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one lemma per row with all possible forms and annotations
https://unimorph.github.io/ | {
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for more info : http://opus.nlpl.eu/fiskmo.php | {
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I have a question regarding "on-the-fly" tokenization. This question was elicited by reading the "How to train a new language model from scratch using Transformers and Tokenizers" [here](https://huggingface.co/blog/how-to-train). Towards the end there is this sentence: "If your dataset is very large, you can opt to load and tokenize examples on the fly, rather than as a preprocessing step". I've tried coming up with a solution that would combine both `datasets` and `tokenizers`, but did not manage to find a good pattern.
I guess the solution would entail wrapping a dataset into a Pytorch dataset.
As a concrete example from the [docs](https://huggingface.co/transformers/custom_datasets.html)
```python
import torch
class SquadDataset(torch.utils.data.Dataset):
def __init__(self, encodings):
# instead of doing this beforehand, I'd like to do tokenization on the fly
self.encodings = encodings
def __getitem__(self, idx):
return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
def __len__(self):
return len(self.encodings.input_ids)
train_dataset = SquadDataset(train_encodings)
```
How would one implement this with "on-the-fly" tokenization exploiting the vectorized capabilities of tokenizers?
----
Edit: I have come up with this solution. It does what I want, but I feel it's not very elegant
```python
class CustomPytorchDataset(Dataset):
def __init__(self):
self.dataset = some_hf_dataset(...)
self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
def __getitem__(self, batch_idx):
instance = self.dataset[text_col][batch_idx]
tokenized_text = self.tokenizer(instance, truncation=True, padding=True)
return tokenized_text
def __len__(self):
return len(self.dataset)
@staticmethod
def collate_fn(batch):
# batch is a list, however it will always contain 1 item because we should not use the
# batch_size argument as batch_size is controlled by the sampler
return {k: torch.tensor(v) for k, v in batch[0].items()}
torch_ds = CustomPytorchDataset()
# NOTE: batch_sampler returns list of integers and since here we have SequentialSampler
# it returns: [1, 2, 3], [4, 5, 6], etc. - check calling `list(batch_sampler)`
batch_sampler = BatchSampler(SequentialSampler(torch_ds), batch_size=3, drop_last=True)
# NOTE: no `batch_size` as now the it is controlled by the sampler!
dl = DataLoader(dataset=torch_ds, sampler=batch_sampler, collate_fn=torch_ds.collate_fn)
``` | {
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more info : http://opus.nlpl.eu/MontenegrinSubs.php | {
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
https://www.aclweb.org/anthology/P18-1198/ | {
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https://github.com/wenhuchen/Table-Fact-Checking
- The tables are stored as individual csv files, so need to download 16,573 🤯 csv files. As a result the `datasets_infos.json` file is huge (6.62 MB).
- Original dataset has nested structure where, where table is one example and each table has multiple statements,
flattening the structure here so that each statement is one example. | {
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} | 2020-12-05T11:25:34Z | https://api.github.com/repos/huggingface/datasets/issues/1158/comments | # Dataset Card for BBC Hindi NLI 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)
## 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. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text 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
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### 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
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## 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/avinsit123/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.",
}
```
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More info : http://opus.nlpl.eu/XhosaNavy.php | {
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[Rikters et al., 2020](https://www.aclweb.org/anthology/D19-5204.pdf). | {
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Followed all steps for adding a new dataset. | {
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} | 2020-12-04T22:36:38Z | https://api.github.com/repos/huggingface/datasets/issues/1144/comments | This PR adds [JFLEG ](https://www.aclweb.org/anthology/E17-2037/), an English grammatical error correction benchmark.
The tests were successful on real data, although it would be great if I can get some guidance on the **dummy data**. Basically, **for each source sentence there are 4 possible gold standard target sentences**. The original dataset comprise files in a flat structure, labelled by split then by source/target (e.g., dev.src, dev.ref0, ..., dev.ref3). Not sure what is the best way of adding this.
