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Discussion using datasets in offline mode

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`datasets.load_dataset("csv", ...)` breaks if you have no connection (There is already this issue https://github.com/huggingface/datasets/issues/761 about it). It seems to be the same for metrics too. I create this ticket to discuss a bit and gather what you have in mind or other propositions. Here are some points to open discussion: - if you want to prepare your code/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine. - AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset("MY_PATH/csv.py", ...)`. But it would be much better if you could run ths same code without modification if files are available locally. - I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least "freeze" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet. WDYT? (thks)

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how processing in batch works in datasets

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Hi, I need to process my datasets before it is passed to dataloader in batch, here is my codes ``` class AbstractTask(ABC): task_name: str = NotImplemented preprocessor: Callable = NotImplemented split_to_data_split: Mapping[str, str] = NotImplemented tokenizer: Callable = NotImplemented max_source_length: str = NotImplemented max_target_length: str = NotImplemented # TODO: should not be a task item, but cannot see other ways. tpu_num_cores: int = None # The arguments set are for all tasks and needs to be kept common. def __init__(self, config): self.max_source_length = config['max_source_length'] self.max_target_length = config['max_target_length'] self.tokenizer = config['tokenizer'] self.tpu_num_cores = config['tpu_num_cores'] def _encode(self, batch) -> Dict[str, torch.Tensor]: batch_encoding = self.tokenizer.prepare_seq2seq_batch( [x["src_texts"] for x in batch], tgt_texts=[x["tgt_texts"] for x in batch], max_length=self.max_source_length, max_target_length=self.max_target_length, padding="max_length" if self.tpu_num_cores is not None else "longest", # TPU hack return_tensors="pt" ) return batch_encoding.data def data_split(self, split): return self.split_to_data_split[split] def get_dataset(self, split, n_obs=None): split = self.data_split(split) if n_obs is not None: split = split+"[:{}]".format(n_obs) dataset = load_dataset(self.task_name, split=split) dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names) dataset = dataset.map(lambda batch: self._encode(batch), batched=True) dataset.set_format(type="torch", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label']) return dataset ``` I call it like `AutoTask.get(task, train_dataset_config).get_dataset(split="train", n_obs=data_args.n_train) ` This gives the following error, to me because the data inside the dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks File "finetune_multitask_trainer.py", line 192, in main if training_args.do_train else None File "finetune_multitask_trainer.py", line 191, in <dictcomp> split="train", n_obs=data_args.n_train) for task in data_args.task} File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 56, in get_dataset dataset = dataset.map(lambda batch: self._encode(batch), batched=True) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1236, in map update_data = does_function_return_dict(test_inputs, test_indices) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1207, in does_function_return_dict function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 56, in <lambda> dataset = dataset.map(lambda batch: self._encode(batch), batched=True) File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 37, in _encode [x["src_texts"] for x in batch], File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 37, in <listcomp> [x["src_texts"] for x in batch], TypeError: string indices must be integers

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datasets freezes

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Hi, I want to load these two datasets and convert them to Dataset format in torch and the code freezes for me, could you have a look please? thanks dataset1 = load_dataset("squad", split="train[:10]") dataset1 = dataset1.set_format(type='torch', columns=['context', 'answers', 'question']) dataset2 = load_dataset("imdb", split="train[:10]") dataset2 = dataset2.set_format(type="torch", columns=["text", "label"]) print(len(dataset1))

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`kor_nli` dataset doesn't being loaded properly

