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"Thanks for your message 😊 \r\nIndeed users shouldn't have to install those dependencies",
"Got it, feel free to close this issue when you think it’s resolved.",
"It should be good now :)"
] | 2020-06-03T09:33:20 | 2020-06-03T15:25:41 | 2020-06-03T15:25:41 | CONTRIBUTOR | null | null | null | null | Hi, first thanks to @lhoestq 's revolutionary work, I successfully downloaded processed wikipedia according to the doc. 😍😍😍
But at the first try, it tell me to install `apache_beam` and `mwparserfromhell`, which I thought wouldn't be used according to #204 , it was kind of confusing me at that time.
Maybe we should not force users to install these ? Or we just add them to`nlp`'s dependency ? | {
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https://api.github.com/repos/huggingface/datasets/issues/225 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/225/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/225/comments | https://api.github.com/repos/huggingface/datasets/issues/225/events | https://github.com/huggingface/datasets/issues/225 | 628,083,366 | MDU6SXNzdWU2MjgwODMzNjY= | 225 | [ROUGE] Different scores with `files2rouge` | {
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"@Colanim unfortunately there are different implementations of the ROUGE metric floating around online which yield different results, and we had to chose one for the package :) We ended up including the one from the google-research repository, which does minimal post-processing before computing the P/R/F scores. If... | 2020-06-01T00:50:36 | 2020-06-03T15:27:18 | 2020-06-03T15:27:18 | NONE | null | null | null | null | It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.
Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rouge1 0.33508031962733364
rouge2 0.14574333776191592
rougeL 0.2321187823256159
`files2rouge` :
>Running ROUGE...
===========================
1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)
1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)
1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)
===========================
1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)
1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)
1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)
===========================
1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)
1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)
1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)
---
When using longer predictions/gold, the difference is bigger.
**How can I reproduce same score as `files2rouge` ?**
@lhoestq
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"Is there any update on this? \r\n\r\nThanks!",
"Hitting this error when using bleurt with PyTorch ...\r\n\r\n```\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n... and I'm assuming because it was built for TF specifically. Is there a way to use this metric in PyTorch?",
"We currently provid... | 2020-05-30T18:30:40 | 2023-08-26T17:38:48 | 2021-01-04T09:53:32 | NONE | null | null | null | null | Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).
I had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried https://colab.research.google.com/drive/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!
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"Hi @lbourdois, yes please share it with us",
"@mariamabarham \r\nI put all the datasets on this drive: https://1drv.ms/u/s!Ao2Rcpiny7RFinDypq7w-LbXcsx9?e=iVsEDh\r\n\r\n\r\nSome information : \r\n• For FLUE, the quote used is\r\n\r\n> @misc{le2019flaubert,\r\n> title={FlauBERT: Unsupervised Language Model Pre... | 2020-05-30T08:52:15 | 2020-12-03T13:39:33 | 2020-12-03T13:39:33 | NONE | null | null | null | null | Hi,
I think it would be interesting to add the FLUE dataset for francophones or anyone wishing to work on French.
In other requests, I read that you are already working on some datasets, and I was wondering if FLUE was planned.
If it is not the case, I can provide each of the cleaned FLUE datasets (in the form of a directly exploitable dataset rather than in the original xml formats which require additional processing, with the French part for cases where the dataset is based on a multilingual dataframe, etc.). | {
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https://api.github.com/repos/huggingface/datasets/issues/222 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/222/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/222/comments | https://api.github.com/repos/huggingface/datasets/issues/222/events | https://github.com/huggingface/datasets/issues/222 | 627,586,690 | MDU6SXNzdWU2Mjc1ODY2OTA= | 222 | Colab Notebook breaks when downloading the squad dataset | {
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"The notebook forces version 0.1.0. If I use the latest, things work, I'll run the whole notebook and create a PR.\r\n\r\nBut in the meantime, this issue gets fixed by changing:\r\n`!pip install nlp==0.1.0`\r\nto\r\n`!pip install nlp`",
"It still breaks very near the end\r\n\r\n
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https://api.github.com/repos/huggingface/datasets/issues/217 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/217/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/217/comments | https://api.github.com/repos/huggingface/datasets/issues/217/events | https://github.com/huggingface/datasets/issues/217 | 627,128,403 | MDU6SXNzdWU2MjcxMjg0MDM= | 217 | Multi-task dataset mixing | {
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"I like this feature! I think the first question we should decide on is how to convert all datasets into the same format. In T5, the authors decided to format every dataset into a text-to-text format. If the dataset had \"multiple\" inputs like MNLI, the inputs were concatenated. So in MNLI the input:\r\n\r\n> - **... | 2020-05-29T09:22:26 | 2025-09-24T08:59:38 | null | CONTRIBUTOR | null | null | null | null | It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).
The [T5 paper](https://arxiv.org/pdf/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:
- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size
- **Equal mixing** - sample uniformly from each task
- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.
Following this discussion https://github.com/huggingface/transformers/issues/4340 in [transformers](https://github.com/huggingface/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.
Some method for combining datasets could be implemented ,e.g.
```
dataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)
```
We would need a few additions:
- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?
- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.
It would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn.
I'm willing to write bits/most of this I just need some guidance on the interface and other library details so I can integrate it properly.
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https://api.github.com/repos/huggingface/datasets/issues/216 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/216/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/216/comments | https://api.github.com/repos/huggingface/datasets/issues/216/events | https://github.com/huggingface/datasets/issues/216 | 626,896,890 | MDU6SXNzdWU2MjY4OTY4OTA= | 216 | ❓ How to get ROUGE-2 with the ROUGE metric ? | {
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"ROUGE-1 and ROUGE-L shouldn't return the same thing. This is weird",
"For the rouge2 metric you can do\r\n\r\n```python\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n for lp, lg in zip(p, g):\r\n rouge.add(lp, lg)\r\nscore = rouge.compute(rouge_types=[\... | 2020-05-28T23:47:32 | 2020-06-01T00:04:35 | 2020-06-01T00:04:35 | NONE | null | null | null | null | I'm trying to use ROUGE metric, but I don't know how to get the ROUGE-2 metric.
---
I compute scores with :
```python
import nlp
rouge = nlp.load_metric('rouge')
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
rouge.add([lp], [lg])
score = rouge.compute()
```
then : _(print only the F-score for readability)_
```python
for k, s in score.items():
print(k, s.mid.fmeasure)
```
It gives :
>rouge1 0.7915168355671788
rougeL 0.7915168355671788
---
**How can I get the ROUGE-2 score ?**
Also, it's seems weird that ROUGE-1 and ROUGE-L scores are the same. Did I made a mistake ?
@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/215 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/215/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/215/comments | https://api.github.com/repos/huggingface/datasets/issues/215/events | https://github.com/huggingface/datasets/issues/215 | 626,867,879 | MDU6SXNzdWU2MjY4Njc4Nzk= | 215 | NonMatchingSplitsSizesError when loading blog_authorship_corpus | {
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"I just ran it on colab and got this\r\n```\r\n[{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train',\r\nnum_bytes=611607465, num_examples=533285, dataset_name='blog_authorship_corpus')},\r\n{'expected': SplitInf... | 2020-05-28T22:55:19 | 2025-01-04T00:03:12 | 2022-02-10T13:05:45 | NONE | null | null | null | null | Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`.
```
raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train',
num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'),
'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323,
dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation',
num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'),
'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766,
dataset_name='blog_authorship_corpus')}]
```
Upon checking it seems like there is a disparity between the information in `datasets/blog_authorship_corpus/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. | {
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https://api.github.com/repos/huggingface/datasets/issues/211 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/211/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/211/comments | https://api.github.com/repos/huggingface/datasets/issues/211/events | https://github.com/huggingface/datasets/issues/211 | 626,565,994 | MDU6SXNzdWU2MjY1NjU5OTQ= | 211 | [Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type | {
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"Here the full error trace:\r\n\r\n```\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-1-7aaf3f011358> in <module>\r\n 1 import nlp\r\n 2 ds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\") # this might take 2.3 min to download but it's... | 2020-05-28T14:38:14 | 2020-07-23T10:15:16 | 2020-07-23T10:15:16 | CONTRIBUTOR | null | null | null | null | Running the following code
```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, load_from_cache_file=False)
```
triggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.
On the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:
```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, remove_columns=["entity_pages"], load_from_cache_file=False)
```
. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?
**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly. | {
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https://api.github.com/repos/huggingface/datasets/issues/207 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/207/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/207/comments | https://api.github.com/repos/huggingface/datasets/issues/207/events | https://github.com/huggingface/datasets/issues/207 | 625,932,200 | MDU6SXNzdWU2MjU5MzIyMDA= | 207 | Remove test set from NLP viewer | {
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"~is the viewer also open source?~\r\n[is a streamlit app!](https://docs.streamlit.io/en/latest/getting_started.html)",
"Appears that [two thirds of those polled on Twitter](https://twitter.com/srush_nlp/status/1265734497632477185) are in favor of _some_ mechanism for averting eyeballs from the test data.",
"We... | 2020-05-27T18:32:07 | 2022-02-10T13:17:45 | 2022-02-10T13:17:45 | NONE | null | null | null | null | While the new [NLP viewer](https://huggingface.co/nlp/viewer/) is a great tool, I think it would be best to outright remove the option of looking at the test sets. At the very least, a warning should be displayed to users before showing the test set. Newcomers to the field might not be aware of best practices, and small things like this can help increase awareness. | {
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https://api.github.com/repos/huggingface/datasets/issues/206 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/206/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/206/comments | https://api.github.com/repos/huggingface/datasets/issues/206/events | https://github.com/huggingface/datasets/issues/206 | 625,842,989 | MDU6SXNzdWU2MjU4NDI5ODk= | 206 | [Question] Combine 2 datasets which have the same columns | {
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"We are thinking about ways to combine datasets for T5 in #217, feel free to share your thoughts about this.",
"Ok great! I will look at it. Thanks"
] | 2020-05-27T16:25:52 | 2020-06-10T09:11:14 | 2020-06-10T09:11:14 | CONTRIBUTOR | null | null | null | null | Hi,
I am using ``nlp`` to load personal datasets. I created summarization datasets in multi-languages based on wikinews. I have one dataset for english and one for german (french is getting to be ready as well). I want to keep these datasets independent because they need different pre-processing (add different task-specific prefixes for T5 : *summarize:* for english and *zusammenfassen:* for german)
My issue is that I want to train T5 on the combined english and german datasets to see if it improves results. So I would like to combine 2 datasets (which have the same columns) to make one and train T5 on it. I was wondering if there is a proper way to do it? I assume that it can be done by combining all examples of each dataset but maybe you have a better solution.
