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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ASCCCCCCCC/amazon_zh_simple | ASCCCCCCCC | 2022-02-22T01:37:48Z | 305 | 1 | null | [
"license:apache-2.0",
"region:us"
] | 2022-02-22T01:37:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: apache-2.0
---
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Abirate/code_net_dev_dataset | Abirate | 2021-12-12T09:26:00Z | 305 | 1 | null | [
"region:us"
] | 2021-12-12T09:26:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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AlexZapolskii/zapolskii-amazon | AlexZapolskii | 2021-12-22T22:13:57Z | 305 | 0 | null | [
"region:us"
] | 2021-12-22T22:13:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | dataset from kaggle https://www.kaggle.com/c/amazon-pet-product-reviews-classification | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Arnold/hausa_common_voice | Arnold | 2022-02-10T03:28:22Z | 305 | 0 | null | [
"region:us"
] | 2022-02-10T03:28:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This dataset is from the common voice corpus 7.0 using the Hausa dataset | [
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Bosio/pacman_descriptions | Bosio | 2021-09-29T14:05:41Z | 305 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Cropinky/rap_lyrics_english | Cropinky | 2021-07-21T03:07:36Z | 305 | 3 | null | [
"region:us"
] | 2021-07-21T03:07:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ## Rap lyrics dataset
this is the repo containing the dataset we made for the hugging face community week, in order to download more songs you need to request and get(it's very simple and fast) your genius API key which ou put in the genius.py file<br/>
#TODO: turn it into an actual huggingface dataset | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/accented_mandarin | Nexdata | 2023-11-22T09:49:28Z | 305 | 3 | null | [
"region:us"
] | 2023-11-22T09:49:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for accented_mandarin
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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://nexdata.ai/?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset contains 2,000 hours of Mandarin Chinese speech data. The data is collected from local speakers in 26 provinces like Henan, Shanxi, Sichuan, Hunan, Fujian, etc.The content covers generic catagory,human machine interaction, smart home command and control, in-car,numbers etc. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 97%.
For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Accented Mandarin
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commercial License
### Citation Information
[More Information Needed]
### Contributions
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Dumiiii/common-voice-romaniarss | Dumiiii | 2022-01-11T11:29:09Z | 305 | 0 | null | [
"region:us"
] | 2022-01-11T11:29:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This datasets consists in the last version of the common-voice-dataset for romanian language.
Also contains data from RSS (Romanian Speech Synthesis Dataset) from this site http://romaniantts.com/ | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
NbAiLab/norec_agg | NbAiLab | 2022-07-01T19:53:24Z | 305 | 0 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2011.02686",
"region:us"
] | 2022-07-01T19:53:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card Creation Guide
## 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-instances)
- [Data Splits](#data-instances)
- [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:** N/A
- **Repository:** [GitHub](https://github.com/ltgoslo/NorBERT/)
- **Paper:** [A Fine-grained Sentiment Dataset for Norwegian](https://www.aclweb.org/anthology/2020.lrec-1.618/)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
Aggregated NoRec_fine: A Fine-grained Sentiment Dataset for Norwegian.
This dataset was created by the Nordic Language Processing Laboratory by aggregating the fine-grained annotations in NoReC_fine and removing sentences with conflicting or no sentiment.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in Norwegian.
## Dataset Structure
### Data Instances
Example of one instance in the dataset.
```{'label': 0, 'text': 'Verre er det med slagsmålene .'}```
### Data Fields
- `id`: index of the example
- `text`: Text of a sentence
- `label`: The sentiment label. Here
- 0 = negative
- 1 = positive
### Data Splits
The dataset is split into a `train`, `validation`, and `test` split with the following sizes:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ----- |
| Number of examples | 2675 | 516 | 417 |
## Dataset Creation
This dataset is based largely on the original data described in the paper _A Fine-Grained Sentiment Dataset for Norwegian_ by L. Øvrelid, P. Mæhlum, J. Barnes, and E. Velldal, accepted at LREC 2020, [paper available](https://www.aclweb.org/anthology/2020.lrec-1.618). However, we have since added annotations for another 3476 sentences, increasing the overall size and scope of the dataset.
## Additional Information
### Licensing Information
This work is licensed under a Creative Commons Attribution 4.0 International License
### Citation Information
```latex
@misc{sheng2020investigating,
title={Investigating Societal Biases in a Poetry Composition System},
author={Emily Sheng and David Uthus},
year={2020},
eprint={2011.02686},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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Samip/func | Samip | 2021-11-15T05:35:47Z | 305 | 0 | null | [
"region:us"
] | 2021-11-15T05:35:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Hello
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/student-question-categories | SetFit | 2022-01-16T18:32:48Z | 305 | 1 | null | [
"region:us"
] | 2022-01-16T18:32:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This is the [IITJEE NEET AIIMS Students Questions Data](https://www.kaggle.com/mrutyunjaybiswal/iitjee-neet-aims-students-questions-data) dataset.
