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https://api.github.com/repos/huggingface/datasets/issues/4773 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4773/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4773/comments | https://api.github.com/repos/huggingface/datasets/issues/4773/events | https://github.com/huggingface/datasets/pull/4773 | 1,322,796,721 | PR_kwDODunzps48WNV3 | 4,773 | Document loading from relative path | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4773). All of your documentation changes will be reflected on that endpoint."
] | 1,659,137,541,000 | 1,659,137,987,000 | null | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/4772 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4772/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4772/comments | https://api.github.com/repos/huggingface/datasets/issues/4772/events | https://github.com/huggingface/datasets/issues/4772 | 1,322,693,123 | I_kwDODunzps5O1rID | 4,772 | AssertionError when using label_cols in to_tf_dataset | {
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] | open | false | null | [] | null | [] | 1,659,130,332,000 | 1,659,130,380,000 | null | NONE | null | null | null | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
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https://api.github.com/repos/huggingface/datasets/issues/4771 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4771/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4771/comments | https://api.github.com/repos/huggingface/datasets/issues/4771/events | https://github.com/huggingface/datasets/pull/4771 | 1,322,600,725 | PR_kwDODunzps48VjWx | 4,771 | Remove dummy data generation docs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4771). All of your documentation changes will be reflected on that endpoint."
] | 1,659,122,446,000 | 1,659,132,435,000 | null | MEMBER | null | false | {
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} | This PR removes instructions to generate dummy data since that is no longer necessary for datasets that are uploaded to the Hub instead of our GitHub repo (see #4744). | {
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https://api.github.com/repos/huggingface/datasets/issues/4770 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4770/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4770/comments | https://api.github.com/repos/huggingface/datasets/issues/4770/events | https://github.com/huggingface/datasets/pull/4770 | 1,322,147,855 | PR_kwDODunzps48UEBT | 4,770 | fix typo | {
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"good catch thanks ! Can you check if the same typo is also present in `add_elasticsearch_index` ? It has a very similar signature",
"> good catch thanks ! Can you check if the same typo is also present in `add_elasticsearch_index` ? It has a very similar signature\r\n\r\nfixed"
] | 1,659,095,172,000 | 1,659,110,527,000 | 1,659,110,527,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/4769 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4769/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4769/comments | https://api.github.com/repos/huggingface/datasets/issues/4769/events | https://github.com/huggingface/datasets/issues/4769 | 1,322,121,554 | I_kwDODunzps5OzflS | 4,769 | Fail to process SQuADv1.1 datasets with max_seq_length=128, doc_stride=96. | {
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] | open | false | null | [] | null | [] | 1,659,093,504,000 | 1,659,093,504,000 | null | NONE | null | null | null | ## Describe the bug
datasets fail to process SQuADv1.1 with max_seq_length=128, doc_stride=96 when calling datasets["train"].train_dataset.map().
## Steps to reproduce the bug
I used huggingface[ TF2 question-answering examples](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering). And my scripts are as follows:
```
python run_qa.py \
--model_name_or_path $BERT_DIR \
--dataset_name $SQUAD_DIR \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 128 \
--doc_stride 96 \
--output_dir $OUTPUT \
--save_steps 10000 \
--overwrite_cache \
--overwrite_output_dir \
```
## Expected results
Normally process SQuADv1.1 datasets with max_seq_length=128, doc_stride=96.
## Actual results
```
INFO:__main__:Padding all batches to max length because argument was set or we're on TPU.