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NONE

There are two issues from `kor_nli` dataset 1. csv.DictReader failed to split features by tab - Should not exist `None` value in label feature, but there it is. ```python kor_nli_train['train'].unique('gold_label') # ['neutral', 'entailment', 'contradiction', None] ``` - I found a reason why there is `None` values in label feature as following code ```python from datasets import load_dataset kor_nli_train = load_dataset('kor_nli', 'multi_nli') for idx, example in enumerate(kor_nli_train['train']): if example['gold_label'] is None: print(idx, example) break # 16835 {'gold_label': None, 'sentence1': '그는 전쟁 전에 가벼운 벅스킨 암말을 가지고 달리기 위해 우유처럼 하얀 스터드를 넣었다.\t전쟁 전에 다인종 여성들과 함께 있는 백인 남자가 있었다.\tentailment\n슬림은 재빨리 옷을 입었고, 순간적으로 미지근한 물을 뿌릴 수 있는 아침 세탁물을 기꺼이 가두었다.\t슬림은 직장에 늦었다.\tneutral\n뉴욕에서 그 식사를 해봤는데, 거기서 소고기의 멋진 소고기 부분을 요리하고 바베큐로 만든 널빤지 같은 걸 가져왔는데, 정말 대단해.\t그들이 거기서 요리하는 쇠고기는 역겹다. 거기서 절대 먹지 마라.\tcontradiction\n판매원의 죽음에서 브라이언 데네히... 크리스 켈리\t크리스 켈리는 세일즈맨의 죽음을 언급하지 않는다.\tcontradiction\n그러는 동안 요리사는 그냥 화가 났어.\t스튜가 끓는 동안 요리사는 화가 났다.\tneutral\n마지막 로마의 맹공격 전날 밤, 900명 이상의 유대인 수비수들이 로마인들에게 그들을 사로잡는 승리를 주기 보다는 대량 자살을 저질렀다.\t로마인들이 그들의 포획에 승리하도록 내버려두기 보다는 900명의 유대인 수비수들이 자살했다.\tentailment\n앞으로 발사하라.\t발사.\tneutral\n그리고 당신은 우리 땅이 에이커에 있다는 것을 알고 있다. 우리 사람들은 어떤 것이 얼마나 많은지 이해하지 못할 것이다.\t모든 사람들은 우리의 측정 시스템이 어떻게 작동하는지 알고 이해합니다.\tcontradiction\n주미게스\tJumiyges는 도시의 이름이다.\tneutral\n사람은 자기 민족을 돌봐야 한다...\t사람은 조국에 공감해야 한다.\tentailment\n또한 PDD 63은 정부와 업계가 컴퓨터 기반 공격에 대해 경고하고 방어할 준비를 더 잘할 수 있도록 시스템 취약성, 위협, 침입 및 이상에 대한 정보를 공유하는 메커니즘을 수립하는 것이 중요하다는 것을 인식했습니다.\t정보 전송 프로토콜을 만드는 것은 중요하다.\tentailment\n카페 링 피아자 델라 레퓌블리카 바로 남쪽에는 피렌체가 알려진 짚 제품 때문에 한때 스트로 마켓이라고 불렸던 16세기 로지아인 메르카토 누오보(Mercato Nuovo)가 있다.\t피아자 델라 레퓌블리카에는 카페가 많이 있다.\tentailment\n우리가 여기 있는 한 트린판이 뭘 주웠는지 살펴봐야겠어\t우리는 트린판이 무엇을 주웠는지 보는 데 시간을 낭비하지 않을 것이다.\tcontradiction\n그러나 켈트족의 문화적 기반을 가진 아일랜드 교회는 유럽의 신흥 기독교 세계와는 다르게 발전했고 결국 로마와 중앙집권적 행정으로 대체되었다.\t아일랜드 교회에는 켈트족의 기지가 있었다.\tentailment\n글쎄, 넌 선택의 여지가 없어\t글쎄, 너에겐 많은 선택권이 있어.\tcontradiction\n사실, 공식적인 보장은 없다.\t내가 산 물건에 대한 보증이 없었다.\tneutral\n덜 활기차긴 하지만, 안시와 르 부르젯의 사랑스러운 호수에서도 삶은 똑같이 상쾌하다.\t안시와 르 부르겟에서는 호수에서의 활동이 서두르고 바쁜 분위기를 연출한다.\tcontradiction\n그의 여행 소식이 이미 퍼졌다면 공격 소식도 퍼졌을 테지만 마을에서는 전혀 공황의 기미가 보이지 않았다.\t그는 왜 마을이 당황하지 않았는지 알 수 없었다.\tneutral\n과거에는 죽음의 위협이 토지의 판매를 막는 데 거의 도움이 되지 않았다.\t토지 판매는 어떠한 위협도 교환하지 않고 이루어진다.\tcontradiction\n어느 시점에 이르러 나는 지금 다가오는 새로운 것들과 나오는 많은 새로운 것들이 내가 늙어가고 있다고 말하는 시대로 접어들고 있다.\t나는 여전히 내가 보는 모든 새로운 것을 사랑한다.\tcontradiction\n뉴스위크는 물리학자들이 경기장 행사에서 고속도로의 자동차 교통과 보행자 교통을 개선하기 위해 새떼의 움직임을 연구하고 있다고 말한다.\t고속도로의 자동차 교통 흐름을 개선하는 것은 물리학자들이 새떼를 연구하는 이유 중 하나이다.\tentailment\n얼마나 다른가? 그는 잠시 말을 멈추었다가 말을 이었다.\t그는 그 소녀가 어디에 있는지 알고 싶었다.\tentailment\n글쎄, 그에게 너무 많은 것을 주지마.\t그는 훨씬 더 많은 것을 요구할 것이다.\tneutral\n아무리 그의 창작물이 완벽해 보인다고 해도, 그들을 믿는 것은 아마도 좋은 생각이 아닐 것이다.\'\t도자기를 잘 만든다고 해서 누군가를 믿는 것은 아마 좋지 않을 것이다.\tneutral\n버스틀링 그란 비아(Bustling Gran Via)는 호텔, 상점, 극장, 나이트클럽, 카페 등이 어우러져 산책과 창가를 볼 수 있다.\tGran Via는 호텔, 상점, 극장, 나이트클럽, 카페의 번화한 조합이다.\tentailment\n정부 인쇄소\t그 사무실은 워싱턴에 위치해 있다.\tneutral\n실제 문화 전쟁이 어디 있는지 알고 싶다면 학원을 잊어버리고 실리콘 밸리와 레드몬드를 생각해 보라.\t실제 문화 전쟁은 레드몬드에서 일어난다.\tentailment\n그리고 페니실린을 주지 않기 위해 침대 위에 올려놨어\t그녀의 방에는 페니실린이 없다는 징후가 전혀 없었다.\tcontradiction\nL.A.의 야외 시장을 활보하는 것은 맛있고 저렴한 그루브를 잡고, 끝이 없는 햇빛을 즐기고, 신선한 농산물, 꽃, 향, 그리고 가젯 갈로어를 구입하면서 현지인들과 어울릴 수 있는 훌륭한 방법이다.\tLA의 야외 시장을 돌아다니는 것은 시간 낭비다.\tcontradiction\n안나는 밖으로 나와 안도의 한숨을 내쉬었다. 단 한 번, 그리고 마리후아쉬 맛의 술로 끝내자는 결심이 뒤섞여 있었다.\t안나는 안심하고 마리후아쉬 맛의 술을 다 마시기로 결심했다.\tentailment\n5 월에 Vajpayee는 핵 실험의 성공적인 완료를 발표했는데, 인도인들은 주권의 표시로 선전했지만 이웃 국가와 서구와의 인도 관계를 복잡하게 만들 수 있습니다.\t인도는 성공적인 핵실험을 한 적이 없다.\tcontradiction\n플라노 원에서 보통 얼마나 많은 것을 가지고 있는가?\t저 사람들 중에 플라노 원에 가본 사람 있어?\tcontradiction\n그것의 전체적인 형태의 우아함은 운하 건너편에서 가장 잘 볼 수 있다. 왜냐하면, 로마에 있는 성 베드로처럼, 돔은 길쭉한 본당 뒤로 더 가까운 곳에 사라지기 때문이다.\t성 베드로의 길쭉한 본당은 돔을 가린다.\tentailment\n당신은 수틴이 살에 강박적인 기쁨을 가지고 누드를 그릴 것이라고 생각하겠지만, 아니오; 그는 그의 모든 경력에서 단 한 점만을 그렸고, 그것은 사소한 그림이다.\t그는 그것이 그를 불편하게 만들었기 때문에 하나만 그렸다.\tneutral\n이 인상적인 풍경은 원래 나포 레온이 루브르 박물관의 침실에서 볼 수 있도록 계획되었는데, 그 당시 궁전이었습니다.\t나폴레옹은 그의 모든 궁전에 있는 그의 침실에서 보는 경치에 많은 관심을 가졌다.\tneutral\n그는 우리에게 문 열쇠를 건네주고는 급히 떠났다.\t그는 긴장해서 우리에게 열쇠를 빨리 주었다.\tneutral\n위원회는 또한 최종 규칙을 OMB에 제출했다.\t위원회는 또한 이 규칙을 다른 그룹에 제출했지만 최종 규칙은 OMB가 평가하기 위한 것이 었습니다.\tneutral\n정원가게에 가보면 올리비아의 복제 화합물 같은 유쾌한 이름을 가진 제품들을 찾을 수 있을 겁니다.이 제품이 뿌리를 내리도록 돕기 위해 촬영의 절단된 끝에 덩크슛을 하는 호르몬의 혼합물이죠.\t정원 가꾸기 가게의 제품들은 종종 그들의 목적을 설명하기 위해 기술적으로나 과학적으로 파생된 이름(올리비아의 복제 화합물처럼)을 부여받는다.\tneutral\n스타는 스틸 자신이나 왜 그녀의 이야기를 바꾸었는지에 훨씬 더 관심이 있을 것이다.\t스틸의 이야기는 조금도 변하지 않았다.\tcontradiction\n남편과의 마지막 대결로 맥티어는 노라의 변신을 너무나 능숙하게 예고해 왔기 때문에, 그녀에게는 당황스러울 정도로 갑작스러운 것처럼 보이지만, 우리에게는 감정적으로 불가피해 보인다.\t노라의 변신은 분명하고 필연적이었다.\tcontradiction\n이집트 최남단 도시인 아스완은 오랜 역사를 통해 중요한 역할을 해왔다.\t아스완은 이집트 국경 바로 위에 위치해 있습니다.\tneutral\n그러나 훨씬 더 우아한 건축적 터치는 신성한 춤인 Bharatanatyam에서 수행된 108 가지 기본 포즈를 시바 패널에서 볼 수 있습니다.\t패널에 대한 시바의 묘사는 일반적인 모티브다.\tneutral\n호화롭게 심어진 계단식 정원은 이탈리아 형식의 가장 훌륭한 앙상블 중 하나입니다.\t아름다운 정원과 희귀한 꽃꽂이 모두 이탈리아의 형식적인 스타일을 보여준다.\tneutral\n음, 그랬으면 좋았을 텐데\t나는 그것을 다르게 할 기회를 몹시 갈망한다.\tentailment\n폐허가 된 성의 기슭에 자리잡고 있는 예쁜 중세 도시 케이서스버그는 노벨 평화상 수상자 알버트 슈바이처(1875년)의 출생지로 널리 알려져 있다.\t알버트 슈바이처는 둘 다 케이서스버그 마을에 있었다.\tentailment\n고감도는 문제가 있는 대부분의 환자들이 발견될 것을 보장한다.\t장비 민감도는 문제 탐지와 관련이 없습니다.\tcontradiction\n오늘은 확실히 반바지 같은 날이었어\t오늘 사무실에 있는 모든 사람들은 반바지를 입었다.\tneutral\n못생긴 턱시도를 입고.\t그것은 분홍색과 주황색입니다.\tneutral\n이주 노동 수용소 오 마이 갓 그들은 판지 상자에 산다.\t노동 수용소에는 판지 상자에 사는 이주 노동자들의 사진이 있다.\tneutral\n그래, 그가 전 세계를 여행한 후에 그런 거야\t그것은 사람들의 세계 여행을 따른다.\tentailment\n건너편에 크고 큰 참나무 몇 그루가 있다.\t우리는 여기 오크나 어떤 종류의 미국 나무도 없다.\tcontradiction\nFort-de-France에서 출발하는 자동차나 여객선으로, 당신은 안세 ? 바다 포도가 그늘을 제공하는 쾌적한 갈색 모래 해변과 피크닉 테이블, 어린이 미끄럼틀, 식당이 있는 안느에 도착할 수 있다.\t프랑스 요새에서 자동차나 페리를 타고 안세로 갈 수 있다.\tentailment\n그리고 그것은 앨라배마주가 예상했던 대로 예산에서 50만 달러를 삭감하지 않을 것이라는 것을 의미한다.\t앨라배마 주는 예산 삭감을 하지 않았다. 왜냐하면 그렇게 하는 것에 대한 초기 정당성이 정밀 조사에 맞서지 않았기 때문이다.\tneutral\n알았어 먼저 어 .. 어 .. 노인이나 가족을 요양원에 보내는 것에 대해 어떻게 생각하니?\t가족을 요양원에 보내서 사는 것에 대해 어떻게 생각하는지 알 필요가 없다.\tcontradiction\n나머지는 너에게 달렸어.\t나머지는 너에게 달렸지만 시간이 많지 않다.\tneutral\n음-흠, 3월에 햇볕에 타는 것에 대해 걱정하면 안 된다는 것을 알고 있는 3월이야.\t3월은 그렇게 덥지 않다.\tneutral\n그리고 어, 그런 작은 것들로 다시 시작해봐. 아직 훨씬 싸. 어, 그 특별한 모델 차는 150달러야.\t그 모형차는 4천 달러가 든다.\tcontradiction\n내일 돌아가야 한다면, 칼이 말했다.\t돌아갈 수 없어. 오늘은 안 돼. 내일은 안 돼. 절대 안 돼." 칼이 말했다.', 'sentence2': 'contradiction'} ``` 2. (Optional) Preferred to change the name of the features for the compatibility with `run_glue.py` in 🤗 Transformers - `kor_nli` dataset has same data structure of multi_nli, xnli - Changing the name of features and the feature type of 'gold_label' to ClassLabel might be helpful ```python def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "premise": datasets.Value("string"), "hypothesis": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]), } ), ``` If you don't mind, I would like to fix this. Thanks!