Hoping this is clear enough,
Thanks a lot 😊
Best | {
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https://api.github.com/repos/huggingface/datasets/issues/202 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/202/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/202/comments | https://api.github.com/repos/huggingface/datasets/issues/202/events | https://github.com/huggingface/datasets/issues/202 | 625,493,983 | MDU6SXNzdWU2MjU0OTM5ODM= | 202 | Mistaken `_KWARGS_DESCRIPTION` for XNLI metric | {
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"Indeed, good catch ! thanks\r\nFixing it right now"
] | 2020-05-27T08:34:42 | 2020-05-28T13:22:36 | 2020-05-28T13:22:36 | NONE | null | null | null | null | Hi!
The [`_KWARGS_DESCRIPTION`](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/xnli/xnli.py#L45) for the XNLI metric uses `Args` and `Returns` text from [BLEU](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/bleu/bleu.py#L58) metric:
```
_KWARGS_DESCRIPTION = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: list of translations to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
'bleu': bleu score,
'precisions': geometric mean of n-gram precisions,
'brevity_penalty': brevity penalty,
'length_ratio': ratio of lengths,
'translation_length': translation_length,
'reference_length': reference_length
"""
```
But it should be something like:
```
_KWARGS_DESCRIPTION = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
'accuracy': accuracy
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https://api.github.com/repos/huggingface/datasets/issues/198 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/198/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/198/comments | https://api.github.com/repos/huggingface/datasets/issues/198/events | https://github.com/huggingface/datasets/issues/198 | 625,200,627 | MDU6SXNzdWU2MjUyMDA2Mjc= | 198 | Index outside of table length | {
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"Sounds like something related to the nlp viewer @srush ",
"Fixed. "
] | 2020-05-26T21:09:40 | 2020-05-26T22:43:49 | 2020-05-26T22:43:49 | NONE | null | null | null | null | The offset input box warns of numbers larger than a limit (like 2000) but then the errors start at a smaller value than that limit (like 1955).
> ValueError: Index (2000) outside of table length (2000).
> Traceback:
> File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
> exec(code, module.__dict__)
> File "/home/sasha/nlp_viewer/run.py", line 116, in <module>
> v = d[item][k]
> File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__
> output_all_columns=self._output_all_columns,
> File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 290, in _getitem
> raise ValueError(f"Index ({key}) outside of table length ({self._data.num_rows}).") | {
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"Hi so there are indeed two configurations in the datasets as you can see [here](https://github.com/huggingface/nlp/blob/master/datasets/scientific_papers/scientific_papers.py#L81-L82).\r\n\r\nYou can load either one with:\r\n```python\r\ndataset = nlp.load_dataset('scientific_papers', 'pubmed')\r\ndataset = nlp.lo... | 2020-05-26T15:18:47 | 2020-05-28T08:19:28 | 2020-05-28T08:19:28 | NONE | null | null | null | null | Hi!
I have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following:
```
dataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.05k/5.05k [00:00<00:00, 2.66MB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.90k/4.90k [00:00<00:00, 2.42MB/s]
Downloading and preparing dataset scientific_papers/pubmed (download: 4.20 GiB, generated: 2.33 GiB, total: 6.53 GiB) to ./scientific_papers/pubmed/1.1.1...
Downloading: 3.62GB [00:40, 90.5MB/s]
Downloading: 880MB [00:08, 101MB/s]
Dataset scientific_papers downloaded and prepared to ./scientific_papers/pubmed/1.1.1. Subsequent calls will reuse this data.
```
only a pubmed folder is created. There doesn't seem to be something for arxiv. Are these two datasets merged? Or have I misunderstood something?
Thanks! | {
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"I guess we can use `tf.data.Dataset.from_generator` instead. I'll give it a try.",
"Is `tf.data.Dataset.from_generator` working on TPU ?",
"`from_generator` is not working on TPU, I met the following error :\r\n\r\n```\r\nFile \"/usr/local/lib/python3.6/contextlib.py\", line 88, in __exit__\r\n next(self.ge... | 2020-05-26T07:19:14 | 2020-10-27T15:28:11 | 2020-10-27T15:28:11 | NONE | null | null | null | null | In the example notebook, the TF Dataset is built using `from_tensor_slices()` :
```python
columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']
train_tf_dataset.set_format(type='tensorflow', columns=columns)
features = {x: train_tf_dataset[x] for x in columns[:3]}
labels = {"output_1": train_tf_dataset["start_positions"]}
labels["output_2"] = train_tf_dataset["end_positions"]
tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
```
But according to [official tensorflow documentation](https://www.tensorflow.org/guide/data#consuming_numpy_arrays), this will load the entire dataset to memory.
**This defeats one purpose of this library, which is lazy loading.**
Is there any other way to load the `nlp` dataset into TF dataset lazily ?
---
For example, is it possible to use [Arrow dataset](https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowDataset) ? If yes, is there any code example ? | {
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https://api.github.com/repos/huggingface/datasets/issues/192 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/192/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/192/comments | https://api.github.com/repos/huggingface/datasets/issues/192/events | https://github.com/huggingface/datasets/issues/192 | 624,397,592 | MDU6SXNzdWU2MjQzOTc1OTI= | 192 | [Question] Create Apache Arrow dataset from raw text file | {
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"We store every dataset in the Arrow format. This is convenient as it supports nested types and memory mapping. If you are curious feel free to check the [pyarrow documentation](https://arrow.apache.org/docs/python/)\r\n\r\nYou can use this library to load your covid papers by creating a dataset script. You can fin... | 2020-05-25T16:42:47 | 2021-12-18T01:45:34 | 2020-10-27T15:20:22 | CONTRIBUTOR | null | null | null | null | Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as "Crime and punishment" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?
Is the worth of send it to you and add it to the NLP library?
Thanks, Manu
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https://api.github.com/repos/huggingface/datasets/issues/189 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/189/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/189/comments | https://api.github.com/repos/huggingface/datasets/issues/189/events | https://github.com/huggingface/datasets/issues/189 | 624,048,881 | MDU6SXNzdWU2MjQwNDg4ODE= | 189 | [Question] BERT-style multiple choice formatting | {
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"Hi @sarahwie, can you details this a little more?\r\n\r\nI'm not sure I understand what you refer to and what you mean when you say \"Previously, this was done by passing a list of InputFeatures to the dataloader instead of a list of InputFeature\"",
"I think I've resolved it. For others' reference: to convert f... | 2020-05-25T05:11:05 | 2020-05-25T18:38:28 | 2020-05-25T18:38:28 | NONE | null | null | null | null | Hello, I am wondering what the equivalent formatting of a dataset should be to allow for multiple-choice answering prediction, BERT-style. Previously, this was done by passing a list of `InputFeatures` to the dataloader instead of a list of `InputFeature`, where `InputFeatures` contained lists of length equal to the number of answer choices in the MCQ instead of single items. I'm a bit confused on what the output of my feature conversion function should be when using `dataset.map()` to ensure similar behavior.
Thanks! | {
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"On a similar note it would be nice to differentiate between train-easy, train-medium, and train-hard",
"Hi @tylerroost, we don't have a timeline for this at the moment.\r\nIf you want to give it a look we would be happy to review a PR on it.\r\nAlso, the library is one week old so everything is quite barebones, ... | 2020-05-24T15:46:52 | 2020-05-24T18:53:48 | 2020-05-24T18:53:48 | NONE | null | null | null | null | Currently only the algebra_linear_1d is supported. Is there a timeline for making the other modules supported. If no timeline is established, how can I help? | {
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https://api.github.com/repos/huggingface/datasets/issues/187 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/187/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/187/comments | https://api.github.com/repos/huggingface/datasets/issues/187/events | https://github.com/huggingface/datasets/issues/187 | 623,627,800 | MDU6SXNzdWU2MjM2Mjc4MDA= | 187 | [Question] How to load wikipedia ? Beam runner ? | {
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"I have seen that somebody is hard working on easierly loadable wikipedia. #129 \r\nMaybe I should wait a few days for that version ?",
"Yes we (well @lhoestq) are very actively working on this."
] | 2020-05-23T10:18:52 | 2020-05-25T00:12:02 | 2020-05-25T00:12:02 | CONTRIBUTOR | null | null | null | null | When `nlp.load_dataset('wikipedia')`, I got
* `WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.`
* `AttributeError: 'NoneType' object has no attribute 'size'`
Could somebody tell me what should I do ?
# Env
On Colab,
```
git clone https://github.com/huggingface/nlp
cd nlp
pip install -q .
```
```
%pip install -q apache_beam mwparserfromhell
-> ERROR: pydrive 1.3.1 has requirement oauth2client>=4.0.0, but you'll have oauth2client 3.0.0 which is incompatible.
ERROR: google-api-python-client 1.7.12 has requirement httplib2<1dev,>=0.17.0, but you'll have httplib2 0.12.0 which is incompatible.
ERROR: chainer 6.5.0 has requirement typing-extensions<=3.6.6, but you'll have typing-extensions 3.7.4.2 which is incompatible.
```
```
pip install -q apache-beam[interactive]
ERROR: google-colab 1.0.0 has requirement ipython~=5.5.0, but you'll have ipython 5.10.0 which is incompatible.
```
# The whole message
```
WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.
Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wikipedia/20200501.aa/1.0.0...
