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I linearized most of the dictionnaries to lists.
Referenced to the authors' datasheet for the dataset card.
🦆🦆🦆
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https://api.github.com/repos/huggingface/datasets/issues/1067 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1067/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1067/comments | https://api.github.com/repos/huggingface/datasets/issues/1067/events | https://github.com/huggingface/datasets/pull/1067 | 756,414,212 | MDExOlB1bGxSZXF1ZXN0NTMxOTYyNDYx | 1,067 | add xquad-r dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/1065 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1065/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1065/comments | https://api.github.com/repos/huggingface/datasets/issues/1065/events | https://github.com/huggingface/datasets/pull/1065 | 756,383,414 | MDExOlB1bGxSZXF1ZXN0NTMxOTM2OTQ3 | 1,065 | add xquad-r dataset | {
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] | closed | false | null | [] | null | 2 | 2020-12-03T17:04:43 | 2021-01-14T02:51:25 | 2021-01-14T02:51:25 | CONTRIBUTOR | null | null | null | I have an issue adding this download link https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz
it might be because it is not a direct link (it returns 302 and redirects to aws that returns 403 for head requests).
```
r.head("https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz", allow_redirects=True)
# <Response [403]>
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1063 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1063/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1063/comments | https://api.github.com/repos/huggingface/datasets/issues/1063/events | https://github.com/huggingface/datasets/pull/1063 | 756,376,374 | MDExOlB1bGxSZXF1ZXN0NTMxOTMxMTMz | 1,063 | Add the Ud treebank | {
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https://api.github.com/repos/huggingface/datasets/issues/1062 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1062/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1062/comments | https://api.github.com/repos/huggingface/datasets/issues/1062/events | https://github.com/huggingface/datasets/pull/1062 | 756,373,187 | MDExOlB1bGxSZXF1ZXN0NTMxOTI4NDY5 | 1,062 | Add KorNLU dataset | {
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} | Added Korean NLU datasets. The link to the dataset can be found [here](https://github.com/kakaobrain/KorNLUDatasets) and the paper can be found [here](https://arxiv.org/abs/2004.03289)
**Note**: The MNLI tsv file is broken, so this code currently excludes the file. Please suggest other alternative if any @lhoestq
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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https://api.github.com/repos/huggingface/datasets/issues/1060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1060/comments | https://api.github.com/repos/huggingface/datasets/issues/1060/events | https://github.com/huggingface/datasets/pull/1060 | 756,349,001 | MDExOlB1bGxSZXF1ZXN0NTMxOTA4MTgx | 1,060 | Fix squad V2 metric script | {
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} | [] | closed | false | null | [] | null | 2 | 2020-12-03T16:23:32 | 2020-12-22T15:02:20 | 2020-12-22T15:02:19 | MEMBER | null | false | {
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} | The current squad v2 metric doesn't work with the squad (v1 or v2) datasets. The script is copied from `squad_evaluate` in transformers that requires the labels (with multiple answers) to be like this:
```
references = [{'id': 'a', 'answers': [
{'text': 'Denver Broncos', 'answer_start': 177},
{'text': 'Denver Broncos', 'answer_start': 177}
]}]
```
while the dataset had references like this:
```
references = [{'id': 'a', 'answers':
{'text': ['Denver Broncos' 'Denver Broncos'], 'answer_start': [177, 177]}
}]
```
Using one or the other format fails with the current squad v2 metric:
```
from datasets import load_metric
metric = load_metric("squad_v2")
predictions = [{'id': 'a', 'prediction_text': 'Denver Broncos', 'no_answer_probability': 0.0}]
references = [{'id': 'a', 'answers': [
{'text': 'Denver Broncos', 'answer_start': 177},
{'text': 'Denver Broncos', 'answer_start': 177}
]}]
metric.compute(predictions=predictions, references=references)
```
fails as well as
```
from datasets import load_metric
metric = load_metric("squad_v2")
predictions = [{'id': 'a', 'prediction_text': 'Denver Broncos', 'no_answer_probability': 0.0}]
references = [{'id': 'a', 'answers':
{'text': ['Denver Broncos' 'Denver Broncos'], 'answer_start': [177, 177]}
}]
metric.compute(predictions=predictions, references=references)
```
This is because arrow reformats the references behind the scene.