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https://github.com/huggingface/datasets/pull/820

MDExOlB1bGxSZXF1ZXN0NTE4MDYwMjQ0

Update quail dataset to v1.3

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Updated quail to most recent version, to address the problem originally discussed [here](https://github.com/huggingface/datasets/issues/806).

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https://github.com/huggingface/datasets/pull/819

MDExOlB1bGxSZXF1ZXN0NTE3OTQ2MjYy

Make save function use deterministic global vars order

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The `dumps` function need to be deterministic for the caching mechanism. However in #816 I noticed that one of dill's method to recursively check the globals of a function may return the globals in different orders each time it's used. To fix that I sort the globals by key in the `globs` dictionary. I had to add a rectified `save_function` to the saving functions registry of the Pickler to make it work. This should fix #816

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MDExOlB1bGxSZXF1ZXN0NTE3ODgzMzk0

Fix type hints pickling in python 3.6

closed

MEMBER

Type hints can't be properly pickled in python 3.6. This was causing errors the `run_mlm.py` script from `transformers` with python 3.6 However Cloupickle proposed a [fix](https://github.com/cloudpipe/cloudpickle/pull/318/files) to make it work anyway. The idea is just to implement the pickling/unpickling of parameterized type hints. There is one detail though: since in python 3.6 we can't use `isinstance` on type hints, then we can't use pickle saving functions registry directly. Therefore we just wrap the `save_global` method of the Pickler. This should fix https://github.com/huggingface/transformers/issues/8212 for python 3.6 and make `run_mlm.py` support python 3.6 cc @sgugger

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MDU6SXNzdWU3MzkxNDUzNjk=

Add MRQA dataset

closed

MEMBER

## Adding a Dataset - **Name:** MRQA - **Description:** Collection of different (subsets of) QA datasets all converted to the same format to evaluate out-of-domain generalization (the datasets come from different domains, distributions, etc.). Some datasets are used for training and others are used for evaluation. This dataset was collected as part of MRQA 2019's shared task - **Paper:** https://arxiv.org/abs/1910.09753 - **Data:** https://github.com/mrqa/MRQA-Shared-Task-2019 - **Motivation:** Out-of-domain generalization is becoming (has become) a de-factor evaluation for NLU systems Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

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MDU6SXNzdWU3MzkxMDI2ODY=

[Caching] Dill globalvars() output order is not deterministic and can cause cache issues.

closed

MEMBER

Dill uses `dill.detect.globalvars` to get the globals used by a function in a recursive dump. `globalvars` returns a dictionary of all the globals that a dumped function needs. However the order of the keys in this dict is not deterministic and can cause caching issues. To fix that one could register an implementation of dill's `save_function` in the `datasets` pickler that sorts the globals keys before dumping a function.

