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https://api.github.com/repos/huggingface/datasets/issues/1831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1831/comments | https://api.github.com/repos/huggingface/datasets/issues/1831/events | https://github.com/huggingface/datasets/issues/1831 | 802,868,854 | MDU6SXNzdWU4MDI4Njg4NTQ= | 1,831 | Some question about raw dataset download info in the project . | {
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https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py
in the _split_generators function is the truly logic of download raw datasets with dl_manager
and use Conll2003 cls by use import_main_class in load_dataset function
My question is that , with this logic it seems that i can not have the raw dataset download location
in variable in downloaded_files in _split_generators.
If someone also want use huggingface datasets as raw dataset downloader,
how can he retrieve the raw dataset download path from attributes in
datasets.dataset_dict.DatasetDict ? | {
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https://api.github.com/repos/huggingface/datasets/issues/1830 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1830/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1830/comments | https://api.github.com/repos/huggingface/datasets/issues/1830/events | https://github.com/huggingface/datasets/issues/1830 | 802,790,075 | MDU6SXNzdWU4MDI3OTAwNzU= | 1,830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | {
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} | [] | open | false | null | [] | null | 9 | "2021-02-06T21:00:26Z" | "2021-02-24T21:56:14Z" | null | NONE | null | null | null | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_unique = set(text.split(" "))
for i in words_unique:
original_tokenizer.add_tokens(i)
original_tokenizer.save_pretrained(path)
tokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,"tokenizer_squad"))
train_set_baby=Dataset.from_dict({"text":[train_set["text"][0][0:50]]})
````
I then applied the dataset map function on a fairly small set of text:
```
%%time
train_set_baby = train_set_baby.map(lambda d:tokenizer2(d["text"]),batched=True)
```
The run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds
**100% 1/1 [00:02<00:00, 2.60s/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**
In comparison using (even after adding additional tokens):
`
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")`
```
%%time
train_set_baby = train_set_baby.map(lambda d:tokenizer2(d["text"]),batched=True)
```
The time is
**100% 1/1 [00:00<00:00, 34.09ba/s] CPU times: user 68.1 ms, sys: 16 µs, total: 68.1 ms Wall time: 62.9 ms**
It seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer to the map function.
I should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory appears to impact the time it takes to run the map function.
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https://api.github.com/repos/huggingface/datasets/issues/1829 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1829/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1829/comments | https://api.github.com/repos/huggingface/datasets/issues/1829/events | https://github.com/huggingface/datasets/pull/1829 | 802,693,600 | MDExOlB1bGxSZXF1ZXN0NTY4NzgzNjA5 | 1,829 | Add Tweet Eval Dataset | {
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} | [] | closed | false | null | [] | null | 0 | "2021-02-06T12:36:25Z" | "2021-02-08T13:17:54Z" | "2021-02-08T13:17:53Z" | CONTRIBUTOR | null | 0 | {
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} | Closes Draft PR #1407.
Notes:
1. I have excluded `mapping.txt` from the dataset at it only contained the name mappings, which are already present in the ClassLabels.
2. I have also exluded the textual names for the emojis mentioned in the [mapping](https://github.com/cardiffnlp/tweeteval/blob/main/datasets/emoji/mapping.txt).
3. I do not understand @abhishekkrthakur's example generator on #1407. Maybe he was trying to build up on code from some other dataset.
Requesting @lhoestq to review. | {
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https://api.github.com/repos/huggingface/datasets/issues/1828 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1828/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1828/comments | https://api.github.com/repos/huggingface/datasets/issues/1828/events | https://github.com/huggingface/datasets/pull/1828 | 802,449,234 | MDExOlB1bGxSZXF1ZXN0NTY4NTkwNDM2 | 1,828 | Add CelebA Dataset | {
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} | Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10). | {
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https://api.github.com/repos/huggingface/datasets/issues/1827 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1827/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1827/comments | https://api.github.com/repos/huggingface/datasets/issues/1827/events | https://github.com/huggingface/datasets/issues/1827 | 802,353,974 | MDU6SXNzdWU4MDIzNTM5NzQ= | 1,827 | Regarding On-the-fly Data Loading | {
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I was wondering if it is possible to load images/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.
Thanks,
Gunjan | {
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Close #1821 | {
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I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training.
Moreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).
Any suggestions?? | {
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https://api.github.com/repos/huggingface/datasets/issues/1824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1824/comments | https://api.github.com/repos/huggingface/datasets/issues/1824/events | https://github.com/huggingface/datasets/pull/1824 | 802,048,281 | MDExOlB1bGxSZXF1ZXN0NTY4MjU3MTU3 | 1,824 | Add OSCAR dataset card | {
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For now it's just basic info for all the different configurations in `Dataset Structure`.
In particular the Data Splits section tells how may samples there are for each config. The Data Instances section show an example for each config, and it also shows the size in MB. Since the Data Instances section is very long the user has to click to expand the info. I was able to generate it thanks to the tools made by @madlag and @yjernite :D
Cc @pjox could you help me with the other sections ? (Dataset Description, Dataset Creation, Considerations for Using the Data, Additional Information)
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https://api.github.com/repos/huggingface/datasets/issues/1823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1823/comments | https://api.github.com/repos/huggingface/datasets/issues/1823/events | https://github.com/huggingface/datasets/pull/1823 | 802,042,181 | MDExOlB1bGxSZXF1ZXN0NTY4MjUyMjIx | 1,823 | Add FewRel Dataset | {
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This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | {
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https://api.github.com/repos/huggingface/datasets/issues/1822 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1822/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1822/comments | https://api.github.com/repos/huggingface/datasets/issues/1822/events | https://github.com/huggingface/datasets/pull/1822 | 802,003,835 | MDExOlB1bGxSZXF1ZXN0NTY4MjIxMzIz | 1,822 | Add Hindi Discourse Analysis Natural Language Inference Dataset | {
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} | # Dataset Card for Hindi Discourse Analysis Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : https://www.aclweb.org/anthology/2020.aacl-main.71
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. Hindi Discourse Analysis (HDA) Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Premise and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Entailed means that hypotheis can be inferred from premise and not-entailed means vice versa
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
- Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- train, test and dev files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह एक वर्णनात्मक कथन है।', 'label': 1, 'premise': 'जैसे उस का सारा चेहरा अपना हो और आँखें किसी दूसरे की जो चेहरे पर पपोटों के पीछे महसूर कर दी गईं।', 'topic': 1}
```
### Data Fields
- Each row contatins 4 columns - premise, hypothesis, label and topic.
### Data Splits
- Train : 31892
- Valid : 9460
- Test : 9970
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available Hindi Discourse Analysis classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper https://www.aclweb.org/anthology/2020.aacl-main.71
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- Initial Data was collected by members of MIDAS Lab from Hindi Websites. They crowd sourced the data annotation process and selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode.
- Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
- The Discourse is further classified into "Argumentative" , "Descriptive" , "Dialogic" , "Informative" and "Narrative" - 5 Clases.
#### Who are the source language producers?
Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically by machine and corresponding recasting process.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
No known bias exist in the dataset.
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations . Size of data may not be enough to train large models
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/midas-research/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to [email protected].
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
### Contributions
Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset.
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```
train_files = glob.glob('rain*.csv')
validation_files = glob.glob(validation*.csv')
datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files))
```
I sometimes encounter an error due to one of the files being misformed (i.e. no data, or a comma in a field that isn't quoted, etc).
For example, this is the tail of an exception which I suspect is due to a stray comma.
> File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read
> File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory
> File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows
> File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows
> File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error
> pandas.errors.ParserError: Error tokenizing data. C error: Expected 2 fields in line 559, saw 3
It would be nice if the exception trace contained the name of the file being processed (I have 250 separate files!) | {
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https://api.github.com/repos/huggingface/datasets/issues/1820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1820/comments | https://api.github.com/repos/huggingface/datasets/issues/1820/events | https://github.com/huggingface/datasets/pull/1820 | 801,529,936 | MDExOlB1bGxSZXF1ZXN0NTY3ODI4OTg1 | 1,820 | Add metrics usage examples and tests | {
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} | [] | closed | false | null | [] | null | 0 | "2021-02-04T18:23:50Z" | "2021-02-05T14:00:01Z" | "2021-02-05T14:00:00Z" | MEMBER | null | 0 | {
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} | All metrics finally have usage examples and proper fast + slow tests :)
I added examples of usage for every metric, and I use doctest to make sure they all work as expected.
For "slow" metrics such as bert_score or bleurt which require to download + run a transformer model, the download + forward pass are only done in the slow test.
In the fast test on the other hand, the download + forward pass are monkey patched.
Metrics that need to be installed from github are not added to setup.py because it prevents uploading the `datasets` package to pypi.
An additional-test-requirements.txt file is used instead. This file also include `comet` in order to not have to resolve its *impossible* dependencies.
Also `comet` is not tested on windows because one of its dependencies (fairseq) can't be installed in the CI for some reason. | {
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https://api.github.com/repos/huggingface/datasets/issues/1819 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1819/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1819/comments | https://api.github.com/repos/huggingface/datasets/issues/1819/events | https://github.com/huggingface/datasets/pull/1819 | 801,448,670 | MDExOlB1bGxSZXF1ZXN0NTY3NzYyMzI2 | 1,819 | Fixed spelling `S3Fileystem` to `S3FileSystem` | {
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} | Fixed documentation spelling errors.
