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PR_kwDODunzps48RMLu

Update CI badge

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-28 18:04:20

2022-07-29 11:36:37

2022-07-29 11:23:51

CONTRIBUTOR

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Replace the old CircleCI badge with a new one for GH Actions.

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PR_kwDODunzps48RMKi

More rigorous shape inference in to_tf_dataset

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['The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4763). All of your documentation changes will be reflected on that endpoint.']

2022-07-28 18:04:15

2022-08-19 11:40:51

MEMBER

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`tf.data` needs to know the shape of tensors emitted from a `tf.data.Dataset`. Although `None` dimensions are possible, overusing them can cause problems - Keras uses the dataset tensor spec at compile-time, and so saying that a dimension is `None` when it's actually constant can hurt performance, or even cause training to fail for dimensions that are needed to determine the shape of weight tensors! The compromise I used here was to sample several batches from the underlying dataset and apply the `collate_fn` to them, and then to see which dimensions were "empirically variable". There's an obvious problem here, though - if you sample 10 batches and they all have the same shape on a certain dimension, there's still a small chance that the 11th batch will be different, and Keras will throw an error if a dataset tries to emit a tensor whose shape doesn't match the spec. I encountered this bug in practice once or twice for datasets that were mostly-but-not-totally constant on a given dimension, and I still don't have a perfect solution, but this PR should greatly reduce the risk. It samples many more batches, and also samples very small batches (size 2) - this increases the variability, making it more likely that a few outlier samples will be detected. Ideally, of course, we'd determine the full output shape analytically, but that's surprisingly tricky when the `collate_fn` can be any arbitrary Python code!

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PR_kwDODunzps48RE56

Improve features resolution in streaming

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['The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4762). All of your documentation changes will be reflected on that endpoint.']

2022-07-28 17:28:11

2022-07-28 17:34:54

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`IterableDataset._resolve_features` was returning the features sorted alphabetically by column name, which is not consistent with non-streaming. I changed this and used the order of columns from the data themselves. It was causing some inconsistencies in the dataset viewer as well. I also fixed `interleave_datasets` that was not filling missing columns with None, because it was not using the columns from `IterableDataset._resolve_features` cc @severo

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I_kwDODunzps5Oved7

parallel searching in multi-gpu setting using faiss

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["And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`." "Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs\r\n\r\n> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.\r\n\r\nThat's unexpected, can you share the code you're running ?" 'here is the code snippet\r\n\r\n```python\r\n\r\n# add faiss index\r\nsource_dataset = load_dataset(source_path)\r\nqueries = load_dataset(query_path)\r\ngpu = [0,1,2,3]\r\nsource_dataset.add_faiss_index(\r\n "embedding",\r\n device=gpu,\r\n )\r\n\r\n\r\n# batch query\r\nbatch_size = 32\r\nfor i in tqdm(range(0, len(queries), batch_size)):\r\n if i + batch_size >= len(queries):\r\n batched_queries = queries[i:]\r\n else:\r\n batched_queries = queries[i:i+batch_size]\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries[\'embedding\']], axis=0)\r\n scores, candidates = source_dataset.get_nearest_examples_batch(\r\n "embedding",\r\n batched_query_embeddings,\r\n k=5\r\n )\r\n```' 'My version of datasets is `2.4.1.dev0`.' 'The code looks all good to me, do you see all the GPUs being utilized ? What version of faiss are you using ?' 'I can see the memory usage of all the GPUs.\r\nMy version of `faiss-gpu` is `1.7.2`' "It looks all good to me then ^^ though you said you didn't experienced speed improvements by adding more GPUs ? What size is your source dataset and what time differences did you experience ?" 'query set: 1e6\r\nsource dataset: 1e6\r\nembedding size: 768\r\nindex: Flat\r\ntopk: 20\r\nGPU: V100\r\n\r\nThe time taken to traverse the query set once is about 1.5h, which is almost not influenced by the value of query batch size or the number of GPUs according to my experiments.' "Hmmm the number of GPUs should divide the time, something is going wrong. Can you check that adding more GPU does divide the memory used per GPU ? Maybe it can be worth looking at similar issues in the FAISS repository or create a noew issue over there to understand what's going on" '> Can you check that adding more GPU does divide the memory used per GPU \r\n\r\nThe memory used per GPU is unchanged while adding more GPU. Is this unexpected?\r\n\r\nI used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆 ' "> I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆\r\n\r\nOh indeed that's possible, I wasn't sure. Anyway you can check that calling get_nearest_examples_batch simply calls search under the hood: \r\n\r\nhttps://github.com/huggingface/datasets/blob/f90f71fbbb33889fe75a3ffc101cdf16a88a3453/src/datasets/search.py#L375" 'Here is a runnable script. \r\nMulti-GPU searching still does not work in my experiments.\r\n\r\n\r\n```python\r\nimport os\r\nfrom tqdm import tqdm\r\nimport numpy as np\r\nimport datasets\r\nfrom datasets import Dataset\r\n\r\nclass DPRSelector:\r\n\r\n def __init__(self, source, target, index_name, gpu=None):\r\n self.source = source\r\n self.target = target\r\n self.index_name = index_name\r\n\r\n cache_path = \'embedding.faiss\'\r\n\r\n if not os.path.exists(cache_path):\r\n self.source.add_faiss_index(\r\n column="embedding",\r\n index_name=index_name,\r\n device=gpu,\r\n )\r\n self.source.save_faiss_index(index_name, cache_path)\r\n else:\r\n self.source.load_faiss_index(\r\n index_name,\r\n cache_path,\r\n device=gpu\r\n )\r\n print(\'index builded!\')\r\n\r\n def build_dataset(self, top_k, batch_size):\r\n print(\'start search\')\r\n\r\n for i in tqdm(range(0, len(self.target), batch_size)):\r\n if i + batch_size >= len(self.target):\r\n batched_queries = self.target[i:]\r\n else:\r\n batched_queries = self.target[i:i+batch_size]\r\n\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries[\'embedding\']], axis=0)\r\n search_res = self.source.get_nearest_examples_batch(\r\n self.index_name,\r\n batched_query_embeddings,\r\n k=top_k\r\n )\r\n \r\n print(\'finish search\')\r\n\r\n\r\ndef get_pseudo_dataset():\r\n pseudo_dict = {"embedding": np.zeros((1000000, 768), dtype=np.float32)}\r\n print(\'generate pseudo data\')\r\n\r\n dataset = Dataset.from_dict(pseudo_dict)\r\n def list_to_array(data):\r\n return {"embedding": [np.array(vector, dtype=np.float32) for vector in data["embedding"]]} \r\n dataset.set_transform(list_to_array, columns=\'embedding\', output_all_columns=True)\r\n\r\n print(\'build dataset\')\r\n return dataset\r\n\r\n\r\n\r\nif __name__=="__main__":\r\n\r\n np.random.seed(42)\r\n\r\n\r\n source_dataset = get_pseudo_dataset()\r\n target_dataset = get_pseudo_dataset()\r\n\r\n gpu = [0,1,2,3,4,5,6,7]\r\n selector = DPRSelector(source_dataset, target_dataset, "embedding", gpu=gpu)\r\n\r\n selector.build_dataset(top_k=20, batch_size=32)\r\n```' '@lhoestq Hi, could you please test the code above if you have time? 😄 ' "Maybe @albertvillanova you can take a look ? I won't be available in the following days" '@albertvillanova Hi, can you help with this issue?' "Hi @xwwwwww I'm investigating it, but I'm not an expert in Faiss. In principle, it is weird that your code does not work properly because it seems right..." 'Have you tried passing `gpu=-1` and check if there is a speedup?' '> Have you tried passing `gpu=-1` and check if there is a speedup?\r\n\r\nyes, there is a speed up using GPU compared with CPU. ' "When passing `device=-1`, ALL existing GPUs are used (multi GPU): this is the maximum speedup you can get. To know the number of total GPUs:\r\n```\r\nimport faiss\r\n\r\nngpus = faiss.get_num_gpus()\r\nprint(ngpus)\r\n```\r\n\r\nWhen passing a list of integers to `device`, then only that number of GPUs are used (multi GPU as well)\r\n- the speedup should be proportional (more or less) to the ratio of the number of elements passed to `device` over `ngpus`\r\n- if this is not the case, then there is an issue in the implementation of this use case (however, I have reviewed the code and in principle I can't find any evident bug)\r\n\r\nWhen passing a positive integer to `device`, then only a single GPU is used.\r\n- this time should be more or less proportional to the time when passing `device=-1` over `ngpus`" 'Thanks for your help!\r\nHave you run the code and replicated the same experimental results (i.e., no speedup while increasing the number of GPUs)?' '@albertvillanova @lhoestq Sorry for the bother, is there any progress on this issue? 😃 ' 'I can confirm `add_faiss_index` calls `index = faiss.index_cpu_to_gpus_list(index, gpus=list(device))`.\r\n\r\nCould this be an issue with your environment ? Could you try running with 1 and 8 GPUs with a code similar to[ this one from the FAISS examples](https://github.com/facebookresearch/faiss/blob/main/tutorial/python/5-Multiple-GPUs.py) but using `gpu_index = faiss.index_cpu_to_gpus_list(cpu_index, gpus=list(device))`, and see if the speed changes ?']

2022-07-28 14:57:03

2022-08-18 14:59:10

CONTRIBUTOR

While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works. Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360

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I_kwDODunzps5OuwCP

Issue with offline mode

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["Hi @SaulLu, thanks for reporting.\r\n\r\nI think offline mode is not supported for datasets containing only data files (without any loading script). I'm having a look into this..." "Thanks for your feedback! \r\n\r\nTo give you a little more info, if you don't set the offline mode flag, the script will load the cache. I first noticed this behavior with the `evaluate` library, and while trying to understand the downloading flow I realized that I had a similar error with datasets." 'This is an issue we have to fix.' 'This is related to https://github.com/huggingface/datasets/issues/3547']

2022-07-28 12:45:14

2022-07-28 16:05:36

NONE

## Describe the bug I can't retrieve a cached dataset with offline mode enabled ## Steps to reproduce the bug To reproduce my issue, first, you'll need to run a script that will cache the dataset ```python import os os.environ["HF_DATASETS_OFFLINE"] = "0" import datasets datasets.logging.set_verbosity_info() ds_name = "SaulLu/toy_struc_dataset" ds = datasets.load_dataset(ds_name) print(ds) ``` then, you can try to reload it in offline mode: ```python import os os.environ["HF_DATASETS_OFFLINE"] = "1" import datasets datasets.logging.set_verbosity_info() ds_name = "SaulLu/toy_struc_dataset" ds = datasets.load_dataset(ds_name) print(ds) ``` ## Expected results I would have expected the 2nd snippet not to return any errors ## Actual results The 2nd snippet returns: ``` Traceback (most recent call last): File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module> ds = datasets.load_dataset(ds_name) File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset builder_instance = load_dataset_builder( File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3 Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?