With this PR (tested locally), both the snippets up there work and return proper results. | {
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https://api.github.com/repos/huggingface/datasets/issues/1059 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1059/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1059/comments | https://api.github.com/repos/huggingface/datasets/issues/1059/events | https://github.com/huggingface/datasets/pull/1059 | 756,348,623 | MDExOlB1bGxSZXF1ZXN0NTMxOTA3ODYy | 1,059 | Add TLC | {
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https://api.github.com/repos/huggingface/datasets/issues/1058 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1058/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1058/comments | https://api.github.com/repos/huggingface/datasets/issues/1058/events | https://github.com/huggingface/datasets/pull/1058 | 756,332,704 | MDExOlB1bGxSZXF1ZXN0NTMxODk0Mjc0 | 1,058 | added paws-x dataset | {
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} | Added paws-x dataset. Updating README and tags in the dataset card in a while | {
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https://api.github.com/repos/huggingface/datasets/issues/1057 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1057/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1057/comments | https://api.github.com/repos/huggingface/datasets/issues/1057/events | https://github.com/huggingface/datasets/pull/1057 | 756,331,419 | MDExOlB1bGxSZXF1ZXN0NTMxODkzMjE4 | 1,057 | Adding TamilMixSentiment | {
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|
https://api.github.com/repos/huggingface/datasets/issues/1056 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1056/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1056/comments | https://api.github.com/repos/huggingface/datasets/issues/1056/events | https://github.com/huggingface/datasets/pull/1056 | 756,309,828 | MDExOlB1bGxSZXF1ZXN0NTMxODc1MjA2 | 1,056 | Add deal_or_no_dialog | {
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} | Add deal_or_no_dialog Dataset
github: https://github.com/facebookresearch/end-to-end-negotiator
Paper: [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://arxiv.org/abs/1706.05125) | {
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https://api.github.com/repos/huggingface/datasets/issues/1055 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1055/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1055/comments | https://api.github.com/repos/huggingface/datasets/issues/1055/events | https://github.com/huggingface/datasets/pull/1055 | 756,298,372 | MDExOlB1bGxSZXF1ZXN0NTMxODY1NjM4 | 1,055 | Add hebrew-sentiment | {
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https://api.github.com/repos/huggingface/datasets/issues/1054 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1054/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1054/comments | https://api.github.com/repos/huggingface/datasets/issues/1054/events | https://github.com/huggingface/datasets/pull/1054 | 756,265,688 | MDExOlB1bGxSZXF1ZXN0NTMxODM3NzQ0 | 1,054 | Add dataset - SemEval 2014 - Task 1 | {
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} | Adding the dataset of SemEval 2014 Task 1
Found the dataset under the shared Google Sheet > Recurring Task Datasets
Task Homepage - https://alt.qcri.org/semeval2014/task1
Thank you! | {
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https://api.github.com/repos/huggingface/datasets/issues/1053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1053/comments | https://api.github.com/repos/huggingface/datasets/issues/1053/events | https://github.com/huggingface/datasets/pull/1053 | 756,176,061 | MDExOlB1bGxSZXF1ZXN0NTMxNzYyNzg4 | 1,053 | Fix dataset URL and file names, and add column name in "Social Bias Frames" dataset | {
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} | # Why I did
When I use "social_bias_frames" datasets in this library, I got 404 Errors.
So, I fixed this error and another some problems that I faced to use the dataset.
# What I did
* Modify this dataset URL
* Modify this dataset file names
* Add a "dataSource" column
Thank you! | {
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https://api.github.com/repos/huggingface/datasets/issues/1052 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1052/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1052/comments | https://api.github.com/repos/huggingface/datasets/issues/1052/events | https://github.com/huggingface/datasets/pull/1052 | 756,171,798 | MDExOlB1bGxSZXF1ZXN0NTMxNzU5MjA0 | 1,052 | add sharc dataset | {
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} | This PR adds the ShARC dataset.