It categorizes university entry questions into 4 categories: Physics, Chemistry, Biology, and Mathematics. | [
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Tevatron/wikipedia-wq | Tevatron | 2021-09-23T01:44:07Z | 305 | 0 | null | [
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TurkuNLP/register_mc4 | TurkuNLP | 2021-12-27T11:35:12Z | 305 | 0 | null | [
"region:us"
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Xenova/sponsorblock-768 | Xenova | 2022-02-08T20:36:56Z | 305 | 2 | null | [
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Xinghua/test | Xinghua | 2021-10-28T07:47:33Z | 305 | 0 | null | [
"region:us"
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jakeazcona/short-text-multi-labeled-emotion-classification | jakeazcona | 2021-12-02T01:08:12Z | 305 | 0 | null | [
"region:us"
] | 2021-12-02T01:08:12Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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lcw99/wikipedia-korean-20221001 | lcw99 | 2022-10-10T03:55:17Z | 305 | 3 | null | [
"language:ko",
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hmao/reformatted_singleapi_openai | hmao | 2023-10-23T23:26:04Z | 305 | 0 | null | [
"region:us"
] | 2023-10-23T23:26:04Z | 2023-10-21T03:43:10.000Z | 2023-10-21T03:43:10 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: api_name
dtype: string
- name: api_definition
dtype: string
- name: dataset_name
dtype: string
splits:
- name: train
num_bytes: 21189
num_examples: 14
download_size: 14297
dataset_size: 21189
---
# Dataset Card for "reformatted_singleapi_openai"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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cawac | null | 2022-11-03T16:15:53Z | 304 | 0 | cawac | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:ca",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-11-03T16:15:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ca
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: cawac
pretty_name: caWaC
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 3987238444
num_examples: 24745986
download_size: 1620361999
dataset_size: 3987238444
---
# Dataset Card for caWaC
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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:** http://nlp.ffzg.hr/resources/corpora/cawac/
- **Repository:** http://nlp.ffzg.hr/data/corpora/cawac.uniq.sortr.gz
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2014/pdf/841_Paper.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr)
### Dataset Summary
caWaC is a 780-million-token web corpus of Catalan built from the .cat top-level-domain in late 2013.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is monolingual in Catalan language.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@inproceedings{DBLP:conf/lrec/LjubesicT14,
author = {Nikola Ljubesic and
Antonio Toral},
editor = {Nicoletta Calzolari and
Khalid Choukri and
Thierry Declerck and
Hrafn Loftsson and
Bente Maegaard and
Joseph Mariani and
Asunci{\'{o}}n Moreno and
Jan Odijk and
Stelios Piperidis},
title = {caWaC - {A} web corpus of Catalan and its application to language
modeling and machine translation},
booktitle = {Proceedings of the Ninth International Conference on Language Resources
and Evaluation, {LREC} 2014, Reykjavik, Iceland, May 26-31, 2014},
pages = {1728--1732},
publisher = {European Language Resources Association {(ELRA)}},
year = {2014},
url = {http://www.lrec-conf.org/proceedings/lrec2014/summaries/841.html},
timestamp = {Mon, 19 Aug 2019 15:23:35 +0200},
biburl = {https://dblp.org/rec/conf/lrec/LjubesicT14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | [
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hda_nli_hindi | null | 2023-01-25T14:31:58Z | 304 | 0 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|hindi_discourse",
"language:hi",
"license:mit",
"region:us"
] | 2023-01-25T14:31:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- hi
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|hindi_discourse
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: Hindi Discourse Analysis Dataset
dataset_info:
- config_name: HDA hindi nli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': not-entailment
'1': entailment
- name: topic
dtype:
class_label:
names:
'0': Argumentative
'1': Descriptive
'2': Dialogic
'3': Informative
'4': Narrative
splits:
- name: train
num_bytes: 8721972
num_examples: 31892
- name: validation
num_bytes: 2556118
num_examples: 9460
- name: test
num_bytes: 2646453
num_examples: 9970
download_size: 13519261
dataset_size: 13924543
- config_name: hda nli hindi
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': not-entailment
'1': entailment
- name: topic
dtype:
class_label:
names:
'0': Argumentative
'1': Descriptive
'2': Dialogic
'3': Informative
'4': Narrative
splits:
- name: train
num_bytes: 8721972
num_examples: 31892
- name: validation
num_bytes: 2556118
num_examples: 9460
- name: test
num_bytes: 2646453
num_examples: 9970
download_size: 13519261
dataset_size: 13924543
---
# Dataset Card for Hindi Discourse Analysis Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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:** [GitHub](https://github.com/midas-research/hindi-nli-data)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71)
- **Point of Contact:** [GitHub](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: string
- hypothesis: string
- label: class label with values that correspond to "not-entailment" (0) or "entailment" (1)
- topic: class label with values that correspond to "Argumentative" (0), "Descriptive" (1), "Dialogic" (2), "Informative" (3) or "Narrative" (4).