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MDU6SXNzdWU3Mzg4NDIwOTI=

Is dataset iterative or not?

closed

NONE

Hi I want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not? could you provide me with example how I can use datasets as iterative datasets? thanks

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MDU6SXNzdWU3Mzg1MDA0NDM=

Joining multiple datasets

closed

NONE

Hi I have multiple iterative datasets from your library with different size and I want to join them in a way that each datasets is sampled equally, so smaller datasets more, larger one less, could you tell me how to implement this in pytorch? thanks

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MDU6SXNzdWU3Mzg0ODk4NTI=

How to implement DistributedSampler with datasets

closed

NONE

Hi, I am using your datasets to define my dataloaders, and I am training finetune_trainer.py in huggingface repo on them. I need a distributedSampler to be able to train the models on TPUs being able to distribute the load across the TPU cores. Could you tell me how I can implement the distribued sampler when using datasets in which datasets are iterative? To give you more context, I have multiple of datasets and I need to write sampler for this case. thanks.

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MDU6SXNzdWU3MzgzNDAyMTc=

Too much logging

closed

NONE

I'm doing this in the beginning of my script: from datasets.utils import logging as datasets_logging datasets_logging.set_verbosity_warning() but I'm still getting these logs: [2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on /home/username/.cache/huggingface/datasets/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock [2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on /home/username/.cache/huggingface/datasets/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock using datasets version = 1.1.2

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nlp viewer error

closed

NONE

Hello, when I select amazon_us_reviews in nlp viewer, it shows error. https://huggingface.co/nlp/viewer/?dataset=amazon_us_reviews 

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MDExOlB1bGxSZXF1ZXN0NTE2ODQzMzQ3

Fix seqeval metric

closed

CONTRIBUTOR

The current seqeval metric returns the following error when computed: ``` ~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/78a944d83252b5a16c9a2e49f057f4c6e02f18cc03349257025a8c9aea6524d8/seqeval.py in _compute(self, predictions, references, suffix) 102 scores = {} 103 for type_name, score in report.items(): --> 104 scores[type_name]["precision"] = score["precision"] 105 scores[type_name]["recall"] = score["recall"] 106 scores[type_name]["f1"] = score["f1-score"] KeyError: 'LOC' ``` This is because the current code basically tries to do: ``` scores = {} scores["LOC"]["precision"] = some_value ``` which does not work in python. This PR fixes that while keeping the previous nested structure of results, with the same keys.

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MDU6SXNzdWU3Mzc4MzI3MDE=

Add Google Taskmaster dataset

closed

MEMBER

## Adding a Dataset - **Name:** Taskmaster - **Description:** A large dataset of task-oriented dialogue with annotated goals (55K dialogues covering entertainment and travel reservations) - **Paper:** https://arxiv.org/abs/1909.05358 - **Data:** https://github.com/google-research-datasets/Taskmaster - **Motivation:** One of few annotated datasets of this size for goal-oriented dialogue Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

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MDExOlB1bGxSZXF1ZXN0NTE2NjQ0NDc0

dataset(dgs): initial dataset loading script

closed

CONTRIBUTOR

When trying to create dummy data I get: > Dataset datasets with config None seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has t o be created with less guidance. Make sure you create the file dummy_data. I am not sure how to manually create the dummy_data (what exactly it should contain) Also note, this library says: > ImportError: To be able to use this dataset, you need to install the following dependencies['pympi'] using 'pip install pympi' for instance' When you actually need to `pip install pympi-ling`

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MDU6SXNzdWU3Mzc1MDk5NTQ=

load_dataset for LOCAL CSV files report CONNECTION ERROR

closed

NONE

## load_dataset for LOCAL CSV files report CONNECTION ERROR - **Description:** A local demo csv file: ``` import pandas as pd import numpy as np from datasets import load_dataset import torch import transformers df = pd.DataFrame(np.arange(1200).reshape(300,4)) df.to_csv('test.csv', header=False, index=False) print('datasets version: ', datasets.__version__) print('pytorch version: ', torch.__version__) print('transformers version: ', transformers.__version__) # output: datasets version: 1.1.2 pytorch version: 1.5.0 transformers version: 3.2.0 ``` when I load data through `dataset`: ``` dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ``` Error infos: ``` ConnectionError Traceback (most recent call last) <ipython-input-17-bbdadb9a0c78> in <module> ----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 588 # Download/copy dataset processing script 589 module_path, hash = prepare_module( --> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True 591 ) 592 ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version) 267 try: --> 268 local_path = cached_path(file_path, download_config=download_config) 269 except FileNotFoundError: 270 if script_version is not None: ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 306 user_agent=download_config.user_agent, 307 local_files_only=download_config.local_files_only, --> 308 use_etag=download_config.use_etag, 309 ) 310 elif os.path.exists(url_or_filename): ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag) 473 elif response is not None and response.status_code == 404: 474 raise FileNotFoundError("Couldn't find file at {}".format(url)) --> 475 raise ConnectionError("Couldn't reach {}".format(url)) 476 477 # Try a second time ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py ``` And I try to connect to the site with requests: ``` import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ``` Similarly Error occurs: ``` --------------------------------------------------------------------------- ConnectionRefusedError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 159 conn = connection.create_connection( --> 160 (self._dns_host, self.port), self.timeout, **extra_kw 161 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock ConnectionRefusedError: [Errno 111] Connection refused During handling of the above exception, another exception occurred: NewConnectionError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 676 headers=headers, --> 677 chunked=chunked, 678 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 380 try: --> 381 self._validate_conn(conn) 382 except (SocketTimeout, BaseSSLError) as e: ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 976 conn.connect() 977 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self) 307 # Add certificate verification --> 308 conn = self._new_conn() 309 hostname = self.host ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 171 raise NewConnectionError( --> 172 self, "Failed to establish a new connection: %s" % e 173 ) NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 448 retries=self.max_retries, --> 449 timeout=timeout 450 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 724 retries = retries.increment( --> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] 726 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 438 if new_retry.is_exhausted(): --> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 440 MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) During handling of the above exception, another exception occurred: ConnectionError Traceback (most recent call last) <ipython-input-20-18cc3eb4a049> in <module> 1 import requests 2 ----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs) 102 103 kwargs.setdefault('allow_redirects', False) --> 104 return request('head', url, **kwargs) 105 106 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs) 59 # cases, and look like a memory leak in others. 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 63 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 528 } 529 send_kwargs.update(settings) --> 530 resp = self.send(prep, **send_kwargs) 531 532 return resp ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs) 641 642 # Send the request --> 643 r = adapter.send(request, **kwargs) 644 645 # Total elapsed time of the request (approximately) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 514 raise SSLError(e, request=request) 515 --> 516 raise ConnectionError(e, request=request) 517 518 except ClosedPoolError as e: ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) ```