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I_kwDODunzps5OuY3E

Dataset Viewer issue for Toygar/turkish-offensive-language-detection

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closed

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['I refreshed the dataset viewer manually, it\'s fixed now. Sorry for the inconvenience.\r\n<img width="1557" alt="Capture d’écran 2022-07-28 à 09 17 39" src="https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png">\r\n\r\n']

2022-07-28 11:21:43

2022-07-28 13:17:56

2022-07-28 13:17:48

NONE

### Link https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection ### Description Status code: 400 Exception: Status400Error Message: The dataset does not exist. Hi, I provided train.csv, test.csv and valid.csv files. However, viewer says dataset does not exist. Should I need to do anything else? ### Owner Yes

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I_kwDODunzps5Otsuk

Document better when relative paths are transformed to URLs

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2022-07-28 08:46:27

2022-08-01 09:06:44

MEMBER

As discussed with @ydshieh, when passing a relative path as `data_dir` to `load_dataset` of a dataset hosted on the Hub, the relative path is transformed to the corresponding URL of the Hub dataset. Currently, we mention this in our docs here: [Create a dataset loading script > Download data files and organize splits](https://huggingface.co/docs/datasets/v2.4.0/en/dataset_script#download-data-files-and-organize-splits) > If the data files live in the same folder or repository of the dataset script, you can just pass the relative paths to the files instead of URLs. Maybe we should document better how relative paths are handled, not only when creating a dataset loading script, but also when passing to `load_dataset`: - `data_dir` - `data_files` CC: @stevhliu

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I_kwDODunzps5OqNOE

Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size

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[]

["I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:\r\n\r\n```\r\nimport datasets\r\nimport transformers\r\npretrained = 'deepset/tinyroberta-squad2'\r\ntokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)\r\n\r\nquestions = ['Can you tell me why?', 'What time is it?']\r\ncontexts = ['This is context zero', 'Another paragraph goes here'] \r\n\r\ndef tok(questions, contexts):\r\n return tokenizer(text=questions,\r\n text_pair=contexts,\r\n truncation='only_second',\r\n return_overflowing_tokens=True,\r\n )\r\nprint(tok(questions, contexts)['overflow_to_sample_mapping'])\r\nassert tok(questions, contexts)['overflow_to_sample_mapping'] == [0, 1] # PASSES\r\n\r\ndef tok2(d):\r\n return tok(d['question'], d['context'])\r\n\r\ndef tok2(d):\r\n return tok(d['question'], d['context'])\r\n\r\nds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})\r\ntokens = ds.map(tok2, batched=True, batch_size=1)\r\nprint(tokens['overflow_to_sample_mapping'])\r\nassert tokens['overflow_to_sample_mapping'] == [0, 1] # FAILS produces [0,0]\r\n```\r\n\r\nNote that even if the batch size would be larger, there will be instances where we will not have a lot of data, and end up using small batches. This can occur e.g. if `n_proc` causes batches to be underfill. I imagine it can also occur in other ways, e.g. the final leftover batch at the end." "A larger batch size does _not_ have this behavior:\r\n\r\n```\r\ndef tok2(d):\r\n return tok(d['question'], d['context'])\r\n\r\nds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})\r\ntokens = ds.map(tok2, batched=True, batch_size=2)\r\nprint(tokens['overflow_to_sample_mapping'])\r\nassert tokens['overflow_to_sample_mapping'] == [0, 1] # PASSES\r\n```"]

2022-07-27 14:54:11

2022-07-27 17:57:28

NONE

## Describe the bug When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences. However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together. ## Steps to reproduce the bug 1. Make a dataset of 3 strings. 2. Tokenize via Dataset.map with n_proc = 8 3. Inspect the `overflow_to_sample_mapping` field ## Expected results `[0, 1, 2]` ## Actual results `[0, 0, 0]` Notes: 1. I have not yet extracted a minimal example, but the above works reliably 2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.

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PR_kwDODunzps48L9p6

Remove "unkown" language tags

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-27 14:50:12

2022-07-27 15:03:00

2022-07-27 14:51:06

MEMBER

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Following https://github.com/huggingface/datasets/pull/4753 there was still a "unknown" langauge tag in `wikipedia` so the job at https://github.com/huggingface/datasets/runs/7542567336?check_suite_focus=true failed for wikipedia

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PR_kwDODunzps48Ll8G

Add `language_bcp47` tag

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-27 13:31:16

2022-07-27 14:50:03

2022-07-27 14:37:56

MEMBER

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Following (internal) https://github.com/huggingface/moon-landing/pull/3509, we need to move the bcp47 tags to `language_bcp47` and keep the `language` tag for iso 639 1-2-3 codes. In particular I made sure that all the tags in `languages` are not longer than 3 characters. I moved the rest to `language_bcp47` and fixed some of them. After this PR is merged I think we can simplify the language validation from the DatasetMetadata class (and keep it bare-bone just for the tagging app) PS: the CI is failing because of missing content in dataset cards that are unrelated to this PR

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I_kwDODunzps5OpW3Z

DatasetInfo issue when testing multiple configs: mixed task_templates

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["I've narrowed down the issue to the `dataset_module_factory` which already creates a `dataset_infos.json` file down in the `.cache/modules/dataset_modules/..` folder. That JSON file already contains the wrong task_templates for `unfiltered`." 'Ugh. Found the issue: apparently `datasets` was reusing the already existing `dataset_infos.json` that is inside `datasets/datasets/hebban-reviews`! Is this desired behavior?\r\n\r\nPerhaps when `--save_infos` and `--all_configs` are given, an existing `dataset_infos.json` file should first be deleted before continuing with the test? Because that would assume that the user wants to create a new infos file for all configs anyway.' 'Hi! I think this is a reasonable solution. Would you be interested in submitting a PR?']

2022-07-27 12:04:54

2022-08-08 18:20:50

CONTRIBUTOR

## Describe the bug When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel. ## Steps to reproduce the bug In summary, what I want to do is create three configs: - unfiltered: no classlabel, no tasks. Gets data from unfiltered.json.gz (I'd want this without splits, just one chunk of data, but that does not seem possible?) - filtered_sentiment: `review_sentiment` as ClassLabel, TextClassification task with `review_sentiment` as label. Gets train/test split from respective json.gz files - filtered_rating: `review_rating0` as ClassLabel, TextClassification task with `review_rating0` as label. Gets train/test split from respective json.gz files This might be a bit tedious to reproduce, so I am sorry, but these are the steps: - Clone datasets -> `datasets/` and install it - Clone `https://huggingface.co/datasets/BramVanroy/hebban-reviews` into `datasets/datasets` so that you have a new folder `datasets/datasets/hebban-reviews/`. - Replace the HebbanReviews class with this new one: ```python class HebbanReviews(datasets.GeneratorBasedBuilder): """The Hebban book reviews dataset.""" BUILDER_CONFIGS = [ HebbanReviewsConfig( name="unfiltered", description=_HEBBAN_REVIEWS_UNFILTERED_DESCRIPTION, version=datasets.Version(_HEBBAN_VERSION) ), HebbanReviewsConfig( name="filtered_sentiment", description=f"This config has the negative, neutral, and positive sentiment scores as ClassLabel in the 'review_sentiment' column.\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}", version=datasets.Version(_HEBBAN_VERSION) ), HebbanReviewsConfig( name="filtered_rating", description=f"This config has the 5-class ratings as ClassLabel in the 'review_rating0' column (which is a variant of 'review_rating' that starts counting from 0 instead of 1).\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}", version=datasets.Version(_HEBBAN_VERSION) ) ] DEFAULT_CONFIG_NAME = "filtered_sentiment" _URLS = { "train": "train.jsonl.gz", "test": "test.jsonl.gz", "unfiltered": "unfiltered.jsonl.gz", } def _info(self): features = { "review_title": datasets.Value("string"), "review_text": datasets.Value("string"), "review_text_without_quotes": datasets.Value("string"), "review_n_quotes": datasets.Value("int32"), "review_n_tokens": datasets.Value("int32"), "review_rating": datasets.Value("int32"), "review_rating0": datasets.Value("int32"), "review_author_url": datasets.Value("string"), "review_author_type": datasets.Value("string"), "review_n_likes": datasets.Value("int32"), "review_n_comments": datasets.Value("int32"), "review_url": datasets.Value("string"), "review_published_date": datasets.Value("string"), "review_crawl_date": datasets.Value("string"), "lid": datasets.Value("string"), "lid_probability": datasets.Value("float32"), "review_sentiment": datasets.features.ClassLabel(names=["negative", "neutral", "positive"]), "review_sentiment_label": datasets.Value("string"), "book_id": datasets.Value("int32"), } if self.config.name == "filtered_sentiment": task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_sentiment")] elif self.config.name == "filtered_rating": # For CrossEntropy, our classes need to start at index 0 -- not 1 features["review_rating0"] = datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"]) features["review_sentiment"] = datasets.Value("int32") task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_rating0")] elif self.config.name == "unfiltered": # no ClassLabels in unfiltered features["review_sentiment"] = datasets.Value("int32") task_templates = None else: raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default)," f" 'filtered_rating', or 'unfiltered'") print("AT INFO", self.config.name, task_templates) return datasets.DatasetInfo( description=self.config.description, features=datasets.Features(features), homepage="https://huggingface.co/datasets/BramVanroy/hebban-reviews", citation=_HEBBAN_REVIEWS_CITATION, task_templates=task_templates, license="cc-by-4.0" ) def _split_generators(self, dl_manager): if self.config.name.startswith("filtered"): files = dl_manager.download_and_extract({"train": "train.jsonl.gz", "test": "test.jsonl.gz"}) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data_file": files["train"] }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "data_file": files["test"] }, ), ] elif self.config.name == "unfiltered": files = dl_manager.download_and_extract({"train": "unfiltered.jsonl.gz"}) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data_file": files["train"] }, ), ] else: raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default)," f" 'filtered_rating', or 'unfiltered'") def _generate_examples(self, data_file): lines = Path(data_file).open(encoding="utf-8").readlines() for line_idx, line in enumerate(lines): row = json.loads(line) yield line_idx, row ``` - finally, run `datasets-cli test ./datasets/hebban-reviews/ --save_infos --all_configs` from within the topmost `datasets` directory ## Expected results Succeeding tests for three different configs. ## Actual results I printed out the values that are given to `DatasetInfo` for config name and task_templates, as you can see. There, as expected, I get `unfiltered None`. I also modified datasets/info.py and added this line [at L.170](https://github.com/huggingface/datasets/blob/f5847a304aa1b38b3a3c54a8318b4df60f1299bc/src/datasets/info.py#L170): ```python print("INTERNALLY AT INFO.PY", self.config_name, self.task_templates) ``` to my surprise, here I get `unfiltered [TextClassification(task='text-classification', text_column='review_text_without_quotes', label_column='review_sentiment')]`. So one way or another, here I suddenly see that `unfiltered` now does have a task_template -- even though that is not what is written in the data loading script, as the first print statement correctly shows. I do not quite understand how, but it seems that the config name and task_templates get mixed. This ultimately leads to the following error, but this trace may not be very useful in itself: ``` Traceback (most recent call last): File "C:\Users\bramv\.virtualenvs\hebban-U6poXNQd\Scripts\datasets-cli-script.py", line 33, in <module> sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')()) File "c:\dev\python\hebban\datasets\src\datasets\commands\datasets_cli.py", line 39, in main service.run() File "c:\dev\python\hebban\datasets\src\datasets\commands\test.py", line 144, in run builder.as_dataset() File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 899, in as_dataset datasets = map_nested( File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 393, in map_nested mapped = [ File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 394, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 330, in _single_map_nested return function(data_struct) File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 930, in _build_single_dataset ds = self._as_dataset( File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 1006, in _as_dataset return Dataset(fingerprint=fingerprint, **dataset_kwargs) File "c:\dev\python\hebban\datasets\src\datasets\arrow_dataset.py", line 661, in __init__ info = info.copy() if info is not None else DatasetInfo() File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 286, in copy return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File "<string>", line 20, in __init__ File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 176, in __post_init__ self.task_templates = [ File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 177, in <listcomp> template.align_with_features(self.features) for template in (self.task_templates) File "c:\dev\python\hebban\datasets\src\datasets\tasks\text_classification.py", line 22, in align_with_features raise ValueError(f"Column {self.label_column} is not a ClassLabel.") ValueError: Column review_sentiment is not a ClassLabel. ``` ## Environment info - `datasets` version: 2.4.1.dev0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.8.8 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps48LJ7U