### 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 midas@iiitd.ac.in.
- 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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hrenwac_para | null | 2022-11-03T16:07:49Z | 304 | 0 | null | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:hr",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-11-03T16:07:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
- hr
license:
- cc-by-sa-3.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: HrenwacPara
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- hr
config_name: hrenWaC
splits:
- name: train
num_bytes: 29602110
num_examples: 99001
download_size: 11640281
dataset_size: 29602110
---
# Dataset Card for hrenwac_para
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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:** http://nlp.ffzg.hr/resources/corpora/hrenwac/
- **Repository:** http://nlp.ffzg.hr/data/corpora/hrenwac/hrenwac.en-hr.txt.gz
- **Paper:** http://workshop2013.iwslt.org/downloads/IWSLT-2013-Cettolo.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr)
### Dataset Summary
The hrenWaC corpus version 2.0 consists of parallel Croatian-English texts crawled from the .hr top-level domain for Croatia. The corpus was built with Spidextor (https://github.com/abumatran/spidextor), a tool that glues together the output of SpiderLing used for crawling and Bitextor used for bitext extraction. The accuracy of the extracted bitext on the segment level is around 80% and on the word level around 84%.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is bilingual with Croatian and English languages.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@misc{11356/1058,
title = {Croatian-English parallel corpus {hrenWaC} 2.0},
author = {Ljube{\v s}i{\'c}, Nikola and Espl{\`a}-Gomis, Miquel and Ortiz Rojas, Sergio and Klubi{\v c}ka, Filip and Toral, Antonio},
url = {http://hdl.handle.net/11356/1058},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {{CLARIN}.{SI} User Licence for Internet Corpora},
year = {2016} }
```
### Contributions
Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
igbo_english_machine_translation | null | 2022-11-18T20:08:55Z | 304 | 1 | igbonlp-datasets | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ig",
"license:unknown",
"arxiv:2004.00648",
"region:us"
] | 2022-11-18T20:08:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- ig
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: igbonlp-datasets
pretty_name: IgboNLP Datasets
dataset_info:
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- ig
- en
config_name: ig-en
splits:
- name: train
num_bytes: 2367989
num_examples: 10000
- name: validation
num_bytes: 60154
num_examples: 200
- name: test
num_bytes: 298670
num_examples: 552
download_size: 2580255
dataset_size: 2726813
---
# Dataset Card for IgboNLP Datasets
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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:** None
- **Repository:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_en_mt
- **Paper:** https://arxiv.org/abs/2004.00648
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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makhzan | null | 2022-11-03T16:07:47Z | 304 | 0 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ur",
"license:other",
"region:us"
] | 2022-11-03T16:07:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: makhzan
dataset_info:
features:
- name: file_id
dtype: string
- name: metadata
dtype: string
- name: title
dtype: string
- name: num-words
dtype: int64
- name: contains-non-urdu-languages
dtype: string
- name: document_body
dtype: string
splits:
- name: train
num_bytes: 35637310
num_examples: 5522
download_size: 15187763
dataset_size: 35637310
---
# Dataset Card for makhzan
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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://matnsaz.net/en/makhzan
- **Repository:** https://github.com/zeerakahmed/makhzan
- **Paper:** [More Information Needed]
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** Zeerak Ahmed
### Dataset Summary
An Urdu text corpus for machine learning, natural language processing and linguistic analysis.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
ur
## Dataset Structure
### Data Instances
```
{
"contains-non-urdu-languages": "No",
"document_body":
"
<body>
<section>
<p>بنگلہ دیش کی عدالتِ عالیہ نے طلاق کے ایک مقدمے کا فیصلہ کرتے ہوئے علما کے فتووں کو غیر قانونی قرار دیا ہے۔ عدالت نے پارلیمنٹ سے یہ درخواست کی ہے کہ وہ جلد ایسا قانون وضع کرے کہ جس کے بعد فتویٰ بازی قابلِ دست اندازیِ پولیس جرم بن جائے۔ بنگلہ دیش کے علما نے اس فیصلے پر بھر پور ردِ عمل ظاہرکرتے ہوئے اس کے خلاف ملک گیر تحریک چلانے کا اعلان کیا ہے۔ اس ضمن میں علما کی ایک تنظیم ”اسلامک یونٹی الائنس“ نے متعلقہ ججوں کو مرتد یعنی دین سے منحرف اور دائرۂ اسلام سے خارج قرار دیا ہے۔</p>
<p>فتوے کا لفظ دو موقعوں پر استعمال ہوتا ہے۔ ایک اس موقع پر جب کوئی صاحبِ علم شریعت کے کسی مئلے کے بارے میں اپنی رائے پیش کرتا ہے۔ دوسرے اس موقع پر جب کوئی عالمِ دین کسی خاص واقعے کے حوالے سے اپنا قانونی فیصلہ صادر کرتا ہے۔ ایک عرصے سے ہمارے علما کے ہاں اس دوسرے موقعِ استعمال کا غلبہ ہو گیا ہے۔ اس کا نتیجہ یہ نکلا ہے کہ اس لفظ کا رائے یا نقطۂ نظر کے مفہوم میں استعمال کم و بیش متروک ہو گیا ہے۔ چنانچہ اب فتوے کا مطلب ہی علما کی طرف سے کسی خاص مألے یا واقعے کے بارے میں حتمی فیصلے کا صدور سمجھا جاتا ہے۔ علما اسی حیثیت سے فتویٰ دیتے ہیں اور عوام الناس اسی اعتبار سے اسے قبول کرتے ہیں۔ اس صورتِ حال میں ہمارے نزدیک، چند مسائل پیدا ہوتے ہیں۔ اس سے پہلے کہ ہم مذکورہ فیصلے کے بارے میں اپنا تاثر بیان کریں، یہ ضروری معلوم ہوتا ہے کہ مختصر طور پر ان مسائل کا جائزہ لے لیا جائے۔</p>
<p>پہلا مألہ یہ پیدا ہوتا ہے کہ قانون سازی اور شرعی فیصلوں کا اختیار ایسے لوگوں کے ہاتھ میں آجاتا ہے جو قانون کی رو سے اس کے مجاز ہی نہیں ہوتے۔ کسی میاں بیوی کے مابین طلاق کے مألے میں کیا طلاق واقع ہوئی ہے یا نہیں ہوئی؟ ان کا نکاح قائم ہے یا باطل ہو گیا ہے؟ رمضان یا عید کا چاند نظر آیا ہے یا نہیں آیا؟کوئی مسلمان اپنے کسی قول یا اقدام کی وجہ سے کہیں دائرۂ اسلام سے خارج اورنتیجۃً مسلم شہریت کے قانونی حقوق سے محروم تو نہیں ہو گیا؟ یہ اور اس نوعیت کے بہت سے دوسرے معاملات سر تا سر قانون اور عدالت سے متعلق ہوتے ہیں۔ علما کی فتویٰ سازی کے نتیجے میںیہ امور گویا حکومت اورعدلیہ کے ہاتھ سے نکل کر غیر متعلق افراد کے ہاتھوں میں آجاتے ہیں۔</p>
<p>دوسرا مألہ یہ پیدا ہوتا ہے کہ قانون کی حاکمیت کا تصور مجروح ہوتا ہے اور لوگوں میں قانون سے روگردانی کے رجحانات کو تقویت ملتی ہے۔ اس کی وجہ یہ ہے کہ قانون اپنی روح میں نفاذ کا متقاضی ہوتا ہے۔ اگر اسے نفاذ سے محروم رکھا جائے تو اس کی حیثیت محض رائے اور نقطۂ نظر کی سی ہوتی ہے۔ غیر مجاز فرد سے صادر ہونے والا فتویٰ یا قانون حکومت کی قوتِ نافذہ سے محروم ہوتا ہے۔ اس کی خلاف ورزی پر کسی قسم کی سزا کا خوف نہیں ہوتا۔ چنانچہ فتویٰ اگر مخاطب کی پسند کے مطابق نہ ہو تو اکثر وہ اسے ماننے سے انکار کر دیتا ہے۔ اس طرح وہ فتویٰ یا قانون بے توقیر ہوتا ہے۔ ایسے ماحول میں رہنے والے شہریوں میں قانون ناپسندی کا رجحان فروغ پاتا ہے اور جیسے ہی انھیں موقع ملتا ہے وہ بے دریغ قانون کی خلاف ورزی کر ڈالتے ہیں۔</p>