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MDU6SXNzdWU3MzcyMTU0MzA=

Quail dataset urls are out of date

closed

CONTRIBUTOR

<h3>Code</h3> ``` from datasets import load_dataset quail = load_dataset('quail') ``` <h3>Error</h3> ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.2/xml/ordered/quail_1.2_train.xml ``` As per [quail v1.3 commit](https://github.com/text-machine-lab/quail/commit/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58](https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore.

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MDU6SXNzdWU3MzcwMTkzNjA=

On loading a metric from datasets, I get the following error

closed

NONE

`from datasets import load_metric` `metric = load_metric('bleurt')` Traceback: 210 class _ArrayXDExtensionType(pa.PyExtensionType): 211 212 ndims: int = None AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' Any help will be appreciated. Thank you.

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MDU6SXNzdWU3MzY4NTg1MDc=

Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa')

closed

CONTRIBUTOR

# The issue It's all in the title, it appears to be fine on the train and validation sets. Is there some kind of mapping to do like for the questions (see https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md) ? # How to reproduce ```py from datasets import load_dataset kilt_tasks = load_dataset("kilt_tasks") trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') # both in "kilt_tasks" In [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']]) Out[18]: False # and "trivia_qa" In [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']]) Out[13]: True # appears to be fine on the train and validation sets. In [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']]) Out[14]: False In [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']]) Out[15]: False In [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']]) Out[16]: True In [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']]) Out[17]: True ```

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MDExOlB1bGxSZXF1ZXN0NTE1OTY1ODE2

fix: typos in tutorial to map KILT and TriviaQA

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NTE1NTM1MDI0

Add XGlue

closed

MEMBER

Dataset is ready to merge. An important feature of this dataset is that for each config the train data is in English, while dev and test data are in multiple languages. Therefore, @lhoestq and I decided offline that we will give the dataset the following API, *e.g.* for ```python load_dataset("xglue", "ner") # would give the splits 'train', 'validation.en', 'test.en', 'validation.es', 'test.es', ... ``` => therefore one can load a single language test via ```python load_dataset("xglue", "ner", split="test.es") ``` Close #749.

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MDU6SXNzdWU3MzU3OTA4NzY=

How to join two datasets?

closed

NONE

Hi, I'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels? I'm currently trying to create paired sentences for BERT from `wikipedia/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence. Thanks!

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MDExOlB1bGxSZXF1ZXN0NTE1MTAyMjc3

Update loading_metrics.rst

closed

CONTRIBUTOR

Minor bug

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MDExOlB1bGxSZXF1ZXN0NTE0OTIzNDMx

switch amazon reviews class label order

closed

CONTRIBUTOR

Switches the label order to be more intuitive for amazon reviews, #791.

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MDU6SXNzdWU3MzU1MTg4MDU=

Cannot load TREC dataset: ConnectionError

closed

NONE

## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.

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MDU6SXNzdWU3MzU0MjAzMzI=

Token classification labels are strings and we don't have the list of labels

closed

CONTRIBUTOR

Not sure if this is an issue we want to fix or not, putting it here so it's not forgotten. Right now, in token classification datasets, the labels for NER, POS and the likes are typed as `Sequence` of `strings`, which is wrong in my opinion. These should be `Sequence` of `ClassLabel` or some types that gives easy access to the underlying labels. The main problem for preprocessing those datasets is that the list of possible labels is not stored inside the `Dataset` object which makes converting the labels to IDs quite difficult (you either have to know the list of labels in advance or run a full pass through the dataset to get the list of labels, the `unique` method being useless with the type `Sequence[str]`).

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MDU6SXNzdWU3MzUxOTgyNjU=

Descriptions of raw and processed versions of wikitext are inverted

closed

NONE

Nothing of importance, but it looks like the descriptions of wikitext-n-v1 and wikitext-n-raw-v1 are inverted for both n=2 and n=103. I just verified by loading them and the `<unk>` tokens are present in the non-raw versions, which confirms that it's a mere inversion of the descriptions and not of the datasets themselves. Also it would be nice if those descriptions appeared in the dataset explorer. https://github.com/huggingface/datasets/blob/87bd0864845ea0a1dd7167918dc5f341bf807bd3/datasets/wikitext/wikitext.py#L52