Wrong `S3Fileystem`
Right `S3FileSystem` | {
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https://api.github.com/repos/huggingface/datasets/issues/1818 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1818/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1818/comments | https://api.github.com/repos/huggingface/datasets/issues/1818/events | https://github.com/huggingface/datasets/issues/1818 | 800,958,776 | MDU6SXNzdWU4MDA5NTg3NzY= | 1,818 | Loading local dataset raise requests.exceptions.ConnectTimeout | {
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} | [] | closed | false | null | [] | null | 1 | "2021-02-04T05:55:23Z" | "2022-06-01T15:38:42Z" | "2022-06-01T15:38:42Z" | NONE | null | null | null | Load local dataset:
```
dataset = load_dataset('json', data_files=["../../data/json.json"])
train = dataset["train"]
print(train.features)
train1 = train.map(lambda x: {"labels": 1})
print(train1[:2])
```
but it raised requests.exceptions.ConnectTimeout:
```
/Users/littlely/myvirtual/tf2/bin/python3.7 /Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py
Traceback (most recent call last):
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 160, in _new_conn
(self._dns_host, self.port), self.timeout, **extra_kw
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py", line 84, in create_connection
raise err
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py", line 74, in create_connection
sock.connect(sa)
socket.timeout: timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 677, in urlopen
chunked=chunked,
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 381, in _make_request
self._validate_conn(conn)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 978, in _validate_conn
conn.connect()
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 309, in connect
conn = self._new_conn()
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 167, in _new_conn
% (self.host, self.timeout),
urllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py", line 449, in send
timeout=timeout
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 727, in urlopen
method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/retry.py", line 439, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py", line 12, in <module>
dataset = load_dataset('json', data_files=["../../data/json.json"])
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py", line 591, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset, max_retries=download_config.max_retries)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 232, in head_hf_s3
max_retries=max_retries,
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 523, in http_head
max_retries=max_retries,
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 458, in _request_with_retry
raise err
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 454, in _request_with_retry
response = requests.request(verb.upper(), url, **params)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py", line 530, in request
resp = self.send(prep, **send_kwargs)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py", line 643, in send
r = adapter.send(request, **kwargs)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
Process finished with exit code 1
```
Why it want to connect a remote url when I load local datasets, and how can I fix it? | {
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https://api.github.com/repos/huggingface/datasets/issues/1817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1817/comments | https://api.github.com/repos/huggingface/datasets/issues/1817/events | https://github.com/huggingface/datasets/issues/1817 | 800,870,652 | MDU6SXNzdWU4MDA4NzA2NTI= | 1,817 | pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 599 but got length 1500 | {
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} | [] | closed | false | null | [] | null | 2 | "2021-02-04T02:30:23Z" | "2022-10-05T12:42:57Z" | "2022-10-05T12:42:57Z" | NONE | null | null | null | I am trying to preprocess any dataset in this package with GPT-2 tokenizer, so I need to structure the datasets as long sequences of text without padding. I've been following a couple of your tutorials and here you can find the script that is failing right at the end
https://github.com/LuCeHe/GenericTools/blob/master/KerasTools/lm_preprocessing.py
In the last iteration of the last dset.map, it gives the error that I copied in the title. Another issue that I have, if I leave the batch_size set as 1000 in the last .map, I'm afraid it's going to lose most text, so I'm considering setting both writer_batch_size and batch_size to 300 K, but I'm not sure it's the best way to go.
Can you help me?
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/1816 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1816/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1816/comments | https://api.github.com/repos/huggingface/datasets/issues/1816/events | https://github.com/huggingface/datasets/pull/1816 | 800,660,995 | MDExOlB1bGxSZXF1ZXN0NTY3MTExMjEx | 1,816 | Doc2dial rc update to latest version | {
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} | [] | closed | false | null | [] | null | 1 | "2021-02-03T20:08:54Z" | "2021-02-15T15:15:24Z" | "2021-02-15T15:04:33Z" | CONTRIBUTOR | null | 0 | {
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|
https://api.github.com/repos/huggingface/datasets/issues/1815 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1815/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1815/comments | https://api.github.com/repos/huggingface/datasets/issues/1815/events | https://github.com/huggingface/datasets/pull/1815 | 800,610,017 | MDExOlB1bGxSZXF1ZXN0NTY3MDY3NjU1 | 1,815 | Add CCAligned Multilingual Dataset | {
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} | [] | closed | false | null | [] | null | 7 | "2021-02-03T18:59:52Z" | "2021-03-01T12:33:03Z" | "2021-03-01T10:36:21Z" | CONTRIBUTOR | null | 0 | {
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} | Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review. | {
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https://api.github.com/repos/huggingface/datasets/issues/1814 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1814/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1814/comments | https://api.github.com/repos/huggingface/datasets/issues/1814/events | https://github.com/huggingface/datasets/pull/1814 | 800,516,236 | MDExOlB1bGxSZXF1ZXN0NTY2OTg4NTI1 | 1,814 | Add Freebase QA Dataset | {
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Requesting @lhoestq to review. | {
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} | If a dataset is available at the version of the local installation of `datasets` (e.g. 1.2.0), then loading this dataset means loading the script at this version.
However when trying to load a dataset that is only available on master, currently users have to specify `script_version="master"` in `load_dataset` to make it work.
However we could automatically get the dataset from master instead in this case.
I added this feature in this PR.
I also added a warning if a dataset is not available at the version of the local installation of `datasets` but is loaded from master:
```python
>>> load_dataset("silicone", "dyda_da")
Couldn't find file locally at silicone/silicone.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/silicone/silicone.py.
The file was picked from the master branch on github instead at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/silicone/silicone.py.
Downloading and preparing dataset silicone/dyda_da (download: 8.46 MiB, generated: 9.39 MiB, post-processed: Unknown size, total: 17.86 MiB) to /Users/quentinlhoest/.cache/huggingface/datasets/silicone/dyda_da/1.0.0/d41d8c0b73c6df035b1369c45774418f0051163ea689b5502b8bda783adf6342...
...
```
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} | [] | closed | false | null | [] | null | 4 | "2021-02-02T11:50:56Z" | "2021-02-18T14:16:31Z" | "2021-02-18T14:16:31Z" | CONTRIBUTOR | null | null | null | I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as
`supervised_keys=("img", "fine_label")` raises no issue. But trying `supervised_keys=("img", "fine_label","coarse_label")` leads to this error :
```python
Traceback (most recent call last):
File "test_script.py", line 2, in <module>
d = load_dataset('./datasets/cifar100')
File "~/datasets/src/datasets/load.py", line 668, in load_dataset
**config_kwargs,
File "~/datasets/src/datasets/builder.py", line 896, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "~/datasets/src/datasets/builder.py", line 247, in __init__
info.update(self._info())
File "~/.cache/huggingface/modules/datasets_modules/datasets/cifar100/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f/cifar100.py", line 79, in _info
citation=_CITATION,
File "<string>", line 19, in __init__
File "~/datasets/src/datasets/info.py", line 136, in __post_init__
self.supervised_keys = SupervisedKeysData(*self.supervised_keys)
TypeError: __init__() takes from 1 to 3 positional arguments but 4 were given
```
Is there a way I can fix this?
Also, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset?
Thanks,
Gunjan | {
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] | open | false | null | [] | null | 4 | "2021-02-02T10:53:59Z" | "2021-12-08T12:03:59Z" | null | CONTRIBUTOR | null | null | null | ## Add Hateful Memes Dataset
- **Name:** Hateful Memes
- **Description:** [https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set]( https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set)
- **Paper:** [https://arxiv.org/pdf/2005.04790.pdf](https://arxiv.org/pdf/2005.04790.pdf)
- **Data:** [This link](https://drivendata-competition-fb-hateful-memes-data.s3.amazonaws.com/XjiOc5ycDBRRNwbhRlgH.zip?AWSAccessKeyId=AKIARVBOBDCY4MWEDJKS&Signature=DaUuGgZWUgDHzEPPbyJ2PhSJ56Q%3D&Expires=1612816874)
- **Motivation:** Including multi-modal datasets to 🤗 datasets.
I will be adding this dataset. It requires the user to sign an agreement on DrivenData. So, it will be used with a manual download.
The issue with this dataset is that the images are of different sizes. The image datasets added so far (CIFAR-10 and MNIST) have a uniform shape throughout.
So something like
```python
datasets.Array2D(shape=(28, 28), dtype="uint8")
```
won't work for the images. How would I add image features then? I checked `datasets/features.py` but couldn't figure out the appropriate class for this. I'm assuming I would want to avoid re-sizing at all since we want the user to be able to access the original images.
Also, in case I want to load only a subset of the data, since the actual data is around 8.8GB, how would that be possible?