<p>تیسرامسئلہ یہ پیدا ہوتا ہے کہ اگرغیر مجاز افراد سے صادر ہونے والے فیصلوں کو نافذ کرنے کی کوشش کی جائے تو ملک میں بد نظمی اور انارکی کا شدید اندیشہ پیدا ہو جاتا ہے۔ جب غیر مجازافراد سے صادر ہونے والے قانونی فیصلوں کو حکومتی سرپرستی کے بغیر نافذ کرنے کی کوشش کی جاتی ہے تو اپنے عمل سے یہ اس بات کا اعلان ہوتا ہے کہ مرجعِ قانون و اقتدارتبدیل ہو چکا ہے۔ جب کوئی عالمِ دین مثال کے طور پر، یہ فتویٰ صادر کرتا ہے کہ سینما گھروں اور ٹی وی اسٹیشنوں کو مسمار کرنامسلمانوں کی ذمہ داری ہے، یا کسی خاص قوم کے خلاف جہاد فرض ہو چکا ہے، یا فلاں کی دی گئی طلاق واقع ہو گئی ہے اور فلاں کی نہیں ہوئی، یا فلاں شخص یا گروہ اپنا اسلامی تشخص کھو بیٹھا ہے تو وہ درحقیقت قانونی فیصلہ جاری کر رہا ہوتا ہے۔ دوسرے الفاظ میں، وہ ریاست کے اندر اپنی ایک الگ ریاست بنانے کا اعلان کر رہا ہوتا ہے۔ اس کا نتیجہ سوائے انتشار اور انارکی کے اور کچھ نہیں نکلتا۔ یہی وجہ ہے کہ جن علاقوں میں حکومت کی گرفت کمزور ہوتی ہے وہاں اس طرح کے فیصلوں کا نفاذ بھی ہو جاتا ہے اور حکومت منہ دیکھتی رہتی ہے۔</p>
<p>چوتھا مسئلہ یہ پیدا ہوتا ہے کہ مختلف مذہبی مسالک کی وجہ سے ایک ہی معاملے میں مختلف اور متضاد فتوے منظرِ عام پر آتے ہیں۔ یہ تو ہمارے روز مرہ کی بات ہے کہ ایک ہی گروہ کو بعض علماے دین کافر قرار دیتے ہیں اور بعض مسلمان سمجھتے ہیں۔ کسی شخص کے منہ سے اگر ایک موقع پر طلاق کے الفاظ تین بار نکلتے ہیں تو بعض علما اس پر ایک طلاق کا حکم لگا کر رجوع کا حق باقی رکھتے ہیں اور بعض تین قرار دے کررجوع کو باطل قرار دیتے ہیں۔ یہ صورتِ حال ایک عام آدمی کے لیے نہایت دشواریاں پیدا کر دیتی ہے۔</p>
<p>پانچواں مسئلہ یہ پیدا ہوتا ہے کہ حکمران اگر دین و شریعت سے کچھ خاص دلچسپی نہ رکھتے ہوں تو وہ اس صورتِ حال میں شریعت کی روشنی میں قانون سازی کی طرف متوجہ نہیں ہوتے۔ کام چل رہا ہے کے اصول پر وہ اس طریقِ قانون سازی سے سمجھوتاکیے رہتے ہیں۔ اس کا نتیجہ یہ نکلتا ہے کہ حکومتی ادارے ضروری قانون سازی کے بارے میں بے پروائی کا رویہ اختیار کرتے ہیں اور قوانین اپنے فطری ارتقا سے محروم رہتے ہیں۔</p>
<p>چھٹا مألہ یہ پیدا ہوتا ہے کہ رائج الوقت قانون اور عدالتوں کی توہین کے امکانات پیدا ہو جاتے ہیں۔ جب کسی مسئلے میں عدالتیں اپنا فیصلہ سنائیں اور علما اسے باطل قرار دیتے ہوئے اس کے برعکس اپنا فیصلہ صادر کریں تو اس سے عدالتوں کا وقار مجروح ہوتا ہے۔ اس کا مطلب یہ ہوتا ہے کہ کوئی شہری عدلیہ کو چیلنج کرنے کے لیے کھڑا ہو گیا ہے۔</p>
<p>ان مسائل کے تناظر میں بنگلہ دیش کی عدالتِ عالیہ کا فیصلہ ہمارے نزدیک، امت کی تاریخ میں ایک عظیم فیصلہ ہے۔ جناب جاوید احمد صاحب غامدی نے اسے بجا طور پر صدی کا بہترین فیصلہ قرار دیا ہے۔ بنگلہ دیش کی عدالت اگر علما کے فتووں اور قانونی فیصلوں پر پابندی لگانے کے بجائے، ان کے اظہارِ رائے پر پابندی عائدکرتی تو ہم اسے صدی کا بدترین فیصلہ قرار دیتے اور انھی صفحات میں بے خوفِ لومۃ و لائم اس پر نقد کر رہے ہوتے۔</p>
<p>موجودہ زمانے میں امتِ مسلمہ کا ایک بڑا المیہ یہ ہے کہ اس کے علما اپنی اصل ذمہ داری کو ادا کرنے کے بجائے ان ذمہ داریوں کو ادا کرنے پر مصر ہیں جن کے نہ وہ مکلف ہیں اور نہ اہل ہیں۔ قرآن و سنت کی رو سے علما کی اصل ذمہ داری دعوت و تبلیغ، انذار و تبشیر اور تعلیم و تحقیق ہے۔ ان کا کام سیاست نہیں، بلکہ سیاست دانوں کو دین کی رہنمائی سے آگاہی ہے؛ ان کا کام حکومت نہیں، بلکہ حکمرانوں کی اصلاح کی کوشش ہے؛ ان کا کام جہاد و قتال نہیں، بلکہ جہادکی تعلیم اور جذبۂ جہاد کی بیداری ہے؛ اسی طرح ان کا کام قانون سازی اور فتویٰ بازی نہیں بلکہ تحقیق و اجتہاد ہے۔ گویا انھیں قرآنِ مجیدکامفہوم سمجھنے، سنتِ ثابتہ کا مدعا متعین کرنے اور قولِ پیغمبر کا منشامعلوم کرنے کے لیے تحقیق کرنی ہے اور جن امور میں قرآن و سنت خاموش ہیں ان میں اپنی عقل و بصیرت سے اجتہادی آراقائم کرنی ہیں۔ ان کی کسی تحقیق یا اجتہاد کو جب عدلیہ یا پارلیمنٹ قبول کرے گی تو وہ قانون قرار پائے گا۔ اس سے پہلے اس کی حیثیت محض ایک رائے کی ہوگی۔ اس لیے اسے اسی حیثیت سے پیش کیا جائے گا۔</p>