WARNING:datasets.fingerprint:Parameter 'function'=<function main.<locals>.prepare_train_features at 0x7f15bc2d07a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
0%| | 0/88 [00:00<?, ?ba/s]thread '<unnamed>' panicked at 'assertion failed: stride < max_len', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/encoding.rs:311:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
0%| | 0/88 [00:00<?, ?ba/s]
Traceback (most recent call last):
File "run_qa.py", line 743, in <module>
main()
File "run_qa.py", line 485, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2394, in map
desc=desc,
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 551, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 518, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2768, in _map_single
offset=offset,
File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2644, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2336, in decorated
result = f(decorated_item, *args, **kwargs)
File "run_qa.py", line 410, in prepare_train_features
padding=padding,
File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2512, in __call__
**kwargs,
File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2703, in batch_encode_plus
**kwargs,
File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 429, in _batch_encode_plus
is_pretokenized=is_split_into_words,
pyo3_runtime.PanicException: assertion failed: stride < max_len
Traceback (most recent call last):
File "./data/SQuADv1.1/evaluate-v1.1.py", line 92, in <module>
with open(args.prediction_file) as prediction_file:
FileNotFoundError: [Errno 2] No such file or directory: './output/bert_base_squadv1.1_tf2/eval_predictions.json'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Ubuntu, pytorch=1.11.0, tensorflow-gpu=2.9.1
- Python version: 2.7
- PyArrow version: 8.0.0
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,659,082,660,000 | 1,659,112,948,000 | 1,659,112,157,000 | MEMBER | null | false | {
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} | Once `rouge-score` has made the 0.1.2 release to fix their issue https://github.com/google-research/google-research/issues/1212, we can unpin it.
Related to:
- #4735 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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https://api.github.com/repos/huggingface/datasets/issues/4766 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4766/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4766/comments | https://api.github.com/repos/huggingface/datasets/issues/4766/events | https://github.com/huggingface/datasets/issues/4766 | 1,321,809,380 | I_kwDODunzps5OyTXk | 4,766 | Dataset Viewer issue for openclimatefix/goes-mrms | {
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"Thanks for reporting, @cheaterHy.\r\n\r\nThe cause of this issue is a misalignment between the names of the repo (`goes-mrms`, with hyphen) and its Python loading scrip file (`goes_mrms.py`, with underscore).\r\n\r\nI've opened an Issue discussion in their repo: https://huggingface.co/datasets/openclimatefix/goes-mrms/discussions/1"
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https://api.github.com/repos/huggingface/datasets/issues/4763 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4763/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4763/comments | https://api.github.com/repos/huggingface/datasets/issues/4763/events | https://github.com/huggingface/datasets/pull/4763 | 1,321,295,876 | PR_kwDODunzps48RMKi | 4,763 | 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."
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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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https://api.github.com/repos/huggingface/datasets/issues/4762 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4762/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4762/comments | https://api.github.com/repos/huggingface/datasets/issues/4762/events | https://github.com/huggingface/datasets/pull/4762 | 1,321,261,733 | PR_kwDODunzps48RE56 | 4,762 | 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."
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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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"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```"
] | 1,659,020,223,000 | 1,659,108,274,000 | null | CONTRIBUTOR | null | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/4760 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4760/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4760/comments | https://api.github.com/repos/huggingface/datasets/issues/4760/events | https://github.com/huggingface/datasets/issues/4760 | 1,320,878,223 | I_kwDODunzps5OuwCP | 4,760 | 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"
] | 1,659,012,314,000 | 1,659,024,336,000 | null | NONE | null | null | null | ## 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 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"
] | 1,659,007,303,000 | 1,659,014,276,000 | 1,659,014,268,000 | NONE | null | null | null | ### 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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https://api.github.com/repos/huggingface/datasets/issues/4757 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4757/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4757/comments | https://api.github.com/repos/huggingface/datasets/issues/4757/events | https://github.com/huggingface/datasets/issues/4757 | 1,320,602,532 | I_kwDODunzps5Otsuk | 4,757 | Document better when relative paths are transformed to URLs | {
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] | open | false | null | [] | null | [] | 1,658,997,987,000 | 1,658,997,987,000 | null | MEMBER | null | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/4755 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4755/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4755/comments | https://api.github.com/repos/huggingface/datasets/issues/4755/events | https://github.com/huggingface/datasets/issues/4755 | 1,319,687,044 | I_kwDODunzps5OqNOE | 4,755 | 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```"
] | 1,658,933,651,000 | 1,658,944,648,000 | null | NONE | null | null | null | ## 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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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,658,933,412,000 | 1,658,934,180,000 | 1,658,933,466,000 | MEMBER | null | false | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4753 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4753/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4753/comments | https://api.github.com/repos/huggingface/datasets/issues/4753/events | https://github.com/huggingface/datasets/pull/4753 | 1,319,571,745 | PR_kwDODunzps48Ll8G | 4,753 | Add `language_bcp47` tag | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,658,928,676,000 | 1,658,933,403,000 | 1,658,932,676,000 | MEMBER | null | false | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4752 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4752/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4752/comments | https://api.github.com/repos/huggingface/datasets/issues/4752/events | https://github.com/huggingface/datasets/issues/4752 | 1,319,464,409 | I_kwDODunzps5OpW3Z | 4,752 | 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."