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MDU6SXNzdWU3MzUxNTg3MjU=

self.options cannot be converted to a Python object for pickling

closed

NONE

Hi, Currently I am trying to load csv file with customized read_options. And the latest master seems broken if we pass the ReadOptions object. Here is a code snippet ```python from datasets import load_dataset from pyarrow.csv import ReadOptions load_dataset("csv", data_files=["out.csv"], read_options=ReadOptions(block_size=16*1024*1024)) ``` error is `self.options cannot be converted to a Python object for pickling` Would you mind to take a look? Thanks! ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-28-ab83fec2ded4> in <module> ----> 1 load_dataset("csv", data_files=["out.csv"], read_options=ReadOptions(block_size=16*1024*1024)) /tmp/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 602 hash=hash, 603 features=features, --> 604 **config_kwargs, 605 ) 606 /tmp/datasets/src/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs) 162 name, 163 custom_features=features, --> 164 **config_kwargs, 165 ) 166 /tmp/datasets/src/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs) 281 ) 282 else: --> 283 suffix = Hasher.hash(config_kwargs_to_add_to_suffix) 284 285 if builder_config.data_files is not None: /tmp/datasets/src/datasets/fingerprint.py in hash(cls, value) 51 return cls.dispatch[type(value)](cls, value) 52 else: ---> 53 return cls.hash_default(value) 54 55 def update(self, value): /tmp/datasets/src/datasets/fingerprint.py in hash_default(cls, value) 44 @classmethod 45 def hash_default(cls, value): ---> 46 return cls.hash_bytes(dumps(value)) 47 48 @classmethod /tmp/datasets/src/datasets/utils/py_utils.py in dumps(obj) 365 file = StringIO() 366 with _no_cache_fields(obj): --> 367 dump(obj, file) 368 return file.getvalue() 369 /tmp/datasets/src/datasets/utils/py_utils.py in dump(obj, file) 337 def dump(obj, file): 338 """pickle an object to a file""" --> 339 Pickler(file, recurse=True).dump(obj) 340 return 341 ~/.local/lib/python3.6/site-packages/dill/_dill.py in dump(self, obj) 444 raise PicklingError(msg) 445 else: --> 446 StockPickler.dump(self, obj) 447 stack.clear() # clear record of 'recursion-sensitive' pickled objects 448 return /usr/lib/python3.6/pickle.py in dump(self, obj) 407 if self.proto >= 4: 408 self.framer.start_framing() --> 409 self.save(obj) 410 self.write(STOP) 411 self.framer.end_framing() /usr/lib/python3.6/pickle.py in save(self, obj, save_persistent_id) 474 f = self.dispatch.get(t) 475 if f is not None: --> 476 f(self, obj) # Call unbound method with explicit self 477 return 478 ~/.local/lib/python3.6/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 931 # we only care about session the first pass thru 932 pickler._session = False --> 933 StockPickler.save_dict(pickler, obj) 934 log.info("# D2") 935 return /usr/lib/python3.6/pickle.py in save_dict(self, obj) 819 820 self.memoize(obj) --> 821 self._batch_setitems(obj.items()) 822 823 dispatch[dict] = save_dict /usr/lib/python3.6/pickle.py in _batch_setitems(self, items) 850 k, v = tmp[0] 851 save(k) --> 852 save(v) 853 write(SETITEM) 854 # else tmp is empty, and we're done /usr/lib/python3.6/pickle.py in save(self, obj, save_persistent_id) 494 reduce = getattr(obj, "__reduce_ex__", None) 495 if reduce is not None: --> 496 rv = reduce(self.proto) 497 else: 498 reduce = getattr(obj, "__reduce__", None) ~/.local/lib/python3.6/site-packages/pyarrow/_csv.cpython-36m-x86_64-linux-gnu.so in pyarrow._csv.ReadOptions.__reduce_cython__() TypeError: self.options cannot be converted to a Python object for pickling ```

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MDExOlB1bGxSZXF1ZXN0NTE0NTU2NzY5

[Datasets] fix discofuse links

closed

MEMBER

The discofuse links were changed: https://github.com/google-research-datasets/discofuse/commit/d27641016eb5b3eb2af03c7415cfbb2cbebe8558. The old links are broken I changed the links and created the new dataset_infos.json. Pinging @thomwolf @lhoestq for notification.

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MDU6SXNzdWU3MzQ2OTM2NTI=

KILT dataset: empty string in triviaqa input field

closed

CONTRIBUTOR

# What happened Both train and test splits of the triviaqa dataset (part of the KILT benchmark) seem to have empty string in their input field (unlike the natural questions dataset, part of the same benchmark) # Versions KILT version is `1.0.0` `datasets` version is `1.1.2` [more here](https://gist.github.com/PaulLerner/3768c8d25f723edbac20d99b6a4056c1) # How to reproduce ```py In [1]: from datasets import load_dataset In [4]: dataset = load_dataset("kilt_tasks") # everything works fine, removed output for a better readibility Dataset kilt_tasks downloaded and prepared to /people/lerner/.cache/huggingface/datasets/kilt_tasks/all_tasks/1.0.0/821c4295a2c35db2847585918d9c47d7f028f1a26b78825d8e77cd3aeb2621a1. Subsequent calls will reuse this data. # empty string in triviaqa input field In [36]: dataset['train_triviaqa'][0] Out[36]: {'id': 'dpql_5197', 'input': '', 'meta': {'left_context': '', 'mention': '', 'obj_surface': {'text': []}, 'partial_evidence': {'end_paragraph_id': [], 'meta': [], 'section': [], 'start_paragraph_id': [], 'title': [], 'wikipedia_id': []}, 'right_context': '', 'sub_surface': {'text': []}, 'subj_aliases': {'text': []}, 'template_questions': {'text': []}}, 'output': {'answer': ['five £', '5 £', '£5', 'five £'], 'meta': [], 'provenance': [{'bleu_score': [1.0], 'end_character': [248], 'end_paragraph_id': [30], 'meta': [], 'section': ['Section::::Question of legal tender.\n'], 'start_character': [246], 'start_paragraph_id': [30], 'title': ['Banknotes of the pound sterling'], 'wikipedia_id': ['270680']}]}} In [35]: dataset['train_triviaqa']['input'][:10] Out[35]: ['', '', '', '', '', '', '', '', '', ''] # same with test set In [37]: dataset['test_triviaqa']['input'][:10] Out[37]: ['', '', '', '', '', '', '', '', '', ''] # works fine with natural questions In [34]: dataset['train_nq']['input'][:10] Out[34]: ['how i.met your mother who is the mother', 'who had the most wins in the nfl', 'who played mantis guardians of the galaxy 2', 'what channel is the premier league on in france', "god's not dead a light in the darkness release date", 'who is the current president of un general assembly', 'when do the eclipse supposed to take place', 'what is the name of the sea surrounding dubai', 'who holds the nba record for most points in a career', 'when did the new maze runner movie come out'] ``` Stay safe :)

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MDExOlB1bGxSZXF1ZXN0NTE0MTg0MzU5

add amazon reviews

closed

CONTRIBUTOR

Adds the Amazon US Reviews dataset as requested in #353. Converted from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/amazon_us_reviews). cc @clmnt @sshleifer

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MDU6SXNzdWU3MzQ0NzAxOTc=

Error running pip install -e ".[dev]" on MacOS 10.13.6: faiss/python does not exist

closed

NONE

I was following along with https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset when I ran into this error. ```sh git clone https://github.com/huggingface/datasets cd datasets virtualenv venv -p python3 --system-site-packages source venv/bin/activate pip install -e ".[dev]" ```   Python 3.7.7

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MDExOlB1bGxSZXF1ZXN0NTEzODM1MzE0

dataset(ncslgr): add initial loading script

closed

CONTRIBUTOR

Its a small dataset, but its heavily annotated https://www.bu.edu/asllrp/ncslgr.html 

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MDU6SXNzdWU3MzQxMzYxMjQ=

failed to reuse cache

closed

NONE

I packed the `load_dataset ` in a function of class, and cached data in a directory. But when I import the class and use the function, the data still have to be downloaded again. The information (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 ******) which logged to terminal shows the path is right to the cache directory, but the files still have to be downloaded again.