More info:
https://sharc-data.github.io/index.html | {
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https://api.github.com/repos/huggingface/datasets/issues/1051 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1051/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1051/comments | https://api.github.com/repos/huggingface/datasets/issues/1051/events | https://github.com/huggingface/datasets/pull/1051 | 756,169,049 | MDExOlB1bGxSZXF1ZXN0NTMxNzU2OTQy | 1,051 | Add Facebook SimpleQuestionV2 | {
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https://api.github.com/repos/huggingface/datasets/issues/1050 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1050/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1050/comments | https://api.github.com/repos/huggingface/datasets/issues/1050/events | https://github.com/huggingface/datasets/pull/1050 | 756,166,728 | MDExOlB1bGxSZXF1ZXN0NTMxNzU1MDQ3 | 1,050 | Add GoEmotions | {
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} | [] | closed | false | null | [] | null | 1 | 2020-12-03T12:49:53 | 2020-12-03T17:37:45 | 2020-12-03T17:30:08 | CONTRIBUTOR | null | false | {
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} | Adds the GoEmotions dataset, a nice emotion classification dataset with 27 (multi-)label annotations on reddit comments. Includes both a large raw version and a narrowed version with predefined train/test/val splits, which I've included as separate configs with the latter as a default.
- Webpage/repo: https://github.com/google-research/google-research/tree/master/goemotions
- Paper: https://arxiv.org/abs/2005.00547 | {
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https://api.github.com/repos/huggingface/datasets/issues/1049 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1049/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1049/comments | https://api.github.com/repos/huggingface/datasets/issues/1049/events | https://github.com/huggingface/datasets/pull/1049 | 756,157,602 | MDExOlB1bGxSZXF1ZXN0NTMxNzQ3NDY0 | 1,049 | Add siswati ner corpus | {
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|
https://api.github.com/repos/huggingface/datasets/issues/1048 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1048/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1048/comments | https://api.github.com/repos/huggingface/datasets/issues/1048/events | https://github.com/huggingface/datasets/pull/1048 | 756,133,072 | MDExOlB1bGxSZXF1ZXN0NTMxNzI3MDA0 | 1,048 | Adding NCHLT dataset | {
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} | https://repo.sadilar.org/handle/20.500.12185/7/discover?filtertype_0=database&filtertype_1=title&filter_relational_operator_1=contains&filter_relational_operator_0=equals&filter_1=&filter_0=Monolingual+Text+Corpora%3A+Annotated&filtertype=project&filter_relational_operator=equals&filter=NCHLT+Text+II | {
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https://api.github.com/repos/huggingface/datasets/issues/1047 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1047/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1047/comments | https://api.github.com/repos/huggingface/datasets/issues/1047/events | https://github.com/huggingface/datasets/pull/1047 | 756,127,490 | MDExOlB1bGxSZXF1ZXN0NTMxNzIyMjk4 | 1,047 | Add KorNLU | {
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} | Added Korean NLU datasets. The link to the dataset can be found [here](https://github.com/kakaobrain/KorNLUDatasets) and the paper can be found [here](https://arxiv.org/abs/2004.03289)
**Note**: The MNLI tsv file is broken, so this code currently excludes the file. Please suggest other alternative if any @lhoestq
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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https://api.github.com/repos/huggingface/datasets/issues/1046 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1046/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1046/comments | https://api.github.com/repos/huggingface/datasets/issues/1046/events | https://github.com/huggingface/datasets/issues/1046 | 756,122,709 | MDU6SXNzdWU3NTYxMjI3MDk= | 1,046 | Dataset.map() turns tensors into lists? | {
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} | [] | open | false | null | [] | null | 2 | 2020-12-03T11:43:46 | 2020-12-23T13:51:12 | null | NONE | null | null | null | I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!