Added dataset information in clinic oos dataset card

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-27 11:44:28

2022-07-28 10:53:21

2022-07-28 10:40:37

CONTRIBUTOR

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This PR aims to add relevant information like the Description, Language and citation information of the clinic oos dataset card.

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I_kwDODunzps5Oo28N

Easily create loading script for benchmark comprising multiple huggingface datasets

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['Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467' 'Ok, I see. Thank you']

2022-07-27 10:13:38

2022-07-27 13:58:07

2022-07-27 13:58:07

CONTRIBUTOR

Hi, I would like to create a loading script for a benchmark comprising multiple huggingface datasets. The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a single interface to all the underlying datasets. I thought about downloading the files with the load_dataset function and then providing the link to the cached file. But this seems a bit inelegant to me. What approach would you propose to do this? Please let me know if you have any questions. Cheers, Joel

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PR_kwDODunzps48JTEb

Add image classification processing guide

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-27 00:11:11

2022-07-27 17:28:21

2022-07-27 17:16:12

MEMBER

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This PR follows up on #4710 to separate the object detection and image classification guides. It expands a little more on the original guide to include a more complete example of loading and transforming a whole dataset.

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PR_kwDODunzps48IWKj

Shard parquet in `download_and_prepare`

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['The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4747). All of your documentation changes will be reflected on that endpoint.']

2022-07-26 18:05:01

2022-07-29 14:19:31

MEMBER

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Following https://github.com/huggingface/datasets/pull/4724 (needs to be merged first) It's good practice to shard parquet files to enable parallelism with spark/dask/etc. I added the `max_shard_size` parameter to `download_and_prepare` (default to 500MB for parquet, and None for arrow). ```python from datasets import * cache_dir = "s3://..." builder = load_dataset_builder("squad", cache_dir=cache_dir) builder.download_and_prepare(file_format="parquet", max_shard_size="5MB") ``` ### Implementation details The examples are written to a parquet file until `ParquetWriter._num_bytes > max_shard_size`. When this happens, a new writer is instantiated to start writing the next shard. At the end, all the shards are renamed to include the total number of shards in their names: `{builder.name}-{split}-{shard_id:05d}-of-{num_shards:05d}.parquet` TODO: - [x] docstrings - [x] docs - [x] tests cc @severo

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I_kwDODunzps5OloJH

Dataset Viewer issue for yanekyuk/wikikey

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['The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main\r\n\r\nMaybe the viewer can display a better message for empty datasets']

2022-07-26 16:25:16

2022-07-26 18:07:37

NONE

### Link _No response_ ### Description _No response_ ### Owner _No response_

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I_kwDODunzps5Oj1aP

Allow `list_datasets` to include private datasets

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[]

["Thanks for opening this issue :)\r\n\r\nIf it can help, I think you can already use `huggingface_hub` to achieve this:\r\n```python\r\n>>> from huggingface_hub import HfApi\r\n>>> [ds_info.id for ds_info in HfApi().list_datasets(use_auth_token=token) if ds_info.private]\r\n['bigscience/xxxx', 'bigscience-catalogue-data/xxxxxxx', ... ]\r\n```\r\n\r\n---------\r\n\r\nThough the latest versions of `huggingface_hub` that contain this feature are not available on python 3.6, so maybe we should first drop support for python 3.6 (see #4460) to update `list_datasets` in `datasets` as well (or we would have to copy/paste some `huggingface_hub` code)" 'Great, thanks @lhoestq the workaround works! I think it would be intuitive to have the support directly in `datasets` but it makes sense to wait given that the workaround exists :)' "i also think that going forward we should replace more and more implementations inside datasets with the corresponding ones from `huggingface_hub` (same as we're doing in `transformers`)"]

2022-07-26 10:16:08

2022-07-26 11:59:25

NONE

I am working with a large collection of private datasets, it would be convenient for me to be able to list them. I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling: ``` list_datasets(use_auth_token="my_token") ``` would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually.

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I_kwDODunzps5OjF-J

Remove instructions to generate dummy data from our docs

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['Note that for me personally, conceptually all the dummy data (even for "canonical" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones)' 'I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.\r\n\r\nBut for now, we could at least start by removing the indication to generate dummy data from our docs.']

2022-07-26 07:32:58

2022-08-02 23:50:30

2022-08-02 23:50:30

MEMBER

In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata However: - dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI - for datasets on the Hub: - they do not pass any CI test requiring dummy data - there are no instructions on how they can test their dataset locally using the dummy data - the generation of the dummy data assumes our GitHub directory structure: - the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue: - #4742 CC: @stevhliu

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PR_kwDODunzps48EUFs

Update map docs

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-25 20:59:35

2022-07-27 16:22:04

2022-07-27 16:10:04

MEMBER

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This PR updates the `map` docs for processing text to include `return_tensors="np"` to make it run faster (see #4676).

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I_kwDODunzps5Og813

Dummy data nowhere to be found

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[]

['Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for datasets on the Hub.\r\n- We should definitely update our docs.\r\n\r\nSecond, the dummy data is generated locally:\r\n- in your case, the dummy data will be generated inside the directory: `./datasets/hebban-reviews/dummy`\r\n- please note the preceding `./datasets` directory: the reason for this is that the command to generate the dummy data was specifically created for our `datasets` library, and therefore assumes our directory structure: commands are run from the root directory of our GitHub repo, and datasets scripts are under `./datasets` \r\n\r\n\r\n ' 'I have opened an Issue to update the instructions on dummy data generation:\r\n- #4744']

2022-07-25 19:18:42

2022-07-26 07:33:47

CONTRIBUTOR

## Describe the bug To finalize my dataset, I wanted to create dummy data as per the guide and I ran ```shell datasets-cli dummy_data datasets/hebban-reviews --auto_generate ``` where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs and shows a message at the end that it succeeded, I cannot find the dummy data anywhere. Where is it? ## Expected results To see the dummy data in the datasets' folder or in the folder where I ran the command. ## Actual results I see the following message but I cannot find the dummy data anywhere. ``` Dummy data generation done and dummy data test succeeded for config 'filtered''. Automatic dummy data generation succeeded for all configs of '.\datasets\hebban-reviews\' ``` ## Environment info - `datasets` version: 2.4.1.dev0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.8.8 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps48B2fl

Fix to dict conversion of `DatasetInfo`/`Features`

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-25 10:41:27

2022-07-25 12:50:36

2022-07-25 12:37:53

CONTRIBUTOR

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Fix #4681

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PR_kwDODunzps48BX5l

Fix multiprocessing in map_nested

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' '@lhoestq as a workaround to preserve previous behavior, the parameter `multiprocessing_min_length=16` is passed from `download` to `map_nested`, so that multiprocessing is only used if at least 16 files to be downloaded.\r\n\r\nNote that there is a small breaking change (I think previously it was unintended behavior, so that I have fixed it):\r\n- Before (with default `num_proc=16`) if there were 16 files to be downloaded, multiprocessing was not used\r\n- Now (with default `num_proc=16`) if there are 16 files to be downloaded, multiprocessing is used' 'Thanks for the workaround !']