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
44 frames
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
/usr/local/lib/python3.6/dist-packages/apache_beam/io/iobase.py in process(self, element, init_result)
1081 writer.write(e)
-> 1082 return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]
1083
/usr/local/lib/python3.6/dist-packages/apache_beam/io/filebasedsink.py in close(self)
422 def close(self):
--> 423 self.sink.close(self.temp_handle)
424 return self.temp_shard_path
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in close(self, writer)
537 if len(self._buffer[0]) > 0:
--> 538 self._flush_buffer()
539 if self._record_batches_byte_size > 0:
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in _flush_buffer(self)
569 for b in x.buffers():
--> 570 size = size + b.size
571 self._record_batches_byte_size = self._record_batches_byte_size + size
AttributeError: 'NoneType' object has no attribute 'size'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-9-340aabccefff> in <module>()
----> 1 dset = nlp.load_dataset('wikipedia')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)
370 verify_infos = not save_infos and not ignore_verifications
371 self._download_and_prepare(
--> 372 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
373 )
374 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
770 with beam.Pipeline(runner=beam_runner, options=beam_options,) as pipeline:
771 super(BeamBasedBuilder, self)._download_and_prepare(
--> 772 dl_manager, pipeline=pipeline, verify_infos=False
773 ) # TODO{beam} verify infos
774
/usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in __exit__(self, exc_type, exc_val, exc_tb)
501 def __exit__(self, exc_type, exc_val, exc_tb):
502 if not exc_type:
--> 503 self.run().wait_until_finish()
504
505 def visit(self, visitor):
/usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in run(self, test_runner_api)
481 return Pipeline.from_runner_api(
482 self.to_runner_api(use_fake_coders=True), self.runner,
--> 483 self._options).run(False)
484
485 if self._options.view_as(TypeOptions).runtime_type_check:
/usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in run(self, test_runner_api)
494 finally:
495 shutil.rmtree(tmpdir)
--> 496 return self.runner.run_pipeline(self, self._options)
497
498 def __enter__(self):
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/direct/direct_runner.py in run_pipeline(self, pipeline, options)
128 runner = BundleBasedDirectRunner()
129
--> 130 return runner.run_pipeline(pipeline, options)
131
132
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_pipeline(self, pipeline, options)
553
554 self._latest_run_result = self.run_via_runner_api(
--> 555 pipeline.to_runner_api(default_environment=self._default_environment))
556 return self._latest_run_result
557
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_via_runner_api(self, pipeline_proto)
563 # TODO(pabloem, BEAM-7514): Create a watermark manager (that has access to
564 # the teststream (if any), and all the stages).
--> 565 return self.run_stages(stage_context, stages)
566
567 @contextlib.contextmanager
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_stages(self, stage_context, stages)
704 stage,
705 pcoll_buffers,
--> 706 stage_context.safe_coders)
707 metrics_by_stage[stage.name] = stage_results.process_bundle.metrics
708 monitoring_infos_by_stage[stage.name] = (
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in _run_stage(self, worker_handler_factory, pipeline_components, stage, pcoll_buffers, safe_coders)
1071 cache_token_generator=cache_token_generator)
1072
-> 1073 result, splits = bundle_manager.process_bundle(data_input, data_output)
1074
1075 def input_for(transform_id, input_id):
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)
2332
2333 with UnboundedThreadPoolExecutor() as executor:
-> 2334 for result, split_result in executor.map(execute, part_inputs):
2335
2336 split_result_list += split_result
/usr/lib/python3.6/concurrent/futures/_base.py in result_iterator()
584 # Careful not to keep a reference to the popped future
585 if timeout is None:
--> 586 yield fs.pop().result()
587 else:
588 yield fs.pop().result(end_time - time.monotonic())
/usr/lib/python3.6/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
/usr/lib/python3.6/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
/usr/local/lib/python3.6/dist-packages/apache_beam/utils/thread_pool_executor.py in run(self)
42 # If the future wasn't cancelled, then attempt to execute it.
43 try:
---> 44 self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))
45 except BaseException as exc:
46 # Even though Python 2 futures library has #set_exection(),
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in execute(part_map)
2329 self._registered,
2330 cache_token_generator=self._cache_token_generator)
-> 2331 return bundle_manager.process_bundle(part_map, expected_outputs)
2332
2333 with UnboundedThreadPoolExecutor() as executor:
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)
2243 process_bundle_descriptor_id=self._bundle_descriptor.id,
2244 cache_tokens=[next(self._cache_token_generator)]))
-> 2245 result_future = self._worker_handler.control_conn.push(process_bundle_req)
2246
2247 split_results = [] # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse]
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in push(self, request)
1557 self._uid_counter += 1
1558 request.instruction_id = 'control_%s' % self._uid_counter
-> 1559 response = self.worker.do_instruction(request)
1560 return ControlFuture(request.instruction_id, response)
1561
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/sdk_worker.py in do_instruction(self, request)
413 # E.g. if register is set, this will call self.register(request.register))
414 return getattr(self, request_type)(
--> 415 getattr(request, request_type), request.instruction_id)
416 else:
417 raise NotImplementedError
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/sdk_worker.py in process_bundle(self, request, instruction_id)
448 with self.maybe_profile(instruction_id):
449 delayed_applications, requests_finalization = (
--> 450 bundle_processor.process_bundle(instruction_id))
451 monitoring_infos = bundle_processor.monitoring_infos()
452 monitoring_infos.extend(self.state_cache_metrics_fn())
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/bundle_processor.py in process_bundle(self, instruction_id)
837 for data in data_channel.input_elements(instruction_id,
838 expected_transforms):
--> 839 input_op_by_transform_id[data.transform_id].process_encoded(data.data)
840
841 # Finish all operations.
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/bundle_processor.py in process_encoded(self, encoded_windowed_values)
214 decoded_value = self.windowed_coder_impl.decode_from_stream(
215 input_stream, True)
--> 216 self.output(decoded_value)
217
218 def try_split(self, fraction_of_remainder, total_buffer_size):
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
/usr/local/lib/python3.6/dist-packages/future/utils/__init__.py in raise_with_traceback(exc, traceback)
417 if traceback == Ellipsis:
418 _, _, traceback = sys.exc_info()
--> 419 raise exc.with_traceback(traceback)
420
421 else:
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
/usr/local/lib/python3.6/dist-packages/apache_beam/io/iobase.py in process(self, element, init_result)
1080 for e in bundle[1]: # values
1081 writer.write(e)
-> 1082 return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]
1083
1084
/usr/local/lib/python3.6/dist-packages/apache_beam/io/filebasedsink.py in close(self)
421
422 def close(self):
--> 423 self.sink.close(self.temp_handle)
424 return self.temp_shard_path
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in close(self, writer)
536 def close(self, writer):
537 if len(self._buffer[0]) > 0:
--> 538 self._flush_buffer()
539 if self._record_batches_byte_size > 0:
540 self._write_batches(writer)
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in _flush_buffer(self)
568 for x in arrays:
569 for b in x.buffers():
--> 570 size = size + b.size
571 self._record_batches_byte_size = self._record_batches_byte_size + size
AttributeError: 'NoneType' object has no attribute 'size' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/186 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/186/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/186/comments | https://api.github.com/repos/huggingface/datasets/issues/186/events | https://github.com/huggingface/datasets/issues/186 | 623,595,180 | MDU6SXNzdWU2MjM1OTUxODA= | 186 | Weird-ish: Not creating unique caches for different phases | {
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"Looks like a duplicate of #120.\r\nThis is already fixed on master. We'll do a new release on pypi soon",
"Good catch, it looks fixed.\r\n"
] | 2020-05-23T06:40:58 | 2020-05-23T20:22:18 | 2020-05-23T20:22:17 | NONE | null | null | null | null | Sample code:
```python
import nlp
dataset = nlp.load_dataset('boolq')
def func1(x):
return x
def func2(x):
return None
train_output = dataset["train"].map(func1)
valid_output = dataset["validation"].map(func1)
print()
print(len(train_output), len(valid_output))
# Output: 9427 9427
```
The map method in both cases seem to be pointing to the same cache, so the latter call based on the validation data will return the processed train data cache.
What's weird is that the following doesn't seem to be an issue:
```python
train_output = dataset["train"].map(func2)
valid_output = dataset["validation"].map(func2)
print()
print(len(train_output), len(valid_output))
# 9427 3270
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/183 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/183/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/183/comments | https://api.github.com/repos/huggingface/datasets/issues/183/events | https://github.com/huggingface/datasets/issues/183 | 623,054,270 | MDU6SXNzdWU2MjMwNTQyNzA= | 183 | [Bug] labels of glue/ax are all -1 | {
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"This is the test set given by the Glue benchmark. The labels are not provided, and therefore set to -1.",
"Ah, yeah. Why it didn’t occur to me. 😂\nThank you for your comment."
] | 2020-05-22T08:43:36 | 2020-05-22T22:14:05 | 2020-05-22T22:14:05 | CONTRIBUTOR | null | null | null | null | ```
ax = nlp.load_dataset('glue', 'ax')
for i in range(30): print(ax['test'][i]['label'], end=', ')
```
```
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/181/comments | https://api.github.com/repos/huggingface/datasets/issues/181/events | https://github.com/huggingface/datasets/issues/181 | 622,634,420 | MDU6SXNzdWU2MjI2MzQ0MjA= | 181 | Cannot upload my own dataset | {
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"It's my misunderstanding. I cannot just upload a csv. I need to write a dataset loading script too.",
"I now try with the sample `datasets/csv` folder. \r\n\r\n nlp-cli upload csv\r\n\r\nThe error is still the same\r\n\r\n```\r\n2020-05-21 17:20:56.394659: I tensorflow/stream_executor/platform/default/dso_loa... | 2020-05-21T16:45:52 | 2020-06-18T22:14:42 | 2020-06-18T22:14:42 | NONE | null | null | null | null | I look into `nlp-cli` and `user.py` to learn how to upload my own data.
It is supposed to work like this
- Register to get username, password at huggingface.co
- `nlp-cli login` and type username, passworld
- I have a single file to upload at `./ttc/ttc_freq_extra.csv`
- `nlp-cli upload ttc/ttc_freq_extra.csv`
But I got this error.
```
2020-05-21 16:33:52.722464: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
About to upload file /content/ttc/ttc_freq_extra.csv to S3 under filename ttc/ttc_freq_extra.csv and namespace korakot
Proceed? [Y/n] y
Uploading... This might take a while if files are large
Traceback (most recent call last):
File "/usr/local/bin/nlp-cli", line 33, in <module>
service.run()
File "/usr/local/lib/python3.6/dist-packages/nlp/commands/user.py", line 234, in run
token=token, filename=filename, filepath=filepath, organization=self.args.organization
File "/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py", line 141, in presign_and_upload
urls = self.presign(token, filename=filename, organization=organization)
File "/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py", line 132, in presign
return PresignedUrl(**d)
TypeError: __init__() got an unexpected keyword argument 'cdn'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/179 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/179/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/179/comments | https://api.github.com/repos/huggingface/datasets/issues/179/events | https://github.com/huggingface/datasets/issues/179 | 622,525,410 | MDU6SXNzdWU2MjI1MjU0MTA= | 179 | [Feature request] separate split name and split instructions | {
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"If your dataset is a collection of sub-datasets, you should probably consider having one config per sub-dataset. For example for Glue, we have sst2, mnli etc.\r\nIf you want to have multiple train sets (for example one per stage). The easiest solution would be to name them `nlp.Split(\"train_stage1\")`, `nlp.Split... | 2020-05-21T14:10:51 | 2020-05-22T13:31:08 | 2020-05-22T13:31:07 | MEMBER | null | null | null | null | Currently, the name of an nlp.NamedSplit is parsed in arrow_reader.py and used as the instruction.
