,092,000 | 1,595,342,092,000 | NONE | null | null | null | `train_test_split` is giving me an error when I try and call it:
`'dict' object has no attribute 'deepcopy'`
## To reproduce
```
dataset = load_dataset('glue', 'mrpc', split='train')
dataset = dataset.train_test_split(test_size=0.2)
```
## Full Stacktrace
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-feb740dbec9a> in <module>
1 dataset = load_dataset('glue', 'mrpc', split='train')
----> 2 dataset = dataset.train_test_split(test_size=0.2)
~/anaconda3/envs/fastai2_me/lib/python3.7/site-packages/nlp/arrow_dataset.py in train_test_split(self, test_size, train_size, shuffle, seed, generator, keep_in_memory, load_from_cache_file, train_cache_file_name, test_cache_file_name, writer_batch_size)
1032 "writer_batch_size": writer_batch_size,
1033 }
-> 1034 train_kwargs = cache_kwargs.deepcopy()
1035 train_kwargs["split"] = "train"
1036 test_kwargs = cache_kwargs.deepcopy()
AttributeError: 'dict' object has no attribute 'deepcopy'
``` | {
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} | Some datasets are available on our google cloud storage in arrow format, so that the users don't need to process the data.
These tests make sure that they're always available. It also makes sure that their scripts are in sync between S3 and the repo.
This should avoid future issues like #407 | {
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"Fixed. Could you try again @mitchellgordon95 ?\r\nIt was due a file not being updated on S3.\r\n\r\nWe need to make sure all the datasets scripts get updated properly @julien-c ",
"Works for me! Thanks.",
"I found the same issue with almost any language other than English. (For English, it works). Will someone... | 1,594,943,283,000 | 1,610,451,676,000 | 1,594,995,868,000 | CONTRIBUTOR | null | null | null | There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):
```
nlp.load_dataset('wikipedia', "20200501.en", split='train')
```
And now, having pulled master, I get:
```
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to /home/hltcoe/mgordon/.cache/huggingface/datasets/wikipedia/20200501.en/1.0.0/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...
Traceback (most recent call last):
File "scripts/download.py", line 11, in <module>
fire.Fire(download_pretrain)
File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 138, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 468, in _Fire
target=component.__name__)
File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 672, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "scripts/download.py", line 6, in download_pretrain
nlp.load_dataset('wikipedia', "20200501.en", split='train')
File "/exp/mgordon/nlp/src/nlp/load.py", line 534, in load_dataset
save_infos=save_infos,
File "/exp/mgordon/nlp/src/nlp/builder.py", line 460, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/exp/mgordon/nlp/src/nlp/builder.py", line 870, in _download_and_prepare
"\n\t`{}`".format(usage_example)
nlp.builder.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, S
park, 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.en', beam_runner='DirectRunner')`
``` | {
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"I think the slowness here probably come from the fact that we are copying from and to python.\r\n\r\n@lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?",
"> @lhoestq for all the `select... | 1,594,934,513,000 | 1,599,489,926,000 | 1,599,489,925,000 | CONTRIBUTOR | null | null | null | Consider shuffling bookcorpus:
```
dataset = nlp.load_dataset('bookcorpus', split='train')
dataset.shuffle()
```
According to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.
But I can also just write the lines to a text file:
```
batch_size = 100000
with open('tmp.txt', 'w+') as out_f:
for i in tqdm(range(0, len(dataset), batch_size)):
batch = dataset[i:i+batch_size]['text']
print("\n".join(batch), file=out_f)
```
Which completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,
```
dataset = nlp.load_dataset('text', data_files='tmp2.txt')
```
Which completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping.