] | 1,658,923,494,000 | 1,658,941,951,000 | null | CONTRIBUTOR | null | null | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/4751 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4751/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4751/comments | https://api.github.com/repos/huggingface/datasets/issues/4751/events | https://github.com/huggingface/datasets/pull/4751 | 1,319,440,903 | PR_kwDODunzps48LJ7U | 4,751 | Added dataset information in clinic oos dataset card | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,658,922,268,000 | 1,659,005,601,000 | 1,659,004,837,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/4750 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4750/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4750/comments | https://api.github.com/repos/huggingface/datasets/issues/4750/events | https://github.com/huggingface/datasets/issues/4750 | 1,319,333,645 | I_kwDODunzps5Oo28N | 4,750 | 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"
] | 1,658,916,818,000 | 1,658,930,287,000 | 1,658,930,287,000 | CONTRIBUTOR | null | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/4748 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4748/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4748/comments | https://api.github.com/repos/huggingface/datasets/issues/4748/events | https://github.com/huggingface/datasets/pull/4748 | 1,318,874,913 | PR_kwDODunzps48JTEb | 4,748 | Add image classification processing guide | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,658,880,671,000 | 1,658,942,901,000 | 1,658,942,172,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/4747 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4747/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4747/comments | https://api.github.com/repos/huggingface/datasets/issues/4747/events | https://github.com/huggingface/datasets/pull/4747 | 1,318,586,932 | PR_kwDODunzps48IWKj | 4,747 | 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."
] | 1,658,858,701,000 | 1,659,104,371,000 | null | MEMBER | null | true | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4746 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4746/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4746/comments | https://api.github.com/repos/huggingface/datasets/issues/4746/events | https://github.com/huggingface/datasets/issues/4746 | 1,318,486,599 | I_kwDODunzps5OloJH | 4,746 | 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"
] | 1,658,852,716,000 | 1,658,858,857,000 | null | NONE | null | null | null | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | {
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https://api.github.com/repos/huggingface/datasets/issues/4745 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4745/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4745/comments | https://api.github.com/repos/huggingface/datasets/issues/4745/events | https://github.com/huggingface/datasets/issues/4745 | 1,318,016,655 | I_kwDODunzps5Oj1aP | 4,745 | 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`)"
] | 1,658,830,568,000 | 1,658,836,765,000 | null | NONE | null | null | null | 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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"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."
] | 1,658,820,778,000 | 1,658,823,453,000 | null | MEMBER | null | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/4743 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4743/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4743/comments | https://api.github.com/repos/huggingface/datasets/issues/4743/events | https://github.com/huggingface/datasets/pull/4743 | 1,317,362,561 | PR_kwDODunzps48EUFs | 4,743 | Update map docs | {
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https://api.github.com/repos/huggingface/datasets/issues/4742 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4742/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4742/comments | https://api.github.com/repos/huggingface/datasets/issues/4742/events | https://github.com/huggingface/datasets/issues/4742 | 1,317,260,663 | I_kwDODunzps5Og813 | 4,742 | 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"
] | 1,658,776,722,000 | 1,658,820,827,000 | null | CONTRIBUTOR | null | null | null | ## 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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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,658,745,687,000 | 1,658,753,436,000 | 1,658,752,673,000 | CONTRIBUTOR | null | false | {
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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 !"
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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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"_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"
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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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"_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 "
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- 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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"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. "
] | 1,658,496,508,000 | 1,658,500,151,000 | null | NONE | null | null | null | ```
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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"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."