This makes it impossible to have several training sets, which can occur when:
- A dataset corresponds to a collection of sub-datasets
- A dataset was built in stages, adding new examples at each stage
Would it be possible to have two separate fields in the Split class, a name /instruction and a unique ID that is used as the key in the builder's split_dict ? | {
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https://api.github.com/repos/huggingface/datasets/issues/175 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/175/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/175/comments | https://api.github.com/repos/huggingface/datasets/issues/175/events | https://github.com/huggingface/datasets/issues/175 | 621,929,428 | MDU6SXNzdWU2MjE5Mjk0Mjg= | 175 | [Manual data dir] Error message: nlp.load_dataset('xsum') -> TypeError | {
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} | [] | closed | false | null | [] | [] | 2020-05-20T17:00:32 | 2020-05-20T18:18:50 | 2020-05-20T18:18:50 | CONTRIBUTOR | null | null | null | null | v 0.1.0 from pip
```python
import nlp
xsum = nlp.load_dataset('xsum')
```
Issue is `dl_manager.manual_dir`is `None`
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-42-8a32f066f3bd> in <module>
----> 1 xsum = nlp.load_dataset('xsum')
~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
515 download_mode=download_mode,
516 ignore_verifications=ignore_verifications,
--> 517 save_infos=save_infos,
518 )
519
~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)
361 verify_infos = not save_infos and not ignore_verifications
362 self._download_and_prepare(
--> 363 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
364 )
365 # Sync info
~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
397 split_dict = SplitDict(dataset_name=self.name)
398 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 399 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
400 # Checksums verification
401 if verify_infos:
~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/datasets/xsum/5c5fca23aaaa469b7a1c6f095cf12f90d7ab99bcc0d86f689a74fd62634a1472/xsum.py in _split_generators(self, dl_manager)
102 with open(dl_path, "r") as json_file:
103 split_ids = json.load(json_file)
--> 104 downloaded_path = os.path.join(dl_manager.manual_dir, "xsum-extracts-from-downloads")
105 return [
106 nlp.SplitGenerator(
~/miniconda3/envs/nb/lib/python3.7/posixpath.py in join(a, *p)
78 will be discarded. An empty last part will result in a path that
79 ends with a separator."""
---> 80 a = os.fspath(a)
81 sep = _get_sep(a)
82 path = a
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
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https://api.github.com/repos/huggingface/datasets/issues/174 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/174/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/174/comments | https://api.github.com/repos/huggingface/datasets/issues/174/events | https://github.com/huggingface/datasets/issues/174 | 621,928,403 | MDU6SXNzdWU2MjE5Mjg0MDM= | 174 | nlp.load_dataset('xsum') -> TypeError | {
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https://api.github.com/repos/huggingface/datasets/issues/172 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/172/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/172/comments | https://api.github.com/repos/huggingface/datasets/issues/172/events | https://github.com/huggingface/datasets/issues/172 | 621,377,386 | MDU6SXNzdWU2MjEzNzczODY= | 172 | Clone not working on Windows environment | {
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"Should be fixed on master now :)",
"Thanks @lhoestq 👍 Now I can uninstall WSL and get back to work with windows.🙂"
] | 2020-05-20T00:45:14 | 2020-05-23T12:49:13 | 2020-05-23T11:27:52 | CONTRIBUTOR | null | null | null | null | Cloning in a windows environment is not working because of use of special character '?' in folder name ..
Please consider changing the folder name ....
Reference to folder -
nlp/datasets/cnn_dailymail/dummy/3.0.0/3.0.0/dummy_data-zip-extracted/dummy_data/uc?export=download&id=0BwmD_VLjROrfM1BxdkxVaTY2bWs/dailymail/stories/
error log:
fatal: cannot create directory at 'datasets/cnn_dailymail/dummy/3.0.0/3.0.0/dummy_data-zip-extracted/dummy_data/uc?export=download&id=0BwmD_VLjROrfM1BxdkxVaTY2bWs': Invalid argument
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https://api.github.com/repos/huggingface/datasets/issues/168 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/168/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/168/comments | https://api.github.com/repos/huggingface/datasets/issues/168/events | https://github.com/huggingface/datasets/issues/168 | 620,959,819 | MDU6SXNzdWU2MjA5NTk4MTk= | 168 | Loading 'wikitext' dataset fails | {
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"Hi, make sure you have a recent version of pyarrow.\r\n\r\nAre you using it in Google Colab? In this case, this error is probably the same as #128",
"Thanks!\r\n\r\nYes I'm using Google Colab, it seems like a duplicate then.",
"Closing as it is a duplicate",
"Hi,\r\nThe squad bug seems to be fixed, but the l... | 2020-05-19T13:04:29 | 2020-05-26T21:46:52 | 2020-05-26T21:46:52 | NONE | null | null | null | null | Loading the 'wikitext' dataset fails with Attribute error:
Code to reproduce (From example notebook):
import nlp
wikitext_dataset = nlp.load_dataset('wikitext')
Error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-17-d5d9df94b13c> in <module>()
11
12 # Load a dataset and print the first examples in the training set
---> 13 wikitext_dataset = nlp.load_dataset('wikitext')
14 print(wikitext_dataset['train'][0])
6 frames
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)
363 verify_infos = not save_infos and not ignore_verifications
364 self._download_and_prepare(
--> 365 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
366 )
367 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
416 try:
417 # Prepare split will record examples associated to the split
--> 418 self._prepare_split(split_generator, **prepare_split_kwargs)
419 except OSError:
420 raise OSError("Cannot find data file. " + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or ""))
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _prepare_split(self, split_generator)
594 example = self.info.features.encode_example(record)
595 writer.write(example)
--> 596 num_examples, num_bytes = writer.finalize()
597
598 assert num_examples == num_examples, f"Expected to write {split_info.num_examples} but wrote {num_examples}"
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in finalize(self, close_stream)
173 def finalize(self, close_stream=True):
174 if self.pa_writer is not None:
--> 175 self.write_on_file()
176 self.pa_writer.close()
177 if close_stream:
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_on_file(self)
124 else:
125 # All good
--> 126 self._write_array_on_file(pa_array)
127 self.current_rows = []
128
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in _write_array_on_file(self, pa_array)
93 def _write_array_on_file(self, pa_array):
94 """Write a PyArrow Array"""
---> 95 pa_batch = pa.RecordBatch.from_struct_array(pa_array)
96 self._num_bytes += pa_array.nbytes
97 self.pa_writer.write_batch(pa_batch)
AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array' | {
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https://api.github.com/repos/huggingface/datasets/issues/166 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/166/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/166/comments | https://api.github.com/repos/huggingface/datasets/issues/166/events | https://github.com/huggingface/datasets/issues/166 | 620,850,218 | MDU6SXNzdWU2MjA4NTAyMTg= | 166 | Add a method to shuffle a dataset | {
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"+1 for the naming convention\r\n\r\nAbout the `shuffle` method, from my understanding it should be done in `Dataloader` (better separation between dataset processing - usage)",
"+1 for shuffle in `Dataloader`. \r\nSome `Dataloader` just store idxs of dataset and just shuffle those idxs, which might(?) be faster ... | 2020-05-19T10:08:46 | 2020-06-23T15:07:33 | 2020-06-23T15:07:32 | MEMBER | null | null | null | null | Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.
Also, we could maybe have a clear indication of which method modify in-place and which methods return/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think? | {
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https://api.github.com/repos/huggingface/datasets/issues/165 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/165/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/165/comments | https://api.github.com/repos/huggingface/datasets/issues/165/events | https://github.com/huggingface/datasets/issues/165 | 620,758,221 | MDU6SXNzdWU2MjA3NTgyMjE= | 165 | ANLI | {
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For ANLI, use https://github.com/facebookresearch/anli. As that paper says, "Our dataset is not
to be confused with abductive NLI (Bhagavatula et al., 2019), which calls itself αNLI, or ART.".
Indeed, the paper cited under what is currently called anli says in the abstract "We introduce a challenge dataset, ART".
The current naming will confuse people :) | {
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https://api.github.com/repos/huggingface/datasets/issues/164 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/164/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/164/comments | https://api.github.com/repos/huggingface/datasets/issues/164/events | https://github.com/huggingface/datasets/issues/164 | 620,540,250 | MDU6SXNzdWU2MjA1NDAyNTA= | 164 | Add Spanish POR and NER Datasets | {
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"Hello @mrm8488, are these datasets official datasets published in an NLP/CL/ML venue?",
"What about this one: https://github.com/ccasimiro88/TranslateAlignRetrieve?"
] | 2020-05-18T22:18:21 | 2020-05-25T16:28:45 | 2020-05-25T16:28:45 | CONTRIBUTOR | null | null | null | null | Hi guys,
In order to cover multilingual support a little step could be adding standard Datasets used for Spanish NER and POS tasks.
