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```
TypeError: _download_and_prepare() got an unexpected keyword argument 'save_infos'
``` | {
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"Hi @leondz, thanks for reporting.\r\n\r\nIndeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions.\r\n\r\nIn particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`.\r\n\r\nLet me know if you need some help: I could push a commit to your repo with the fix.",
"Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)"
] | 1,651,838,607,000 | 1,652,084,758,000 | 1,652,084,758,000 | NONE | null | null | null | ### Link
https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train
### Description
The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss?
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/4288 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4288/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4288/comments | https://api.github.com/repos/huggingface/datasets/issues/4288/events | https://github.com/huggingface/datasets/pull/4288 | 1,226,821,732 | PR_kwDODunzps43XLKi | 4,288 | Add missing `faiss` import to fix https://github.com/huggingface/datasets/issues/4287 | {
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https://api.github.com/repos/huggingface/datasets/issues/4287 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4287/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4287/comments | https://api.github.com/repos/huggingface/datasets/issues/4287/events | https://github.com/huggingface/datasets/issues/4287 | 1,226,806,652 | I_kwDODunzps5JH5V8 | 4,287 | "NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None | {
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"So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above.\r\n\r\nAs it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered.\r\n\r\nSo on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU.\r\n\r\nI'll add the code in a PR linked to this issue in case you want to merge it!",
"Adding here the complete error traceback!\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/alvarobartt/lol.py\", line 12, in <module>\r\n ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3656, in add_faiss_index\r\n super().add_faiss_index(\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 478, in add_faiss_index\r\n faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True)\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 281, in add_vectors\r\n self.faiss_index = self._faiss_index_to_device(index, self.device)\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 327, in _faiss_index_to_device\r\n faiss_res = faiss.StandardGpuResources()\r\nNameError: name 'faiss' is not defined\r\n```"
] | 1,651,763,385,000 | 1,651,837,063,000 | null | NONE | null | null | null | ## Describe the bug
When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception.
All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
import torch
torch.set_grad_enabled(False)
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
from datasets import load_dataset
ds = load_dataset('crime_and_punish', split='train[:100]')
ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()})
ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`
```
## Expected results
A new column named `embeddings` in the dataset that we're adding the index to.
## Actual results
An exception is triggered with the following message `NameError: name 'faiss' is not defined`.
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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https://api.github.com/repos/huggingface/datasets/issues/4286 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4286/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4286/comments | https://api.github.com/repos/huggingface/datasets/issues/4286/events | https://github.com/huggingface/datasets/pull/4286 | 1,226,758,621 | PR_kwDODunzps43W-DI | 4,286 | Add Lahnda language tag | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4286). All of your documentation changes will be reflected on that endpoint."
] | 1,651,761,260,000 | 1,651,762,010,000 | null | CONTRIBUTOR | null | false | {
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} | This language is present in [Wikimedia's WIT](https://huggingface.co/datasets/wikimedia/wit_base) dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/4285 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4285/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4285/comments | https://api.github.com/repos/huggingface/datasets/issues/4285/events | https://github.com/huggingface/datasets/pull/4285 | 1,226,374,831 | PR_kwDODunzps43VtEa | 4,285 | Update LexGLUE README.md | {
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https://api.github.com/repos/huggingface/datasets/issues/4284 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4284/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4284/comments | https://api.github.com/repos/huggingface/datasets/issues/4284/events | https://github.com/huggingface/datasets/issues/4284 | 1,226,200,727 | I_kwDODunzps5JFlaX | 4,284 | Issues in processing very large datasets | {
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] | open | false | null | [] | null | [] | 1,651,726,869,000 | 1,651,726,869,000 | null | NONE | null | null | null | ## Describe the bug
I'm trying to add a feature called "subgraph" to CNN/DM dataset (modifications on run_summarization.py of Huggingface Transformers script) --- I'm not quite sure if I'm doing it the right way, though--- but the main problem appears when the training starts where the error ` [OSError: [Errno 12] Cannot allocate memory]` appears. I suppose this problem roots in RAM issues and how the dataset is loaded during training, but I have no clue of what I can do to fix it. Observing the dataset's cache directory, I see that it takes ~600GB of memory and that's why I believe special care is needed when loading it into the memory.