I imagine I can treat each distinct source-target pair as its own split? But having so many copies of the source sentence feels redundant, and it would make it less convenient to end-users who might want to access multiple gold standard targets simultaneously. | {
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} | 2020-12-04T22:26:59Z | https://api.github.com/repos/huggingface/datasets/issues/1143/comments | Adds the Winograd Schema Challenge, including configs for the more canonical wsc273 as well as wsc285 with 12 new examples.
- https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html
The data format was a bit of a nightmare but I think I got it to a workable format. | {
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757,399,142 | https://api.github.com/repos/huggingface/datasets/issues/1140 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1140/events | [] | null | 2020-12-07T03:27:23Z | [] | https://github.com/huggingface/datasets/pull/1140 | CONTRIBUTOR | null | false | null | [] | Add Urdu Sentiment Corpus (USC). | {
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757,393,158 | https://api.github.com/repos/huggingface/datasets/issues/1139 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1139/events | [] | null | 2020-12-16T16:01:18Z | [] | https://github.com/huggingface/datasets/pull/1139 | CONTRIBUTOR | null | false | null | [] | Add ReFreSD dataset | {
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} | 2020-12-04T20:45:11Z | https://api.github.com/repos/huggingface/datasets/issues/1139/comments | This PR adds the **ReFreSD dataset**.
The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.
Need feedback on:
- I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.
- The feature names.
- I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.
- There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.
- The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.
Thanks in advance | {
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757,358,145 | https://api.github.com/repos/huggingface/datasets/issues/1137 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1137/events | [] | null | 2020-12-06T19:59:44Z | [] | https://github.com/huggingface/datasets/pull/1137 | CONTRIBUTOR | null | true | null | [] | add wmt mlqe 2020 shared task | {
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Note that I copied the tags in the README for only one (of the 7 configurations): `en-de`.
There is one configuration for each pair of languages. | {
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757,301,368 | https://api.github.com/repos/huggingface/datasets/issues/1132 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1132/events | [] | null | 2020-12-04T20:52:48Z | [] | https://github.com/huggingface/datasets/pull/1132 | CONTRIBUTOR | null | false | null | [] | Add Urdu Sentiment Corpus (USC). | {
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### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
### TODO:
- [x] add more metadata
- [x] add full text
- [x] add pre-computed document embedding | {
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Samples are taken from ParlAI for consistency with the main users at the moment.
Supersede #945 (problem with the rebase) and adresses the issues mentioned in the review (dummy data are smaller now and code comments are fixed). | {
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757,133,789 | https://api.github.com/repos/huggingface/datasets/issues/1117 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1117/events | [] | null | 2020-12-06T17:14:11Z | [] | https://github.com/huggingface/datasets/pull/1117 | CONTRIBUTOR | null | false | null | [] | Fix incorrect MRQA train+SQuAD URL | {
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757,127,527 | https://api.github.com/repos/huggingface/datasets/issues/1115 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1115/events | [] | null | 2020-12-06T17:14:22Z | [] | https://github.com/huggingface/datasets/issues/1115 | CONTRIBUTOR | completed | null | null | [] | Incorrect URL for MRQA SQuAD train subset | {
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The URL for `train+SQuAD` subset of MRQA points to the dev set instead of train set. It should be `https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SQuAD.jsonl.gz`. | {
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https://github.com/google-research-datasets/QED
https://arxiv.org/abs/2009.06354 | {
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757,108,151 | https://api.github.com/repos/huggingface/datasets/issues/1112 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1112/events | [] | null | 2020-12-04T16:16:40Z | [] | https://github.com/huggingface/datasets/pull/1112 | CONTRIBUTOR | null | false | null | [] | Initial version of cord-19 dataset from AllenAI with only the abstract | {
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### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [ ] Both tests for the real data and the dummy data pass.