Thanks,
Gunjan | {
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} | Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review. | {
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I see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable format to a file like json? thanks @lhoestq | {
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} | [] | closed | false | null | [] | null | 1 | "2021-02-02T00:39:53Z" | "2021-02-02T22:48:41Z" | "2021-02-02T18:06:58Z" | MEMBER | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/1807.diff",
"html_url": "https://github.com/huggingface/datasets/pull/1807",
"merged_at": "2021-02-02T18:06:58Z",
"patch_url": "https://github.com/huggingface/datasets/pull/1807.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/1807"
} | This dataset gathers modified versions of several other conditional text generation datasets which together make up the shared task for the Generation Evaluation and Metrics workshop (think GLUE for text generation)
The changes from the original datasets are detailed in the Dataset Cards on the GEM website, which are linked to in this dataset card.
cc @sebastianGehrmann
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https://api.github.com/repos/huggingface/datasets/issues/1806 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1806/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1806/comments | https://api.github.com/repos/huggingface/datasets/issues/1806/events | https://github.com/huggingface/datasets/pull/1806 | 798,607,869 | MDExOlB1bGxSZXF1ZXN0NTY1Mzk0ODIz | 1,806 | Update details to MLSUM dataset | {
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"diff_url": "https://github.com/huggingface/datasets/pull/1806.diff",
"html_url": "https://github.com/huggingface/datasets/pull/1806",
"merged_at": "2021-02-01T18:46:21Z",
"patch_url": "https://github.com/huggingface/datasets/pull/1806.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/1806"
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https://api.github.com/repos/huggingface/datasets/issues/1805 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1805/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1805/comments | https://api.github.com/repos/huggingface/datasets/issues/1805/events | https://github.com/huggingface/datasets/issues/1805 | 798,498,053 | MDU6SXNzdWU3OTg0OTgwNTM= | 1,805 | can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index | {
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} | [] | closed | false | null | [] | null | 2 | "2021-02-01T16:14:17Z" | "2021-03-06T14:32:46Z" | "2021-03-06T14:32:46Z" | CONTRIBUTOR | null | null | null | So, I have the following instances in my dataset
```
{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of
this increase in rotation?',
'answer': 'C',
'example_id': 'ARCCH_Mercury_7175875',
'options':[{'option_context': 'One effect of increased amperage in the planetary world (..)', 'option_id': 'A', 'option_text': 'Planetary density will decrease.'},
(...)]}
```
The `options` value is always an list with 4 options, each one is a dict with `option_context`; `option_id` and `option_text`.
I would like to overwrite the `option_context` of each instance of my dataset for a dpr result that I am developing. Then, I trained a model already and save it in a FAISS index
```
dpr_dataset = load_dataset(
"text",
data_files=ARC_CORPUS_TEXT,
cache_dir=CACHE_DIR,
split="train[:100%]",
)
dpr_dataset.load_faiss_index("embeddings", f"{ARC_CORPUS_FAISS}")
torch.set_grad_enabled(False)
```
Then, as a processor of my dataset, I created a map function that calls the `dpr_dataset` for each _option_
```
def generate_context(example):
question_text = example['question']
for option in example['options']:
question_with_option = question_text + " " + option['option_text']
tokenize_text = question_tokenizer(question_with_option, return_tensors="pt").to(device)
question_embed = (
question_encoder(**tokenize_text)
)[0][0].cpu().numpy()
_, retrieved_examples = dpr_dataset.get_nearest_examples(
"embeddings", question_embed, k=10
)
# option["option_context"] = retrieved_examples["text"]
# option["option_context"] = " ".join(option["option_context"]).strip()
#result_dict = {
# 'example_id': example['example_id'],
# 'answer': example['answer'],
# 'question': question_text,
#options': example['options']
# }
return example
```
I intentionally commented on this portion of the code.
But when I call the `map` method, `ds_with_context = dataset.map(generate_context,load_from_cache_file=False)`
It calls the following error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-55-75a458ce205c> in <module>
----> 1 ds_with_context = dataset.map(generate_context,load_from_cache_file=False)
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1257 fn_kwargs=fn_kwargs,
1258 new_fingerprint=new_fingerprint,
-> 1259 update_data=update_data,
1260 )
1261 else:
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
155 }
156 # apply actual function
--> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
159 # re-apply format to the output
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name
157 kwargs[fingerprint_name] = update_fingerprint(
--> 158 self._fingerprint, transform, kwargs_for_fingerprint
159 )
160
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)
103 for key in sorted(transform_args):
104 hasher.update(key)
--> 105 hasher.update(transform_args[key])
106 return hasher.hexdigest()
107
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update(self, value)
55 def update(self, value):
56 self.m.update(f"=={type(value)}==".encode("utf8"))
---> 57 self.m.update(self.hash(value).encode("utf-8"))
58
59 def hexdigest(self):
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash(cls, value)
51 return cls.dispatch[type(value)](cls, value)
52 else:
---> 53 return cls.hash_default(value)
54
55 def update(self, value):
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash_default(cls, value)
44 @classmethod
45 def hash_default(cls, value):
---> 46 return cls.hash_bytes(dumps(value))
47
48 @classmethod
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dumps(obj)
387 file = StringIO()
388 with _no_cache_fields(obj):
--> 389 dump(obj, file)
390 return file.getvalue()
391
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dump(obj, file)
359 def dump(obj, file):
360 """pickle an object to a file"""
--> 361 Pickler(file, recurse=True).dump(obj)
362 return
363
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in dump(self, obj)
452 raise PicklingError(msg)
453 else:
--> 454 StockPickler.dump(self, obj)
455 stack.clear() # clear record of 'recursion-sensitive' pickled objects
456 return
/usr/lib/python3.7/pickle.py in dump(self, obj)
435 if self.proto >= 4:
436 self.framer.start_framing()
--> 437 self.save(obj)
438 self.write(STOP)
439 self.framer.end_framing()
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in save_function(pickler, obj)
554 dill._dill._create_function,
555 (obj.__code__, globs, obj.__name__, obj.__defaults__, obj.__closure__, obj.__dict__, fkwdefaults),
--> 556 obj=obj,
557 )
558 else:
/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
636 else:
637 save(func)
--> 638 save(args)
639 write(REDUCE)
640
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/usr/lib/python3.7/pickle.py in save_tuple(self, obj)
784 write(MARK)
785 for element in obj:
--> 786 save(element)
787
788 if id(obj) in memo:
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
880 for k, v in tmp:
881 save(k)
--> 882 save(v)
883 write(SETITEMS)
884 elif n:
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
880 for k, v in tmp:
881 save(k)
--> 882 save(v)
883 write(SETITEMS)
884 elif n:
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
885 k, v = tmp[0]
886 save(k)
--> 887 save(v)
888 write(SETITEM)
889 # else tmp is empty, and we're done
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
880 for k, v in tmp:
881 save(k)
--> 882 save(v)
883 write(SETITEMS)
884 elif n:
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
885 k, v = tmp[0]
886 save(k)
--> 887 save(v)
888 write(SETITEM)
889 # else tmp is empty, and we're done
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
522 reduce = getattr(obj, "__reduce_ex__", None)
523 if reduce is not None:
--> 524 rv = reduce(self.proto)
525 else:
526 reduce = getattr(obj, "__reduce__", None)
TypeError: can't pickle SwigPyObject objects
```
Which I have no idea how to solve/deal with it
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https://api.github.com/repos/huggingface/datasets/issues/1804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1804/comments | https://api.github.com/repos/huggingface/datasets/issues/1804/events | https://github.com/huggingface/datasets/pull/1804 | 798,483,881 | MDExOlB1bGxSZXF1ZXN0NTY1MjkzMTc3 | 1,804 | Add SICK dataset | {
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} | [] | closed | false | null | [] | null | 0 | "2021-02-01T15:57:44Z" | "2021-02-05T17:46:28Z" | "2021-02-05T15:49:25Z" | CONTRIBUTOR | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/1804.diff",
"html_url": "https://github.com/huggingface/datasets/pull/1804",
"merged_at": "2021-02-05T15:49:25Z",
"patch_url": "https://github.com/huggingface/datasets/pull/1804.patch",
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} | Adds the SICK dataset (http://marcobaroni.org/composes/sick.html).
Closes #1772.
Edit: also closes #1632, which is the original issue requesting the dataset. The newer one is a duplicate. | {
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https://api.github.com/repos/huggingface/datasets/issues/1803 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1803/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1803/comments | https://api.github.com/repos/huggingface/datasets/issues/1803/events | https://github.com/huggingface/datasets/issues/1803 | 798,243,904 | MDU6SXNzdWU3OTgyNDM5MDQ= | 1,803 | Querying examples from big datasets is slower than small datasets | {
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For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_dataset("bookcorpus", split="train[:50%]")
b100 = load_dataset("bookcorpus", split="train[:100%]")
%timeit _ = b1[-1]
# 12.2 µs ± 70.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit _ = b50[-1]
# 92.5 µs ± 1.24 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
%timeit _ = b100[-1]
# 177 µs ± 3.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
It looks like the time to fetch the example increases with the size of the dataset.
This is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.
Maybe switching to the Arrow IPC file format could help fixing this issue.
Indeed according to the [documentation](https://arrow.apache.org/docs/format/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:
> We define a “file format” supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.
cc @gaceladri since it can help speed up your training when this one is fixed. | {
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} | This PR will add contributors GitHub id at the end of every dataset cards. | {
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|
https://api.github.com/repos/huggingface/datasets/issues/1800 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1800/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1800/comments | https://api.github.com/repos/huggingface/datasets/issues/1800/events | https://github.com/huggingface/datasets/pull/1800 | 797,798,689 | MDExOlB1bGxSZXF1ZXN0NTY0NzE5MjA3 | 1,800 | Add DuoRC Dataset | {
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} | [] | closed | false | null | [] | null | 1 | "2021-01-31T20:01:59Z" | "2021-02-03T05:01:45Z" | "2021-02-02T22:49:26Z" | CONTRIBUTOR | null | 0 | {
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} | Hi,
DuoRC SelfRC is one type of the [DuoRC Dataset](https://duorc.github.io/). DuoRC SelfRC is a crowdsourced Abstractive/Extractive Question-Answering dataset based on Wikipedia movie plots. It contains examples that may have answers in the movie plot, synthesized answers which are not present in the movie plot, or no answers. I have also added ParaphraseRC - the other type of DuoRC dataset where questions are based on Wikipedia movie plots and answers are based on corresponding IMDb movie plots.