2022-07-25 08:44:19

2022-07-28 10:53:23

2022-07-28 10:40:31

MEMBER

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As previously discussed: Before, multiprocessing was not used in `map_nested` if `num_proc` was greater than or equal to `len(iterable)`. - Multiprocessing was not used e.g. when passing `num_proc=20` but having 19 files to download - As by default, `DownloadManager` sets `num_proc=16`, before multiprocessing was only used when `len(iterable)>16` by default Now, if `num_proc` is greater than or equal to ``len(iterable)``, `num_proc` is set to ``len(iterable)`` and multiprocessing is used. - We pass the variable `parallel_min_length=16`, so that multiprocessing is only used if at least 16 files to be downloaded - ~As by default, `DownloadManager` sets `num_proc=16`, now multiprocessing is used when `len(iterable)>1` by default~ See discussion below. ~After having had to fix some tests (87602ac), I am wondering:~ - ~do we want to have multiprocessing by default?~ - ~please note that `DownloadManager.download` sets `num_proc=16` by default~ - ~or would it be better to ask the user to set it explicitly if they want multiprocessing (and default to `num_proc=1`)?~ Fix #4636. CC: @nateraw

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PR_kwDODunzps48BHdE

Deprecate metrics

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['_The documentation is not available anymore as the PR was closed or merged._' 'I mark this as Draft because the deprecated version number needs being updated after the latest release.' 'Perhaps now is the time to also update the `inspect_metric` from `evaluate` with the changes introduced in https://github.com/huggingface/datasets/pull/4433 (cc @lvwerra) ' 'What do you think of including what changes users have to do to switch to `evaluate` in the warning message ?\r\n(basically replace `datasets.load_metric` by `evaluate.load`)\r\n\r\nI think it can help users migrate to `evaluate` and silence the warnings']

2022-07-25 07:35:55

2022-07-28 11:44:27

2022-07-28 11:32:16

MEMBER

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Deprecate metrics: - deprecate public functions: `load_metric`, `list_metrics` and `inspect_metric`: docstring and warning - test deprecation warnings are issues - deprecate metrics in all docs - remove mentions to metrics in docs and README - deprecate internal functions/classes Maybe we should also stop testing metrics?

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PR_kwDODunzps479hq4

Use CI unit/integration tests

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'I think this PR can be merged. Willing to see it in action.\r\n\r\nCC: @lhoestq ']

2022-07-22 16:48:00

2022-07-26 20:19:22

2022-07-26 20:07:05

MEMBER

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This PR: - Implements separate unit/integration tests - A fail in integration tests does not cancel the rest of the jobs - We should implement more robust integration tests: work in progress in a subsequent PR - For the moment, test involving network requests are marked as integration: to be evolved

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I_kwDODunzps5OYXm8

Download error on scene_parse_150

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[]

["Hi! The server with the data seems to be down. I've reported this issue (https://github.com/CSAILVision/sceneparsing/issues/34) in the dataset repo. "]