Is shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.) | {
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https://api.github.com/repos/huggingface/datasets/issues/405 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/405/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/405/comments | https://api.github.com/repos/huggingface/datasets/issues/405/events | https://github.com/huggingface/datasets/pull/405 | 658,580,192 | MDExOlB1bGxSZXF1ZXN0NDUwNTI1MTc3 | 405 | Make select() faster by batching reads | {
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} | Here's a benchmark:
```
dataset = nlp.load_dataset('bookcorpus', split='train')
start = time.time()
dataset.select(np.arange(1000), reader_batch_size=1, load_from_cache_file=False)
end = time.time()
print(f'{end - start}')
start = time.time()
dataset.select(np.arange(1000), reader_batch_size=1000, load_from_cache_file=False)
end = time.time()
print(f'{end - start}')
```
Without batching, select takes around 1.27 seconds. With batching, it takes around 0.01 seconds. The slowness was upsetting me because dataset.shuffle() was supposed to take ~27 hours for bookcorpus. Now with the fix it takes ~2.5 hours (which still is pretty slow, but I'll open a separate issue for that). | {
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https://api.github.com/repos/huggingface/datasets/issues/404 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/404/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/404/comments | https://api.github.com/repos/huggingface/datasets/issues/404/events | https://github.com/huggingface/datasets/pull/404 | 658,400,987 | MDExOlB1bGxSZXF1ZXN0NDUwMzY4Mjg4 | 404 | Add seed in metrics | {
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} | With #361 we noticed that some metrics were not deterministic.
In this PR I allow the user to specify numpy's seed when instantiating a metric with `load_metric`.
The seed is set only when `compute` is called, and reset afterwards.
Moreover when calling `compute` with the same metric instance (i.e. same experiment_id), the metric will always return the same results given the same inputs. This is the case even if the seed is was not specified by the user, as the previous seed is going to be reused.
However, instantiating twice a metric (two different experiments) without specifying a seed can create different results. | {
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} | We were using to_pandas() to convert from arrow types, however it returns numpy arrays instead of python lists.
I fixed it by using to_pydict/to_pylist instead.
Fix #387
It was mentioned in https://github.com/huggingface/transformers/issues/5729
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#336 | {
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"What does the `nlp-cli dummy_data` command returns ?",
"`test.json` -> `test` \r\nand \r\n`train.json` -> `train`\r\n\r\nas shown by the `nlp-cli dummy_data` command ;-)",
"LGTM for merge @lhoestq - I let you merge if you want to."
] | 1,594,889,699,000 | 1,596,694,580,000 | 1,596,694,579,000 | CONTRIBUTOR | null | false | {
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} | add Web Question dataset
#336
Maybe @patrickvonplaten you can help with the dummy_data structure? it still broken | {
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#336 | {
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"Thanks!"
] | 1,594,874,278,000 | 1,594,882,188,000 | 1,594,882,177,000 | CONTRIBUTOR | null | false | {
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"Do you mind adding a link to the much more extended pages on adding and sharing a dataset in the new documentation?",
"Sure, I will do that too"
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https://api.github.com/repos/huggingface/datasets/issues/397 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/397/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/397/comments | https://api.github.com/repos/huggingface/datasets/issues/397/events | https://github.com/huggingface/datasets/pull/397 | 657,510,856 | MDExOlB1bGxSZXF1ZXN0NDQ5NjE1MDA4 | 397 | Add contiguous sharding | {
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Usage:
```
nlp.concatenate_datasets([dset.shard(n, i, contiguous=True) for i in range(n)])
``` | {
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Fix #395 | {
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```python
import nlp
w = nlp.load_dataset("wikipedia", "20200501.en", split="train")
w.select([0])
```
This is caused by [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/arrow_dataset.py#L626) for some reason, that tries to serialize the function with all the wikipedia data with it.
It's not the case with `.map` or `.filter`.
However functions that are based on `.select` like `.shuffle`, `.shard`, `.train_test_split`, `.sort` are affected.
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#378 | {
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For downloaded files, we are using the `downloads` directory to make things easier to navigate, but extracted files were still placed at the root of the cache directory. To fix that I changed the directory for extracted files to cache_dir/downloads/extracted. | {
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https://api.github.com/repos/huggingface/datasets/issues/392 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/392/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/392/comments | https://api.github.com/repos/huggingface/datasets/issues/392/events | https://github.com/huggingface/datasets/pull/392 | 657,313,738 | MDExOlB1bGxSZXF1ZXN0NDQ5NDUwOTkx | 392 | Style change detection | {
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- There's the possibility of adding the [PAN19](https://zenodo.org/record/3577602) and PAN18 style change detection tasks too (these are datasets whose labels are a subset of PAN20's). These would probably make more sense as separate datasets (like wmt is now)
- I've converted the integer 0,1 values to a boolean
- Using manually downloaded data again. This might be changed at some point following the discussion in https://github.com/huggingface/nlp/pull/349. | {
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"Looks cool :)\r\n\r\nI feel like \r\n```python\r\nconcatenated_dataset = dataset1.concatenate(dataset2)\r\n```\r\ncould be more natural. What do you think ?\r\n\r\nAlso could you also concatenate the `nlp.Dataset._data_files` ?\r\n```python\r\nreturn cls(table, info=info, split=split, data_files=self._data_files +... | 1,594,769,077,000 | 1,595,411,398,000 | 1,595,411,398,000 | CONTRIBUTOR | null | false | {
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} | I'm constructing the "WikiBooks" dataset, which is a concatenation of Wikipedia & BookCorpus. So I implemented the `Dataset.from_concat()` method, which concatenates two datasets with the same schema.