Here are my modifications to `run_summarization.py` code.
```
# loading pre-computed dictionary where keys are 'id' of article and values are corresponding subgraph
graph_data_train = get_graph_data('train')
graph_data_validation = get_graph_data('val')
...
...
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function_train,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
```
And here is the modified preprocessed function:
```
def preprocess_function_train(examples):
inputs, targets, sub_graphs, ids = [], [], [], []
for i in range(len(examples[text_column])):
if examples[text_column][i] is not None and examples[summary_column][i] is not None:
# if examples['doc_id'][i] in graph_data.keys():
inputs.append(examples[text_column][i])
targets.append(examples[summary_column][i])
sub_graphs.append(graph_data_train[examples['id'][i]])
ids.append(examples['id'][i])
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True,
sub_graphs=sub_graphs, ids=ids)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == "max_length" and data_args.ignore_pad_token_for_loss:
labels["input_ids"] = [
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
]
model_inputs["labels"] = labels["input_ids"]
return model_inputs
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/4282 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4282/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4282/comments | https://api.github.com/repos/huggingface/datasets/issues/4282/events | https://github.com/huggingface/datasets/pull/4282 | 1,225,616,545 | PR_kwDODunzps43TZYL | 4,282 | Don't do unnecessary list type casting to avoid replacing None values by empty lists | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Quick question about the message in the warning. You say \"will be fixed in a future major version\" but don't you mean \"will raise an error in a future major version\"?",
"Right ! Good catch, thanks, I updated the message to say \"will raise an error in a future major version\""
] | 1,651,682,221,000 | 1,651,833,838,000 | 1,651,833,420,000 | MEMBER | null | false | {
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} | In certain cases, `None` values are replaced by empty lists when casting feature types.
It happens every time you cast an array of nested lists like [None, [0, 1, 2, 3]] to a different type (to change the integer precision for example). In this case you'd get [[], [0, 1, 2, 3]] for example. This issue comes from PyArrow, see the discussion in https://github.com/huggingface/datasets/issues/3676
This issue also happens when no type casting is needed, because casting is supposed to be a no-op in this case. But as https://github.com/huggingface/datasets/issues/3676 shown, it's not the case and `None` are replaced by empty lists even if we cast to the exact same type.
In this PR I just workaround this bug in the case where no type casting is needed. In particular, I only call `pa.ListArray.from_arrays` only when necessary.
I also added a warning when some `None` are effectively replaced by empty lists. I wanted to raise an error in this case, but maybe we should wait a major update to do so
This PR fixes this particular case, that is occurring in `run_qa.py` in `transformers`:
```python
from datasets import Dataset
ds = Dataset.from_dict({"a": range(4)})
ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"])
print(ds.to_pandas())
# before:
# b
# 0 [None, [0]]
# 1 [[], [0]]
# 2 [[], [0]]
# 3 [[], [0]]
#
# now:
# b
# 0 [None, [0]]
# 1 [None, [0]]
# 2 [None, [0]]
# 3 [None, [0]]
```
cc @sgugger | {
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https://api.github.com/repos/huggingface/datasets/issues/4281 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4281/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4281/comments | https://api.github.com/repos/huggingface/datasets/issues/4281/events | https://github.com/huggingface/datasets/pull/4281 | 1,225,556,939 | PR_kwDODunzps43TNBm | 4,281 | Remove a copy-paste sentence in dataset cards | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The non-passing tests have nothing to do with this PR."
] | 1,651,678,915,000 | 1,651,826,283,000 | 1,651,689,196,000 | MEMBER | null | false | {
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} | Remove the following copy-paste sentence from dataset cards:
```
We show detailed information for up to 5 configurations of the dataset.
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4280 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4280/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4280/comments | https://api.github.com/repos/huggingface/datasets/issues/4280/events | https://github.com/huggingface/datasets/pull/4280 | 1,225,446,844 | PR_kwDODunzps43S2xg | 4,280 | Add missing features to commonsense_qa dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@albertvillanova it adds question_concept and id which is great. I suppose we'll talk about staying true to the format on another PR. ",
"Yes, let's merge this PR as it is: it adds missing features.\r\n\r\nA subsequent PR may address the request on changing the dataset feature structure."