```import datasets
import torch
from datasets import load_dataset
print("version datasets", datasets.__version__)
dataset = load_dataset("snli", split='train[0:50]')
def tokenizer_fn(example):
# actually uses a tokenizer which does something like:
return {'input_ids': torch.tensor([[0, 1, 2]])}
print("First item in dataset:\n", dataset[0])
tokenized = tokenizer_fn(dataset[0])
print("Tokenized hyp:\n", tokenized)
dataset_tok = dataset.map(tokenizer_fn, batched=False,
remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
dataset_tok = dataset.map(tokenizer_fn, batched=False,
remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
```
The output is:
```
version datasets 1.1.3
Reusing dataset snli (/home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)
First item in dataset:
{'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}
Tokenized hyp:
{'input_ids': tensor([[0, 1, 2]])}
Loading cached processed dataset at /home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c/cache-fe38f449fe9ac46f.arrow
Tokenized using map:
{'input_ids': [[0, 1, 2]]}
<class 'torch.Tensor'> <class 'list'>
```
Or am I doing something wrong?
| {
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https://api.github.com/repos/huggingface/datasets/issues/1045 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1045/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1045/comments | https://api.github.com/repos/huggingface/datasets/issues/1045/events | https://github.com/huggingface/datasets/pull/1045 | 756,120,760 | MDExOlB1bGxSZXF1ZXN0NTMxNzE2NzIy | 1,045 | Add xitsonga ner corpus | {
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} | github: https://github.com/fbougares/TSAC
paper: https://www.aclweb.org/anthology/W17-1307/ | {
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https://api.github.com/repos/huggingface/datasets/issues/1042 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1042/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1042/comments | https://api.github.com/repos/huggingface/datasets/issues/1042/events | https://github.com/huggingface/datasets/pull/1042 | 756,097,583 | MDExOlB1bGxSZXF1ZXN0NTMxNjk3NDU4 | 1,042 | Add Big Patent dataset | {
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} | - More info on the dataset: https://evasharma.github.io/bigpatent/
- There's another raw version of the dataset available from tfds. However, they're quite large so I don't have the resources to fully test all the configs for that version yet. We'll try to add it later.
- ~Currently, there are no dummy data for this dataset yet as I'm facing some problems with generating them. I'm trying to add them later.~ | {
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https://api.github.com/repos/huggingface/datasets/issues/1040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1040/comments | https://api.github.com/repos/huggingface/datasets/issues/1040/events | https://github.com/huggingface/datasets/pull/1040 | 756,050,387 | MDExOlB1bGxSZXF1ZXN0NTMxNjU4MTU3 | 1,040 | Add UN Universal Declaration of Human Rights (UDHR) | {
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} | Universal declaration of human rights with translations in 464 languages and dialects.
- UN page: https://www.ohchr.org/EN/UDHR/Pages/UDHRIndex.aspx
- Raw data source: https://unicode.org/udhr/index.html
Each instance of the dataset corresponds to one translation of the document. Since there's only one instance per language (and because there are 500 languages so the dummy data would be messy), I opted to just include them all under the same single config. I wasn't able to find any kind of license so I just copied the copyright notice.