<p>اس کا مطلب یہ ہے کہ کوئی حکم نہیں لگایا جائے گا، کوئی فیصلہ نہیں سنایا جائے گا، کوئی فتویٰ نہیں دیا جائے گا، بلکہ طالبِ علمانہ لب و لہجے میں محض علم و استدلال کی بنا پر اپنا نقطۂ نظر پیش کیا جائے گا۔ یہ نہیں کہا جائے گا کہ فلاں شخص کافر ہے، بلکہ اس کی اگر ضرورت پیش آئے تو یہ کہا جائے گا کہ فلاں شخص کا فلاں عقیدہ کفر ہے۔ یہ نہیں کہا جائے گا کہ فلاں آدمی دائرۂ اسلام سے خارج ہو گیا ہے، بلکہ یہ کہا جائے گا کہ فلاں آدمی کا فلاں نقطۂ نظر اسلام کے دائرے میں نہیں آتا۔ یہ نہیں کہا جائے گا فلاں آدمی مشرک ہے، بلکہ یہ کہا جائے گا فلاں نظریہ یا فلاں طرزِ عمل شرک ہے۔ یہ نہیں کہا جائے گا کہ زید کی طرف سے دی گئی ایک وقت کی تین طلاقیں واقع ہو گئی ہیں، بلکہ یہ کہا جائے گا کہ ایک وقت کی تین طلاقیں واقع ہو نی چاہییں۔</p>
<p>حکم لگانا، فیصلہ سنانا، قانون وضع کرنا اورفتویٰ جاری کرنا درحقیقت، عدلیہ اور حکومت کا کام ہے کسی عالمِ دین یا کسی اور غیر مجاز فرد کی طرف سے اس کام کو انجام دینے کی کوشش سراسر تجاوز ہے۔ خلافتِ راشدہ کے زمانے میں اس اصول کو ہمیشہ ملحوظ رکھا گیا۔ شاہ ولی اللہ محدث دہلوی اپنی کتاب ”ازالتہ الخفا ء“ میں لکھتے ہیں:</p>
<blockquote>
<p>”اس زمانے تک وعظ اور فتویٰ خلیفہ کی رائے پر موقوف تھا۔ خلیفہ کے حکم کے بغیر نہ وعظ کہتے تھے اور نہ فتویٰ دیتے تھے۔ بعد میں خلیفہ کے حکم کے بغیر وعظ کہنے اور فتویٰ دینے لگے اور فتویٰ کے معاملے میں جماعت (مجلسِ شوریٰ) کے مشورہ کی جو صورت پہلے تھی وہ باقی نہ رہی——- (اس زمانے میں) جب کوئی اختلافی صورت نمودار ہوتی، خلیفہ کے سامنے معاملہ پیش کرتے، خلیفہ اہلِ علم و تقویٰ سے مشورہ کرنے کے بعد ایک رائے قائم کرتا اور وہی سب لوگوں کی رائے بن جاتی۔ حضرت عثمان کی شہادت کے بعد ہر عالم بطورِ خود فتویٰ دینے لگا اور اس طرح مسلمانوں میں اختلاف برپا ہوا۔“ (بحوالہ ”اسلامی ریاست میں فقہی اختلافات کا حل“، مولاناامین احسن اصلاحی، ص۳۲)</p>
</blockquote>
</section>
</body>
",
"file_id": "0001.xml",
"metadata":
"
<meta>
<title>بنگلہ دیش کی عدالت کا تاریخی فیصلہ</title>
<author>
<name>سید منظور الحسن</name>
<gender>Male</gender>
</author>
<publication>
<name>Mahnama Ishraq February 2001</name>
<year>2001</year>
<city>Lahore</city>
<link>https://www.javedahmedghamidi.org/#!/ishraq/5adb7341b7dd1138372db999?articleId=5adb7452b7dd1138372dd6fb&year=2001&decade=2000</link>
<copyright-holder>Al-Mawrid</copyright-holder>
</publication>
<num-words>1694</num-words>
<contains-non-urdu-languages>No</contains-non-urdu-languages>
</meta>
",
"num-words": 1694,
"title": "بنگلہ دیش کی عدالت کا تاریخی فیصلہ"
}
```
### Data Fields
```file_id (str)```: Document file_id corresponding to filename in repository.
```metadata(str)```: XML formatted string containing metadata on the document such as the document's title, information about the author and publication, as well as other potentially useful facts such as the number of Urdu words in the document and whether the document contains text in any other languages.
```title (str)```: Title of the document.
```num-words (int)```: Number of words in document.
```contains-non-urdu-languages (str)```: ```Yes``` if document contains words other than urdu, ```No``` otherwise.
```document_body```: XML formatted body of the document. Details below:
The document is divided into ```<section>``` elements. In general the rule is that a clear visual demarkation in the original text (such as a page break, or a horizontal rule) is used to indicate a section break. A heading does not automatically create a new section.