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https://api.github.com/repos/huggingface/datasets/issues/4279 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4279/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4279/comments | https://api.github.com/repos/huggingface/datasets/issues/4279/events | https://github.com/huggingface/datasets/pull/4279 | 1,225,300,273 | PR_kwDODunzps43SXw5 | 4,279 | Update minimal PyArrow version warning | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,667,169,000 | 1,651,740,658,000 | 1,651,740,227,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/4278 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4278/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4278/comments | https://api.github.com/repos/huggingface/datasets/issues/4278/events | https://github.com/huggingface/datasets/pull/4278 | 1,225,122,123 | PR_kwDODunzps43RyTs | 4,278 | Add missing features to openbookqa dataset for additional config | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Let's merge this PR as it is: it adds missing features.\r\n\r\nA subsequent PR may address the request on changing the data feature structure."
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https://api.github.com/repos/huggingface/datasets/issues/4277 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4277/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4277/comments | https://api.github.com/repos/huggingface/datasets/issues/4277/events | https://github.com/huggingface/datasets/pull/4277 | 1,225,002,286 | PR_kwDODunzps43RZV9 | 4,277 | Enable label alignment for token classification datasets | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hmm, not sure why the Windows tests are failing with:\r\n\r\n```\r\nDid not find path entry C:\\tools\\miniconda3\\bin\r\nC:\\tools\\miniconda3\\envs\\py37\\python.exe: No module named pytest\r\n```\r\n\r\nEdit: running the CI again fixed the problem 🙃 ",
"> One last nit and we can merge then\r\n\r\nThanks, done!"
] | 1,651,648,516,000 | 1,651,851,735,000 | 1,651,851,391,000 | MEMBER | null | false | {
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} | This PR extends the `Dataset.align_labels_with_mapping()` method to support alignment of label mappings between datasets and models for token classification (e.g. NER).
Example of usage:
```python
from datasets import load_dataset
ner_ds = load_dataset("conll2003", split="train")
# returns [3, 0, 7, 0, 0, 0, 7, 0, 0]
ner_ds[0]["ner_tags"]
# hypothetical model mapping with O <--> B-LOC
label2id = {
"B-LOC": "0",
"B-MISC": "7",
"B-ORG": "3",
"B-PER": "1",
"I-LOC": "6",
"I-MISC": "8",
"I-ORG": "4",
"I-PER": "2",
"O": "5"
}
ner_aligned_ds = ner_ds.align_labels_with_mapping(label2id, "ner_tags")
# returns [3, 5, 7, 5, 5, 5, 7, 5, 5]
ner_aligned_ds[0]["ner_tags"]
```
Context: we need this in AutoTrain to automatically align datasets / models during evaluation. cc @abhishekkrthakur | {
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"Thanks for reporting, @vblagoje.\r\n\r\nIndeed, I noticed some of these issues while reviewing this PR:\r\n- #4259 \r\n\r\nThis is in my TODO list. ",
"Ok, awesome @albertvillanova How about #4275 ?",
"On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency.\r\n\r\nFor example, other datasets also flatten \"question.stem\" into \"question\":\r\n- ai2_arc:\r\n ```python\r\n question = data[\"question\"][\"stem\"]\r\n choices = data[\"question\"][\"choices\"]\r\n text_choices = [choice[\"text\"] for choice in choices]\r\n label_choices = [choice[\"label\"] for choice in choices]\r\n yield id_, {\r\n \"id\": id_,\r\n \"answerKey\": answerkey,\r\n \"question\": question,\r\n \"choices\": {\"text\": text_choices, \"label\": label_choices},\r\n }\r\n ```\r\n- commonsense_qa:\r\n ```python\r\n question = data[\"question\"]\r\n stem = question[\"stem\"]\r\n yield id_, {\r\n \"answerKey\": answerkey,\r\n \"question\": stem,\r\n \"choices\": {\"label\": labels, \"text\": texts},\r\n }\r\n ```\r\n- cos_e:\r\n ```python\r\n \"question\": cqa[\"question\"][\"stem\"],\r\n ```\r\n- qasc\r\n- quartz\r\n- wiqa\r\n\r\nExceptions:\r\n- exams\r\n\r\nI think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.",
"@albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq ",
"I'm opening a PR that adds the missing fields.\r\n\r\nLet's agree on the feature structure: @lhoestq @mariosasko @polinaeterna ",
"IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).",
"I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new. ",
"I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets.\r\n\r\nThere is always the tension between:\r\n- preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset),\r\n- and on the other hand performing some king of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain \"standard\" structure).\r\n - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model\r\n\r\nRecently, in the BigScience BioMedical hackathon, they adopted a different approach:\r\n- they implement a \"source\" config, respecting the original structure as much as possible\r\n- they implement additional config for each task, with a \"standard\" nested structure per task, which is most useful for users.",