This would also be useful if someone wants to pretrain on a large generic dataset + their own custom dataset. Not in love with the method name, so would love to hear suggestions.
Usage:
```python
from nlp import Dataset, load_dataset
data1, data2 = {"id": [0, 1, 2]}, {"id": [3, 4, 5]}
dset1, dset2 = Dataset.from_dict(data1), Dataset.from_dict(data2)
dset_concat = Dataset.from_concat([dset1, dset2])
print(dset_concat)
# Dataset(schema: {'id': 'int64'}, num_rows: 6)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/389 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/389/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/389/comments | https://api.github.com/repos/huggingface/datasets/issues/389/events | https://github.com/huggingface/datasets/pull/389 | 656,921,768 | MDExOlB1bGxSZXF1ZXN0NDQ5MTMyOTU5 | 389 | Fix pickling of SplitDict | {
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"By the way, the reason this is an issue for me is because I want to be able to \"save\" changes made to a dataset by writing something to disk. In this case, I would like to pre-process my dataset once, and then train multiple models on the dataset later without having to re-process the data. \r\n\r\nIs pickling/u... | 1,594,763,619,000 | 1,596,551,890,000 | 1,596,551,890,000 | CONTRIBUTOR | null | false | {
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} | It would be nice to pickle and unpickle Datasets, as done in [this tutorial](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb). Example:
```
wiki = nlp.load_dataset('wikipedia', split='train')
def sentencize(examples):
...
wiki = wiki.map(sentencize, batched=True)
torch.save(wiki, 'sentencized_wiki_dataset.pt')
```
However, upon unpickling the dataset via torch.load(...), this error is raised:
```
ValueError("Cannot add elem. Use .add() instead.")
```
On line [492 of splits.py](https://github.com/huggingface/nlp/blob/master/src/nlp/splits.py#L492). This is because SplitDict subclasses dict, and pickle treats [dicts specially](https://github.com/huggingface/nlp/blob/master/src/nlp/splits.py#L492). Pickle expects access to `dict.__setitem__`, but this is disallowed by the class.
The workaround is to provide an explicit interface for pickle to call when pickling and unpickling, thereby avoiding the use of `__setitem__`.
Testing:
- Manually pickled and unpickled a modified wikipedia dataset.
- Ran `make style`
I would be happy to run any other tests, but I couldn't find any in the contributing guidelines. | {
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https://api.github.com/repos/huggingface/datasets/issues/388 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/388/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/388/comments | https://api.github.com/repos/huggingface/datasets/issues/388/events | https://github.com/huggingface/datasets/issues/388 | 656,707,497 | MDU6SXNzdWU2NTY3MDc0OTc= | 388 | 🐛 [Dataset] Cannot download wmt14, wmt15 and wmt17 | {
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"name": "dataset bug",
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"follo... | null | [
"similar slow download speed here for nlp.load_dataset('wmt14', 'fr-en')\r\n`\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 658M/658M [1:00:42<00:00, 181kB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 918M/918M [1:39:38<00:00, 154kB/s]\r\nDow... | 1,594,741,001,000 | 1,596,639,392,000 | null | NONE | null | null | null | 1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code:
```
nlp.load_dataset('wmt14','de-en')
nlp.load_dataset('wmt15','de-en')
nlp.load_dataset('wmt17','de-en')
nlp.load_dataset('wmt19','de-en')
```
The code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18`
2. When trying to download `wmt17 zh-en`, I got the following error:
> ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz | {
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https://api.github.com/repos/huggingface/datasets/issues/387 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/387/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/387/comments | https://api.github.com/repos/huggingface/datasets/issues/387/events | https://github.com/huggingface/datasets/issues/387 | 656,361,357 | MDU6SXNzdWU2NTYzNjEzNTc= | 387 | Conversion through to_pandas output numpy arrays for lists instead of python objects | {
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"To convert from arrow type we have three options: to_numpy, to_pandas and to_pydict/to_pylist.\r\n\r\n- to_numpy and to_pandas return numpy arrays instead of lists but are very fast.\r\n- to_pydict/to_pylist can be 100x slower and become the bottleneck for reading data, but at least they return lists.\r\n\r\nMaybe... | 1,594,707,841,000 | 1,594,985,820,000 | 1,594,985,820,000 | MEMBER | null | null | null | In a related question, the conversion through to_pandas output numpy arrays for the lists instead of python objects.