Each paragraph is a ```<p>``` element.
Headings are wrapped in an ```<heading>``` element.
Blockquotes are wrapped in a ```<blockquote>``` element. Blockquotes may themselves contain other elements.
Lists are wrapped in an ```<list>```. Individual items in each list are wrapped in an ```<li>``` element.
Poetic verses are wrapped in a ```<verse>``` element. Each verse is on a separate line but is not wrapped in an individual element.
Tables are wrapped in a ```<table>``` element. A table is divided into rows marked by ```<tr>``` and columns marked by ```<td>```.
Text not in the Urdu language is wrapped in an ```<annotation>``` tag (more below).
```<p>, <heading>, <li>, <td>``` and ```<annotation>``` tags are inline with the text (i.e. there is no new line character before and after the tag). Other tags have a new line after the opening and before the closing tag.
Due to the use of XML syntax, ```<```, ```>``` and ```&``` characters have been escaped as ```<```;, ```>```;, and ```&```; respectively. This includes the use of these characters in URLs inside metadata.
### Data Splits
All the data is in one split ```train```
## Dataset Creation
### Curation Rationale
All text in this repository has been selected for quality of language, upholding high editorial standards. Given the poor quality of most published Urdu text in digital form, this selection criteria allows the use of this text for natural language processing, and machine learning applications without the need to address fundamental quality issues in the text.
We have made efforts to ensure this text is as broadly representative as possible. Specifically we have attempted to select for as many authors as possible, and diversity in the gender of the author, as well as years and city of publication. This effort is imperfect, and we appreciate any attempts at pointing us to resources that can help diversify this text further.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Makhzan has been started with generous initial donations of text from two renowned journals Bunyad, from the Gurmani Center of Literature and Languages at the Lahore University of Management Sciences (LUMS), and Ishraq, from the Al-Mawrid Institute. This choice of sources allowed us to get a diversity of voices even in a small initial corpus, while ensuring the highest editorial standards available in published Urdu text. As a result your models can also maintain high linguistic standards.
### Annotations
#### Annotation process
Text is structured and annotated using XML syntax. The ontology of elements used is loosely based around HTML, with simplifications made when HTML's specificity is not needed, and additions made to express common occurences in this corpus that would be useful for linguistic analysis. The semantic tagging of text is editorial in nature, which is to say that another person semantically tagging the text may do so differently. Effort has been made however to ensure consistency, and to retain the original meaning of the text while making it easy to parse through linguistically different pieces of text for analysis.
Annotations have been made inline using an ```<annotation>``` element.
A language (lang) attribute is added to the ```<annotation>``` element to indicate text in other languages (such as quoted text or technical vocabulary presented in other languages and scripts). The attribute value a two-character ISO 639-1 code. So the resultant annotation for an Arabic quote for example, will be ```<annotation lang="ar"></annotation>```.
A type (type) attributed is added to indicate text that is not in a language per se but is not Urdu text. URLs for example are wrapped in an ```<annotation type="url">``` tag.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
A few of the files do not have valid XML and cannot be loaded. This issue is tracked [here](https://github.com/zeerakahmed/makhzan/issues/28)
## Additional Information
### Dataset Curators
Zeerak Ahmed
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{makhzan,
title={Maḵẖzan},
howpublished = "\url{https://github.com/zeerakahmed/makhzan/}",
}
```
### Contributions
Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset. | [
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nkjp-ner | null | 2023-01-25T14:41:28Z | 304 | 1 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:gpl-3.0",
"region:us"
] | 2023-01-25T14:41:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: NJKP NER
dataset_info:
features:
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': geogName
'1': noEntity
'2': orgName
'3': persName
'4': placeName
'5': time
splits:
- name: train
num_bytes: 1612125
num_examples: 15794
- name: test
num_bytes: 221092
num_examples: 2058
- name: validation
num_bytes: 196652
num_examples: 1941
download_size: 821629
dataset_size: 2029869
---
# Dataset Card for NJKP NER
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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:**
http://nkjp.pl/index.php?page=0&lang=1
- **Repository:**
- **Paper:**
@book{przepiorkowski2012narodowy,
title={Narodowy korpus j{\k{e}}zyka polskiego},
author={Przepi{\'o}rkowski, Adam},
year={2012},
publisher={Naukowe PWN}
- **Leaderboard:**
- **Point of Contact:**
adamp@ipipan.waw.pl
### Dataset Summary
A linguistic corpus is a collection of texts where one can find the typical use of a single word or a phrase, as well as their meaning and grammatical function. Nowadays, without access to a language corpus, it has become impossible to do linguistic research, to write dictionaries, grammars and language teaching books, to create search engines sensitive to Polish inflection, machine translation engines and software of advanced language technology. Language corpora have become an essential tool for linguists, but they are also helpful for software engineers, scholars of literature and culture, historians, librarians and other specialists of art and computer sciences.