Paper : [https://arxiv.org/abs/1804.07927](https://arxiv.org/abs/1804.07927)
I want to add this to 🤗 datasets to make it more accessible to the community. I have added all the details that I could find. Please let me know if anything else is needed from my end.
Thanks,
Gunjan
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https://api.github.com/repos/huggingface/datasets/issues/1799 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1799/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1799/comments | https://api.github.com/repos/huggingface/datasets/issues/1799/events | https://github.com/huggingface/datasets/pull/1799 | 797,789,439 | MDExOlB1bGxSZXF1ZXN0NTY0NzEyMzUy | 1,799 | Update: SWDA - Fixed code to use all metadata features. Added comments and cleaned c… | {
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} | [] | closed | false | null | [] | null | 1 | "2021-01-31T19:18:55Z" | "2021-02-09T22:06:13Z" | "2021-02-09T15:49:58Z" | CONTRIBUTOR | null | 0 | {
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} | This is a dataset I currently use my research and I realized some features are not being returned.
Previous code was not using all available metadata and was kind of messy
I fixed code to use all metadata and made some modification to be more efficient and better formatted.
Please let me know if I need to make any changes. | {
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} | [] | closed | false | null | [] | null | 1 | "2021-01-31T17:38:55Z" | "2021-02-10T20:39:13Z" | "2021-02-03T10:35:54Z" | CONTRIBUTOR | null | 0 | {
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} | This MIT license dataset: https://github.com/iabufarha/ArSarcasm
Via https://sites.google.com/view/ar-sarcasm-sentiment-detection/ | {
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} | [] | closed | false | null | [] | null | 1 | "2021-01-30T07:32:45Z" | "2021-08-04T18:09:37Z" | "2021-08-04T18:09:37Z" | NONE | null | null | null | Hi
I am hitting to the error, help me and thanks.
`train_data = datasets.load_dataset("xsum", split="train")`
`ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/xsum/xsum.py` | {
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} | [] | open | false | null | [] | null | 10 | "2021-01-30T04:09:19Z" | "2024-01-19T13:25:21Z" | null | NONE | null | null | null | I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.
When I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.
Below are the variants I tried.
1. filter() with batch size 1024, single process (takes roughly 3 hr)
2. filter() with batch size 1024, 96 processes (takes 5-6 hrs ¯\\\_(ツ)\_/¯)
3. filter() with loading all data in memory, only a single boolean column (never ends).
Can someone please help?
Below is a sample code for small dataset.
```
from datasets import load_dataset
dataset = load_dataset('glue', 'mrpc', split='train')
dataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})
def _amplify(data):
return data
dataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])
```
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https://api.github.com/repos/huggingface/datasets/issues/1795 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1795/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1795/comments | https://api.github.com/repos/huggingface/datasets/issues/1795/events | https://github.com/huggingface/datasets/pull/1795 | 797,021,730 | MDExOlB1bGxSZXF1ZXN0NTY0MDk5OTUz | 1,795 | Custom formatting for lazy map + arrow data extraction refactor | {
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} | Hi !
This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions.
While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly.
A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`.
### What's new:
You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`.
A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch.
Here is an example to tokenize and pad tokens on-the-fly when accessing the samples:
```python
from datasets import load_dataset
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def encode(batch):
return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
dataset = load_dataset("glue", "mrpc", split="train")
dataset.set_format(transform=encode)
dataset.format
# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
dataset[:2]
# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}
```
Let me know what you think of this API !
We can still change it if we want to.
Especially @sgugger since this may be useful when using `datasets` to train models.
EDIT: this was changed to `dataset.set_transform(encode)`
-------------------
Note:
I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them.
Some bugs I found and fixed:
- certain slices/ranges were not supported because negative ids were passed to pyarrow
- formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy)
- on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32
The unit tests for those are now really extensive :) | {
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} | The dataset was added in #1761 but not in the right directory. I'm moving it to /datasets | {
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} | Minor fix:
- duplicated attributes
- format fix | {
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} | Allow loading datasets either from:
- memory-mapped file (current implementation)
- from file descriptor, copying data to physical memory
Close #708 | {
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} | Now you can set `train_size` to the whole dataset size via `train_size = -1` and login writes not `Training the index with the first -1 vectors` but (for example) `Training the index with the first 16123 vectors`. And maybe more than dataset length. Logging will be correct | {
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import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct? | {
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https://api.github.com/repos/huggingface/datasets/issues/1787 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1787/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1787/comments | https://api.github.com/repos/huggingface/datasets/issues/1787/events | https://github.com/huggingface/datasets/pull/1787 | 795,485,842 | MDExOlB1bGxSZXF1ZXN0NTYyODI1NTI3 | 1,787 | Update the CommonGen citation information | {
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https://api.github.com/repos/huggingface/datasets/issues/1786 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1786/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1786/comments | https://api.github.com/repos/huggingface/datasets/issues/1786/events | https://github.com/huggingface/datasets/issues/1786 | 795,462,816 | MDU6SXNzdWU3OTU0NjI4MTY= | 1,786 | How to use split dataset | {
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] | closed | false | null | [] | null | 2 | "2021-01-27T21:37:47Z" | "2021-04-23T15:17:39Z" | "2021-04-23T15:17:39Z" | NONE | null | null | null | 
Hey,
I want to split the lambada dataset into corpus, test, train and valid txt files (like penn treebank) but I am not able to achieve this. What I am doing is, executing the lambada.py file in my project but its not giving desired results. Any help will be appreciated! | {
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https://api.github.com/repos/huggingface/datasets/issues/1785 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1785/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1785/comments | https://api.github.com/repos/huggingface/datasets/issues/1785/events | https://github.com/huggingface/datasets/issues/1785 | 795,458,856 | MDU6SXNzdWU3OTU0NTg4NTY= | 1,785 | Not enough disk space (Needed: Unknown size) when caching on a cluster | {
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} | [] | closed | false | null | [] | null | 8 | "2021-01-27T21:30:59Z" | "2022-11-07T16:33:03Z" | "2021-01-30T01:07:56Z" | CONTRIBUTOR | null | null | null | I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path")
OSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)
```
[`utils.has_sufficient_disk_space`](https://github.com/huggingface/datasets/blob/8a03ab7d123a76ee744304f21ce868c75f411214/src/datasets/utils/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(".").free` can't compute on the cluster's shared disk).
This is exactly where the error gets thrown:
https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L502
```python
if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):
raise IOError(
"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})".format(
utils.size_str(self.info.size_in_bytes or 0),
utils.size_str(self.info.download_size or 0),
utils.size_str(self.info.dataset_size or 0),
utils.size_str(self.info.post_processing_size or 0),
)
)
```
What would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal.
Would it be possible to pass a flag to skip this check on disk space? | {
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https://api.github.com/repos/huggingface/datasets/issues/1784 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1784/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1784/comments | https://api.github.com/repos/huggingface/datasets/issues/1784/events | https://github.com/huggingface/datasets/issues/1784 | 794,659,174 | MDU6SXNzdWU3OTQ2NTkxNzQ= | 1,784 | JSONDecodeError on JSON with multiple lines | {
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} | [] | closed | false | null | [] | null | 2 | "2021-01-27T00:19:22Z" | "2021-01-31T08:47:18Z" | "2021-01-31T08:47:18Z" | CONTRIBUTOR | null | null | null | Hello :),
I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported:
```json
{"key1":11, "key2":12, "key3":13}
{"key1":21, "key2":22, "key3":23}
```
But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported.
When I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either.
Please let me know :)
Thanks,
Gunjan | {
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https://api.github.com/repos/huggingface/datasets/issues/1783 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1783/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1783/comments | https://api.github.com/repos/huggingface/datasets/issues/1783/events | https://github.com/huggingface/datasets/issues/1783 | 794,544,495 | MDU6SXNzdWU3OTQ1NDQ0OTU= | 1,783 | Dataset Examples Explorer | {
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} | [] | closed | false | null | [] | null | 2 | "2021-01-26T20:39:02Z" | "2021-02-01T13:58:44Z" | "2021-02-01T13:58:44Z" | NONE | null | null | null | In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.
Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation. | {
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https://api.github.com/repos/huggingface/datasets/issues/1782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1782/comments | https://api.github.com/repos/huggingface/datasets/issues/1782/events | https://github.com/huggingface/datasets/pull/1782 | 794,167,920 | MDExOlB1bGxSZXF1ZXN0NTYxNzI5OTc3 | 1,782 | Update pyarrow import warning | {
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} | [] | closed | false | null | [] | null | 0 | "2021-01-26T11:47:11Z" | "2021-01-26T13:50:50Z" | "2021-01-26T13:50:49Z" | MEMBER | null | 0 | {
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} | Update the minimum version to >=0.17.1 in the pyarrow version check and update the message.