"@albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best. "
] | 1,651,643,512,000 | 1,652,013,223,000 | null | CONTRIBUTOR | null | null | null | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanScore'],
- 'clarity': row['clarity'],
- 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2 | {
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"Thanks for reporting, @vblagoje.\r\n\r\nI'm opening a PR to address this. "
] | 1,651,642,739,000 | 1,651,664,478,000 | null | CONTRIBUTOR | null | null | null | ## Describe the bug
In short, CommonSenseQA implementation is inconsistent with the original dataset.
More precisely, we need to:
1. Add the dataset matching "id" field. The current dataset, instead, regenerates monotonically increasing id.
2. The [“question”][“stem”] field is flattened into "question". We should match the original dataset and unflatten it
3. Add the missing "question_concept" field in the question tree node
4. Anything else? Go over the data structure of the newly repaired CommonSenseQA and make sure it matches the original
## Expected results
Every data item of the CommonSenseQA should structurally and data-wise match the original CommonSenseQA dataset.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
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"> This PR fixes #4271.\r\n\r\nThings have not changed when searching \"tqdm\" in the Dataset document. The second result still performs as \"Enable\".",
"Hi @jiangwy99, the fix will appear on the `main` version of the docs:\r\n\r\n![Screen Shot 2022-05-04 at 8 38 29 AM](https://user-images.githubusercontent.com/59462357/166718225-6848ab91-87d1-4572-9912-40a909af6cb9.png)\r\n",
"Fixed now, thanks."
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"Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)"
] | 1,651,599,896,000 | 1,651,647,515,000 | 1,651,647,515,000 | NONE | null | null | null | ## Describe the bug
in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable". | {
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was green, but after merging it to master, a code quality error appeared. | {
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https://api.github.com/repos/huggingface/datasets/issues/4269 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4269/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4269/comments | https://api.github.com/repos/huggingface/datasets/issues/4269/events | https://github.com/huggingface/datasets/pull/4269 | 1,223,865,145 | PR_kwDODunzps43Nzwh | 4,269 | Add license and point of contact to big_patent dataset | {
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- license
- point of contact | {
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https://api.github.com/repos/huggingface/datasets/issues/4268 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4268/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4268/comments | https://api.github.com/repos/huggingface/datasets/issues/4268/events | https://github.com/huggingface/datasets/issues/4268 | 1,223,331,964 | I_kwDODunzps5I6pB8 | 4,268 | error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered | {
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"It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for [\"word\"](https://en.wiktionary.org/wiki/word),\r\n\r\nPronunciation\r\n([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/\r\n([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/",
"Hi @i-am-neo, thanks for reporting.\r\n\r\nNormally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public.\r\n\r\nAlso note that last commit \"Add metadata\" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well.\r\n\r\n@i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.",
"Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!",
"All datasets are private now. \r\n\r\nRe:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)",
"Thanks a lot, @cakiki.\r\n\r\n@i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets. ",
"Thanks for letting me know, @albertvillanova @cakiki.\r\nAny chance of having a subset alpha version in the meantime? \r\nI only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes.\r\n\r\nWould like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. \r\n\r\n(Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!",
"Hey @i-am-neo,\r\n\r\nCool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)",
"@i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/)\r\nYou're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file",
"thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!",
"thanks @patrickvonplaten. will do - getting my observations together."