### TODO:
- [ ] add more metadata
- [ ] add full text
- [ ] add pre-computed document embedding | {
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} | MDU6SXNzdWU3NTcwODI2Nzc= | null | 2020-12-04T12:56:33Z | https://api.github.com/repos/huggingface/datasets/issues/1110/comments | A column named `_type` leads to a `TypeError: unhashable type: 'dict'` for certain operations:
```python
from datasets import Dataset, concatenate_datasets
ds = Dataset.from_dict({"_type": ["whatever"]}).map()
concatenate_datasets([ds])
# or simply
Dataset(ds._data)
```
Context: We are using datasets to persist data coming from elasticsearch to feed to our pipeline, and elasticsearch has a `_type` field, hence the strange name of the column.
Not sure if you wish to support this specific column name, but if you do i would be happy to try a fix and provide a PR. I already had a look into it and i think the culprit is the `datasets.features.generate_from_dict` function. It uses the hard coded `_type` string to figure out if it reached the end of the nested feature object from a serialized dict.
Best wishes and keep up the awesome work! | {
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https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz
https://arxiv.org/abs/1604.04562 | {
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Paper: [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://arxiv.org/abs/1906.01830)
Homepage: http://oma-project.com/ | {
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} | 2020-12-04T04:37:30Z | https://api.github.com/repos/huggingface/datasets/issues/1087/comments | * More info on the dataset: https://evasharma.github.io/bigpatent/
* There's another raw version of the dataset available from tfds. However, they're quite large so I don't have the resources to fully test all the configs for that version yet. We'll try to add it later. | {
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} | 2020-12-04T01:28:11Z | https://api.github.com/repos/huggingface/datasets/issues/1086/comments | - **Name:** *Cyberbullying Detection Task*
- **Description:** *The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.*
- **Data:** *https://github.com/ptaszynski/cyberbullying-Polish*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.* | {
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WIP
TODO:
- scenario_kbs (bug with pyarrow conversion)
- download from codalab checksums bug | {
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- **Description**: *Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (2017) for a detailed description of the resource.*
- **Data**: *http://2019.poleval.pl/index.php/tasks/*
- **Motivation**: *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.* | {
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`multilingual` configs have all languages mixed together
`crosslingual` mixes the languages for test but separates them for train and dec, so I've made one config per language for train/dev data and one config with the joint test set | {
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} | 2020-12-03T23:39:00Z | https://api.github.com/repos/huggingface/datasets/issues/1082/comments | Add news topic classification dataset in Myanmar / Burmese languagess
This data was collected in 2017 by Aye Hninn Khine , and published on GitHub with a GPL license
https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
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- Webpage/repo: https://github.com/google-research-datasets/KELM-corpus
- Paper: https://arxiv.org/pdf/2010.12688.pdf | {
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} | 2020-12-03T23:09:24Z | https://api.github.com/repos/huggingface/datasets/issues/1080/comments | This PR adds the full set of 176 languages from the balanced train/dev/test splits of WikiANN / PAN-X from: https://github.com/afshinrahimi/mmner
Until now, only 40 of these languages were available in `datasets` as part of the XTREME benchmark
Courtesy of the dataset author, we can now download this dataset from a Dropbox URL without needing a manual download anymore 🥳, so at some point it would be worth updating the PAN-X subset of XTREME as well 😄
Link to gist with some snippets for producing dummy data: https://gist.github.com/lewtun/5b93294ab6dbcf59d1493dbe2cfd6bb9
P.S. @yjernite I think I was confused about needing to generate a set of YAML tags per config, so ended up just adding a single one in the README. | {
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} | 2020-12-03T22:47:26Z | https://api.github.com/repos/huggingface/datasets/issues/1079/comments | - **Name:** *nkjp-ner*
- **Description:** *The NKJP-NER is based on a human-annotated part of NKJP. We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.*
- **Data:** *https://klejbenchmark.com/tasks/*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.*
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756,617,964 | https://api.github.com/repos/huggingface/datasets/issues/1077 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1077/events | [] | null | 2020-12-04T09:55:53Z | [] | https://github.com/huggingface/datasets/pull/1077 | CONTRIBUTOR | null | false | null | [] | Added glucose dataset | {
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