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```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 simila... | {
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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.... | {
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---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-48907f2ad433> in <module>
----> 1 features = {x: trai... | {
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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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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 t... | {
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I think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https://github.com/huggingfa... | {
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Fix partially:
- #353
Subsequent PR after:
- #449 | {
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```
Bilingual data: {Arabic, German, French, Japanese, Korean, Chinese} <-> English
Multilingual data: German, English, Italian, Dutch, Romanian. (Any pair)
```
I'm unsure how to h... | {
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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 ... | {
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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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## 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-inp... | {
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- Allow to directly feed numpy/pytorch/tensorflow/pandas objects in metrics | {
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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... | {
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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 th... | {
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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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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... | {
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- 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 s... | {
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Also changed the function call to use named rather than positional argum... | {
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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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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 usi... | {
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**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:... | {
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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
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 ... | {
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Added a test to recognize import errors of all metrics | {
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When loading the metric "sacrebleu" there is an import error due to the wrong path
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```
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... | {
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```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... | {
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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 als... | {
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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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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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] | open | false | null | [] | null | 2 | 2020-07-26T21:43:04Z | 2020-08-24T15:12:15Z | null | CONTRIBUTOR | 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)
![image](https://user-images.githubusercontent.com/60450... | {
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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 sen... | {
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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 rest... | {
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https://api.github.com/repos/huggingface/datasets/issues/433 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/433/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/433/comments | https://api.github.com/repos/huggingface/datasets/issues/433/events | https://github.com/huggingface/datasets/issues/433 | 665,311,025 | MDU6SXNzdWU2NjUzMTEwMjU= | 433 | How to reuse functionality of a (generic) dataset? | {
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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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```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).
... | {
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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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### 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.Dat... | {
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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... | {
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### 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 outp... | {
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] | closed | false | null | [] | null | 6 | 2020-07-23T05:00:41Z | 2021-03-12T09:34:12Z | 2020-09-07T14:48:04Z | NONE | 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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## 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['tr... | {
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https://api.github.com/repos/huggingface/datasets/issues/424 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/424/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/424/comments | https://api.github.com/repos/huggingface/datasets/issues/424/events | https://github.com/huggingface/datasets/pull/424 | 663,858,552 | MDExOlB1bGxSZXF1ZXN0NDU1MTk4MTY0 | 424 | Web of science | {
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#353 | {
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https://api.github.com/repos/huggingface/datasets/issues/423 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/423/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/423/comments | https://api.github.com/repos/huggingface/datasets/issues/423/events | https://github.com/huggingface/datasets/pull/423 | 663,079,359 | MDExOlB1bGxSZXF1ZXN0NDU0NTU4OTA0 | 423 | Change features vs schema logic | {
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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 `sche... | {
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https://api.github.com/repos/huggingface/datasets/issues/420 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/420/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/420/comments | https://api.github.com/repos/huggingface/datasets/issues/420/events | https://github.com/huggingface/datasets/pull/420 | 662,029,782 | MDExOlB1bGxSZXF1ZXN0NDUzNjI5OTk2 | 420 | Better handle nested features | {
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- 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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This is the script for me:
```python
class EmoConfig(nlp.BuilderConfig):
"""BuilderConfig ... | {
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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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This should fix #304 | {
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"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library",
"id": 2067388877,