] | 1,651,523,665,000 | 1,651,852,410,000 | 1,651,577,028,000 | NONE | null | null | null | ## Describe the bug
Error generated when attempting to download dataset
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
ExpectedMoreDownloadedFiles Traceback (most recent call last)
[<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
3 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
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"_The documentation is not available anymore as the PR was closed or merged._"
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,510,771,000 | 1,651,740,440,000 | 1,651,740,009,000 | CONTRIBUTOR | null | false | {
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} | Adds the HF Speech Bench to Librispeech Dataset Card in place of the Papers With Code Leaderboard. Should improve usage and visibility of this leaderboard! Wondering whether this can also be done for [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) and [8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) through someone with permissions?
cc @patrickvonplaten: more leaderboard promotion! | {
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https://api.github.com/repos/huggingface/datasets/issues/4263 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4263/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4263/comments | https://api.github.com/repos/huggingface/datasets/issues/4263/events | https://github.com/huggingface/datasets/pull/4263 | 1,222,723,083 | PR_kwDODunzps43KLnD | 4,263 | Rename imagenet2012 -> imagenet-1k | {
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"> Later we can add imagenet-21k as a new dataset if we want.\r\n\r\nisn't it what models refer to as `imagenet` already?",
"> isn't it what models refer to as imagenet already?\r\n\r\nI wasn't sure, but it looks like it indeed. Therefore having a dataset `imagenet` for ImageNet 21k makes sense actually.\r\n\r\nEDIT: actually not all `imagenet` tag refer to ImageNet 21k - we will need to correct some of them",
"_The documentation is not available anymore as the PR was closed or merged._",
"should we remove the repo mirror on the hub side or will you do it?"
] | 1,651,487,181,000 | 1,651,513,846,000 | 1,651,509,177,000 | MEMBER | null | false | {
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} | On the Hugging Face Hub, users refer to imagenet2012 (from #4178 ) as imagenet-1k in their model tags.
To correctly link models to imagenet, we should rename this dataset `imagenet-1k`.
Later we can add `imagenet-21k` as a new dataset if we want.
Once this one is merged we can delete the `imagenet2012` dataset repository on the Hub.
EDIT: to complete the rationale on why we should name it `imagenet-1k`:
If users specifically added the tag `imagenet-1k` , then it could be for two reasons (not sure which one is predominant), either they
- wanted to make it explicit that it’s not 21k -> the distinction is important for the community
- or they have been following this convention from other models -> the convention implicitly exists already | {
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https://api.github.com/repos/huggingface/datasets/issues/4262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4262/comments | https://api.github.com/repos/huggingface/datasets/issues/4262/events | https://github.com/huggingface/datasets/pull/4262 | 1,222,130,749 | PR_kwDODunzps43IOye | 4,262 | Add YAML tags to Dataset Card rotten tomatoes | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,406,348,000 | 1,651,588,053,000 | 1,651,587,635,000 | CONTRIBUTOR | null | false | {
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"Hi @xflashxx, thanks for reporting.\r\n\r\nPlease note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis\r\n\r\nWe are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.",
"i'd suggest just pinging the authors here in the issue if possible?",
"Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.",
"Hi @xflashxx and @albertvillanova,\r\n\r\nI have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to \"new\" items. The length of `ids_validation` and `ids_testing` is zero.\r\n\r\nRegarding impact on scores:\r\n1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. \r\n\r\n2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository.\r\n\r\nHope this helps!",
"Thanks @shahbazsyed for your fast fix.\r\n\r\nAs a side note:\r\n- Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de\r\n- Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings."
] | 1,651,340,617,000 | 1,651,557,866,000 | 1,651,557,866,000 | NONE | null | null | null | ## Describe the bug
Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results.
Furthermore, all splits contain duplicate samples.