I can provide it in raw and preprocessed formats. | {
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https://api.github.com/repos/huggingface/datasets/issues/163 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/163/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/163/comments | https://api.github.com/repos/huggingface/datasets/issues/163/events | https://github.com/huggingface/datasets/issues/163 | 620,534,307 | MDU6SXNzdWU2MjA1MzQzMDc= | 163 | [Feature request] Add cos-e v1.0 | {
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"Sounds good, @mariamabarham do you want to give a look?\r\nI think we should have two configurations so we can allow either version of the dataset to be loaded with the `1.0` version being the default maybe.\r\n\r\nCc some authors of the great cos-e: @nazneenrajani @bmccann",
"cos_e v1.0 is related to CQA v1.0 b... | 2020-05-18T22:05:26 | 2020-06-16T23:15:25 | 2020-06-16T18:52:06 | NONE | null | null | null | null | I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https://www.aclweb.org/anthology/P19-1487/), and v1.11 has noted [annotation](https://github.com/salesforce/cos-e/issues/2) [issues](https://arxiv.org/pdf/2004.14546.pdf). | {
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https://api.github.com/repos/huggingface/datasets/issues/161 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/161/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/161/comments | https://api.github.com/repos/huggingface/datasets/issues/161/events | https://github.com/huggingface/datasets/issues/161 | 620,487,535 | MDU6SXNzdWU2MjA0ODc1MzU= | 161 | Discussion on version identifier & MockDataLoaderManager for test data | {
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"usually you can replace `download` in your dataset script with `download_and_prepare()` - could you share the code for your dataset here? :-) ",
"I have an initial version here: https://github.com/EntilZha/nlp/tree/master/datasets/qanta Thats pretty close to what I'll do as a PR, but still want to do some more s... | 2020-05-18T20:31:30 | 2020-05-24T18:10:03 | null | CONTRIBUTOR | null | null | null | null | Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp/utils/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/160 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/160/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/160/comments | https://api.github.com/repos/huggingface/datasets/issues/160/events | https://github.com/huggingface/datasets/issues/160 | 620,448,236 | MDU6SXNzdWU2MjA0NDgyMzY= | 160 | caching in map causes same result to be returned for train, validation and test | {
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"Hi @dpressel, \r\n\r\nthanks for posting your issue! Can you maybe add a complete code snippet that we can copy paste to reproduce the error? For example, I'm not sure where the variable `train_set` comes from in your code and it seems like you are loading multiple datasets at once? ",
"Hi, the full example was... | 2020-05-18T19:22:03 | 2020-05-18T21:36:20 | 2020-05-18T21:36:20 | NONE | null | null | null | null | hello,
I am working on a program that uses the `nlp` library with the `SST2` dataset.
The rough outline of the program is:
```
import nlp as nlp_datasets
...
parser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')
...
dataset = nlp_datasets.load_dataset(*args.dataset)
...
# Create feature vocabs
vocabs = create_vocabs(dataset.values(), vectorizers)
...
# Create a function to vectorize based on vectorizers and vocabs:
print('TS', train_set.num_rows)
print('VS', valid_set.num_rows)
print('ES', test_set.num_rows)
# factory method to create a `convert_to_features` function based on vocabs
convert_to_features = create_featurizer(vectorizers, vocabs)
train_set = train_set.map(convert_to_features, batched=True)
train_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)
valid_set = valid_set.map(convert_to_features, batched=True)
valid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)
test_set = test_set.map(convert_to_features, batched=True)
test_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])
test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)
print('TS', train_set.num_rows)
print('VS', valid_set.num_rows)
print('ES', test_set.num_rows)
```
Im not sure if Im using it incorrectly, but the results are not what I expect. Namely, the `.map()` seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:
```
TS 67349
VS 872
ES 1821
TS 67349
VS 67349
ES 67349
```
The behavior changes if I turn off the caching but then the results fail:
```
train_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)
...
valid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)
...
test_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)
```
Now I get the right set of features back...
```
TS 67349
VS 872
ES 1821
100%|██████████| 68/68 [00:00<00:00, 92.78it/s]
100%|██████████| 1/1 [00:00<00:00, 75.47it/s]
0%| | 0/2 [00:00<?, ?it/s]TS 67349
VS 872
ES 1821
100%|██████████| 2/2 [00:00<00:00, 77.19it/s]
```
but I think its losing track of the original training set:
```
Traceback (most recent call last):
File "/home/dpressel/dev/work/baseline/api-examples/layers-classify-hf-datasets.py", line 148, in <module>
for x in train_loader:
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
data = self._next_data()
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__
output_all_columns=self._output_all_columns,
File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 294, in _getitem
outputs = self._unnest(self._data.slice(key, 1).to_pydict())
File "pyarrow/table.pxi", line 1211, in pyarrow.lib.Table.slice
File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table
File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)
Process finished with exit code 1
```
The full-example program (minus the print stmts) is here:
https://github.com/dpressel/mead-baseline/pull/620/files
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https://api.github.com/repos/huggingface/datasets/issues/159 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/159/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/159/comments | https://api.github.com/repos/huggingface/datasets/issues/159/events | https://github.com/huggingface/datasets/issues/159 | 620,420,700 | MDU6SXNzdWU2MjA0MjA3MDA= | 159 | How can we add more datasets to nlp library? | {
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"Found it. https://github.com/huggingface/nlp/tree/master/datasets"
] | 2020-05-18T18:35:31 | 2020-05-18T18:37:08 | 2020-05-18T18:37:07 | NONE | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/157 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/157/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/157/comments | https://api.github.com/repos/huggingface/datasets/issues/157/events | https://github.com/huggingface/datasets/issues/157 | 620,356,542 | MDU6SXNzdWU2MjAzNTY1NDI= | 157 | nlp.load_dataset() gives "TypeError: list_() takes exactly one argument (2 given)" | {
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"You can just run: \r\n`val = nlp.load_dataset('squad')` \r\n\r\nif you want to have just the validation script you can also do:\r\n\r\n`val = nlp.load_dataset('squad', split=\"validation\")`",
"If you want to load a local dataset, make sure you include a `./` before the folder name. ",
"This happens by just do... | 2020-05-18T16:46:38 | 2020-06-05T08:08:58 | 2020-06-05T08:08:58 | NONE | null | null | null | null | I'm trying to load datasets from nlp but there seems to have error saying
"TypeError: list_() takes exactly one argument (2 given)"
gist can be found here
https://gist.github.com/saahiluppal/c4b878f330b10b9ab9762bc0776c0a6a | {
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https://api.github.com/repos/huggingface/datasets/issues/156 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/156/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/156/comments | https://api.github.com/repos/huggingface/datasets/issues/156/events | https://github.com/huggingface/datasets/issues/156 | 620,263,687 | MDU6SXNzdWU2MjAyNjM2ODc= | 156 | SyntaxError with WMT datasets | {
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"Jeez - don't know what happened there :D Should be fixed now! \r\n\r\nThanks a lot for reporting this @tomhosking !",
"Hi @patrickvonplaten!\r\n\r\nI'm now getting the below error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError ... | 2020-05-18T14:38:18 | 2020-07-23T16:41:55 | 2020-07-23T16:41:55 | NONE | null | null | null | null | The following snippet produces a syntax error:
```
import nlp
dataset = nlp.load_dataset('wmt14')
print(dataset['train'][0])
```
```
Traceback (most recent call last):
File "/home/tom/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-8-3206959998b9>", line 3, in <module>
dataset = nlp.load_dataset('wmt14')
File "/home/tom/.local/lib/python3.6/site-packages/nlp/load.py", line 505, in load_dataset
builder_cls = import_main_class(module_path, dataset=True)
File "/home/tom/.local/lib/python3.6/site-packages/nlp/load.py", line 56, in import_main_class
module = importlib.import_module(module_path)
File "/usr/lib/python3.6/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 994, in _gcd_import
File "<frozen importlib._bootstrap>", line 971, in _find_and_load
File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 678, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/tom/.local/lib/python3.6/site-packages/nlp/datasets/wmt14/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2/wmt14.py", line 21, in <module>
from .wmt_utils import Wmt, WmtConfig
File "/home/tom/.local/lib/python3.6/site-packages/nlp/datasets/wmt14/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2/wmt_utils.py", line 659
<<<<<<< HEAD
^
SyntaxError: invalid syntax
```
Python version:
`3.6.9 (default, Apr 18 2020, 01:56:04) [GCC 8.4.0]`
Running on Ubuntu 18.04, via a Jupyter notebook | {
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https://api.github.com/repos/huggingface/datasets/issues/153 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/153/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/153/comments | https://api.github.com/repos/huggingface/datasets/issues/153/events | https://github.com/huggingface/datasets/issues/153 | 619,972,246 | MDU6SXNzdWU2MTk5NzIyNDY= | 153 | Meta-datasets (GLUE/XTREME/...) – Special care to attributions and citations | {
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"As @yoavgo suggested, there should be the possibility to call a function like nlp.bib that outputs all bibtex ref from the datasets and models actually used and eventually nlp.bib.forreadme that would output the same info + versions numbers so they can be included in a readme.md file.",
"Actually, double checki... | 2020-05-18T07:24:22 | 2020-05-18T21:18:16 | null | MEMBER | null | null | null | null | Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation/specific homepage/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.
Let's take GLUE as an example:
The configuration has the citation for each dataset included (e.g. [here](https://github.com/huggingface/nlp/blob/master/datasets/glue/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/149 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/149/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/149/comments | https://api.github.com/repos/huggingface/datasets/issues/149/events | https://github.com/huggingface/datasets/issues/149 | 619,735,739 | MDU6SXNzdWU2MTk3MzU3Mzk= | 149 | [Feature request] Add Ubuntu Dialogue Corpus dataset | {
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"@AlphaMycelium the Ubuntu Dialogue Corpus [version 2]( https://github.com/rkadlec/ubuntu-ranking-dataset-creator) is added. Note that it requires a manual download by following the download instructions in the [repos]( https://github.com/rkadlec/ubuntu-ranking-dataset-creator).\r\nMaybe we can close this issue for... | 2020-05-17T15:42:39 | 2020-05-18T17:01:46 | 2020-05-18T17:01:46 | NONE | null | null | null | null | https://github.com/rkadlec/ubuntu-ranking-dataset-creator or http://dataset.cs.mcgill.ca/ubuntu-corpus-1.0/ | {
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https://api.github.com/repos/huggingface/datasets/issues/148 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/148/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/148/comments | https://api.github.com/repos/huggingface/datasets/issues/148/events | https://github.com/huggingface/datasets/issues/148 | 619,590,555 | MDU6SXNzdWU2MTk1OTA1NTU= | 148 | _download_and_prepare() got an unexpected keyword argument 'verify_infos' | {
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"Same error for dataset 'wiki40b'",
"Should be fixed on master :)"
] | 2020-05-17T01:48:53 | 2020-05-18T07:38:33 | 2020-05-18T07:38:33 | CONTRIBUTOR | null | null | null | null | # Reproduce
In Colab,
```
%pip install -q nlp
%pip install -q apache_beam mwparserfromhell
dataset = nlp.load_dataset('wikipedia')
```
get
```
Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wikipedia/20200501.aa/1.0.0...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-52471d2a0088> in <module>()
----> 1 dataset = nlp.load_dataset('wikipedia')
1 frames
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
515 download_mode=download_mode,
516 ignore_verifications=ignore_verifications,
--> 517 save_infos=save_infos,
518 )
519
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)
361 verify_infos = not save_infos and not ignore_verifications
362 self._download_and_prepare(
--> 363 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
364 )
365 # Sync info
TypeError: _download_and_prepare() got an unexpected keyword argument 'verify_infos'
``` | {
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"Indeed. Probably we will want to have a similar method directly in the library",
"Related: #166 "
] | 2020-05-17T00:28:24 | 2020-06-18T16:23:23 | 2020-06-18T16:23:23 | NONE | null | null | null | null | It would be nice if we could use sklearn `train_test_split` to quickly generate subsets from the dataset objects returned by `nlp.load_dataset`. At the moment the code:
```python
data = nlp.load_dataset('imdb', cache_dir=data_cache)
f_half, s_half = train_test_split(data['train'], test_size=0.5, random_state=seed)
```
throws:
```
ValueError: Can only get row(s) (int or slice) or columns (string).