Here is an example:
```python
>>> dataset._data.slice(key, 1).to_pandas().to_dict("list")
{'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [array([ 101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292,
1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938,
4267, 12223, 21811, 1117, 2554, 119, 102])], 'token_type_ids': [array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0])], 'attention_mask': [array([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])]}
>>> type(dataset._data.slice(key, 1).to_pandas().to_dict("list")['input_ids'][0])
<class 'numpy.ndarray'>
>>> dataset._data.slice(key, 1).to_pydict()
{'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [[101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[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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https://api.github.com/repos/huggingface/datasets/issues/386 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/386/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/386/comments | https://api.github.com/repos/huggingface/datasets/issues/386/events | https://github.com/huggingface/datasets/pull/386 | 655,839,067 | MDExOlB1bGxSZXF1ZXN0NDQ4MjQ1NDI4 | 386 | Update dataset loading and features - Add TREC dataset | {
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"I just copied the files that are on google storage to follow the new `_relative_data_dir ` format. It should be good to merge now :)\r\n\r\nWell actually it seems there are some merge conflicts to fix first"
] | 1,594,645,818,000 | 1,594,887,478,000 | 1,594,887,478,000 | MEMBER | null | false | {
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} | This PR:
- add a template for a new dataset script
- update the caching structure so that the path to the cached data files is also a function of the dataset loading script hash. This way when you update a loading script the data will be automatically updated instead of falling back to the previous version (which is usually a outdated). This makes it in particular easier to iterate when writing a new dataset loading script.
- fix a bug in the `ClassLabel` feature and make it more flexible so that its methods `str2int` and `int2str` can also accept list, numpy arrays and PyTorch/TensorFlow tensors.
- add the TREC-6 dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/385 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/385/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/385/comments | https://api.github.com/repos/huggingface/datasets/issues/385/events | https://github.com/huggingface/datasets/pull/385 | 655,663,997 | MDExOlB1bGxSZXF1ZXN0NDQ4MTAzMjY5 | 385 | Remove unnecessary nested dict | {
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"We can probably scan the dataset scripts with a regexpr to try to identify this pattern cc @patrickvonplaten maybe",
"@mariamabarham This script should work. I tested it for a couple of datasets. There might be exceptions where the script breaks - did not test everything.\r\n\r\n```python\r\n#!/usr/bin/env pytho... | 1,594,629,983,000 | 1,594,812,458,000 | 1,594,807,433,000 | CONTRIBUTOR | null | false | {
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} | This PR is removing unnecessary nested dictionary used in some datasets. For now the following datasets are updated:
- MLQA
- RACE
Will be adding more if necessary.
#378 | {
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https://api.github.com/repos/huggingface/datasets/issues/383 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/383/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/383/comments | https://api.github.com/repos/huggingface/datasets/issues/383/events | https://github.com/huggingface/datasets/pull/383 | 655,291,201 | MDExOlB1bGxSZXF1ZXN0NDQ3ODI0OTky | 383 | Adding the Linguistic Code-switching Evaluation (LinCE) benchmark | {
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"I am checking the details of the CI log for the failed test, but I don't see how the error relates to the code I added; the error is coming from a config builder different than the `LinceConfig`, and it crashes when `self.config.data_files` because is self.config is None. I would appreciate if someone could help m... | 1,594,506,920,000 | 1,594,916,386,000 | 1,594,916,386,000 | CONTRIBUTOR | null | false | {
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First of all, this library is really cool! Thanks for putting all of this together!