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MDExOlB1bGxSZXF1ZXN0NTEzNjk5MTQz

Adding nli_tr dataset

closed

CONTRIBUTOR

Hello, In this pull request, we have implemented the necessary interface to add our recent dataset [NLI-TR](https://github.com/boun-tabi/NLI-TR). The datasets will be presented on a full paper at EMNLP 2020 this month. [[arXiv link] ](https://arxiv.org/pdf/2004.14963.pdf) The dataset is the neural machine translation of SNLI and MultiNLI datasets into Turkish. So, we followed a similar format with the original datasets hosted in the HuggingFace datasets hub. Our dataset is designed to be accessed as follows by following the interface of the GLUE dataset that provides multiple datasets in a single interface over the HuggingFace datasets hub. ``` from datasets import load_dataset multinli_tr = load_dataset("nli_tr", "multinli_tr") snli_tr = load_dataset("nli_tr", "snli_tr") ``` Thanks for your help in reviewing our pull request.

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MDU6SXNzdWU3MzM3NjE3MTc=

feat(dataset): multiprocessing _generate_examples

closed

CONTRIBUTOR

forking this out of #741, this issue is only regarding multiprocessing I'd love if there was a dataset configuration parameter `workers`, where when it is `1` it behaves as it does right now, and when its `>1` maybe `_generate_examples` can also get the `pool` and return an iterable using the pool. In my use case, I would instead of: ```python for datum in data: yield self.load_datum(datum) ``` do: ```python return pool.map(self.load_datum, data) ``` As the dataset in question, as an example, has **only** 7000 rows, and takes 10 seconds to load each row on average, it takes almost 20 hours to load the entire dataset. If this was a larger dataset (and many such datasets exist), it would take multiple days to complete. Using multiprocessing, for example, 40 cores, could speed it up dramatically. For this dataset, hopefully to fully load in under an hour.

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MDExOlB1bGxSZXF1ZXN0NTEzNDMyNTM1

feat(aslg_pc12): add dev and test data splits

closed

CONTRIBUTOR

For reproducibility sake, it's best if there are defined dev and test splits. The original paper author did not define splits for the entire dataset, not for the sample loaded via this library, so I decided to define: - 5/7th for train - 1/7th for dev - 1/7th for test

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MDU6SXNzdWU3MzM3MDA0NjM=

Issue with downloading Wikipedia data for low resource language

closed

NONE

Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet ``` jv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner') su_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner') ``` And I get the following error for these two languages: Javanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json ``` Sundanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json ``` I found from https://github.com/huggingface/datasets/issues/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json` and `https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json` are no longer valid. Any suggestions on how to handle this issue? Thanks!

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MDExOlB1bGxSZXF1ZXN0NTEzMzAwODUz

updated links to v1.3 of quail, fixed the description

closed

NONE

updated links to v1.3 of quail, fixed the description

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MDExOlB1bGxSZXF1ZXN0NTEzMTE2MTM0

Fix metric deletion when attribuets are missing

closed

MEMBER

When you call `del` on a metric we want to make sure that the arrow attributes are not already deleted. I just added `if hasattr(...)` to make sure it doesn't crash

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MDExOlB1bGxSZXF1ZXN0NTEyOTkyMzQw

Add XNLI train set

closed

MEMBER

I added the train set that was built using the translated MNLI. Now you can load the dataset specifying one language: ```python from datasets import load_dataset xnli_en = load_dataset("xnli", "en") print(xnli_en["train"][0]) # {'hypothesis': 'Product and geography are what make cream skimming work .', 'label': 1, 'premise': 'Conceptually cream skimming has two basic dimensions - product and geography .'} print(xnli_en["test"][0]) # {'hypothesis': 'I havent spoken to him again.', 'label': 2, 'premise': "Well, I wasn't even thinking about that, but I was so frustrated, and, I ended up talking to him again."} ``` Cc @sgugger

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MDExOlB1bGxSZXF1ZXN0NTEyNjM0MzI0

Add ASNQ dataset

closed

CONTRIBUTOR

This pull request adds the ASNQ dataset. It is a dataset for answer sentence selection derived from Google Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). The dataset details can be found in the paper at https://arxiv.org/abs/1911.04118 The dataset is authored by Siddhant Garg, Thuy Vu and Alessandro Moschitti. _Please note that I have no affiliation with the authors._ Repo: https://github.com/alexa/wqa_tanda

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MDExOlB1bGxSZXF1ZXN0NTEyNDQzMjY0

Feature/fidelity metrics from emnlp2020 evaluating and characterizing human rationales

closed

NONE

This metric computes fidelity (Yu et al. 2019, DeYoung et al. 2019) and normalized fidelity (Carton et al. 2020).

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MDU6SXNzdWU3MzI0NDk2NTI=

Unexpected behavior when loading cached csv file?

closed

CONTRIBUTOR

I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode="force_redownload"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset. Small snippet to reproduce the behavior: ```python import datasets with open("dummy_data.csv", "w") as file: file.write("test,this;text\n") print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train").column_names) # ["test", "this;text"] print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train", delimiter=";").column_names) # still ["test", "this;text"] ``` By the way, thanks a lot for this amazing library! :)

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MDExOlB1bGxSZXF1ZXN0NTEyMzI2ODM2

Better error message for uninitialized metric

closed

MEMBER

When calling `metric.compute()` without having called `metric.add` or `metric.add_batch` at least once, the error was quite cryptic. I added a better error message Fix #729

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MDExOlB1bGxSZXF1ZXN0NTEyMjk5NzQx

Allow custom split names in text dataset

closed

MEMBER

The `text` dataset used to return only splits like train, test and validation. Other splits were ignored. Now any split name is allowed. I did the same for `json`, `pandas` and `csv` Fix #735

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MDExOlB1bGxSZXF1ZXN0NTEyMjUyODI3

Properly delete metrics when a process is killed

closed

MEMBER

Tests are flaky when using metrics in distributed setup. There is because of one test that make sure that using two possibly incompatible metric computation (same exp id) either works or raises the right error. However if the error is raised, all the processes of the metric are killed, and the open files (arrow + lock files) are not closed correctly. This causes PermissionError on Windows when deleting the temporary directory. To fix that I added a `finally` clause in the function passed to multiprocess to properly close the files when the process exits.

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MDExOlB1bGxSZXF1ZXN0NTEyMjM0NjA0

[ROUGE] Add description to Rouge metric

closed

MEMBER

Add information about case sensitivity to ROUGE.