```
It's not a big deal, since there are other ways to split the data, but it would be a cool thing to have. | {
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https://api.github.com/repos/huggingface/datasets/issues/143 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/143/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/143/comments | https://api.github.com/repos/huggingface/datasets/issues/143/events | https://github.com/huggingface/datasets/issues/143 | 619,457,641 | MDU6SXNzdWU2MTk0NTc2NDE= | 143 | ArrowTypeError in squad metrics | {
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"There was an issue in the format, thanks.\r\nNow you can do\r\n```python3\r\nsquad_dset = nlp.load_dataset(\"squad\")\r\nsquad_metric = nlp.load_metric(\"/Users/quentinlhoest/Desktop/hf/nlp-bis/metrics/squad\")\r\npredictions = [\r\n {\"id\": v[\"id\"], \"prediction_text\": v[\"answers\"][\"text\"][0]} # take ... | 2020-05-16T12:06:37 | 2020-05-22T13:38:52 | 2020-05-22T13:36:48 | CONTRIBUTOR | null | null | null | null | `squad_metric.compute` is giving following error
```
ArrowTypeError: Could not convert [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}] with type list: was not a dict, tuple, or recognized null value for conversion to struct type
```
This is how my predictions and references look like
```
predictions[0]
# {'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}
```
```
references[0]
# {'answers': [{'text': 'Denver Broncos'},
{'text': 'Denver Broncos'},
{'text': 'Denver Broncos'}],
'id': '56be4db0acb8001400a502ec'}
```
These are structured as per the `squad_metric.compute` help string. | {
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"I would suggest `nlds`. NLP is a very general, broad and ambiguous term, the library is not about NLP (as in processing) per se, it is about accessing Natural Language related datasets. So the name should reflect its purpose.\r\n",
"Chiming in to second everything @honnibal said, and to add that I think the curr... | 2020-05-15T20:23:27 | 2022-09-16T05:18:22 | 2020-09-28T00:08:10 | NONE | null | null | null | null | Hey :)
Just making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.
The issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.
If you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.
If people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.
I don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.
I suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. | {
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https://api.github.com/repos/huggingface/datasets/issues/133 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/133/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/133/comments | https://api.github.com/repos/huggingface/datasets/issues/133/events | https://github.com/huggingface/datasets/issues/133 | 619,094,954 | MDU6SXNzdWU2MTkwOTQ5NTQ= | 133 | [Question] Using/adding a local dataset | {
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"Hi @zphang,\r\n\r\nSo you can just give the local path to a dataset script file and it should work.\r\n\r\nHere is an example:\r\n- you can download one of the scripts in the `datasets` folder of the present repo (or clone the repo)\r\n- then you can load it with `load_dataset('PATH/TO/YOUR/LOCAL/SCRIPT.py')`\r\n\... | 2020-05-15T16:26:06 | 2020-07-23T16:44:09 | 2020-07-23T16:44:09 | NONE | null | null | null | null | Users may want to either create/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.
It appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.
A notebook/example script demonstrating this would be very helpful. | {
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https://api.github.com/repos/huggingface/datasets/issues/132 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/132/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/132/comments | https://api.github.com/repos/huggingface/datasets/issues/132/events | https://github.com/huggingface/datasets/issues/132 | 619,077,851 | MDU6SXNzdWU2MTkwNzc4NTE= | 132 | [Feature Request] Add the OpenWebText dataset | {
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"We're experimenting with hosting the OpenWebText corpus on Zenodo for easier downloading. https://zenodo.org/record/3834942#.Xs1w8i-z2J8",
"Closing since it's been added in #660 "
] | 2020-05-15T15:57:29 | 2020-10-07T14:22:48 | 2020-10-07T14:22:48 | MEMBER | null | null | null | null | The OpenWebText dataset is an open clone of OpenAI's WebText dataset. It can be used to train ELECTRA as is specified in the [README](https://www.github.com/google-research/electra).
More information and the download link are available [here](https://skylion007.github.io/OpenWebTextCorpus/). | {
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https://api.github.com/repos/huggingface/datasets/issues/131 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/131/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/131/comments | https://api.github.com/repos/huggingface/datasets/issues/131/events | https://github.com/huggingface/datasets/issues/131 | 619,073,731 | MDU6SXNzdWU2MTkwNzM3MzE= | 131 | [Feature request] Add Toronto BookCorpus dataset | {
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"As far as I understand, `wikitext` is refer to `WikiText-103` and `WikiText-2` that created by researchers in Salesforce, and mostly used in traditional language modeling.\r\n\r\nYou might want to say `wikipedia`, a dump from wikimedia foundation.\r\n\r\nAlso I would like to have Toronto BookCorpus too ! Though it... | 2020-05-15T15:50:44 | 2020-06-28T21:27:31 | 2020-06-28T21:27:31 | CONTRIBUTOR | null | null | null | null | I know the copyright/distribution of this one is complex, but it would be great to have! That, combined with the existing `wikitext`, would provide a complete dataset for pretraining models like BERT. | {
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https://api.github.com/repos/huggingface/datasets/issues/130 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/130/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/130/comments | https://api.github.com/repos/huggingface/datasets/issues/130/events | https://github.com/huggingface/datasets/issues/130 | 619,035,440 | MDU6SXNzdWU2MTkwMzU0NDA= | 130 | Loading GLUE dataset loads CoLA by default | {
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"As a follow-up to this: It looks like the actual GLUE task name is supplied as the `name` argument. Is there a way to check what `name`s/sub-datasets are available under a grouping like GLUE? That information doesn't seem to be readily available in info from `nlp.list_datasets()`.\r\n\r\nEdit: I found the info und... | 2020-05-15T14:55:50 | 2020-05-27T22:08:15 | 2020-05-27T22:08:15 | NONE | null | null | null | null | If I run:
```python
dataset = nlp.load_dataset('glue')
```
The resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:
```python
metric = nlp.load_metric("glue")
```
which throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets? | {
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"Indeed, I think this one is almost ready cc @lhoestq ",
"I'm doing the latest adjustments to make the processing of the dataset run on Dataflow",
"Is there an update to this? It will be very beneficial for the QA community!",
"Still work in progress :)\r\nThe idea is to have the dataset already processed som... | 2020-05-15T14:14:20 | 2020-07-23T13:21:29 | 2020-07-23T13:21:29 | NONE | null | null | null | null | Would be great to have https://github.com/google-research-datasets/natural-questions as an alternative to SQuAD. | {
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"Google colab has an old version of Apache Arrow built-in.\r\nBe sure you execute the \"pip install\" cell and restart the notebook environment if the colab asks for it.",
"Thanks for reply, worked fine!\r\n"
] | 2020-05-15T13:01:29 | 2020-05-15T13:10:40 | 2020-05-15T13:10:40 | NONE | null | null | null | null | First of all, nice work!
I am going through [this overview notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb)
In simple step `dataset = nlp.load_dataset('squad', split='validation[:10%]')`
I get an error, which is connected with some inner code, I think:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-d848d3a99b8c> in <module>()
1 # Downloading and loading a dataset
2
----> 3 dataset = nlp.load_dataset('squad', split='validation[:10%]')
8 frames
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
515 download_mode=download_mode,
516 ignore_verifications=ignore_verifications,
--> 517 save_infos=save_infos,
518 )
519
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)
361 verify_infos = not save_infos and not ignore_verifications
362 self._download_and_prepare(
--> 363 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
364 )
365 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
414 try:
415 # Prepare split will record examples associated to the split
--> 416 self._prepare_split(split_generator, **prepare_split_kwargs)
417 except OSError:
418 raise OSError("Cannot find data file. " + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or ""))
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _prepare_split(self, split_generator)
585 fname = "{}-{}.arrow".format(self.name, split_generator.name)
586 fpath = os.path.join(self._cache_dir, fname)
--> 587 examples_type = self.info.features.type
588 writer = ArrowWriter(data_type=examples_type, path=fpath, writer_batch_size=self._writer_batch_size)
589
/usr/local/lib/python3.6/dist-packages/nlp/features.py in type(self)
460 @property
461 def type(self):
--> 462 return get_nested_type(self)
463
464 @classmethod
/usr/local/lib/python3.6/dist-packages/nlp/features.py in get_nested_type(schema)
370 # Nested structures: we allow dict, list/tuples, sequences
371 if isinstance(schema, dict):
--> 372 return pa.struct({key: get_nested_type(value) for key, value in schema.items()})
373 elif isinstance(schema, (list, tuple)):
374 assert len(schema) == 1, "We defining list feature, you should just provide one example of the inner type"
/usr/local/lib/python3.6/dist-packages/nlp/features.py in <dictcomp>(.0)
370 # Nested structures: we allow dict, list/tuples, sequences
371 if isinstance(schema, dict):
--> 372 return pa.struct({key: get_nested_type(value) for key, value in schema.items()})
373 elif isinstance(schema, (list, tuple)):
374 assert len(schema) == 1, "We defining list feature, you should just provide one example of the inner type"
/usr/local/lib/python3.6/dist-packages/nlp/features.py in get_nested_type(schema)
379 # We allow to reverse list of dict => dict of list for compatiblity with tfds
380 if isinstance(inner_type, pa.StructType):
--> 381 return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))
382 return pa.list_(inner_type, schema.length)
383
/usr/local/lib/python3.6/dist-packages/nlp/features.py in <genexpr>(.0)
379 # We allow to reverse list of dict => dict of list for compatiblity with tfds
380 if isinstance(inner_type, pa.StructType):
--> 381 return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))
382 return pa.list_(inner_type, schema.length)
383
TypeError: list_() takes exactly one argument (2 given)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/120 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/120/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/120/comments | https://api.github.com/repos/huggingface/datasets/issues/120/events | https://github.com/huggingface/datasets/issues/120 | 618,737,783 | MDU6SXNzdWU2MTg3Mzc3ODM= | 120 | 🐛 `map` not working | {
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"I didn't assign the output 🤦♂️\r\n\r\n```python\r\ndataset.map(test)\r\n```\r\n\r\nshould be :\r\n\r\n```python\r\ndataset = dataset.map(test)\r\n```"
] | 2020-05-15T06:43:08 | 2020-05-15T07:02:38 | 2020-05-15T07:02:38 | NONE | null | null | null | null | I'm trying to run a basic example (mapping function to add a prefix).
