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one lemma per row with all possible forms and annotations
https://unimorph.github.io/ | {
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} | This PR adds the [Naver sentiment movie corpus](https://github.com/e9t/nsmc), a dataset containing Korean movie reviews from Naver, the most commonly used search engine in Korea. This dataset is often used to benchmark models on Korean NLP tasks, as seen in [this paper](https://www.aclweb.org/anthology/2020.lrec-1.199.... | {
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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 c... | {
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## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
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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 s... | {
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- https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html
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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... | {
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} | First commit for Shared task 1 (wmt_mlqw_task1) of WMT20 MLQE (quality estimation of machine translation)
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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1132/comments | https://api.github.com/repos/huggingface/datasets/issues/1132/events | https://github.com/huggingface/datasets/pull/1132 | 757,301,368 | MDExOlB1bGxSZXF1ZXN0NTMyNzAwNTY5 | 1,132 | 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_CONFIG... | {
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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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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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} | Initial version only reading the metadata in CSV.
### 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_CONFIG... | {
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```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 persi... | {
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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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* 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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- **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*
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WIP
TODO:
- scenario_kbs (bug with pyarrow conversion)
- download from codalab checksums bug | {
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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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"merged_at": "2020-12-04T10:13... | 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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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... | {
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"merged_at": "2020-12-04T09:42... | - **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 L... | {
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