2022-07-22 13:28:28

2022-07-22 14:29:11

NONE

``` from datasets import load_dataset dataset = load_dataset("scene_parse_150", "scene_parsing") FileNotFoundError: Couldn't find file at http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip ```

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I_kwDODunzps5OYEUc

Dataset Viewer issue for deepklarity/huggingface-spaces-dataset

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["Thanks for reporting. You're right, workers were under-provisioned due to a manual error, and the job queue was full. It's fixed now."]

2022-07-22 12:14:18

2022-07-22 13:46:38

2022-07-22 13:46:38

NONE

### Link https://huggingface.co/datasets/deepklarity/huggingface-spaces-dataset/viewer/deepklarity--huggingface-spaces-dataset/train ### Description Hi Team, I'm getting the following error on a uploaded dataset. I'm getting the same status for a couple of hours now. The dataset size is `<1MB` and the format is csv, so I'm not sure if it's supposed to take this much time or not. ``` Status code: 400 Exception: Status400Error Message: The split is being processed. Retry later. ``` Is there any explicit step to be taken to get the viewer to work? ### Owner Yes

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PR_kwDODunzps477CuP

Pin rouge_score test dependency

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-22 07:18:21

2022-07-22 07:58:14

2022-07-22 07:45:18

MEMBER

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Temporarily pin `rouge_score` (to avoid latest version 0.7.0) until the issue is fixed. Fix #4734

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I_kwDODunzps5OWZuW

Package rouge-score cannot be imported

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['We have added a comment on an existing issue opened in their repo: https://github.com/google-research/google-research/issues/1212#issuecomment-1192267130\r\n- https://github.com/google-research/google-research/issues/1212']

2022-07-22 07:15:05

2022-07-22 07:45:19

2022-07-22 07:45:18

MEMBER

## Describe the bug After the today release of `rouge_score-0.0.7` it seems no longer importable. Our CI fails: https://github.com/huggingface/datasets/runs/7463218591?check_suite_focus=true ``` FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_bigbench FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_configs_bigbench FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_bigbench FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_rouge ``` with errors: ``` > from rouge_score import rouge_scorer E ModuleNotFoundError: No module named 'rouge_score' ``` ``` E ImportError: To be able to use rouge, you need to install the following dependency: rouge_score. E Please install it using 'pip install rouge_score' for instance' ```

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I_kwDODunzps5OWV4A

rouge metric

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['Fixed by:\r\n- #4735']

2022-07-22 07:06:51

2022-07-22 09:08:02

2022-07-22 09:05:35

NONE

## Describe the bug A clear and concise description of what the bug is. Loading Rouge metric gives error after latest rouge-score==0.0.7 release. Downgrading rougemetric==0.0.4 works fine. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` ## Expected results A clear and concise description of the expected results. from rouge_score import rouge_scorer, scoring should run ## Actual results Specify the actual results or traceback. File "/root/.cache/huggingface/modules/datasets_modules/metrics/rouge/0ffdb60f436bdb8884d5e4d608d53dbe108e82dac4f494a66f80ef3f647c104f/rouge.py", line 21, in <module> from rouge_score import rouge_scorer, scoring ImportError: cannot import name 'rouge_scorer' from 'rouge_score' (unknown location) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Linux - Python version:3.9 - PyArrow version:

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I_kwDODunzps5OV7fu

Document better that loading a dataset passing its name does not use the local script

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open

[]

['Thanks for the feedback!\r\n\r\nI think since this issue is closely related to loading, I can add a clearer explanation under [Load > local loading script](https://huggingface.co/docs/datasets/main/en/loading#local-loading-script).' 'That makes sense but I think having a line about it under https://huggingface.co/docs/datasets/installation#source the "source" header here would be useful. My mental model of `pip install -e .` does not include the fact that the source files aren\'t actually being used. ' "Thanks for sharing your perspective. I think the `load_dataset` function is the only one that pulls from GitHub, and since this use-case is very specific, I don't think we need to include such a broad clarification in the Installation section.\r\n\r\nFeel free to check out the linked PR and let me know if it needs any additional explanation 😊"]

2022-07-22 06:07:31

2022-08-01 20:32:13

MEMBER

As reported by @TrentBrick here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191858596, it could be more clear that loading a dataset by passing its name does not use the (modified) local script of it. What he did: - he installed `datasets` from source - he modified locally `datasets/the_pile/the_pile.py` loading script - he tried to load it but using `load_dataset("the_pile")` instead of `load_dataset("datasets/the_pile")` - as explained here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191040245: - the former does not use the local script, but instead it downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that. He suggests adding a more clear explanation about this. He suggests adding it maybe in [Installation > source](https://huggingface.co/docs/datasets/installation)) CC: @stevhliu

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PR_kwDODunzps474dlZ

docs: ✏️ fix TranslationVariableLanguages example

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-21 20:35:41

2022-07-22 07:01:00

2022-07-22 06:48:42

CONTRIBUTOR

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I_kwDODunzps5OSTfP

Loading imagenet-1k validation split takes much more RAM than expected

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closed

[]

['My bad, `482 * 418 * 50000 * 3 / 1000000 = 30221 MB` ( https://stackoverflow.com/a/42979315 ).\r\n\r\nMeanwhile `256 * 256 * 50000 * 3 / 1000000 = 9830 MB`. We are loading the non-cropped images and that is why we take so much RAM.']

2022-07-21 15:14:06

2022-07-21 16:41:04

2022-07-21 16:41:04

CONTRIBUTOR

## Describe the bug Loading into memory the validation split of imagenet-1k takes much more RAM than expected. Assuming ImageNet-1k is 150 GB, split is 50000 validation images and 1,281,167 train images, I would expect only about 6 GB loaded in RAM. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("imagenet-1k", split="validation") print(dataset) """prints Dataset({ features: ['image', 'label'], num_rows: 50000 }) """ pipe_inputs = dataset["image"] # and wait :-) ``` ## Expected results Use only < 10 GB RAM when loading the images. ## Actual results  ``` Using custom data configuration default Reusing dataset imagenet-1k (/home/fxmarty/.cache/huggingface/datasets/imagenet-1k/default/1.0.0/a1e9bfc56c3a7350165007d1176b15e9128fcaf9ab972147840529aed3ae52bc) Killed ``` ## Environment info - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.35 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - datasets commit: 4e4222f1b6362c2788aec0dd2cd8cede6dd17b80

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PR_kwDODunzps473GmR

Refactor Hub tests

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-21 14:43:13

2022-07-22 15:09:49

2022-07-22 14:56:29

MEMBER

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This PR refactors `test_upstream_hub` by removing unittests and using the following pytest Hub fixtures: - `ci_hub_config` - `set_ci_hub_access_token`: to replace setUp/tearDown - `temporary_repo` context manager: to replace `try... finally` - `cleanup_repo`: to delete repo accidentally created if one of the tests fails This is a preliminary work done to manage unit/integration tests separately.

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I_kwDODunzps5OQTmu

load_dataset gives "403" error when using Financial Phrasebank

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[]

closed

[]

['Hi @rohitvincent, thanks for reporting.\r\n\r\nUnfortunately I\'m not able to reproduce your issue:\r\n```python\r\nIn [2]: from datasets import load_dataset, DownloadMode\r\n ...: load_dataset(path=\'financial_phrasebank\',name=\'sentences_allagree\', download_mode="force_redownload")\r\nDownloading builder script: 6.04kB [00:00, 2.87MB/s] \r\nDownloading metadata: 13.7kB [00:00, 7.24MB/s] \r\nDownloading and preparing dataset financial_phrasebank/sentences_allagree (download: 665.91 KiB, generated: 296.26 KiB, post-processed: Unknown size, total: 962.17 KiB) to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141...\r\nDownloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 682k/682k [00:00<00:00, 7.66MB/s]\r\nDataset financial_phrasebank downloaded and prepared to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 918.80it/s]\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: [\'sentence\', \'label\'],\r\n num_rows: 2264\r\n })\r\n})\r\n```\r\n\r\nAre you able to access the link? https://www.researchgate.net/profile/Pekka-Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip' 'Yes was able to download from the link manually. But still, get the same error when I use load_dataset.' 'Fixed once data files are hosted on the Hub:\r\n- #4598']

2022-07-21 08:43:32

2022-08-04 08:32:35

2022-08-04 08:32:35

NONE

I tried both codes below to download the financial phrasebank dataset (https://huggingface.co/datasets/financial_phrasebank) with the sentences_allagree subset. However, the code gives a 403 error when executed from multiple machines locally or on the cloud. ``` from datasets import load_dataset, DownloadMode load_dataset(path='financial_phrasebank',name='sentences_allagree',download_mode=DownloadMode.FORCE_REDOWNLOAD) ``` ``` from datasets import load_dataset, DownloadMode load_dataset(path='financial_phrasebank',name='sentences_allagree') ``` **Error** ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)

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I_kwDODunzps5OPWEP

Dataset Viewer issue for TheNoob3131/mosquito-data

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closed

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['The preview is working OK:\r\n\r\n\r\n\r\n']

2022-07-21 05:24:48

2022-07-21 07:51:56

2022-07-21 07:45:01

NONE

### Link https://huggingface.co/datasets/TheNoob3131/mosquito-data/viewer/TheNoob3131--mosquito-data/test ### Description Dataset preview not showing with large files. Says 'split cache is empty' even though there are train and test splits. ### Owner _No response_

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PR_kwDODunzps47ykPI

Fix broken link to the Hub

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-20 22:57:27

2022-07-21 14:33:18

2022-07-21 08:00:54

MEMBER

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The Markdown link fails to render if it is in the same line as the `<span>`. This PR implements @mishig25's fix by using `<a href=" ">` instead. 

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I_kwDODunzps5OMh0Y

the_pile datasets URL broken.

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['Thanks for reporting, @TrentBrick. We are addressing the change with their data host server.\r\n\r\nOn the meantime, if you would like to work with your fixed local copy of the_pile script, you should use:\r\n```python\r\nload_dataset("path/to/your/local/the_pile/the_pile.py",...\r\n```\r\ninstead of just `load_dataset("the_pile",...`.\r\n\r\nThe latter downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.' '@TrentBrick, I have checked the URLs and both hosts work, the original (https://the-eye.eu/) and the mirror (https://mystic.the-eye.eu/). See e.g.:\r\n- https://mystic.the-eye.eu/public/AI/pile/\r\n- https://mystic.the-eye.eu/public/AI/pile_preliminary_components/\r\n\r\nPlease, let me know if you still find any issue loading this dataset by using current server URLs.' "Great this is working now. Re the download from GitHub... I'm sure thought went into doing this but could it be made more clear maybe here? https://huggingface.co/docs/datasets/installation for example under installing from source? I spent over an hour questioning my sanity as I kept trying to edit this file, uninstall and reinstall the repo, git reset to previous versions of the file etc." 'Thanks for the quick reply and help too\r\n' "Thanks @TrentBrick for the suggestion about improving our docs: we should definitely do this if you find they are not clear enough.\r\n\r\nCurrently, our docs explain how to load a dataset from a local loading script here: [Load > Local loading script](https://huggingface.co/docs/datasets/loading#local-loading-script)\r\n\r\nI've opened an issue here:\r\n- #4732\r\n\r\nFeel free to comment on it any additional explanation/suggestion/requirement related to this problem."]

2022-07-20 20:57:30

2022-07-22 06:09:46

2022-07-21 07:38:19

NONE

https://github.com/huggingface/datasets/pull/3627 changed the Eleuther AI Pile dataset URL from https://the-eye.eu/ to https://mystic.the-eye.eu/ but the latter is now broken and the former works again. Note that when I git clone the repo and use `pip install -e .` and then edit the URL back the codebase doesn't seem to use this edit so the mystic URL is also cached somewhere else that I can't find?

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PR_kwDODunzps47vLrP

Download and prepare as Parquet for cloud storage

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['The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4724). All of your documentation changes will be reflected on that endpoint.' 'Added some docs for dask and took your comments into account\r\n\r\ncc @philschmid if you also want to take a look :)' 'Just noticed that it would be more convenient to pass the output dir to download_and_prepare directly, to bypass the caching logic which prepares the dataset at `<cache_dir>/<name>/<version>/<hash>/`. And this way the cache is only used for the downloaded files. What do you think ?\r\n\r\n```python \r\n\r\nbuilder = load_datadet_builder("squad")\r\n# or with a custom cache\r\nbuilder = load_datadet_builder("squad", cache_dir="path/to/local/cache/for/downloaded/files")\r\n\r\n# download and prepare to s3\r\nbuilder.download_and_prepare("s3://my_bucket/squad")\r\n```' 'Might be of interest: \r\nPyTorch and AWS introduced better support for S3 streaming in `torchtext`. \r\n\r\n' 'Having thought about it a bit more, I also agree with @philschmid in that it\'s important to follow the existing APIs (pandas/dask), which means we should support the following at some point:\r\n\r\n* remote data files resolution for the packaged modules to support `load_dataset("<format>", data_files="<fs_url>")`\r\n* `to_<format>("<fs_url>")`\r\n* `load_from_disk` and `save_to_disk` already expose the `fs` param, but it would be cool to support specifying `fsspec` URLs directly as the source/destination path (perhaps we can then deprecate `fs` to be fully aligned with pandas/dask)\r\n\r\nIMO these are the two main issues with the current approach:\r\n* relying on the builder API to generate the formatted files results in a non-friendly format due to how our caching works (a lot of nested subdirectories)\r\n* this approach still downloads the files needed to generate a dataset locally. Considering one of our goals is to align the streaming API with the non-streaming one, this could be avoided by running `to_<format>` on streamed/iterable datasets']