This PR contains the [Linguistic Code-switching Evaluation (LinCE) benchmark](https://ritual.uh.edu/lince). As described in the official website (FAQ):
> 1. Why do we need LinCE?
>LinCE brings 10 code-switching datasets together for 4 tasks and 4 language pairs with 5 leaderboards in a single evaluation platform. We examined each dataset and fixed major issues on the partitions (or even define official partitions) with a comprehensive stratification method (see our paper for more details).
>Besides, we believe that online benchmarks like LinCE bring steady research progress and allow to compare state-of-the-art models at the pace of the progress in NLP. We expect to benefit greatly the code-switching community with this benchmark.
The data comes from social media and here's the summary table of tasks per language pair:
| Language Pairs | LID | POS | NER | SA |
|----------------------------------------|-----|-----|-----|----|
| Spanish-English | ✅ | ✅ | ✅ | ✅ |
| Hindi-English | ✅ | ✅ | ✅ | |
| Modern Standard Arabic-Egyptian Arabic | ✅ | | ✅ | |
| Nepali-English | ✅ | | | |
The tasks are as follows:
* LID: token-level language identification
* POS: part-of-speech tagging
* NER: named entity recognition
* SA: sentiment analysis
With the exception of MSA-EA, the rest of the datasets contain token-level LID labels.
## Usage
For Spanish-English LID, we can load the data as follows:
```
import nlp
data = nlp.load_dataset('./datasets/lince/lince.py', 'lid_spaeng')
for split in data:
print(data[split])
```
Here's the output:
```
Dataset(schema: {'idx': 'int32', 'tokens': 'list<item: string>', 'lid': 'list<item: string>'}, num_rows: 21030)
Dataset(schema: {'idx': 'int32', 'tokens': 'list<item: string>', 'lid': 'list<item: string>'}, num_rows: 3332)
Dataset(schema: {'idx': 'int32', 'tokens': 'list<item: string>', 'lid': 'list<item: string>'}, num_rows: 8289)
```
Here's the list of shortcut names for every dataset available in LinCE:
* `lid_spaeng`
* `lid_hineng`
* `lid_nepeng`
* `lid_msaea`
* `pos_spaeng`
* `pos_hineng`
* `ner_spaeng`
* `ner_hineng`
* `ner_msaea`
* `sa_spaeng`
All the numbers match with Table 3 in the LinCE [paper](https://www.aclweb.org/anthology/2020.lrec-1.223.pdf). Also, note that the MSA-EA datasets use the Persian script while the other datasets use the Roman script.
## Features
Here is how the features look in the case of language identification (LID) tasks:
| LID Feature | Type | Description |
|----------------------|---------------|-------------------------------------------|
| `idx` | `int` | Dataset index of current sentence |
| `tokens` | `list<str>` | List of tokens (string) of a sentence |
| `lid` | `list<str>` | List of LID labels (string) of a sentence |
For part-of-speech (POS) tagging:
| POS Feature | Type | Description |
|----------------------|---------------|-------------------------------------------|
| `idx` | `int` | Dataset index of current sentence |
| `tokens` | `list<str>` | List of tokens (string) of a sentence |
| `lid` | `list<str>` | List of LID labels (string) of a sentence |
| `pos` | `list<str>` | List of POS tags (string) of a sentence |
For named entity recognition (NER):
| NER Feature | Type | Description |
|----------------------|---------------|-------------------------------------------|
| `idx` | `int` | Dataset index of current sentence |
| `tokens` | `list<str>` | List of tokens (string) of a sentence |
| `lid` | `list<str>` | List of LID labels (string) of a sentence |
| `ner` | `list<str>` | List of NER labels (string) of a sentence |
**NOTE**: the MSA-EA NER dataset does not contain the `lid` feature.
For sentiment analysis (SA):
| SA Feature | Type | Description |
|---------------------|-------------|-------------------------------------------|
| `idx` | `int` | Dataset index of current sentence |
| `tokens` | `list<str>` | List of tokens (string) of a sentence |
| `lid` | `list<str>` | List of LID labels (string) of a sentence |
| `sa` | `str` | Sentiment label (string) of a sentence |
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