## Steps to reproduce the bug
```python
from datasets import load_dataset
training = load_dataset("webis/conclugen", "base", split="train")
validation = load_dataset("webis/conclugen", "base", split="validation")
testing = load_dataset("webis/conclugen", "base", split="test")
# collect which sample id's are present in the training split
ids_validation = list()
ids_testing = list()
for train_sample in training:
train_argument = train_sample["argument"]
train_conclusion = train_sample["conclusion"]
train_id = train_sample["id"]
# test if current sample is in validation split
if train_argument in validation["argument"]:
for validation_sample in validation:
validation_argument = validation_sample["argument"]
validation_conclusion = validation_sample["conclusion"]
validation_id = validation_sample["id"]
if train_argument == validation_argument and train_conclusion == validation_conclusion:
ids_validation.append(validation_id)
# test if current sample is in test split
if train_argument in testing["argument"]:
for testing_sample in testing:
testing_argument = testing_sample["argument"]
testing_conclusion = testing_sample["conclusion"]
testing_id = testing_sample["id"]
if train_argument == testing_argument and train_conclusion == testing_conclusion:
ids_testing.append(testing_id)
```
## Expected results
Length of both lists `ids_validation` and `ids_testing` should be zero.
## Actual results
Length of `ids_validation` = `2556`
Length of `ids_testing` = `287`
Furthermore, there seems to be duplicate samples in (at least) the *training* split, since:
`print(len(set(ids_validation)))` = `950`
`print(len(set(ids_testing)))` = `101`
All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: macOS-12.3.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4260/comments | https://api.github.com/repos/huggingface/datasets/issues/4260/events | https://github.com/huggingface/datasets/pull/4260 | 1,221,830,292 | PR_kwDODunzps43HSfs | 4,260 | Add mr_polarity movie review sentiment classification | {
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"whoops just found https://huggingface.co/datasets/rotten_tomatoes"
] | 1,651,324,773,000 | 1,651,328,185,000 | 1,651,328,185,000 | CONTRIBUTOR | null | false | {
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} | Add the MR (Movie Review) dataset. The original dataset contains sentences from Rotten Tomatoes labeled as either "positive" or "negative".
Homepage: [https://www.cs.cornell.edu/people/pabo/movie-review-data/](https://www.cs.cornell.edu/people/pabo/movie-review-data/)
paperswithcode: [https://paperswithcode.com/dataset/mr](https://paperswithcode.com/dataset/mr)
- [ ] I was not able to generate dummy data, the original dataset files have ".pos" and ".neg" as file extensions so the auto-generator does not work. Is it fine like this or should dummy data be added?
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https://api.github.com/repos/huggingface/datasets/issues/4259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4259/comments | https://api.github.com/repos/huggingface/datasets/issues/4259/events | https://github.com/huggingface/datasets/pull/4259 | 1,221,768,025 | PR_kwDODunzps43HHGc | 4,259 | Fix bug in choices labels in openbookqa dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,304,499,000 | 1,651,645,891,000 | 1,651,590,861,000 | CONTRIBUTOR | null | false | {
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} | This PR fixes the Bug in the openbookqa dataset as mentioned in this issue #3550.
Fix #3550.
cc. @lhoestq @mariosasko | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Good catch ! Thanks :)\r\n> \r\n> Next time can you describe your fix in the Pull Request description please ?\r\n\r\nThanks. Also whoops, sorry about not being very descriptive. I updated the pull request description, and will keep this in mind for future PRs."
] | 1,651,272,164,000 | 1,651,509,200,000 | 1,651,508,772,000 | MEMBER | null | false | {
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} | This pr fixes a couple bugs:
1) the perplexity was calculated with a 0 in the attention mask for the start token, which was causing high perplexity scores that were not correct
2) the documentation was not updated | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,257,447,000 | 1,651,503,018,000 | 1,651,502,604,000 | CONTRIBUTOR | null | false | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"CI is failing because some sections are missing in the dataset card, but this is unrelated to this PR - Merging !"
] | 1,651,247,746,000 | 1,651,249,495,000 | 1,651,249,136,000 | MEMBER | null | false | {
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cc @stas00 | {
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} | This PR replaces data URL in SAMSum dataset:
- original host (arxiv.org) does not allow HTTP Range requests
- we have hosted the data on the Hub (license: CC BY-NC-ND 4.0)
Moreover, it implements support for streaming.