2022-07-20 13:39:02

2022-08-10 12:58:02

MEMBER

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Download a dataset as Parquet in a cloud storage can be useful for streaming mode and to use with spark/dask/ray. This PR adds support for `fsspec` URIs like `s3://...`, `gcs://...` etc. and ads the `file_format` to save as parquet instead of arrow: ```python from datasets import * cache_dir = "s3://..." builder = load_dataset_builder("crime_and_punish", cache_dir=cache_dir) builder.download_and_prepare(file_format="parquet") ``` credentials to cloud storage can be passed using the `storage_options` argument in `load_dataset_builder` For consistency with the BeamBasedBuilder, I name the parquet files `{builder.name}-{split}-xxxxx-of-xxxxx.parquet`. I think this is fine since we'll need to implement parquet sharding after this PR, so that a dataset can be used efficiently with dask for example. Note that images/audio files are not embedded yet in the parquet files, this will added in a subsequent PR TODO: - [x] docs - [x] tests

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PR_kwDODunzps47uoSj

Refactor conftest fixtures

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-20 12:15:22

2022-07-21 14:37:11

2022-07-21 14:24:18

MEMBER

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Previously, fixture modules `hub_fixtures` and `s3_fixtures`: - were both at the root test directory - were imported using `import *` - as a side effect, the modules `os` and `pytest` were imported from `s3_fixtures` into `conftest` This PR: - puts both fixture modules in a dedicated directory `fixtures` - renames both to: `fixtures.hub` and `fixtures.s3` - imports them into `conftest` as plugins, using the `pytest_plugins`: this avoids the `import *` - additionally creates a new fixture module `fixtures.files` with all file-related fixtures

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PR_kwDODunzps47t_HJ

Docs: Fix same-page haslinks

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-20 10:04:37

2022-07-20 17:02:33

2022-07-20 16:49:36

CONTRIBUTOR

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`href="/docs/datasets/quickstart#audio"` implicitly goes to `href="/docs/datasets/{$LATEST_STABLE_VERSION}/quickstart#audio"`. Therefore, https://huggingface.co/docs/datasets/quickstart#audio #audio hashlink does not work since the new docs were not added to v2.3.2 (LATEST_STABLE_VERSION) to preserve the version, it should be just `href="#audio"`, which will implicilty go to curren_page + #audio element

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I_kwDODunzps5OGOHw

PyArrow Dataset error when calling `load_dataset`

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open

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["Hi ! It looks like a bug in `pyarrow`. If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nTo achieve that you can try to lower the value of `max_shard_size` and also don't use `map` before `push_to_hub`.\r\n\r\nDo you have a minimum reproducible example that we can share with the Arrow team for further debugging ?" "> If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nYup, I did not encounter this bug when I was testing my script with a slice of <1000 samples for my dataset.\r\n\r\n> Do you have a minimum reproducible example...\r\n\r\nNot sure if I can get more minimal than the script I shared above. Are you asking for a sample json file?\r\nJust generate a random manifest list, I can add that to the above script if that's what you mean?\r\n" 'Actually this is probably linked to this open issue: https://issues.apache.org/jira/browse/ARROW-5030.\r\n\r\nsetting `max_shard_size="2GB"` should do the job (or `max_shard_size="1GB"` if you want to be on the safe side, especially given that there can be some variance in the shard sizes if the dataset is not evenly distributed)']

2022-07-20 01:16:03

2022-07-22 14:11:47

NONE

## Describe the bug I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error: ```sh File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main raw_datasets = load_dataset( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split for key, table in logging.tqdm( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables for batch_idx, record_batch in enumerate( File "pyarrow/_parquet.pyx", line 1309, in iter_batches File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs ``` ## Steps to reproduce the bug I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`. When creating the dataset, I do something like this: ```python import json from datasets import Dataset, Audio # manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text for line in manifest_lines: line = line.strip() if line: line_dict = json.loads(line) manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}") manifest_dict["duration"].append(line_dict["duration"]) manifest_dict["transcription"].append(line_dict["text"]) # Create a HF dataset dataset = Dataset.from_dict(manifest_dict).cast_column( "audio", Audio(sampling_rate=16_000), ) # From the docs for saving to disk # https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk def read_audio_file(example): with open(example["audio"]["path"], "rb") as f: return {"audio": {"bytes": f.read()}} dataset = dataset.map(read_audio_file, num_proc=70) dataset.save_to_disk(f"/audio-data/hf/{artifact_name}") dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True) ``` Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace. I am able to load the dataset fine if I use `load_from_disk()`. ## Expected results `load_dataset()` should behave just like `load_from_disk()` and not cause any errors. ## Actual results See above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> I am using the `huggingface/transformers-pytorch-gpu:latest` image - `datasets` version: 2.3.0 - Platform: Docker/Ubuntu 20.04 - Python version: 3.8 - PyArrow version: 8.0.0

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I_kwDODunzps5OFLYj

Dataset Viewer issue for shamikbose89/lancaster_newsbooks

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['It seems like the list of splits could not be obtained:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names("shamikbose89/lancaster_newsbooks", "default")\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/shamikbose89--lancaster_newsbooks/2d1c63d269bf7b9342accce0a95960b1710ab4bc774248878bd80eb96c1afaf7/lancaster_newsbooks.py", line 73, in _split_generators\r\n data_dir = dl_manager.download_and_extract(_URL)\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 916, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 879, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 348, in map_nested\r\n return function(data_struct)\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 884, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 388, in _get_extraction_protocol\r\n return _get_extraction_protocol_with_magic_number(f)\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 354, in _get_extraction_protocol_with_magic_number\r\n f.seek(0)\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 684, in seek\r\n raise ValueError("Cannot seek streaming HTTP file")\r\nValueError: Cannot seek streaming HTTP file\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File "<stdin>", line 1, in <module>\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nping @huggingface/datasets ' "Oh, I removed the 'split' key from `kwargs`. I put it back in, but there's still the same error" "It looks like the data host doesn't support http range requests, which is necessary to glob inside a ZIP archive in streaming mode. Can you try hosting the dataset elsewhere ? Or download each file separately from https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2531 ?" '@lhoestq Thanks! That seems to have solved it. I can get the splits with the `get_dataset_split_names()` function. The dataset viewer is still not loading properly, though. The new error is\r\n```\r\nStatus code: 400\r\nException: BadZipFile\r\nMessage: File is not a zip file\r\n```\r\n\r\nPS. The dataset loads properly and can be accessed']

2022-07-19 20:00:07

2022-07-20 17:06:02

NONE

### Link https://huggingface.co/datasets/shamikbose89/lancaster_newsbooks ### Description Status code: 400 Exception: ValueError Message: Cannot seek streaming HTTP file I am able to use the dataset loading script locally and it also runs when I'm using the one from the hub, but the viewer still doesn't load ### Owner Yes

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I_kwDODunzps5OEssc

Issue loading TheNoob3131/mosquito-data dataset

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["I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated." "Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Datasets library, but it has to do with the specific implementation of your dataset on the Hub.\r\n\r\nTherefore, I'm transferring this discussion to your own dataset Community tab: https://huggingface.co/datasets/TheNoob3131/mosquito-data/discussions/1"]

2022-07-19 17:47:37

2022-07-20 06:46:57

2022-07-20 06:46:02

NONE

 So my dataset is public in the Huggingface Hub, but when I try to load it using the load_dataset command, it shows that it is downloading the files, but throws a ValueError. When I went to my directory to see if the files were downloaded, the folder was blank. Here is the error below: ValueError Traceback (most recent call last) Input In [8], in <cell line: 3>() 1 from datasets import load_dataset ----> 3 dataset = load_dataset("TheNoob3131/mosquito-data", split="train") File ~\Anaconda3\lib\site-packages\datasets\load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1678 # Download and prepare data -> 1679 builder_instance.download_and_prepare( 1680 download_config=download_config, 1681 download_mode=download_mode, 1682 ignore_verifications=ignore_verifications, 1683 try_from_hf_gcs=try_from_hf_gcs, 1684 use_auth_token=use_auth_token, 1685 ) 1687 # Build dataset for splits 1688 keep_in_memory = ( 1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1690 ) Is the dataset in the wrong format or is there some security permission that I should enable?

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PR_kwDODunzps47prWR

Make Extractor accept Path as input

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-19 13:25:06

2022-07-22 13:42:27

2022-07-22 13:29:43

MEMBER

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This PR: - Makes `Extractor` accept instance of `Path` as input - Removes unnecessary castings of `Path` to `str`

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I_kwDODunzps5ODZMj

Dataset Viewer issue for LawalAfeez/englishreview-ds-mini

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closed

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['It\'s currently working, as far as I understand\r\n\r\nhttps://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train\r\n\r\n<img width="1556" alt="Capture d’écran 2022-07-19 à 09 24 01" src="https://user-images.githubusercontent.com/1676121/179761130-2d7980b9-c0f6-4093-8b1d-f0a3872fef3f.png">\r\n\r\n---\r\n\r\nWhat was your issue?']

2022-07-19 13:19:39

2022-07-20 08:32:57

2022-07-20 08:32:57

NONE

### Link _No response_ ### Description Unable to view the split data ### Owner _No response_

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PR_kwDODunzps47pdbh

Support "tags" yaml tag

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['_The documentation is not available anymore as the PR was closed or merged._' "IMO `DatasetMetadata` shouldn't crash with attributes that it doesn't know, btw" 'Yea this PR is mostly to have a validation that this field contains a list of strings.\r\n\r\nRegarding unknown fields, the tagging app currently returns an error if a field is unknown using the `DatasetMetadata`. We can change that though']

2022-07-19 12:34:31

2022-07-20 13:44:50

2022-07-20 13:31:56

MEMBER

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Added the "tags" YAML tag, so that users can specify data domain/topics keywords for dataset search

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PR_kwDODunzps47pSui

Fix POS tags

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'CI failures are about missing content in the dataset cards or bad tags, and this is unrelated to this PR. Merging :)']

2022-07-19 11:52:54

2022-07-19 12:54:34

2022-07-19 12:41:16

MEMBER

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We're now using `part-of-speech` and not `part-of-speech-tagging`, see discussion here: https://github.com/huggingface/datasets/commit/114c09aff2fa1519597b46fbcd5a8e0c0d3ae020#r78794777

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PR_kwDODunzps47o0YG

Fix named split sorting and remove unnecessary casting

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' "hahaha what a timing, I added my comment right after you merged x)\r\n\r\nyou can ignore my (nit), it's fine" 'Sorry, just too sync... :sweat_smile: ']

2022-07-19 09:48:28

2022-07-22 09:39:45

2022-07-22 09:10:57

MEMBER

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This PR: - makes `NamedSplit` sortable: so that `sorted()` can be called on them - removes unnecessary `sorted()` on `dict.keys()`: `dict_keys` view is already like a `set` - removes unnecessary casting of `NamedSplit` to `str`

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PR_kwDODunzps47ojC1

Document installation of sox OS dependency for audio

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-19 08:42:35

2022-07-21 08:16:59

2022-07-21 08:04:15

MEMBER

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The `sox` OS package needs being installed manually using the distribution package manager. This PR adds this explanation to the docs.

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PR_kwDODunzps47ohdr

Highlight non-commercial license in amazon_reviews_multi dataset card

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-19 08:36:20

2022-07-27 16:09:40

2022-07-27 15:57:41

CONTRIBUTOR

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Highlight that the licence granted by Amazon only covers non-commercial research use.

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I_kwDODunzps5OB96K

Document how to create a dataset loading script for audio/vision

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open

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2022-07-19 08:03:40

2022-08-01 15:08:11

MEMBER

Currently, in our docs for Audio/Vision/Text, we explain how to: - Load data - Process data However we only explain how to *Create a dataset loading script* for text data. I think it would be useful that we add the same for Audio/Vision as these have some specificities different from Text. See, for example: - #4697 - and comment there: https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492 CC: @stevhliu

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PR_kwDODunzps47ny0L

Add object detection processing tutorial

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' "Great idea! Now that we have more than one task, it makes sense to separate image classification and object detection so it'll be easier for users to follow." '@lhoestq do we want to do that in this PR, or should we merge it and let @stevhliu reorganize separately? ']

2022-07-19 04:23:46

2022-07-21 20:10:35