"name": "dataset bug",
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] | open | false | null | [] | null | 0 | 2020-07-19T08:18:51Z | 2020-07-20T09:54:26Z | null | NONE | 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... | {
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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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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", bea... | {
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## 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 `AmazonPho... | {
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dataset homepage: https://homes.cs.washington.edu/~msap/social-bias-frames/ | {
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#353 | {
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"gists... | null | 2 | 2020-07-17T10:36:28Z | 2020-07-21T14:34:52Z | 2020-07-21T14:34:52Z | NONE | 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
```
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https://api.github.com/repos/huggingface/datasets/issues/408 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/408/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/408/comments | https://api.github.com/repos/huggingface/datasets/issues/408/events | https://github.com/huggingface/datasets/pull/408 | 659,064,144 | MDExOlB1bGxSZXF1ZXN0NDUwOTU1MTE0 | 408 | Add tests datasets gcp | {
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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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"gists... | null | 4 | 2020-07-16T23:48:03Z | 2021-01-12T11:41:16Z | 2020-07-17T14:24:28Z | CONTRIBUTOR | 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... | {
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```
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`... | {
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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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```
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_fr... | {
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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... | {
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https://api.github.com/repos/huggingface/datasets/issues/403 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/403/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/403/comments | https://api.github.com/repos/huggingface/datasets/issues/403/events | https://github.com/huggingface/datasets/pull/403 | 658,325,756 | MDExOlB1bGxSZXF1ZXN0NDUwMzAzNjI2 | 403 | return python objects instead of arrays by default | {
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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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#336
Maybe @patrickvonplaten you can help with the dummy_data structure? it still broken | {
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#336 | {
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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_datas... | {
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https://api.github.com/repos/huggingface/datasets/issues/396 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/396/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/396/comments | https://api.github.com/repos/huggingface/datasets/issues/396/events | https://github.com/huggingface/datasets/pull/396 | 657,477,952 | MDExOlB1bGxSZXF1ZXN0NDQ5NTg3MDQ4 | 396 | Fix memory issue when doing select | {
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Fix #395 | {
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"gists... | null | 1 | 2020-07-15T15:43:38Z | 2020-07-16T08:07:31Z | 2020-07-16T08:07:31Z | MEMBER | null | As noticed in #389, the following code loads the entire wikipedia in memory.
```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 ... | {
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https://api.github.com/repos/huggingface/datasets/issues/394 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/394/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/394/comments | https://api.github.com/repos/huggingface/datasets/issues/394/events | https://github.com/huggingface/datasets/pull/394 | 657,425,548 | MDExOlB1bGxSZXF1ZXN0NDQ5NTQzNTE0 | 394 | Remove remaining nested dict | {
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#378 | {
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https://api.github.com/repos/huggingface/datasets/issues/393 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/393/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/393/comments | https://api.github.com/repos/huggingface/datasets/issues/393/events | https://github.com/huggingface/datasets/pull/393 | 657,330,911 | MDExOlB1bGxSZXF1ZXN0NDQ5NDY1MTAz | 393 | Fix extracted files directory for the DownloadManager | {
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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 ca... | {
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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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"gist... | [] | closed | false | null | [] | null | 0 | 2020-07-15T12:32:14Z | 2020-07-21T13:18:36Z | 2020-07-17T17:13:23Z | CONTRIBUTOR | null | Another [PAN task](https://pan.webis.de/clef20/pan20-web/style-change-detection.html). This time about identifying when the style/author changes in documents.
- 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... | {
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"gists... | [] | closed | false | null | [] | null | 6 | 2020-07-14T23:24:37Z | 2020-07-22T09:49:58Z | 2020-07-22T09:49:58Z | CONTRIBUTOR | null | 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... | {
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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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```
wiki = nlp.load_dataset('wikipedia', split='train')
def sentencize(examples):
...
wiki = wiki.map(sentencize, batched=True)
torch.save(wiki, '... | {
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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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```
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 ob... | {
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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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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 hi... | {
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- 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 ... | {
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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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- 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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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 t... | {
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https://api.github.com/repos/huggingface/datasets/issues/378 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/378/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/378/comments | https://api.github.com/repos/huggingface/datasets/issues/378/events | https://github.com/huggingface/datasets/issues/378 | 655,226,316 | MDU6SXNzdWU2NTUyMjYzMTY= | 378 | [dataset] Structure of MLQA seems unecessary nested | {
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Should we keep this @mariamabarham @patrickvonplaten? Was this added for compatibility with tfds?
```python
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