Fix #4146.
Related to: #4236.
CC: @severo | {
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sorry for spamming you with review requests, @albertvillanova ! :hugs: | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Updated meta.yaml as well. Thanks.",
"I'm OK with bumping PyArrow to version 6 to match the version in Colab, but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues.",
"> but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues.\r\n\r\nI agree, not much attention has been payed to extension arrays in the latest developments of Arrow anyway.\r\n\r\nLet's not use them more that what we do right now, and try to remove them at one point"
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This PR updates the PyArrow version to 6 in setup.py, CI job files .circleci/config.yaml and .github/workflows/benchmarks.yaml files.
This will fix ArrayND error which exists in pyarrow 5. | {
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Fix #4247.
CC: @severo | {
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"Thanks for reporting @sue99.\r\n\r\nUnfortunately. I'm not able to reproduce your problem:\r\n```python\r\nIn [1]: import datasets\r\n ...: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"conll2003\")\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 14042\r\n })\r\n validation: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3251\r\n })\r\n test: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3454\r\n })\r\n})\r\n\r\nIn [3]: dataset[\"train\"][0]\r\nOut[3]: \r\n{'id': '0',\r\n 'tokens': ['EU',\r\n 'rejects',\r\n 'German',\r\n 'call',\r\n 'to',\r\n 'boycott',\r\n 'British',\r\n 'lamb',\r\n '.'],\r\n 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\r\n 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\r\n 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}\r\n```\r\n\r\nJust guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub. "
] | 1,651,138,411,000 | 1,651,163,473,000 | null | NONE | null | null | null | ## Describe the bug
I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text.
Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ?
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
dataset = load_dataset("conll2003")
```
## Expected results
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
## Actual results
```python
print(dataset)
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 219554
})
test: Dataset({
features: ['text'],
num_rows: 50350
})
validation: Dataset({
features: ['text'],
num_rows: 55044
})
})
```
```python
for i in range(20):
print(dataset['train'][i])
{'text': '-DOCSTART- -X- -X- O'}
{'text': ''}
{'text': 'EU NNP B-NP B-ORG'}
{'text': 'rejects VBZ B-VP O'}
{'text': 'German JJ B-NP B-MISC'}
{'text': 'call NN I-NP O'}
{'text': 'to TO B-VP O'}
{'text': 'boycott VB I-VP O'}
{'text': 'British JJ B-NP B-MISC'}
{'text': 'lamb NN I-NP O'}
{'text': '. . O O'}
{'text': ''}
{'text': 'Peter NNP B-NP B-PER'}
{'text': 'Blackburn NNP I-NP I-PER'}
{'text': ''}
{'text': 'BRUSSELS NNP B-NP B-LOC'}
{'text': '1996-08-22 CD I-NP O'}
{'text': ''}
{'text': 'The DT B-NP O'}
{'text': 'European NNP I-NP B-ORG'}
```
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"![image](https://user-images.githubusercontent.com/49108847/165700611-915b4343-766f-4b81-bdaa-b31950250f06.png)\r\n",
"Thanks for reporting @czq1999.\r\n\r\nNote that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet.\r\n\r\nThat is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming. ",
"Fixed, thanks @albertvillanova !\r\n\r\nhttps://huggingface.co/datasets/xglue\r\n\r\n<img width=\"824\" alt=\"Capture d’écran 2022-04-29 à 10 23 14\" src=\"https://user-images.githubusercontent.com/1676121/165909391-9f98d98a-665a-4e57-822d-8baa2dc9b7c9.png\">\r\n"
] | 1,651,131,050,000 | 1,651,220,608,000 | 1,651,162,083,000 | NONE | null | null | null | It seems that something wrong with the data previvew of XGLUE | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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} | This PR implements support to load a dataset (w/o script) containing TSV files by passing only the dataset name (no need to pass `sep='\t'`):
```python
ds = load_dataset("dataset/name")
```
The refactoring allows for future builder kwargs customizations based on file extension.
Related to #4238. | {
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