I was pretty careful careful generating the language tags so they _should_ all be correct & consistent BCP-47 codes per the docs. | {
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https://api.github.com/repos/huggingface/datasets/issues/1039 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1039/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1039/comments | https://api.github.com/repos/huggingface/datasets/issues/1039/events | https://github.com/huggingface/datasets/pull/1039 | 756,000,478 | MDExOlB1bGxSZXF1ZXN0NTMxNjE3MDI2 | 1,039 | Update ADD NEW DATASET | {
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} | This PR adds a couple of detail on cloning/rebasing the repo. | {
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https://api.github.com/repos/huggingface/datasets/issues/1038 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1038/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1038/comments | https://api.github.com/repos/huggingface/datasets/issues/1038/events | https://github.com/huggingface/datasets/pull/1038 | 755,987,997 | MDExOlB1bGxSZXF1ZXN0NTMxNjA2Njgw | 1,038 | add med_hop | {
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} | This PR adds the MedHop dataset from the QAngaroo multi hop reading comprehension datasets
More info:
http://qangaroo.cs.ucl.ac.uk/index.html | {
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https://api.github.com/repos/huggingface/datasets/issues/1036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1036/comments | https://api.github.com/repos/huggingface/datasets/issues/1036/events | https://github.com/huggingface/datasets/pull/1036 | 755,953,294 | MDExOlB1bGxSZXF1ZXN0NTMxNTc4MjQ4 | 1,036 | Add PerSenT | {
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https://api.github.com/repos/huggingface/datasets/issues/1035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1035/comments | https://api.github.com/repos/huggingface/datasets/issues/1035/events | https://github.com/huggingface/datasets/pull/1035 | 755,947,097 | MDExOlB1bGxSZXF1ZXN0NTMxNTczMTc3 | 1,035 | add wiki_hop | {
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} | This PR adds the WikiHop dataset from the QAngaroo multi hop reading comprehension datasets
More info:
http://qangaroo.cs.ucl.ac.uk/index.html
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https://api.github.com/repos/huggingface/datasets/issues/1034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1034/comments | https://api.github.com/repos/huggingface/datasets/issues/1034/events | https://github.com/huggingface/datasets/pull/1034 | 755,936,327 | MDExOlB1bGxSZXF1ZXN0NTMxNTY0MjA0 | 1,034 | add scb_mt_enth_2020 | {
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} | ## scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
The primary objective of our work is to build a large-scale English-Thai dataset for machine translation.
We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources,
namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents.
Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner.
We train machine translation models based on this dataset. Our models' performance are comparable to that of
Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is
included in the training data for both Thai-English and English-Thai translation.
The dataset, pre-trained models, and source code to reproduce our work are available for public use. | {
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https://api.github.com/repos/huggingface/datasets/issues/1033 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1033/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1033/comments | https://api.github.com/repos/huggingface/datasets/issues/1033/events | https://github.com/huggingface/datasets/pull/1033 | 755,921,927 | MDExOlB1bGxSZXF1ZXN0NTMxNTUxNzYw | 1,033 | Add support for ".txm" format | {
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} | In dummy data generation, add support for XML-like ".txm" file format.
Also support filenames with additional compression extension: ".txm.gz". | {
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https://api.github.com/repos/huggingface/datasets/issues/1032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1032/comments | https://api.github.com/repos/huggingface/datasets/issues/1032/events | https://github.com/huggingface/datasets/pull/1032 | 755,858,785 | MDExOlB1bGxSZXF1ZXN0NTMxNDk2MTU2 | 1,032 | IIT B English to Hindi machine translation dataset | {
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more info : http://www.cfilt.iitb.ac.in/iitb_parallel/ | {
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https://api.github.com/repos/huggingface/datasets/issues/1031 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1031/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1031/comments | https://api.github.com/repos/huggingface/datasets/issues/1031/events | https://github.com/huggingface/datasets/pull/1031 | 755,844,004 | MDExOlB1bGxSZXF1ZXN0NTMxNDgyMzEy | 1,031 | add crows_pairs | {
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} | This PR adds CrowS-Pairs datasets.
More info:
https://github.com/nyu-mll/crows-pairs/
https://arxiv.org/pdf/2010.00133.pdf | {
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https://api.github.com/repos/huggingface/datasets/issues/1030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1030/comments | https://api.github.com/repos/huggingface/datasets/issues/1030/events | https://github.com/huggingface/datasets/pull/1030 | 755,777,438 | MDExOlB1bGxSZXF1ZXN0NTMxNDI0MDM3 | 1,030 | allegro_reviews dataset | {
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} | - **Name:** *allegro_reviews*
- **Description:** *Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review).*
- **Data:** *https://github.com/allegro/klejbenchmark-allegroreviews*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.* | {
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https://api.github.com/repos/huggingface/datasets/issues/1029 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1029/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1029/comments | https://api.github.com/repos/huggingface/datasets/issues/1029/events | https://github.com/huggingface/datasets/pull/1029 | 755,767,616 | MDExOlB1bGxSZXF1ZXN0NTMxNDE2NzE4 | 1,029 | Add PEC | {
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https://api.github.com/repos/huggingface/datasets/issues/1028 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1028/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1028/comments | https://api.github.com/repos/huggingface/datasets/issues/1028/events | https://github.com/huggingface/datasets/pull/1028 | 755,712,854 | MDExOlB1bGxSZXF1ZXN0NTMxMzc0MTYw | 1,028 | Add ASSET dataset for text simplification evaluation | {
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} | Adding the ASSET dataset from https://github.com/facebookresearch/asset
One config for the simplification data, one for the human ratings of quality.