I also moved the check at the top of the __init__.py | {
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} | [] | closed | false | null | [] | null | 8 | "2021-01-26T04:18:35Z" | "2022-10-05T12:37:06Z" | "2022-10-05T12:37:06Z" | NONE | null | null | null | I'm using Colab. And suddenly this morning, there is this error. Have a look below!
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https://api.github.com/repos/huggingface/datasets/issues/1780 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1780/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1780/comments | https://api.github.com/repos/huggingface/datasets/issues/1780/events | https://github.com/huggingface/datasets/pull/1780 | 793,882,132 | MDExOlB1bGxSZXF1ZXN0NTYxNDkxNTgy | 1,780 | Update SciFact URL | {
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} | [] | closed | false | null | [] | null | 7 | "2021-01-26T02:49:06Z" | "2021-01-28T18:48:00Z" | "2021-01-28T10:19:45Z" | CONTRIBUTOR | null | 0 | {
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} | Hi,
I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset!
Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running
```
python datasets-cli test datasets/scifact --save_infos --all_configs
```
which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening?
Can you help me switch the download URL?
```
(datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs
Checking datasets/scifact/scifact.py for additional imports.
Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact
Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py
Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json
Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json
Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534
Testing builder 'corpus' (1/2)
Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534)
Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534...
Downloading took 0.0 min
Checksum Computation took 0.0 min
Traceback (most recent call last):
File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module>
service.run()
File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run
builder.download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare
self._download_and_prepare(
File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare
verify_checksums(
File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'}
``` | {
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} | [] | closed | false | null | [] | null | 0 | "2021-01-25T16:42:29Z" | "2021-01-26T10:20:20Z" | "2021-01-26T10:20:19Z" | MEMBER | null | 0 | {
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} | As noticed in #1718 , when a function used for processing with `map` is moved inside its python file, then the change of line number causes the caching mechanism to consider it as a different function. Therefore in this case, it recomputes everything.
This is because we were not ignoring the line number definition for such functions (even though we're doing it for lambda functions).
For example this code currently prints False:
```python
from datasets.fingerprint import Hasher
# define once
def foo(x):
return x
h = Hasher.hash(foo)
# define a second time elsewhere
def foo(x):
return x
print(h == Hasher.hash(foo))
```
I changed this by ignoring the line number for all functions. | {
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} | [] | closed | false | null | [] | null | 6 | "2021-01-25T15:22:31Z" | "2021-01-29T09:35:14Z" | "2021-01-29T09:34:51Z" | CONTRIBUTOR | null | 0 | {
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} | Submitting the manual version of Narrative QA script which requires a manual download from the original repository | {
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https://api.github.com/repos/huggingface/datasets/issues/1777 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1777/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1777/comments | https://api.github.com/repos/huggingface/datasets/issues/1777/events | https://github.com/huggingface/datasets/issues/1777 | 793,273,770 | MDU6SXNzdWU3OTMyNzM3NzA= | 1,777 | GPT2 MNLI training using run_glue.py | {
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} | [] | closed | false | null | [] | null | 0 | "2021-01-25T10:53:52Z" | "2021-01-25T11:12:53Z" | "2021-01-25T11:12:53Z" | NONE | null | null | null | Edit: I'm closing this because I actually meant to post this in `transformers `not `datasets`
Running this on Google Colab,
```
!python run_glue.py \
--model_name_or_path gpt2 \
--task_name mnli \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_gpu_train_batch_size 10 \
--gradient_accumulation_steps 32\
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir models/gpt2/mnli/
```
I get the following error,
```
"Asking to pad but the tokenizer does not have a padding token. "
ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.
```
Do I need to modify the trainer to work with GPT2 ? | {
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} | [] | closed | false | null | [] | null | 6 | "2021-01-24T09:28:24Z" | "2021-05-20T04:15:58Z" | "2021-05-20T04:15:58Z" | NONE | null | null | null | I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache?
BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:
https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532 | {
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} | [] | closed | false | null | [] | null | 2 | "2021-01-24T07:54:31Z" | "2021-01-24T09:50:39Z" | "2021-01-24T09:50:39Z" | CONTRIBUTOR | null | null | null | For a large dataset that does not fits the memory, how can I select only a subset of features from each example?
If I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this?
Thanks | {
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] | null | 2 | "2021-01-24T06:15:52Z" | "2024-01-31T15:54:18Z" | "2024-01-31T15:54:18Z" | NONE | null | null | null | Hi,
see below error:
```
AssertionError: Requested slice [:10000000000000000] incompatible with 20 examples.
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1773 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1773/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1773/comments | https://api.github.com/repos/huggingface/datasets/issues/1773/events | https://github.com/huggingface/datasets/issues/1773 | 792,708,160 | MDU6SXNzdWU3OTI3MDgxNjA= | 1,773 | bug in loading datasets | {
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"type": "User",
"url": "https://api.github.com/users/ghost"
} | [] | closed | false | null | [] | null | 3 | "2021-01-24T02:53:45Z" | "2021-09-06T08:54:46Z" | "2021-08-04T18:13:01Z" | NONE | null | null | null | Hi,
I need to load a dataset, I use these commands:
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files={'train': 'sick/train.csv',
'test': 'sick/test.csv',
'validation': 'sick/validation.csv'})
print(dataset['validation'])
```
the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq
```
Using custom data configuration default
Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...
Traceback (most recent call last):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir
yield tmp_dir
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__
return self.get_chunk()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk
return self.read(nrows=size)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read
index, columns, col_dict = self._engine.read(nrows)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "write_sick.py", line 19, in <module>
'validation': 'sick/validation.csv'})
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare
self._save_info()
File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__
self.gen.throw(type, value, traceback)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir
shutil.rmtree(tmp_dir)
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'
```
| {
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https://api.github.com/repos/huggingface/datasets/issues/1772 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1772/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1772/comments | https://api.github.com/repos/huggingface/datasets/issues/1772/events | https://github.com/huggingface/datasets/issues/1772 | 792,703,797 | MDU6SXNzdWU3OTI3MDM3OTc= | 1,772 | Adding SICK dataset | {
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{
"color": "e99695",
"default": false,
"description": "Requesting to add a new dataset",
"id": 2067376369,
"name": "dataset request",
"node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request"
}
] | closed | false | null | [] | null | 0 | "2021-01-24T02:15:31Z" | "2021-02-05T15:49:25Z" | "2021-02-05T15:49:25Z" | NONE | null | null | null | Hi
It would be great to include SICK dataset.
## Adding a Dataset
- **Name:** SICK
- **Description:** a well known entailment dataset
- **Paper:** http://marcobaroni.org/composes/sick.html
- **Data:** http://marcobaroni.org/composes/sick.html
- **Motivation:** this is an important NLI benchmark
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/1771 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1771/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1771/comments | https://api.github.com/repos/huggingface/datasets/issues/1771/events | https://github.com/huggingface/datasets/issues/1771 | 792,701,276 | MDU6SXNzdWU3OTI3MDEyNzY= | 1,771 | Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py | {
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"url": "https://api.github.com/users/world2vec"
} | [] | closed | false | null | [] | null | 3 | "2021-01-24T01:53:52Z" | "2021-01-24T23:06:29Z" | "2021-01-24T23:06:29Z" | NONE | null | null | null | Hi,
When I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset?
```
Traceback (most recent call last):
File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
max_retries=download_config.max_retries,
File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1770 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1770/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1770/comments | https://api.github.com/repos/huggingface/datasets/issues/1770/events | https://github.com/huggingface/datasets/issues/1770 | 792,698,148 | MDU6SXNzdWU3OTI2OTgxNDg= | 1,770 | how can I combine 2 dataset with different/same features? | {
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"url": "https://api.github.com/users/world2vec"
} | [] | closed | false | null | [] | null | 3 | "2021-01-24T01:26:06Z" | "2022-06-01T15:43:15Z" | "2022-06-01T15:43:15Z" | NONE | null | null | null | to combine 2 dataset by one-one map like ds = zip(ds1, ds2):
ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'}
or different feature:
ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} | {
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https://api.github.com/repos/huggingface/datasets/issues/1769 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1769/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1769/comments | https://api.github.com/repos/huggingface/datasets/issues/1769/events | https://github.com/huggingface/datasets/issues/1769 | 792,523,284 | MDU6SXNzdWU3OTI1MjMyODQ= | 1,769 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 | {
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"url": "https://api.github.com/users/shuaihuaiyi"
} | [] | closed | false | null | [] | null | 4 | "2021-01-23T10:13:00Z" | "2022-10-05T12:38:51Z" | "2022-10-05T12:38:51Z" | NONE | null | null | null | It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chinese-roberta-wwm-ext
--train_file
/nfs/volume-377-2/bert/data/test/train.txt
--output_dir
test
--do_train
--per_device_train_batch_size
2
--gradient_accumulation_steps
2
--learning_rate
1e-4
--max_steps
1000
--warmup_steps
10
--save_steps
1000
--save_total_limit
1
--seed
23333
--max_seq_length
512
--preprocessing_num_workers
2
--cache_dir
/nfs/volume-377-2/bert/data/test/cache
```
Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.