] | 1,658,492,058,000 | 1,658,497,598,000 | 1,658,497,598,000 | NONE | null | null | null | ### 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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} | Temporarily pin `rouge_score` (to avoid latest version 0.7.0) until the issue is fixed.
Fix #4734 | {
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https://api.github.com/repos/huggingface/datasets/issues/4734 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4734/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4734/comments | https://api.github.com/repos/huggingface/datasets/issues/4734/events | https://github.com/huggingface/datasets/issues/4734 | 1,314,495,382 | I_kwDODunzps5OWZuW | 4,734 | 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"
] | 1,658,474,105,000 | 1,658,475,919,000 | 1,658,475,918,000 | MEMBER | null | null | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/4733 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4733/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4733/comments | https://api.github.com/repos/huggingface/datasets/issues/4733/events | https://github.com/huggingface/datasets/issues/4733 | 1,314,479,616 | I_kwDODunzps5OWV4A | 4,733 | rouge metric | {
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"Fixed by:\r\n- #4735"
] | 1,658,473,611,000 | 1,658,480,882,000 | 1,658,480,735,000 | NONE | null | null | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/4732 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4732/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4732/comments | https://api.github.com/repos/huggingface/datasets/issues/4732/events | https://github.com/huggingface/datasets/issues/4732 | 1,314,371,566 | I_kwDODunzps5OV7fu | 4,732 | Document better that loading a dataset passing its name does not use the local script | {
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"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. "
] | 1,658,470,051,000 | 1,658,777,132,000 | null | MEMBER | null | null | null | 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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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,658,435,741,000 | 1,658,473,260,000 | 1,658,472,522,000 | CONTRIBUTOR | null | false | {
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"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."
] | 1,658,416,446,000 | 1,658,421,664,000 | 1,658,421,664,000 | CONTRIBUTOR | null | null | null | ## 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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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,658,414,593,000 | 1,658,502,589,000 | 1,658,501,789,000 | MEMBER | null | false | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4728 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4728/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4728/comments | https://api.github.com/repos/huggingface/datasets/issues/4728/events | https://github.com/huggingface/datasets/issues/4728 | 1,312,897,454 | I_kwDODunzps5OQTmu | 4,728 | load_dataset gives "403" error when using Financial Phrasebank | {
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"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."
] | 1,658,393,012,000 | 1,658,478,333,000 | null | NONE | null | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/4727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4727/comments | https://api.github.com/repos/huggingface/datasets/issues/4727/events | https://github.com/huggingface/datasets/issues/4727 | 1,312,645,391 | I_kwDODunzps5OPWEP | 4,727 | Dataset Viewer issue for TheNoob3131/mosquito-data | {
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"The preview is working OK:\r\n\r\n\r\n\r\n"
] | 1,658,381,088,000 | 1,658,389,916,000 | 1,658,389,501,000 | NONE | null | null | null | ### 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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https://api.github.com/repos/huggingface/datasets/issues/4726 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4726/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4726/comments | https://api.github.com/repos/huggingface/datasets/issues/4726/events | https://github.com/huggingface/datasets/pull/4726 | 1,312,082,175 | PR_kwDODunzps47ykPI | 4,726 | Fix broken link to the Hub | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,658,357,847,000 | 1,658,413,998,000 | 1,658,390,454,000 | MEMBER | null | false | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4725 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4725/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4725/comments | https://api.github.com/repos/huggingface/datasets/issues/4725/events | https://github.com/huggingface/datasets/issues/4725 | 1,311,907,096 | I_kwDODunzps5OMh0Y | 4,725 | 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."
] | 1,658,350,650,000 | 1,658,470,186,000 | 1,658,389,099,000 | NONE | null | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/4724 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4724/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4724/comments | https://api.github.com/repos/huggingface/datasets/issues/4724/events | https://github.com/huggingface/datasets/pull/4724 | 1,311,127,404 | PR_kwDODunzps47vLrP | 4,724 | 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 :)"
] | 1,658,324,342,000 | 1,659,105,431,000 | null | MEMBER | null | false | {
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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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