The README.md borrows from that written by @juand-r | {
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https://api.github.com/repos/huggingface/datasets/issues/1027 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1027/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1027/comments | https://api.github.com/repos/huggingface/datasets/issues/1027/events | https://github.com/huggingface/datasets/issues/1027 | 755,695,420 | MDU6SXNzdWU3NTU2OTU0MjA= | 1,027 | Hi | {
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} | [] | closed | false | null | [] | null | 0 | 2020-12-02T23:47:14 | 2020-12-03T16:42:41 | 2020-12-03T16:42:41 | NONE | null | null | null | ## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | {
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https://api.github.com/repos/huggingface/datasets/issues/1026 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1026/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1026/comments | https://api.github.com/repos/huggingface/datasets/issues/1026/events | https://github.com/huggingface/datasets/issues/1026 | 755,689,195 | MDU6SXNzdWU3NTU2ODkxOTU= | 1,026 | Lío o | {
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```
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`````
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````
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1025 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1025/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1025/comments | https://api.github.com/repos/huggingface/datasets/issues/1025/events | https://github.com/huggingface/datasets/pull/1025 | 755,673,371 | MDExOlB1bGxSZXF1ZXN0NTMxMzQxNjE5 | 1,025 | Add Sesotho Ner | {
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https://api.github.com/repos/huggingface/datasets/issues/1024 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1024/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1024/comments | https://api.github.com/repos/huggingface/datasets/issues/1024/events | https://github.com/huggingface/datasets/pull/1024 | 755,664,113 | MDExOlB1bGxSZXF1ZXN0NTMxMzMzOTc5 | 1,024 | Add ZEST: ZEroShot learning from Task descriptions | {
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} | Adds the ZEST dataset on zero-shot learning from task descriptions from AI2.
- Webpage: https://allenai.org/data/zest
- Paper: https://arxiv.org/abs/2011.08115
The nature of this dataset made the supported task tags tricky if you wouldn't mind giving any feedback @yjernite. Also let me know if you think we should have a `other-task-generalization` or something like that... | {
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https://api.github.com/repos/huggingface/datasets/issues/1023 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1023/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1023/comments | https://api.github.com/repos/huggingface/datasets/issues/1023/events | https://github.com/huggingface/datasets/pull/1023 | 755,655,752 | MDExOlB1bGxSZXF1ZXN0NTMxMzI3MTMy | 1,023 | Add Schema Guided Dialogue dataset | {
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} | This PR adds the Schema Guided Dialogue dataset created for the DSTC8 challenge
- https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
A bit simpler than MultiWOZ, the only tricky thing is the sequence of dictionaries that had to be linearized. There is a config for the data proper, and a config for the schemas. | {
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https://api.github.com/repos/huggingface/datasets/issues/1022 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1022/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1022/comments | https://api.github.com/repos/huggingface/datasets/issues/1022/events | https://github.com/huggingface/datasets/pull/1022 | 755,651,377 | MDExOlB1bGxSZXF1ZXN0NTMxMzIzNTkw | 1,022 | add MRQA | {
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} | MRQA (shared task 2019)
out of distribution generalization
Framed as extractive question answering
Dataset is the concatenation (of subsets) of existing QA datasets processed to match the SQuAD format | {
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} | This PR adds the gutenberg_time dataset: https://arxiv.org/abs/2011.04124 | {
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https://api.github.com/repos/huggingface/datasets/issues/1020 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1020/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1020/comments | https://api.github.com/repos/huggingface/datasets/issues/1020/events | https://github.com/huggingface/datasets/pull/1020 | 755,601,450 | MDExOlB1bGxSZXF1ZXN0NTMxMjgyODQy | 1,020 | Add Setswana NER | {
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- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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Added the `README.md` card with tags to the best of my knowledge
Multi-target summaries or TLDRs of Scientific Documents
Continued from #986 | {
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- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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https://arxiv.org/abs/1904.09679 | {
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} | This PR adds yahoo answers topic classification dataset.