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MDU6SXNzdWU3MzE2ODQxNTM=

Adding CC-100: Monolingual Datasets from Web Crawl Data

closed

MEMBER

## Adding a Dataset - **Name:** CC-100: Monolingual Datasets from Web Crawl Data - **Description:** https://twitter.com/alex_conneau/status/1321507120848625665 - **Paper:** https://arxiv.org/abs/1911.02116 - **Data:** http://data.statmt.org/cc-100/ - **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how "Wikipedia-like" it is, hopefully helping avoid some of the worst parts of the common crawl. Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

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MDExOlB1bGxSZXF1ZXN0NTExNjg4ODMx

Fix metric with cache dir

closed

MEMBER

The cache_dir provided by the user was concatenated twice and therefore causing FileNotFound errors. The tests didn't cover the case of providing `cache_dir=` for metrics because of a stupid issue (it was not using the right parameter). I remove the double concatenation and I fixed the tests Fix #728

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MDU6SXNzdWU3MzE0ODIyMTM=

Using `Dataset.map` with `n_proc>1` print multiple progress bars

closed

CONTRIBUTOR

When using `Dataset.map` with `n_proc > 1`, only one of the processes should print a progress bar (to make the output readable). Right now, `n_proc` progress bars are printed.

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MDExOlB1bGxSZXF1ZXN0NTExNTQ5MTg1

Fix custom builder caching

closed

MEMBER

The cache directory of a dataset didn't take into account additional parameters that the user could specify such as `features` or any parameter of the builder configuration kwargs (ex: `encoding` for the `text` dataset). To fix that, the cache directory name now has a suffix that depends on all of them. Fix #730 Fix #750

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MDU6SXNzdWU3MzEyNTcxMDQ=

How to choose proper download_mode in function load_dataset?

closed

NONE

Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue?

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MDU6SXNzdWU3MzA5MDgwNjA=

Add a `lazy_map` method to `Dataset` and `DatasetDict`

open

CONTRIBUTOR

The library is great, but it would be even more awesome with a `lazy_map` method implemented on `Dataset` and `DatasetDict`. This would apply a function on a give item but when the item is requested. Two use cases: 1. load image on the fly 2. apply a random function and get different outputs at each epoch (like data augmentation or randomly masking a part of a sentence for BERT-like objectives).

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MDU6SXNzdWU3MzA3NzE2MTA=

Add option for named splits when using ds.train_test_split

open

CONTRIBUTOR

### Feature Request 🚀 Can we add a way to name your splits when using the `.train_test_split` function? In almost every use case I've come across, I have a `train` and a `test` split in my `DatasetDict`, and I want to create a `validation` split. Therefore, its kinda useless to get a `test` split back from `train_test_split`, as it'll just overwrite my real `test` split that I intended to keep. ### Workaround this is my hack for dealin with this, for now :slightly_smiling_face: ```python from datasets import load_dataset ​ ​ ds = load_dataset('imdb') ds['train'], ds['validation'] = ds['train'].train_test_split(.1).values() ```

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MDU6SXNzdWU3MzA2Njk1OTY=

[GEM] add DART data-to-text generation dataset

closed

MEMBER

## Adding a Dataset - **Name:** DART - **Description:** DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. - **Paper:** https://arxiv.org/abs/2007.02871v1 - **Data:** https://github.com/Yale-LILY/dart - **Motivation:** the dataset will likely be included in the GEM benchmark Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

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MDU6SXNzdWU3MzA2NjgzMzI=

[GEM] Add DART data-to-text generation dataset

closed

MEMBER

## Adding a Dataset - **Name:** DART - **Description:** DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. - **Paper:** https://arxiv.org/abs/2007.02871v1 - **Data:** https://github.com/Yale-LILY/dart - **Motivation:** It will likely be included in the GEM generation evaluation benchmark Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

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MDExOlB1bGxSZXF1ZXN0NTEwODkyMTk2

Adding Issue Template for Dataset Requests

closed

MEMBER

adding .github/ISSUE_TEMPLATE/add-dataset.md

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MDExOlB1bGxSZXF1ZXN0NTEwODcyMDYx

Fixed errors in bertscore related to custom baseline

closed

CONTRIBUTOR

[bertscore version 0.3.6 ](https://github.com/Tiiiger/bert_score) added support for custom baseline files. This update added extra argument `baseline_path` to BERTScorer class as well as some extra boolean parameters `use_custom_baseline` in functions like `get_hash(model, num_layers, idf, rescale_with_baseline, use_custom_baseline)`. This PR fix those matching errors in bertscore metric implementation.

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MDU6SXNzdWU3MzA1ODY5NzI=

[GEM] Add Czech Restaurant data-to-text generation dataset

closed

MEMBER

- Paper: https://www.aclweb.org/anthology/W19-8670.pdf - Data: https://github.com/UFAL-DSG/cs_restaurant_dataset - The dataset will likely be part of the GEM benchmark

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MDU6SXNzdWU3Mjk4OTg4Njc=

Downloaded datasets are not usable offline

closed

CONTRIBUTOR

I've been trying to use the IMDB dataset offline, but after downloading it and turning off the internet it still raises an error from the ```requests``` library trying to reach for the online dataset. Is this the intended behavior ? (Sorry, I wrote the the first version of this issue while still on nlp 0.3.0).

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MDU6SXNzdWU3Mjk2Mzc5MTc=

Add meta-data to the HANS dataset

closed

MEMBER

The current version of the [HANS dataset](https://github.com/huggingface/datasets/blob/master/datasets/hans/hans.py) is missing the additional information provided for each example, including the sentence parses, heuristic and subcase.

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MDU6SXNzdWU3MjkwNDY5MTY=

(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py

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NONE

Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?

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MDU6SXNzdWU3Mjg2Mzg1NTk=

Process 0 very slow when using num_procs with map to tokenizer

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NONE

<img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ```

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MDU6SXNzdWU3MjgyNDE0OTQ=

CUDA out of memory

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NONE

In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.

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https://github.com/huggingface/datasets/pull/756

MDExOlB1bGxSZXF1ZXN0NTA4OTYwNTc3

Start community-provided dataset docs

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CONTRIBUTOR

Continuation of #736 with clean fork. #### Old description This is what I did to get the pseudo-labels updated. Not sure if it generalizes, but I figured I would write it down. It was pretty easy because all I had to do was make properly formatted directories and change URLs. In slack @thomwolf called it a user-namespace dataset, but the docs call it community dataset. I think the first naming is clearer, but I didn't address that here. I didn't add metadata, will try that.

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MDExOlB1bGxSZXF1ZXN0NTA4OTU0NDI2

Start community-provided dataset docs V2

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CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NTA4NjczNzM2

Use full released xsum dataset

closed

CONTRIBUTOR

#672 Fix xsum to expand coverage and include IDs Code based on parser from older version of `datasets/xsum/xsum.py` @lhoestq

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