[Here is the colab notebook I'm using.](https://colab.research.google.com/drive/1YH4JCAy0R1MMSc-k_Vlik_s1LEzP_t1h?usp=sharing)
```python
import nlp
dataset = nlp.load_dataset('squad', split='validation[:10%]')
def test(sample):
sample['title'] = "test prefix @@@ " + sample["title"]
return sample
print(dataset[0]['title'])
dataset.map(test)
print(dataset[0]['title'])
```
Output :
> Super_Bowl_50
Super_Bowl_50
Expected output :
> Super_Bowl_50
test prefix @@@ Super_Bowl_50 | {
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https://api.github.com/repos/huggingface/datasets/issues/119 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/119/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/119/comments | https://api.github.com/repos/huggingface/datasets/issues/119/events | https://github.com/huggingface/datasets/issues/119 | 618,652,145 | MDU6SXNzdWU2MTg2NTIxNDU= | 119 | 🐛 Colab : type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array' | {
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"It's strange, after installing `nlp` on Colab, the `pyarrow` version seems fine from `pip` but not from python :\r\n\r\n```python\r\nimport pyarrow\r\n\r\n!pip show pyarrow\r\nprint(\"version = {}\".format(pyarrow.__version__))\r\n```\r\n\r\n> Name: pyarrow\r\nVersion: 0.17.0\r\nSummary: Python library for Apache ... | 2020-05-15T02:27:26 | 2020-05-15T05:11:22 | 2020-05-15T02:45:28 | NONE | null | null | null | null | I'm trying to load CNN/DM dataset on Colab.
[Colab notebook](https://colab.research.google.com/drive/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)
But I meet this error :
> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
| {
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https://api.github.com/repos/huggingface/datasets/issues/118 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/118/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/118/comments | https://api.github.com/repos/huggingface/datasets/issues/118/events | https://github.com/huggingface/datasets/issues/118 | 618,643,088 | MDU6SXNzdWU2MTg2NDMwODg= | 118 | ❓ How to apply a map to all subsets ? | {
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"That's the way!"
] | 2020-05-15T01:58:52 | 2020-05-15T07:05:49 | 2020-05-15T07:04:25 | NONE | null | null | null | null | I'm working with CNN/DM dataset, where I have 3 subsets : `train`, `test`, `validation`.
Should I apply my map function on the subsets one by one ?
```python
import nlp
cnn_dm = nlp.load_dataset('cnn_dailymail')
for corpus in ['train', 'test', 'validation']:
cnn_dm[corpus] = cnn_dm[corpus].map(my_func)
```
Or is there a better way to do this ? | {
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https://api.github.com/repos/huggingface/datasets/issues/117 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/117/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/117/comments | https://api.github.com/repos/huggingface/datasets/issues/117/events | https://github.com/huggingface/datasets/issues/117 | 618,632,573 | MDU6SXNzdWU2MTg2MzI1NzM= | 117 | ❓ How to remove specific rows of a dataset ? | {
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"Hi, you can't do that at the moment.",
"Can you do it by now? Coz it would be awfully helpful!",
"you can convert dataset object to pandas and remove a feature and convert back to dataset .",
"That's what I ended up doing too. but it feels like a workaround to a feature that should be added to the datasets c... | 2020-05-15T01:25:06 | 2022-07-15T08:36:44 | 2020-05-15T07:04:32 | NONE | null | null | null | null | I saw on the [example notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb#scrollTo=efFhDWhlvSVC) how to remove a specific column :
```python
dataset.drop('id')
```
But I didn't find how to remove a specific row.
**For example, how can I remove all sample with `id` < 10 ?** | {
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https://api.github.com/repos/huggingface/datasets/issues/116 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/116/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/116/comments | https://api.github.com/repos/huggingface/datasets/issues/116/events | https://github.com/huggingface/datasets/issues/116 | 618,628,264 | MDU6SXNzdWU2MTg2MjgyNjQ= | 116 | 🐛 Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323 | {
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{
"color": "25b21e",
"default": false,
"description": "A bug in a metric script",
"id": 2067393914,
"name": "metric bug",
"node_id": "MDU6TGFiZWwyMDY3MzkzOTE0",
"url": "https://api.github.com/repos/huggingface/datasets/labels/metric%20bug"
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"Can you share your data files or a minimally reproducible example?",
"Sure, [here is a Colab notebook](https://colab.research.google.com/drive/1uiS89fnHMG7HV_cYxp3r-_LqJQvNNKs9?usp=sharing) reproducing the error.\r\n\r\n> ArrowInvalid: Column 1 named references expected length 36 but got length 56",
"This is b... | 2020-05-15T01:12:06 | 2020-05-28T23:43:07 | 2020-05-28T23:43:07 | NONE | null | null | null | null | I'm trying to use rouge metric.
I have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.
I tried :
```python
import nlp
rouge = nlp.load_metric('rouge')
with open("test.pred.tokenized") as p, open("test.gold.tokenized") as g:
for lp, lg in zip(p, g):
rouge.add(lp, lg)
```
But I meet following error :
> pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323
---
Full stack-trace :
```
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/metric.py", line 224, in add
self.writer.write_batch(batch)
File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/arrow_writer.py", line 148, in write_batch
pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)
File "pyarrow/table.pxi", line 1550, in pyarrow.lib.Table.from_pydict
File "pyarrow/table.pxi", line 1503, in pyarrow.lib.Table.from_arrays
File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table
File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323
```
(`nlp` installed from source) | {
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https://api.github.com/repos/huggingface/datasets/issues/115 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/115/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/115/comments | https://api.github.com/repos/huggingface/datasets/issues/115/events | https://github.com/huggingface/datasets/issues/115 | 618,615,855 | MDU6SXNzdWU2MTg2MTU4NTU= | 115 | AttributeError: 'dict' object has no attribute 'info' | {
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"I could access the info by first accessing the different splits :\r\n\r\n```python\r\nimport nlp\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\nprint(cnn_dm['train'].info)\r\n```\r\n\r\nInformation seems to be duplicated between the subsets :\r\n\r\n```python\r\nprint(cnn_dm[\"train\"].info == cnn_dm[\"test\... | 2020-05-15T00:29:47 | 2020-05-17T13:11:00 | 2020-05-17T13:11:00 | NONE | null | null | null | null | I'm trying to access the information of CNN/DM dataset :
```python
cnn_dm = nlp.load_dataset('cnn_dailymail')
print(cnn_dm.info)
```
returns :
> AttributeError: 'dict' object has no attribute 'info' | {
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https://api.github.com/repos/huggingface/datasets/issues/114 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/114/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/114/comments | https://api.github.com/repos/huggingface/datasets/issues/114/events | https://github.com/huggingface/datasets/issues/114 | 618,611,310 | MDU6SXNzdWU2MTg2MTEzMTA= | 114 | Couldn't reach CNN/DM dataset | {
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"Installing from source (instead of Pypi package) solved the problem."
] | 2020-05-15T00:16:17 | 2020-05-15T00:19:52 | 2020-05-15T00:19:51 | NONE | null | null | null | null | I can't get CNN / DailyMail dataset.
```python
import nlp
assert "cnn_dailymail" in [dataset.id for dataset in nlp.list_datasets()]
cnn_dm = nlp.load_dataset('cnn_dailymail')
```
[Colab notebook](https://colab.research.google.com/drive/1zQ3bYAVzm1h0mw0yWPqKAg_4EUlSx5Ex?usp=sharing)
gives following error :
```
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/cnn_dailymail/cnn_dailymail.py
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/38 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/38/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/38/comments | https://api.github.com/repos/huggingface/datasets/issues/38/events | https://github.com/huggingface/datasets/issues/38 | 611,677,656 | MDU6SXNzdWU2MTE2Nzc2NTY= | 38 | [Checksums] Error for some datasets | {
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"@lhoestq - could you take a look? It's not very urgent though!",
"Fixed with 06882b4\r\n\r\nNow your command works :)\r\nNote that you can also do\r\n```\r\nnlp-cli test datasets/nlp/xnli --save_checksums\r\n```\r\nSo that it will save the checksums directly in the right directory.",
"Awesome!"
] | 2020-05-04T08:00:16 | 2020-05-04T09:48:20 | 2020-05-04T09:48:20 | CONTRIBUTOR | null | null | null | null | The checksums command works very nicely for `squad`. But for `crime_and_punish` and `xnli`,
the same bug happens:
When running:
```
python nlp-cli nlp-cli test xnli --save_checksums
```
leads to:
```
File "nlp-cli", line 33, in <module>
service.run()
File "/home/patrick/python_bin/nlp/commands/test.py", line 61, in run
ignore_checksums=self._ignore_checksums,
File "/home/patrick/python_bin/nlp/builder.py", line 383, in download_and_prepare
self._download_and_prepare(dl_manager=dl_manager, download_config=download_config)
File "/home/patrick/python_bin/nlp/builder.py", line 627, in _download_and_prepare
dl_manager=dl_manager, max_examples_per_split=download_config.max_examples_per_split,
File "/home/patrick/python_bin/nlp/builder.py", line 431, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/patrick/python_bin/nlp/datasets/xnli/8bf4185a2da1ef2a523186dd660d9adcf0946189e7fa5942ea31c63c07b68a7f/xnli.py", line 95, in _split_generators
dl_dir = dl_manager.download_and_extract(_DATA_URL)
File "/home/patrick/python_bin/nlp/utils/download_manager.py", line 246, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/patrick/python_bin/nlp/utils/download_manager.py", line 186, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/home/patrick/python_bin/nlp/utils/download_manager.py", line 166, in _record_sizes_checksums
self._recorded_sizes_checksums[url] = get_size_checksum(path)
File "/home/patrick/python_bin/nlp/utils/checksums_utils.py", line 81, in get_size_checksum
with open(path, "rb") as f:
TypeError: expected str, bytes or os.PathLike object, not tuple
```
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https://api.github.com/repos/huggingface/datasets/issues/6 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6/comments | https://api.github.com/repos/huggingface/datasets/issues/6/events | https://github.com/huggingface/datasets/issues/6 | 600,330,836 | MDU6SXNzdWU2MDAzMzA4MzY= | 6 | Error when citation is not given in the DatasetInfo | {
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"Yes looks good to me.\r\nNote that we may refactor quite strongly the `info.py` to make it a lot simpler (it's very complicated for basically a dictionary of info I think)",
"No, problem ^^ It might just be a temporary fix :)",
"Fixed."