Full Traceback:
```
Traceback (most recent call last):
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module>
main()
File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks
put(task)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump
StockPickler.dump(self, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump
self.save(obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function
obj.__dict__, fkwdefaults), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce
save(cls)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type
obj.__bases__, _dict), obj=obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save
self.save_global(obj, rv)
File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union
```
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https://api.github.com/repos/huggingface/datasets/issues/1768 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1768/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1768/comments | https://api.github.com/repos/huggingface/datasets/issues/1768/events | https://github.com/huggingface/datasets/pull/1768 | 792,150,745 | MDExOlB1bGxSZXF1ZXN0NTYwMDgyNzIx | 1,768 | Mention kwargs in the Dataset Formatting docs | {
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} | [] | closed | false | null | [] | null | 0 | "2021-01-22T16:43:20Z" | "2021-01-31T12:33:10Z" | "2021-01-25T09:14:59Z" | CONTRIBUTOR | null | 0 | {
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} | Hi,
This was discussed in Issue #1762 where the docs didn't mention that keyword arguments to `datasets.Dataset.set_format()` are allowed.
To prevent people from having to check the code/method docs, I just added a couple of lines in the docs.
Please let me know your thoughts on this.
Thanks,
Gunjan
@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/1767 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1767/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1767/comments | https://api.github.com/repos/huggingface/datasets/issues/1767/events | https://github.com/huggingface/datasets/pull/1767 | 792,068,497 | MDExOlB1bGxSZXF1ZXN0NTYwMDE2MzE2 | 1,767 | Add Librispeech ASR | {
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} | [] | closed | false | null | [] | null | 1 | "2021-01-22T14:54:37Z" | "2021-01-25T20:38:07Z" | "2021-01-25T20:37:42Z" | MEMBER | null | 0 | {
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} | This PR adds the librispeech asr dataset: https://www.tensorflow.org/datasets/catalog/librispeech
There are 2 configs: "clean" and "other" whereas there are two "train" datasets for "clean", hence the name "train.100" and "train.360".
As suggested by @lhoestq, due to the enormous size of the dataset in `.arrow` format, the speech files are not directly prepared to a float32-array, but instead just the path to the array file is stored. | {
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https://api.github.com/repos/huggingface/datasets/issues/1766 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1766/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1766/comments | https://api.github.com/repos/huggingface/datasets/issues/1766/events | https://github.com/huggingface/datasets/issues/1766 | 792,044,105 | MDU6SXNzdWU3OTIwNDQxMDU= | 1,766 | Issues when run two programs compute the same metrics | {
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} | [] | closed | false | null | [] | null | 2 | "2021-01-22T14:22:55Z" | "2021-02-02T10:38:06Z" | "2021-02-02T10:38:06Z" | NONE | null | null | null | I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:
```
File "train_matching_min.py", line 160, in <module>ch_9_label
avg_loss = valid(epoch, args.batch, args.validation, args.with_label)
File "train_matching_min.py", line 93, in valid
bleu += eval.compute()
File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute
return self.metric.compute()['score']
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute
self._finalize()
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize
self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths]))
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files
pa_table = self._read_files(files)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict)
File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename
pa_table = f.read_all()
File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1765 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1765/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1765/comments | https://api.github.com/repos/huggingface/datasets/issues/1765/events | https://github.com/huggingface/datasets/issues/1765 | 791,553,065 | MDU6SXNzdWU3OTE1NTMwNjU= | 1,765 | Error iterating over Dataset with DataLoader | {
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} | [] | closed | false | null | [] | null | 6 | "2021-01-21T22:56:45Z" | "2022-10-28T02:16:38Z" | "2021-01-23T03:44:14Z" | NONE | null | null | null | I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113,
102]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}
```
When I try to iterate as in the docs, I get errors:
```
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
next(iter(dataloader))
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-05180ba8aa35> in <module>()
1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
----> 2 next(iter(dataloader))
3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader)
411 self._timeout = loader.timeout
412 self._collate_fn = loader.collate_fn
--> 413 self._sampler_iter = iter(self._index_sampler)
414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
415 self._persistent_workers = loader.persistent_workers
TypeError: 'int' object is not iterable
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1764 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1764/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1764/comments | https://api.github.com/repos/huggingface/datasets/issues/1764/events | https://github.com/huggingface/datasets/issues/1764 | 791,486,860 | MDU6SXNzdWU3OTE0ODY4NjA= | 1,764 | Connection Issues | {
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} | [] | closed | false | null | [] | null | 1 | "2021-01-21T20:56:09Z" | "2021-01-21T21:00:19Z" | "2021-01-21T21:00:02Z" | NONE | null | null | null | Today, I am getting connection issues while loading a dataset and the metric.
```
Traceback (most recent call last):
File "src/train.py", line 180, in <module>
train_dataset, dev_dataset, test_dataset = create_race_dataset()
File "src/train.py", line 130, in create_race_dataset
train_dataset = load_dataset("race", "all", split="train")
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 591, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
max_retries=download_config.max_retries,
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/race/race.py
```
Or
```
Traceback (most recent call last):
File "src/train.py", line 105, in <module>
rouge = datasets.load_metric("rouge")
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 500, in load_metric
dataset=False,
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
max_retries=download_config.max_retries,
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/metrics/rouge/rouge.py
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1763 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1763/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1763/comments | https://api.github.com/repos/huggingface/datasets/issues/1763/events | https://github.com/huggingface/datasets/pull/1763 | 791,389,763 | MDExOlB1bGxSZXF1ZXN0NTU5NDU3MTY1 | 1,763 | PAWS-X: Fix csv Dictreader splitting data on quotes | {
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```python
from datasets import load_dataset
# load english paws-x dataset
datasets = load_dataset('paws-x', 'en')
print(len(datasets['train'])) # outputs 49202 but official dataset has 49401 pairs
print(datasets['train'].unique('label')) # outputs [1, 0, -1] but labels are binary [0,1]
```
changed `data = csv.DictReader(f, delimiter="\t")` to `data = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)` in the dataloader to make csv module not split by quotes.
The results are as expected for all languages after the change. | {
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https://api.github.com/repos/huggingface/datasets/issues/1762 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1762/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1762/comments | https://api.github.com/repos/huggingface/datasets/issues/1762/events | https://github.com/huggingface/datasets/issues/1762 | 791,226,007 | MDU6SXNzdWU3OTEyMjYwMDc= | 1,762 | Unable to format dataset to CUDA Tensors | {
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} | [] | closed | false | null | [] | null | 6 | "2021-01-21T15:31:23Z" | "2021-02-02T07:13:22Z" | "2021-02-02T07:13:22Z" | CONTRIBUTOR | null | null | null | Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't support assignment:
```
columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']
samples.set_format(type='torch', columns = columns)
for column in columns:
samples[column].to(torch.device(self.config.device))
```
There should be an option to do so, or if there is already a way to do this, please let me know.
Thanks,
Gunjan | {
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https://api.github.com/repos/huggingface/datasets/issues/1761 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1761/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1761/comments | https://api.github.com/repos/huggingface/datasets/issues/1761/events | https://github.com/huggingface/datasets/pull/1761 | 791,150,858 | MDExOlB1bGxSZXF1ZXN0NTU5MjUyMzEw | 1,761 | Add SILICONE benchmark | {
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} | [] | closed | false | null | [] | null | 8 | "2021-01-21T14:29:12Z" | "2021-02-04T14:32:48Z" | "2021-01-26T13:50:31Z" | CONTRIBUTOR | null | 0 | {
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} | My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication.
This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
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https://api.github.com/repos/huggingface/datasets/issues/1760 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1760/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1760/comments | https://api.github.com/repos/huggingface/datasets/issues/1760/events | https://github.com/huggingface/datasets/pull/1760 | 791,110,857 | MDExOlB1bGxSZXF1ZXN0NTU5MjE3MjY0 | 1,760 | More tags | {
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} | [] | closed | false | null | [] | null | 2 | "2021-01-21T13:50:10Z" | "2021-01-22T09:40:01Z" | "2021-01-22T09:40:00Z" | MEMBER | null | 0 | {
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} | Since the hub v2 is going to be released soon I figured it would be great to add the missing tags at least for some of the datasets of reference listed [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#write-the-loadingprocessing-code) | {
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https://api.github.com/repos/huggingface/datasets/issues/1759 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1759/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1759/comments | https://api.github.com/repos/huggingface/datasets/issues/1759/events | https://github.com/huggingface/datasets/issues/1759 | 790,992,226 | MDU6SXNzdWU3OTA5OTIyMjY= | 1,759 | wikipedia dataset incomplete | {
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} | [] | closed | false | null | [] | null | 4 | "2021-01-21T11:47:15Z" | "2021-01-21T17:22:11Z" | "2021-01-21T17:21:06Z" | NONE | null | null | null | Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche).
The pickle file however shows: französische Gemeinde mit Einwohnern (Stand).
Is it possible to fix this?
Best regards
Chris
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https://api.github.com/repos/huggingface/datasets/issues/1758 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1758/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1758/comments | https://api.github.com/repos/huggingface/datasets/issues/1758/events | https://github.com/huggingface/datasets/issues/1758 | 790,626,116 | MDU6SXNzdWU3OTA2MjYxMTY= | 1,758 | dataset.search() (elastic) cannot reliably retrieve search results | {
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} | [] | closed | false | null | [] | null | 2 | "2021-01-21T02:26:37Z" | "2021-01-22T00:25:50Z" | "2021-01-22T00:25:50Z" | NONE | null | null | null | I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.