More info:
https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset
cc @joeddav, @yjernite | {
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I want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks | {
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github repo: https://github.com/yaolu/Multi-XScience
paper: [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://arxiv.org/abs/2010.14235) | {
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} | Adds Urdu-English dataset for machine translation: http://ufal.ms.mff.cuni.cz/umc/005-en-ur/ | {
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} | `generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated_reviews_yn` in [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)) are English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858), translated by Google Translate API and annotated as accepted or rejected (`correct`) based on fluency and adequacy of the translation by human annotators. This allows it to be used for English-to-Thai translation quality esitmation (binary label), machine translation, and sentiment analysis. | {
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More info:
https://ciir.cs.umass.edu/downloads/nfL6/ | {
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} | Datasets from https://github.com/microsoft/CodeXGLUE
This contains 13 datasets:
code_x_glue_cc_clone_detection_big_clone_bench
code_x_glue_cc_clone_detection_poj_104
code_x_glue_cc_cloze_testing_all
code_x_glue_cc_cloze_testing_maxmin
code_x_glue_cc_code_completion_line
code_x_glue_cc_code_completion_token
code_x_glue_cc_code_refinement
code_x_glue_cc_code_to_code_trans
code_x_glue_cc_defect_detection
code_x_glue_ct_code_to_text
code_x_glue_tc_nl_code_search_adv
code_x_glue_tc_text_to_code
code_x_glue_tt_text_to_text
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Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-9-cd4bf8bea840> in <module>()
22
23
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')
5 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' | {
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After trying to load the dataset, I am getting the following error:
`ConnectionError: Couldn't reach https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_fr_train.json
`
I used the following code to load the dataset:
`load_dataset(
dataset_name,
"all_languages",
cache_dir=".data"
)`
I am using version 1.1.3 of `datasets`
Note that I can perform a successfull `wget https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_fr_train.json` | {
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} | Adding the E2E NLG dataset.
More info here : http://www.macs.hw.ac.uk/InteractionLab/E2E/
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
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I am dealing with large-scale datasets which I need to train distributedly, I used the shard function to divide the dataset across the cores, without any sampler, this does not work for distributed training and does not become any faster than 1 TPU core. 1) how I can make sure data is not loaded in memory 2) in case of distributed training with iterative datasets which measures needs to be taken? Is this all sharding the data only. I was wondering if there can be possibility for me to discuss this with someone with distributed training with iterative datasets using dataset library. thanks | {
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Added README card with tags to the best of my knowledge
Multi-target summaries or TLDRs of Scientific Documents | {
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} | I decided it will be faster to create a new pull request instead of fixing the rebase issues.
continuing from https://github.com/huggingface/datasets/pull/954
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} | 40,000 reviews, previously released on GitHub ( https://github.com/wongnai/wongnai-corpus ) with an LGPL license, and on a closed Kaggle competition ( https://www.kaggle.com/c/wongnai-challenge-review-rating-prediction/ ) | {
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} | This PR adds version 2.2 of the Multi-domain Wizard of OZ dataset: https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2
It was a pretty big chunk of work to figure out the structure, so I stil have tol add the description to the README.md
On the plus side the structure is broadly similar to that of the Google Schema Guided dialogue [dataset](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue), so will take care of that one next. | {
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} | ### OVERVIEW
Millions of open-source projects with numerous bug fixes
are available in code repositories. This proliferation
of software development histories can be leveraged to
learn how to fix common programming bugs
Code refinement aims to automatically fix bugs in the code,
which can contribute to reducing the cost of bug-fixes for developers.
Given a piece of Java code with bugs,
the task is to remove the bugs to output the refined code. | {
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