] | 2020-04-15T14:14:54 | 2020-04-29T09:23:22 | 2020-04-29T09:23:22 | CONTRIBUTOR | null | null | null | null | The following error is raised when the `citation` parameter is missing when we instantiate a `DatasetInfo`:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/dev/jplu/datasets/src/nlp/info.py", line 338, in __repr__
citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
AttributeError: 'NoneType' object has no attribute 'strip'
```
I propose to do the following change in the `info.py` file. The method:
```python
def __repr__(self):
splits_pprint = _indent("\n".join(["{"] + [
" '{}': {},".format(k, split.num_examples)
for k, split in sorted(self.splits.items())
] + ["}"]))
features_pprint = _indent(repr(self.features))
citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
return INFO_STR.format(
name=self.name,
version=self.version,
description=self.description,
total_num_examples=self.splits.total_num_examples,
features=features_pprint,
splits=splits_pprint,
citation=citation_pprint,
homepage=self.homepage,
supervised_keys=self.supervised_keys,
# Proto add a \n that we strip.
license=str(self.license).strip())
```
Becomes:
```python
def __repr__(self):
splits_pprint = _indent("\n".join(["{"] + [
" '{}': {},".format(k, split.num_examples)
for k, split in sorted(self.splits.items())
] + ["}"]))
features_pprint = _indent(repr(self.features))
## the strip is done only is the citation is given
citation_pprint = self.citation
if self.citation:
citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
return INFO_STR.format(
name=self.name,
version=self.version,
description=self.description,
total_num_examples=self.splits.total_num_examples,
features=features_pprint,
splits=splits_pprint,
citation=citation_pprint,
homepage=self.homepage,
supervised_keys=self.supervised_keys,
# Proto add a \n that we strip.
license=str(self.license).strip())
```
And now it is ok. @thomwolf are you ok with this fix? | {
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https://api.github.com/repos/huggingface/datasets/issues/5 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5/comments | https://api.github.com/repos/huggingface/datasets/issues/5/events | https://github.com/huggingface/datasets/issues/5 | 600,295,889 | MDU6SXNzdWU2MDAyOTU4ODk= | 5 | ValueError when a split is empty | {
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"To fix this I propose to modify only the file `arrow_reader.py` with few updates. First update, the following method:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n name,\r\n name2len,\r\n absolute_instructions,\r\n):\r\n \"\"\"Returns the files instructions from the absolu... | 2020-04-15T13:25:13 | 2020-04-29T09:23:05 | 2020-04-29T09:23:05 | CONTRIBUTOR | null | null | null | null | When a split is empty either TEST, VALIDATION or TRAIN I get the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/dev/jplu/datasets/src/nlp/load.py", line 295, in load
ds = dbuilder.as_dataset(**as_dataset_kwargs)
File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 587, in as_dataset
datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in map_nested
for k, v in data_struct.items()
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in <dictcomp>
for k, v in data_struct.items()
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
return function(data_struct)
File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 601, in _build_single_dataset
split=split,
File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 625, in _as_dataset
split_infos=self.info.splits.values(),
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 200, in read
return py_utils.map_nested(_read_instruction_to_ds, instructions)
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
return function(data_struct)
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 191, in _read_instruction_to_ds
file_instructions = make_file_instructions(name, split_infos, instruction)
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 104, in make_file_instructions
absolute_instructions=absolute_instructions,
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 122, in _make_file_instructions_from_absolutes
'Split empty. This might means that dataset hasn\'t been generated '
ValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.
```
How to reproduce:
```python
import csv
import nlp
class Bbc(nlp.GeneratorBasedBuilder):
VERSION = nlp.Version("1.0.0")
def __init__(self, **config):
self.train = config.pop("train", None)
self.validation = config.pop("validation", None)
super(Bbc, self).__init__(**config)
def _info(self):
return nlp.DatasetInfo(builder=self, description="bla", features=nlp.features.FeaturesDict({"id": nlp.int32, "text": nlp.string, "label": nlp.string}))
def _split_generators(self, dl_manager):
return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": self.train}),
nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": self.validation}),
nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={"filepath": None})]
def _generate_examples(self, filepath):
if not filepath:
return None, {}
with open(filepath) as f:
reader = csv.reader(f, delimiter=',', quotechar="\"")
lines = list(reader)[1:]
for idx, line in enumerate(lines):
yield idx, {"id": idx, "text": line[1], "label": line[0]}
```
```python
import nlp
dataset = nlp.load("bbc", builder_kwargs={"train": "bbc/data/train.csv", "validation": "bbc/data/test.csv"})
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4/comments | https://api.github.com/repos/huggingface/datasets/issues/4/events | https://github.com/huggingface/datasets/issues/4 | 600,185,417 | MDU6SXNzdWU2MDAxODU0MTc= | 4 | [Feature] Keep the list of labels of a dataset as metadata | {
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"Yes! I see mostly two options for this:\r\n- a `Feature` approach like currently (but we might deprecate features)\r\n- wrapping in a smart way the Dictionary arrays of Arrow: https://arrow.apache.org/docs/python/data.html?highlight=dictionary%20encode#dictionary-arrays",
"I would have a preference for the secon... | 2020-04-15T10:17:10 | 2020-07-08T16:59:46 | 2020-05-04T06:11:57 | CONTRIBUTOR | null | null | null | null | It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata. | {
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https://api.github.com/repos/huggingface/datasets/issues/3 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3/comments | https://api.github.com/repos/huggingface/datasets/issues/3/events | https://github.com/huggingface/datasets/issues/3 | 600,180,050 | MDU6SXNzdWU2MDAxODAwNTA= | 3 | [Feature] More dataset outputs | {
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"Yes!\r\n- pandas will be a one-liner in `arrow_dataset`: https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.to_pandas\r\n- for Spark I have no idea. let's investigate that at some point",
"For Spark it looks to be pretty straightforward as well https://spark.apache.org/docs/latest/sq... | 2020-04-15T10:08:14 | 2020-05-04T06:12:27 | 2020-05-04T06:12:27 | CONTRIBUTOR | null | null | null | null | Add the following dataset outputs:
- Spark
- Pandas | {
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https://api.github.com/repos/huggingface/datasets/issues/2 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2/comments | https://api.github.com/repos/huggingface/datasets/issues/2/events | https://github.com/huggingface/datasets/issues/2 | 599,767,671 | MDU6SXNzdWU1OTk3Njc2NzE= | 2 | Issue to read a local dataset | {
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"My first bug report ❤️\r\nLooking into this right now!",
"Ok, there are some news, most good than bad :laughing: \r\n\r\nThe dataset script now became:\r\n```python\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n VERSION = nlp.Version(\"1.0.0\")\r\n\r\n def __init__(sel... | 2020-04-14T18:18:51 | 2020-05-11T18:55:23 | 2020-05-11T18:55:22 | CONTRIBUTOR | null | null | null | null | Hello,
As proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:
```python
import os
import csv
import nlp
class BbcConfig(nlp.BuilderConfig):
def __init__(self, **kwargs):
super(BbcConfig, self).__init__(**kwargs)
class Bbc(nlp.GeneratorBasedBuilder):
_DIR = "./data"
_DEV_FILE = "test.csv"
_TRAINING_FILE = "train.csv"
BUILDER_CONFIGS = [BbcConfig(name="bbc", version=nlp.Version("1.0.0"))]
def _info(self):
return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({"id": nlp.string, "text": nlp.string, "label": nlp.string}))
def _split_generators(self, dl_manager):
files = {"train": os.path.join(self._DIR, self._TRAINING_FILE), "dev": os.path.join(self._DIR, self._DEV_FILE)}
return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": files["train"]}),
nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": files["dev"]})]
def _generate_examples(self, filepath):
with open(filepath) as f:
reader = csv.reader(f, delimiter=',', quotechar="\"")
lines = list(reader)[1:]
for idx, line in enumerate(lines):
yield idx, {"idx": idx, "text": line[1], "label": line[0]}
```
The dataset is attached to this issue as well:
[data.zip](https://github.com/huggingface/datasets/files/4476928/data.zip)
Now the steps to reproduce what I would like to do:
1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)
2. create the `bbc.py` script as above at the same location than the unziped `data` folder.
Now I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:
```python
import nlp
from bbc import Bbc
dataset = nlp.load("bbc")
```
I get:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
builder_cls = load_dataset(path, name=name, **builder_kwargs)
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 88, in load_dataset
local_files_only=local_files_only,
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/utils/file_utils.py", line 214, in cached_path
if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
File "/opt/anaconda3/envs/transformers/lib/python3.7/zipfile.py", line 203, in is_zipfile
with open(filename, "rb") as fp:
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
But @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:
```python
import nlp
dataset = nlp.load("bbc.py")
```
And
```python
import nlp
dataset = nlp.load("./bbc.py")
```
And
```python
import nlp
dataset = nlp.load("/absolute/path/to/bbc.py")
```
These three ways gives me:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
builder_cls = load_dataset(path, name=name, **builder_kwargs)
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 124, in load_dataset
dataset_module = importlib.import_module(module_path)
File "/opt/anaconda3/envs/transformers/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 965, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'
```
Any idea of what I'm missing? or I might have spot a bug :) | {
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