I am indexing data that looks like the following from the HF SQuAD 2.0 data set:
```
['57318658e6313a140071d02b',
'56f7165e3d8e2e1400e3733a',
'570e2f6e0b85d914000d7d21',
'5727e58aff5b5019007d97d0',
'5a3b5a503ff257001ab8441f',
'57262fab271a42140099d725']
```
To reproduce the issue, try:
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# build base examples, features set of training data
shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list()
examples = load_dataset("squad_v2").shuffle(seed=1)['train']
features = load_dataset("squad_v2").shuffle(seed=1)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# reorder features by the training process
features = features.select(indices=shuffled_idx)
# get the example ids to match with the "example" data; get unique entries
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions in the examples data set; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# retrieve the example index
example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]
example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]
example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]
len(example_idx_k1) # should be 130319
len(example_idx_k2) # should be 130319
#trial 1 lengths:
# k=1: 130314
# k=3: 130319
# trial 2:
# just run k=3 first: 130310
# try k=1 after k=3: 130319
```
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https://api.github.com/repos/huggingface/datasets/issues/1757 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1757/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1757/comments | https://api.github.com/repos/huggingface/datasets/issues/1757/events | https://github.com/huggingface/datasets/issues/1757 | 790,466,509 | MDU6SXNzdWU3OTA0NjY1MDk= | 1,757 | FewRel | {
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] | closed | false | null | [] | null | 5 | "2021-01-20T23:56:03Z" | "2021-03-09T02:52:05Z" | "2021-03-08T14:34:52Z" | NONE | null | null | null | ## Adding a Dataset
- **Name:** FewRel
- **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset
- **Paper:** @inproceedings{han2018fewrel,
title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},
author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong},
booktitle={EMNLP},
year={2018}}
- **Data:** https://github.com/ProKil/FewRel
- **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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] | closed | false | null | [] | null | 0 | "2021-01-20T22:18:44Z" | "2021-03-01T10:36:21Z" | "2021-03-01T10:36:21Z" | CONTRIBUTOR | null | null | null | ## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French).
- **Paper:** *link to the dataset paper if available*
- https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf
- **Data:** *link to the Github repository or current dataset location*
- http://www.statmt.org/cc-aligned/
- **Motivation:** *what are some good reasons to have this dataset*
- The authors says it's an high quality dataset.
- it's pretty large and includes many language pairs. It could be interesting training mt5 on this task.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/1755 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1755/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1755/comments | https://api.github.com/repos/huggingface/datasets/issues/1755/events | https://github.com/huggingface/datasets/issues/1755 | 790,324,734 | MDU6SXNzdWU3OTAzMjQ3MzQ= | 1,755 | Using select/reordering datasets slows operations down immensely | {
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The below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score.
The problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes.
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30):
all_start_logits, all_end_logits = starting_logits, ending_logits
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
predictions = collections.OrderedDict()
# Logging.
print(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
# Let's loop over all the examples!
for example_index, example in enumerate(tqdm(examples)):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_score = None # Only used if squad_v2 is True.
valid_answers = []
context = example["context"]
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_logits = all_start_logits[feature_index]
end_logits = all_end_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Update minimum null prediction.
cls_index = features[feature_index]["input_ids"].index(tokenizer.cls_token_id)
feature_null_score = start_logits[cls_index] + end_logits[cls_index]
if min_null_score is None or min_null_score < feature_null_score:
min_null_score = feature_null_score
# Go through all possibilities for the `n_best_size` greater start and end logits.
start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
for start_index in start_indexes:
for end_index in end_indexes:
# Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
# to part of the input_ids that are not in the context.
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or offset_mapping[end_index] is None
):
continue
# Don't consider answers with a length that is either < 0 or > max_answer_length.
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
start_char = offset_mapping[start_index][0]
end_char = offset_mapping[end_index][1]
valid_answers.append(
{
"score": start_logits[start_index] + end_logits[end_index],
"text": context[start_char: end_char]
}
)
if len(valid_answers) > 0:
best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0]
else:
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
best_answer = {"text": "", "score": 0.0}
# Let's pick our final answer: the best one or the null answer (only for squad_v2)
if not squad_v2:
predictions[example["id"]] = best_answer["text"]
else:
answer = best_answer["text"] if best_answer["score"] > min_null_score else ""
predictions[example["id"]] = answer
return predictions
# build base examples, features from training data
examples = load_dataset("squad_v2").shuffle(seed=5)['train']
features = load_dataset("squad_v2").shuffle(seed=5)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# sim some shuffled training indices that we want to use to re-order the data to compare how we did
shuffle_idx = np.arange(0, 131754)
np.random.shuffle(shuffle_idx)
# create a new dataset with rows selected following the training shuffle
features = features.select(indices=shuffle_idx)
# get unique example ids to match with the "example" data
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# search the examples for their index position
example_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
# drop the elastic search
examples.drop_index(index_name='id')
# put examples in the right order
examples = examples.select(indices=example_idx)
# generate some fake data
logits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)}
def score_squad(logits, n_best_size, max_answer):
# proceed with QA calculation
final_predictions = postprocess_qa_predictions(examples=examples,
features=features,
starting_logits=logits['starting_logits'],
ending_logits=logits['ending_logits'],
n_best_size=20,
max_answer_length=30)
metric = load_metric("squad_v2")
formatted_predictions = [{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in final_predictions.items()]
references = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples]
metrics = metric.compute(predictions=formatted_predictions, references=references)
return metrics
metrics = score_squad(logits, n_best_size=20, max_answer=30)
```
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https://api.github.com/repos/huggingface/datasets/issues/1754 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1754/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1754/comments | https://api.github.com/repos/huggingface/datasets/issues/1754/events | https://github.com/huggingface/datasets/pull/1754 | 789,881,730 | MDExOlB1bGxSZXF1ZXN0NTU4MTU5NjEw | 1,754 | Use a config id in the cache directory names for custom configs | {
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} | [] | closed | false | null | [] | null | 0 | "2021-01-20T11:11:00Z" | "2021-01-25T09:12:07Z" | "2021-01-25T09:12:06Z" | MEMBER | null | 0 | {
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} | As noticed by @JetRunner there was some issues when trying to generate a dataset using a custom config that is based on an existing config.
For example in the following code the `mnli_custom` would reuse the cache used to create `mnli` instead of generating a new dataset with the new label classes:
```python
from datasets import load_dataset
mnli = load_dataset("glue", "mnli")
mnli_custom = load_dataset("glue", "mnli", label_classes=["contradiction", "entailment", "neutral"])
```
I fixed that by extending the cache directory definition of a dataset that is being generated.
Instead of using the config name in the cache directory name, I switched to using a `config_id`.
By default it is equal to the config name.
However the name of a config is not sufficent to have a unique identifier for the dataset being generated since it doesn't take into account:
- the config kwargs that can be used to overwrite attributes
- the custom features used to write the dataset
- the data_files for json/text/csv/pandas datasets
Therefore the config id is just the config name with an optional suffix based on these.
In particular taking into account the config kwargs fixes the issue with the `label_classes` above.
I completed the current test cases by adding the case that was missing: overwriting an already existing config. | {
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} | I realized COMET citations were not showing in the hugging face metrics page:
<img width="814" alt="Screenshot 2021-01-20 at 09 48 44" src="https://user-images.githubusercontent.com/17256847/105164848-8b9da900-5b0d-11eb-9e20-a38f559d2037.png">
This pull request is intended to fix that.
Thanks! | {
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} | In my last pull request to add COMET metric, the citations where not following the usual "format". Because of that they where not correctly displayed on the website:
<img width="814" alt="Screenshot 2021-01-20 at 09 48 44" src="https://user-images.githubusercontent.com/17256847/105158000-686efb80-5b05-11eb-8bb0-9c85fdac2938.png">
This pull request is only intended to fix that. | {
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} | See the updated card at https://github.com/mcmillanmajora/datasets/tree/add-SBIC-card/datasets/social_bias_frames. I incorporated information from the [SBIC data statement](https://homes.cs.washington.edu/~msap/social-bias-frames/DATASTATEMENT.html), paper, and the corpus README file included with the dataset download. | {
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} | When I read the README.md of `CNN/DailyMail Dataset`, there seems to be a typo `CCN`.
I am afraid this is a trivial matter, but I would like to make a suggestion for revision. | {
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} | Switchboard Dialog Act Corpus
I made some changes following @bhavitvyamalik recommendation in #1678:
* Contains all metadata.
* Used official implementation from the [/swda](https://github.com/cgpotts/swda) repo.