2022-07-21 19:56:42

CONTRIBUTOR

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The following adds a quick guide on how to process object detection datasets with `albumentations`.

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I_kwDODunzps5OACgF

WMT21 & WMT22

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open

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["Hi ! That would be awesome to have them indeed, thanks for opening this issue\r\n\r\nI just added you to the WMT org on the HF Hub if you're interested in adding those datasets.\r\n\r\nFeel free to create a dataset repository for each dataset and upload the data files there :) preferably in ZIP archives instead of TAR archives (the current WMT scripts don't support streaming TAR archives, so it would break the dataset preview). We've also had issues with the `statmt.org` host (data unavailable, slow download speed), that's why I think it's better if we re-host the files on the Hub.\r\n\r\n`wmt21` (and wmt22) can be added in this GitHub repository I think, for consistency with the previous ones.\r\nTo add it, you can copy paste the code of the previous one (e.g. wmt19), and add the new data:\r\n- in wmt_utils.py, add the new data subsets. You need to provide the download URLs, as well as the target and source languages\r\n- in wmt21.py (renamed from wmt19.py), you can specify the subsets that WMT21 uses (i.e. the one you just added)\r\n- in wmt_utils.py, define the python function that must be used to parse the subsets you added. To do so, you must go in `_generate_examples` and chose the proper `sub_generator` based on the subset name. For example, the `paracrawl_v3` subset uses the `_parse_tmx` function:\r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/datasets/wmt19/wmt_utils.py#L834-L835\r\n\r\nHopefully the data is in a format that is already supported and there's no need to write a new `_parse_*` function for the new subsets. Let me know if you have questions or if I can help :)"]

2022-07-18 21:05:33

2022-07-19 10:07:44

NONE

## Adding a Dataset - **Name:** WMT21 & WMT22 - **Description:** We are going to have three tracks: two small tasks and a large task. The small tracks evaluate translation between fairly related languages and English (all pairs). The large track uses 101 languages. - **Paper:** / - **Data:** https://statmt.org/wmt21/large-scale-multilingual-translation-task.html https://statmt.org/wmt22/large-scale-multilingual-translation-task.html - **Motivation:** Many more languages than previous WMT versions - Could be very high impact Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md). I could also tackle this. I saw the existing logic for WMT models is a bit complex (datasets are stored on the wmt account & retrieved in separate wmt datasets afaict). How long do you think it would take me? @lhoestq

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PR_kwDODunzps47lewm

Fix require torchaudio and refactor test requirements

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-18 17:24:28

2022-07-22 06:30:56

2022-07-22 06:18:11

MEMBER

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Currently there is a bug in `require_torchaudio` (indeed it is requiring `sox` instead): ```python def require_torchaudio(test_case): if find_spec("sox") is None: ... ``` The bug was introduced by: - #3685 - Commit: https://github.com/huggingface/datasets/pull/3685/commits/b5a3e7122d49c4dcc9333b1d8d18a833fc04b940 which moved ```python require_sndfile = pytest.mark.skipif( # In Windows and OS X, soundfile installs sndfile (sys.platform != "linux" and find_spec("soundfile") is None) # In Linux, soundfile throws RuntimeError if sndfile not installed with distribution package manager or (sys.platform == "linux" and find_library("sndfile") is None), reason="Test requires 'sndfile': `pip install soundfile`; " "Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`", ) require_sox = pytest.mark.skipif( find_library("sox") is None, reason="Test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`", ) require_torchaudio = pytest.mark.skipif(find_spec("torchaudio") is None, reason="Test requires 'torchaudio'") ``` to ```python def require_sndfile(test_case): """ Decorator marking a test that requires soundfile. These tests are skipped when soundfile isn't installed. """ if (sys.platform != "linux" and find_spec("soundfile") is None) or ( sys.platform == "linux" and find_library("sndfile") is None ): test_case = unittest.skip( "test requires 'sndfile': `pip install soundfile`; " "Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`", )(test_case) return test_case def require_sox(test_case): """ Decorator marking a test that requires sox. These tests are skipped when sox isn't installed. """ if find_library("sox") is None: return unittest.skip("test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`")( test_case ) return test_case def require_torchaudio(test_case): """ Decorator marking a test that requires torchaudio. These tests are skipped when torchaudio isn't installed. """ if find_spec("sox") is None: return unittest.skip("test requires 'torchaudio'")(test_case) return test_case ``` This PR; - fixes the bug in `require_torchaudio` - refactors the test requirements back to using `pytest` instead of `unittest` - the text in `pytest.skipif` `reason` can be used if needed in a test body: `require_torchaudio.kwargs["reason"]`

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I_kwDODunzps5N-lUN

Dataset Viewer issue for TheNoob3131/mosquito-data

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closed

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['Thanks for reporting. I refreshed the dataset viewer and it now works as expected.\r\n\r\nhttps://huggingface.co/datasets/TheNoob3131/mosquito-data\r\n\r\n<img width="1135" alt="Capture d’écran 2022-07-18 à 13 15 22" src="https://user-images.githubusercontent.com/1676121/179566497-e47f1a27-fd84-4a8d-9d7f-2e0f2da803df.png">\r\n\r\nWe will investigate why it occurred in the first place\r\n' 'By chance, could you provide some details about the operations done on the dataset: was it private? gated?' 'Yes, it was a private dataset, and when I made it public, the Dataset Preview did not work. \r\n\r\nHowever, now when I make the dataset private, it says that the Dataset Preview has been disabled. Why is this?' 'Thanks for the details. For now, the dataset viewer is always disabled on private datasets (see https://huggingface.co/docs/hub/datasets-viewer for more details)' "Hi, it was working fine for a few hours, but then I can't see the dataset viewer again (public dataset). Why is this still happening?\r\nIt's the same error too:\r\n\r\n" "OK? This is a bug, thanks for help spotting and reproducing it (it occurs when a dataset is switched to private, then to public). We will be working on it, meanwhile, I've restored the dataset viewer manually again."]

2022-07-18 17:07:19

2022-07-18 19:44:46

2022-07-18 17:15:50

NONE

### Link _No response_ ### Description Getting this error when trying to view dataset preview: Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv') ### Owner _No response_

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PR_kwDODunzps47lNBg

Fix empty examples in xtreme dataset for bucc18 config

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'I guess the report link is this instead: https://huggingface.co/datasets/xtreme/discussions/1']

2022-07-18 16:22:46

2022-07-19 06:41:14

2022-07-19 06:29:17

MEMBER

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As reported in https://huggingface.co/muibk, there are empty examples in xtreme/bucc18.de I applied your fix @mustaszewski I also used a dict to make the dataset generation much faster

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PR_kwDODunzps47lFDo

Fix crd3

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-18 15:53:44

2022-07-21 17:18:44

2022-07-21 17:06:30

MEMBER

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As reported in https://huggingface.co/datasets/crd3/discussions/1#62cc377073b2512b81662794, each split of the dataset was containing the same data. This issues comes from a bug in the dataset script I fixed it and also uploaded the data to hf.co to make the dataset work in streaming mode

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PR_kwDODunzps47lCFt

Skip tests only for lz4/zstd params if not installed

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-18 15:41:40

2022-07-19 13:02:31

2022-07-19 12:49:18

MEMBER

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Currently, if `zstandard` or `lz4` are not installed, `test_compression_filesystems` and `test_streaming_dl_manager_extract_all_supported_single_file_compression_types` are skipped for all compression format parameters. This PR fixes these tests, so that if `zstandard` or `lz4` are not installed, the tests are skipped only for the corresponding compression parameters (`zstd` or `lz4`), whereas the tests are not skipped for all the other compression parameters (`gzip`, `xz` and `bz2`). Related to: - #4688

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PR_kwDODunzps47kABf

Make cast in `from_pandas` more robust

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-18 11:55:49

2022-07-22 11:17:42

2022-07-22 11:05:24

CONTRIBUTOR

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Make the cast in `from_pandas` more robust (as it was done for the packaged modules in https://github.com/huggingface/datasets/pull/4364) This should be useful in situations like [this one](https://discuss.huggingface.co/t/loading-custom-audio-dataset-and-fine-tuning-model/8836/4).

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I_kwDODunzps5N81mT

Domain specific dataset discovery on the Hugging Face hub

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open

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["Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a good idea to make our current taxonomy more complex." "> Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a good idea to make our current taxonomy more complex.\r\n\r\nThanks, for letting me know. Will you allow the topic tags to be user-generated or only chosen from a list?" 'Thanks for opening this issue @davanstrien.\r\n\r\nAs we discussed last week, the tag approach would be in principle the simpler to be implemented, either the domain tag (with closed vocabulary: more reliable but also more rigid), or the topic tag (with open vocabulary: more flexible for user needs)' 'Hi @davanstrien If i remember correctly this was also discussed inside a hf.co Discussion, would you be able to link it here too?\r\n\r\n(where i suggested using `tags: - foo - bar` IIRC.\r\n\r\nThanks a ton!' "> Hi @davanstrien If i remember correctly this was also discussed inside a hf.co Discussion, would you be able to link it here too?\r\n> \r\n> (where i suggested using `tags: - foo - bar` IIRC.\r\n> \r\n> Thanks a ton!\r\n\r\nThis doesn't ring a bell - I did a quick search of https://discuss.huggingface.co but didn't find anything. \r\n\r\nThe `tags: ` approach sounds like a good option for this. It would be especially nice if these could suggest existing tags, but this probably won't be easily possible through the current interface. \r\n" 'I opened a PR to add "tags" to the YAML validator:\r\nhttps://github.com/huggingface/datasets/pull/4716\r\n\r\nI also added "tags" to the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), with suggestions like "bio" or "newspapers"' 'Thanks @lhoestq for the initiative.\r\n \r\nJust one question: are "tags" already supported on the Hub? \r\n\r\nI think they aren\'t. Thus, the Hub should support them so that they are properly displayed.' "I think they're not displayed, but at least it should enable users to filter by tag in using `huggingface_hub` or using the appropriate query params on the website (not sure if it's possible yet though)" "> I think they're not displayed, but at least it should enable users to filter by tag in using `huggingface_hub` or using the appropriate query params on the website (not sure if it's possible yet though)\r\n\r\nI think this would already be a helpful start. I'm happy to try this out with the datasets added to https://huggingface.co/organizations/biglam and use the `huggingface_hub` to filter those datasets using the tags. "]

2022-07-18 11:14:03

2022-07-19 15:18:11

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** ## The problem The datasets hub currently has `8,239` datasets. These datasets span a wide range of different modalities and tasks (currently with a bias towards textual data). There are various ways of identifying datasets that may be relevant for a particular use case: - searching - various filters Currently, however, there isn't an easy way to identify datasets belonging to a specific domain. For example, I want to browse machine learning datasets related to 'social science' or 'climate change research'. The ability to identify datasets relating to a specific domain has come up in discussions around the [BigLA](https://github.com/bigscience-workshop/lam/) datasets hackathon https://github.com/bigscience-workshop/lam/discussions/31#discussioncomment-3123610. As part of the hackathon, we're currently collecting datasets related to Libraries, Archives and Museums and making them available via the hub. We currently do this under a Hugging Face organization (https://huggingface.co/biglam). However, going forward, I can see some of these datasets being migrated to sit under an organization that is the custodian of the dataset (for example, a national library the data was originally from). At this point, it becomes more difficult to quickly identify datasets from this domain without relying on search. This is also related to some existing issues on Github related to metadata on the hub: - https://github.com/huggingface/datasets/issues/3625 - https://github.com/huggingface/datasets/issues/3877 **Describe the solution you'd like** ### Some possible solutions that may help with this: #### Enable domain tags (from a controlled vocabulary) - This would add metadata field to the YAML for the domain a dataset relates to - Advantages: - the list is controlled, allowing it to be more easily integrated into the datasets tag app (https://huggingface.co/space/huggingface/datasets-tagging) - the controlled vocabulary could align with an existing controlled vocabulary - this additional metadata can be used to perform filtering by domain - disadvantages - choosing the best controlled vocab may be difficult - there are many datasets that are likely to fit into the 'machine learning' domain (i.e. there is a long tail of datasets that aren't in more 'generic' machine learning domain #### Enable topic tags (user-generated) Enable 'free form' topic tags for datasets and models. This would be closer to GitHub's repository topics which can be chosen from a controlled list (https://github.com/topics/) but can also be more user/org specific. This could potentially be useful for organizations to also manage their own models and datasets as the number they hold in their org grows. For example, they may create 'topic tags' for a specific project, so it's clearer which datasets /models are related to that project. #### Collections This solution would likely be the biggest shift and may require significant changes in the hub fronted. Collections could work in several different ways but would include: Users can curate particular datasets, models, spaces, etc., into a collection. For example, they may create a collection of 'historic newspapers suitable for training language models'. These collections would not be mutually exclusive, i.e. a dataset can belong to zero, one or many collections. Collections can also potentially be nested under other collections. This is fairly common on other data reposotiores for example the following collections: <img width="293" alt="Screenshot 2022-07-18 at 11 50 44" src="https://user-images.githubusercontent.com/8995957/179496445-963ed122-5e26-4574-96e8-41081bce3e2b.png"> all belong under a higher level collection (https://bl.iro.bl.uk/collections/353c908d-b495-4413-b047-87236d2573e3?locale=en). There are different models one could use for how these collections could be created: - only within an org - for any dataset/model - the owner or a dataset/model has to agree to be added to a collection - a collection owner can have people suggest additions to their collection - other models.... These collections could be thematic, related to particular training approaches, curate models with particular inference properties etc. Whilst some of these features may duplicate current/or future tag filters on the hub, they offer the advantage of being flexible and not having to predict what users will want to do upfront. There is also potential for automating the creation of these collections based on existing metadata. For example, one could collect models trained on a collection of datasets so for example, if we had a collection of 'historic newspapers suitable for training language models' that contained 30 datasets, we could create another collection 'historic newspaper language models' that takes any model on the hub whose metadata says it used one or more of those 30 datasets. There is also the option of exploring ML approaches to suggest models/datasets may be relevant to a particular collection. This approach is likely to be quite difficult to implement well and would require significant thought. There is also likely to be a benefit in doing quite a bit of upfront work in curating useful collections to demonstrate the benefits of collections. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. It is possible to collate this information externally, i.e. one could link back to the relevant models/datasets from an external platform. **Additional context** Add any other context about the feature request here. I'm cc'ing others involved in the BigLAM hackathon who may also have thoughts @cakiki @clancyoftheoverflow @albertvillanova

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PR_kwDODunzps47jeE9

Added more information in the README about contributors of the Arabic Speech Corpus

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2022-07-18 09:48:03

2022-07-28 10:33:05

2022-07-28 10:33:05

CONTRIBUTOR

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Added more information in the README about contributors and encouraged reading the thesis for more infos

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PR_kwDODunzps47jKNx

Support extract lz4 compressed data files

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-07-18 08:41:31

2022-07-18 14:43:59

2022-07-18 14:31:47

MEMBER

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PR_kwDODunzps47jA6Z

Fix Authentification Error while streaming

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['Hi, thanks for working on this, but the fix for this has already been merged in https://github.com/huggingface/datasets/pull/4608.']

2022-07-18 08:03:41

2022-07-20 13:10:44

2022-07-20 13:10:43

NONE

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I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub. ``` from datasets import load_dataset dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True) for d in dataset['train']: print(d) break # this is for checking ``` This code is an example for streaming private datasets. when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this, ``` /usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self) 1007 status=self.status, 1008 message=self.reason, → 1009 headers=self.headers, 1010 ) 1011 ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet') ``` (this is an example on the dataset has `parquet` extenstion) It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~". so I add three lines. With this change, there is no error anymore(but this code is ad-hoc).

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PR_kwDODunzps47i9gN

Enable streaming dataset to use the "all" split

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['The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4698). All of your documentation changes will be reflected on that endpoint.' "@albertvillanova \r\nAdding the validation split causes these two `assert_called_once` assertions to fail with `AssertionError: Expected 'ArrowWriter' to have been called once. Called 2 times`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/main/tests/test_builder.py#L548-L562\r\n\r\nIt might be better to create a new dummy generator for the streaming tests, WDYT? Alternatively we could test for `self.call_count` equalling 2." '@cakiki have you read my comment in the issue page?\r\nhttps://github.com/huggingface/datasets/issues/4637#issuecomment-1175984812' 'Streaming with `split=all` seems to be working, will fix the failing test next' 'Not sure if marking the PR as "ready for review" actually notified you, so tagging @albertvillanova just in case :smiley_cat: ']

2022-07-18 07:47:39

2022-08-10 08:35:33

CONTRIBUTOR

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Fixes #4637

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I_kwDODunzps5N7E6d

Trouble with streaming frgfm/imagenette vision dataset with TAR archive

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closed

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['Hi @frgfm, thanks for reporting.\r\n\r\nAs the error message says, streaming mode is not supported out of the box when the dataset contains TAR archive files.\r\n\r\nTo make the dataset streamable, you have to use `dl_manager.iter_archive`.\r\n\r\nThere are several examples in other datasets, e.g. food101: https://huggingface.co/datasets/food101/blob/main/food101.py\r\n\r\nAnd yes, as the link you pointed out, for the streaming to be possible, the metadata file must be loaded before all of the images:\r\n- either this is the case when iterating the archive (and you get the metadata file before the images)\r\n- or you have to extract the metadata file by hand and upload it separately to the Hub' "Hi @albertvillanova :wave:\r\n\r\nThanks! Yeah I saw that but since I didn't have any metadata, I wasn't sure whether I should create them myself.\r\n\r\nSo one last question:\r\nWhat is the metadata supposed to be for archives? The relative path of all files in it?\r\n_(Sorry I'm a bit confused since it's quite hard to debug using the single error message from the data preview :sweat_smile: )_" 'Hi @frgfm, streaming a dataset that contains a TAR file requires some tweaks because (contrary to ZIP files), tha TAR archive does not allow random access to any of the contained member files. Instead they have to be accessed sequentially (in the order in which they were put into the TAR file when created) and yielded.\r\n\r\nSo when iterating over the TAR file content, when an image file is found, we need to yield it (and not keeping it in memory, which will require huge RAM memory for large datasets). But when yielding an image file, we also need to yield with it what we call "metadata": the class label, and other textual information (for example, for audio files, sometimes we also add info such as the speaker ID, their sex, their age,...).\r\n\r\nAll this information usually is stored in what we call the metadata file: either a JSON or a CSV/TSV file.\r\n\r\nBut if this is also inside the TAR archive, we need to find this file in the first place when iterating the TAR archive, so that we already have this information when we find an image file and we can yield the image file and its metadata info.\r\n\r\nTherefore:\r\n- either the TAR archive contains the metadata file as the first member when iterating it (something we cannot change as it is done at the creation of the TAR file)\r\n- or if not, then we need to have the metadata file elsewhere\r\n - in these cases, what we do (if the dataset license allows it) is:\r\n - we download the TAR file locally, we extract the metadata file and we host the metadata on the Hub\r\n - we modify the dataset loading script so that it first downloads the metadata file (and reads it) and only then starts iterating the content of the TAR archive file\r\n\r\nSee an example of this process we recently did for "google/fleurs" (their metadata files for "train" were at the end of the TAR archives, after all audio files): https://huggingface.co/datasets/google/fleurs/discussions/4\r\n- we uploaded the metadata file to the Hub\r\n- we adapted the loading script to use it' 'Hi @albertvillanova :wave: \r\n\r\nThanks, since my last message, I went through the repo of https://huggingface.co/datasets/food101/blob/main/food101.py and managed to get it to work in the end :pray: \r\n\r\nHere it is: https://huggingface.co/datasets/frgfm/imagenette\r\n\r\nI appreciate you opening an issue to document the process, it might help a few!' "Great to see that you manage to make your dataset streamable. :rocket: \r\n\r\nI'm closing this issue, as for the docs update there is another issue opened:\r\n- #4711"]

2022-07-18 02:51:09

2022-08-01 15:10:57

2022-08-01 15:10:57

NONE

### Link https://huggingface.co/datasets/frgfm/imagenette ### Description Hello there :wave: Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile: Basically, I get a: ``` Status code: 400 Exception: NotImplementedError Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead. ``` I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far! Could you point me in the right direction please? :pray: ### Owner Yes

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I_kwDODunzps5N6gf7

Cannot load LinCE dataset

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closed

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['Hi @finiteautomata, thanks for reporting.\r\n\r\nUnfortunately, I\'m not able to reproduce your issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset("lince", "ner_spaeng")\r\nDownloading builder script: 20.8kB [00:00, 9.09MB/s] \r\nDownloading metadata: 31.2kB [00:00, 13.5MB/s] \r\nDownloading and preparing dataset lince/ner_spaeng (download: 2.93 MiB, generated: 18.45 MiB, post-processed: Unknown size, total: 21.38 MiB) to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.08M/3.08M [00:01<00:00, 2.73MB/s]\r\nDataset lince downloaded and prepared to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 630.66it/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: [\'idx\', \'words\', \'lid\', \'ner\'],\r\n num_rows: 33611\r\n })\r\n validation: Dataset({\r\n features: [\'idx\', \'words\', \'lid\', \'ner\'],\r\n num_rows: 10085\r\n })\r\n test: Dataset({\r\n features: [\'idx\', \'words\', \'lid\', \'ner\'],\r\n num_rows: 23527\r\n })\r\n})\r\n``` \r\n\r\nPlease note that for this dataset, the original data files are not hosted on the Hugging Face Hub, but on https://ritual.uh.edu\r\nAnd sometimes, the server might be temporarily unavailable, as your error message said (trying to connect to the server timed out):\r\n```\r\nConnectionError: Couldn\'t reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host=\'ritual.uh.edu\', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, \'Connection to ritual.uh.edu timed out. (connect timeout=100)\'))")))\r\n```\r\nIn these cases you could:\r\n- either contact the owners of the data server where the data is hosted to inform them about the issue in their server\r\n- or re-try after waiting some time: usually these issues are just temporary' 'Great, thanks for checking out!']

2022-07-17 19:01:54

2022-07-18 09:20:40

2022-07-18 07:24:22

NONE

## Describe the bug Cannot load LinCE dataset due to a connection error ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("lince", "ner_spaeng") ``` A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2 ## Expected results It should load the dataset ## Actual results ```python --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-2-fc551ddcebef> in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("lince", "ner_spaeng") 10 frames /usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1682 ignore_verifications=ignore_verifications, 1683 try_from_hf_gcs=try_from_hf_gcs, -> 1684 use_auth_token=use_auth_token, 1685 ) 1686 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 703 if not downloaded_from_gcs: 704 self._download_and_prepare( --> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1219 1220 def _download_and_prepare(self, dl_manager, verify_infos): -> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1222 1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 772 773 # Checksums verification /root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager) 481 def _split_generators(self, dl_manager): 482 """Returns SplitGenerators.""" --> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip") 484 data_dir = os.path.join(lince_dir, self.config.data_dir) 485 return [ /usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) 432 433 def get_recorded_sizes_checksums(self): /usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls) 313 num_proc=download_config.num_proc, 314 disable_tqdm=not is_progress_bar_enabled(), --> 315 desc="Downloading data files", 316 ) 317 duration = datetime.now() - start_time /usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 346 # Singleton 347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 348 return function(data_struct) 349 350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled() /usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config) 333 # append the relative path to the base_path 334 url_or_filename = url_or_path_join(self._base_path, url_or_filename) --> 335 return cached_path(url_or_filename, download_config=download_config) 336 337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]): /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 195 use_auth_token=download_config.use_auth_token, 196 ignore_url_params=download_config.ignore_url_params, --> 197 download_desc=download_config.download_desc, 198 ) 199 elif os.path.exists(url_or_filename): /usr/local/lib/python3.7/dist-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, max_retries, use_auth_token, ignore_url_params, download_desc) 531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 532 if head_error is not None: --> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})") 534 elif response is not None: 535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))"))) ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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https://github.com/huggingface/datasets/pull/4695

PR_kwDODunzps47hobQ

Add MANtIS dataset

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[]

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[]

['The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4695). All of your documentation changes will be reflected on that endpoint.']

2022-07-17 15:53:05

2022-07-17 16:00:15

CONTRIBUTOR

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This PR adds MANtIS dataset. Arxiv: [https://arxiv.org/abs/1912.04639](https://arxiv.org/abs/1912.04639) Github: [https://github.com/Guzpenha/MANtIS](https://github.com/Guzpenha/MANtIS) README and dataset tags are WIP.

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