* Add official train and test splits used in [Stolcke et al. (2000)](https://web.stanford.edu/~jurafsky/ws97) and validation split used in [Probabilistic-RNN-DA-Classifier](https://github.com/NathanDuran/Probabilistic-RNN-DA-Classifier). | {
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I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq | {
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I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq | {
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} | This brings the number of examples for ModApte to match the stated `Training set (9,603 docs)...Test Set (3,299 docs)` | {
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https://api.github.com/repos/huggingface/datasets/issues/1743 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1743/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1743/comments | https://api.github.com/repos/huggingface/datasets/issues/1743/events | https://github.com/huggingface/datasets/issues/1743 | 787,631,412 | MDU6SXNzdWU3ODc2MzE0MTI= | 1,743 | Issue while Creating Custom Metric | {
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} | [] | closed | false | null | [] | null | 3 | "2021-01-17T07:01:14Z" | "2022-06-01T15:49:34Z" | "2022-06-01T15:49:34Z" | CONTRIBUTOR | null | null | null | Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}),
# Homepage of the metric for documentation
homepage="http://metric.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_metric"],
reference_urls=["http://path.to.reference.url/new_metric"]
)
def _compute(self,predictions,references,text,offset_mapping,spans):
pred_spans = []
for i,preds in enumerate(predictions):
current_preds = []
for j,token_preds in enumerate(preds):
if (preds>0.5):
current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))
pred_spans.append(current_spans)
return {
"Token Wise F1": f1_score(references,predictions,labels=[0,1]),
"Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])
}
```
I believe this is not correct. But that's not the issue I am facing right now. I get this error :
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-144-ed7349b50821> in <module>()
----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] )
2 frames
/usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch)
802 encoded_batch = {}
803 if set(batch) != set(self):
--> 804 print(batch)
805 print(self)
806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self)))
ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}
```
On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.
How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?
Thanks,
Gunjan | {
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https://api.github.com/repos/huggingface/datasets/issues/1742 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1742/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1742/comments | https://api.github.com/repos/huggingface/datasets/issues/1742/events | https://github.com/huggingface/datasets/pull/1742 | 787,623,640 | MDExOlB1bGxSZXF1ZXN0NTU2MjgyMDYw | 1,742 | Add GLUE Compat (compatible with transformers<3.5.0) | {
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} | [] | closed | false | null | [] | null | 2 | "2021-01-17T05:54:25Z" | "2023-09-24T09:52:12Z" | "2021-03-29T12:43:30Z" | CONTRIBUTOR | null | 0 | {
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} | Link to our discussion on Slack (HF internal)
https://huggingface.slack.com/archives/C014N4749J9/p1609668119337400
The next step is to add a compatible option in the new `run_glue.py`
I duplicated `glue` and made the following changes:
1. Change the name to `glue_compat`.
2. Change the label assignments for MNLI and AX. | {
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https://api.github.com/repos/huggingface/datasets/issues/1741 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1741/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1741/comments | https://api.github.com/repos/huggingface/datasets/issues/1741/events | https://github.com/huggingface/datasets/issues/1741 | 787,327,060 | MDU6SXNzdWU3ODczMjcwNjA= | 1,741 | error when run fine_tuning on text_classification | {
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} | [] | closed | false | null | [] | null | 1 | "2021-01-16T02:23:19Z" | "2021-01-16T02:39:28Z" | "2021-01-16T02:39:18Z" | NONE | null | null | null | dataset:sem_eval_2014_task_1
pretrained_model:bert-base-uncased
error description:
when i use these resoruce to train fine_tuning a text_classification on sem_eval_2014_task_1,there always be some problem(when i use other dataset ,there exist the error too). And i followed the colab code (url:https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb#scrollTo=TlqNaB8jIrJW).
the error is like this :
`File "train.py", line 69, in <module>
trainer.train()
File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/transformers/trainer.py", line 784, in train
for step, inputs in enumerate(epoch_iterator):
File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/projects/anaconda3/envs/calibration/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/projects/anaconda3/envs/calibration/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]
KeyError: 2`
this is my code :
```dataset_name = 'sem_eval_2014_task_1'
num_labels_size = 3
batch_size = 4
model_checkpoint = 'bert-base-uncased'
number_train_epoch = 5
def tokenize(batch):
return tokenizer(batch['premise'], batch['hypothesis'], truncation=True, )
def compute_metrics(pred):
labels = pred.label_ids
preds = pred.predictions.argmax(-1)
precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro')
acc = accuracy_score(labels, preds)
return {
'accuracy': acc,
'f1': f1,
'precision': precision,
'recall': recall
}
model = BertForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels_size)
tokenizer = BertTokenizerFast.from_pretrained(model_checkpoint, use_fast=True)
train_dataset = load_dataset(dataset_name, split='train')
test_dataset = load_dataset(dataset_name, split='test')
train_encoded_dataset = train_dataset.map(tokenize, batched=True)
test_encoded_dataset = test_dataset.map(tokenize, batched=True)
args = TrainingArguments(
output_dir='./results',
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
num_train_epochs=number_train_epoch,
weight_decay=0.01,
do_predict=True,
)
trainer = Trainer(
model=model,
args=args,
compute_metrics=compute_metrics,
train_dataset=train_encoded_dataset,
eval_dataset=test_encoded_dataset,
tokenizer=tokenizer
)
trainer.train()
trainer.evaluate()
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} | [] | closed | false | null | [] | null | 0 | "2021-01-15T22:58:34Z" | "2021-01-20T13:41:26Z" | "2021-01-20T13:41:26Z" | CONTRIBUTOR | null | 0 | {
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} | id_liputan6 is a large-scale Indonesian summarization dataset. The articles were harvested from an online news portal, and obtain 215,827 document-summary pairs: https://arxiv.org/abs/2011.00679 | {
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} | - fixes test sets loading in v3.0
- adds additional fields for v3.0_ru
- adds info to the WebNLG data card | {
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} | [] | closed | false | null | [] | null | 3 | "2021-01-14T15:11:25Z" | "2021-01-15T10:08:20Z" | "2021-01-15T10:08:19Z" | MEMBER | null | 0 | {
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} | Will push a new version on anaconda cloud every time a tag starting with `v` is pushed (like `v1.2.2`).
Will appear here: https://anaconda.org/huggingface/datasets
Depends on `conda-forge` for now, so the following is required for installation:
```
conda install -c huggingface -c conda-forge datasets
``` | {
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} | [] | closed | false | null | [] | null | 1 | "2021-01-14T02:49:21Z" | "2021-01-14T10:25:24Z" | "2021-01-14T10:25:24Z" | CONTRIBUTOR | null | 0 | {
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} | Base on this issue https://github.com/huggingface/datasets/issues/1064, I can now use the official links.
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https://api.github.com/repos/huggingface/datasets/issues/1736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1736/comments | https://api.github.com/repos/huggingface/datasets/issues/1736/events | https://github.com/huggingface/datasets/pull/1736 | 785,433,854 | MDExOlB1bGxSZXF1ZXN0NTU0NDYyNjYw | 1,736 | Adjust BrWaC dataset features name | {
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} | [] | closed | false | null | [] | null | 0 | "2021-01-13T20:39:04Z" | "2021-01-14T10:29:38Z" | "2021-01-14T10:29:38Z" | CONTRIBUTOR | null | 0 | {
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} | I added this dataset some days ago, and today I used it to train some models and realized that the names of the features aren't so good.
Looking at the current features hierarchy, we have "paragraphs" with a list of "sentences" with a list of "sentences?!". But the actual hierarchy is a "text" with a list of "paragraphs" with a list of "sentences".
I confused myself trying to use the dataset with these names. So I think it's better to change it. | {
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https://api.github.com/repos/huggingface/datasets/issues/1735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1735/comments | https://api.github.com/repos/huggingface/datasets/issues/1735/events | https://github.com/huggingface/datasets/pull/1735 | 785,184,740 | MDExOlB1bGxSZXF1ZXN0NTU0MjUzMDcw | 1,735 | Update add new dataset template | {
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} | [] | closed | false | null | [] | null | 2 | "2021-01-13T15:08:09Z" | "2021-01-14T15:16:01Z" | "2021-01-14T15:16:00Z" | CONTRIBUTOR | null | 0 | {
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} | This PR fixes a few typos in the "Add new dataset template" and clarifies a bit what to do for the dummy data creation when the `auto_generate` flag can't work. | {
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https://api.github.com/repos/huggingface/datasets/issues/1734 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1734/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1734/comments | https://api.github.com/repos/huggingface/datasets/issues/1734/events | https://github.com/huggingface/datasets/pull/1734 | 784,956,707 | MDExOlB1bGxSZXF1ZXN0NTU0MDYxMzMz | 1,734 | Fix empty token bug for `thainer` and `lst20` | {
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} | [] | closed | false | null | [] | null | 0 | "2021-01-13T09:55:09Z" | "2021-01-14T10:42:18Z" | "2021-01-14T10:42:18Z" | CONTRIBUTOR | null | 0 | {
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} | add a condition to check if tokens exist before yielding in `thainer` and `lst20` | {
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https://api.github.com/repos/huggingface/datasets/issues/1733 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1733/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1733/comments | https://api.github.com/repos/huggingface/datasets/issues/1733/events | https://github.com/huggingface/datasets/issues/1733 | 784,903,002 | MDU6SXNzdWU3ODQ5MDMwMDI= | 1,733 | connection issue with glue, what is the data url for glue? | {
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} | [] | closed | false | null | [] | null | 1 | "2021-01-13T08:37:40Z" | "2021-08-04T18:13:55Z" | "2021-08-04T18:13:55Z" | NONE | null | null | null | Hi
my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not
thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/1732 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1732/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1732/comments | https://api.github.com/repos/huggingface/datasets/issues/1732/events | https://github.com/huggingface/datasets/pull/1732 | 784,874,490 | MDExOlB1bGxSZXF1ZXN0NTUzOTkzNTAx | 1,732 | [GEM Dataset] Added TurkCorpus, an evaluation dataset for sentence simplification. | {
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