The manually annotated 1-million word subcorpus of the NJKP, available on GNU GPL v.3
### Supported Tasks and Leaderboards
Named entity recognition
[More Information Needed]
### Languages
Polish
## Dataset Structure
### Data Instances
Two tsv files (train, dev) with two columns (sentence, target) and one (test) with just one (sentence).
### Data Fields
- sentence
- target
### Data Splits
Data is splitted in train/dev/test split.
## Dataset Creation
### Curation Rationale
This dataset is one of nine evaluation tasks to improve polish language processing.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
GNU GPL v.3
### Citation Information
@book{przepiorkowski2012narodowy,
title={Narodowy korpus j{\k{e}}zyka polskiego},
author={Przepi{\'o}rkowski, Adam},
year={2012},
publisher={Naukowe PWN}
}
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ofis_publik | null | 2022-11-03T16:15:15Z | 304 | 0 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:br",
"language:fr",
"license:unknown",
"region:us"
] | 2022-11-03T16:15:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- br
- fr
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OfisPublik
dataset_info:
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- br
- fr
config_name: br-fr
splits:
- name: train
num_bytes: 12256825
num_examples: 63422
download_size: 3856983
dataset_size: 12256825
---
# Dataset Card for OfisPublik
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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:** http://opus.nlpl.eu/OfisPublik.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
reclor | null | 2022-11-18T21:41:37Z | 304 | 1 | reclor | [
"region:us"
] | 2022-11-18T21:41:37Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
paperswithcode_id: reclor
pretty_name: ReClor
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: label
dtype: string
- name: id_string
dtype: string
splits:
- name: train
num_bytes: 4711114
num_examples: 4638
- name: test
num_bytes: 1017354
num_examples: 1000
- name: validation
num_bytes: 518604
num_examples: 500
download_size: 0
dataset_size: 6247072
---
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@JetRunner](https://github.com/JetRunner), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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sede | null | 2022-11-18T21:44:41Z | 304 | 5 | sede | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2106.05006",
"arxiv:2005.02539",
"region:us"
] | 2022-11-18T21:44:41Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: SEDE (Stack Exchange Data Explorer)
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
paperswithcode_id: sede
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- parsing
dataset_info:
features:
- name: QuerySetId
dtype: uint32
- name: Title
dtype: string
- name: Description
dtype: string
- name: QueryBody
dtype: string
- name: CreationDate
dtype: string
- name: validated
dtype: bool
config_name: sede
splits:
- name: train
num_bytes: 4410584
num_examples: 10309
- name: validation
num_bytes: 380942
num_examples: 857
- name: test
num_bytes: 386599
num_examples: 857
download_size: 6318959
dataset_size: 5178125
---
# Dataset Card for SEDE
## 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-instances)
- [Data Splits](#data-instances)
- [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
- **Repository:** https://github.com/hirupert/sede
- **Paper:** https://arxiv.org/abs/2106.05006
- **Leaderboard:** https://paperswithcode.com/sota/text-to-sql-on-sede
- **Point of Contact:** [email](moshe@hirupert.com)
### Dataset Summary
SEDE (Stack Exchange Data Explorer) is a dataset for Text-to-SQL tasks with more than 12,000 SQL queries and their natural language description. It's based on a real usage of users from the Stack Exchange Data Explorer platform, which brings complexities and challenges never seen before in any other semantic parsing dataset like including complex nesting, dates manipulation, numeric and text manipulation, parameters, and most importantly: under-specification and hidden-assumptions.
### Supported Tasks and Leaderboards
- `parsing`: The dataset can be used to train a model for Text-to-SQL task. A Seq2Seq model (e.g. T5) can be used to solve the task. A model with more inductive-bias (e.g. a model with a grammar-based decoder) or an interactive settings for Text-to-SQL (https://arxiv.org/abs/2005.02539) can improve the results further. The model performance is measured by how high its [PCM-F1](https://arxiv.org/abs/2106.05006) score is. A [t5-large](https://huggingface.co/t5-large) achieves a [PCM-F1 of 50.6](https://arxiv.org/abs/2106.05006).
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point comprises a question title, (optionally) a description and its underlying SQL query. In addition, each sample has a unique ID (from the Stack Exchange Data Explorer), its creation date and a boolean flag named `validated` if this sample was validated to be in gold quality by humans, see the paper for full details regarding the `validated` flag.
An instance for example:
```
{
'QuerySetId':1233,
'Title':'Top 500 Askers on the site',
'Description':'A list of the top 500 askers of questions ordered by average answer score excluding community wiki closed posts.',
'QueryBody':'SELECT * FROM (\nSELECT \n TOP 500\n OwnerUserId as [User Link],\n Count(Posts.Id) AS Questions,\n CAST(AVG(CAST(Score AS float)) as numeric(6,2)) AS [Average Question Score]\nFROM\n Posts\nWHERE \n PostTypeId = 1 and CommunityOwnedDate is null and ClosedDate is null\nGROUP BY\n OwnerUserId\nORDER BY\n Count(Posts.Id) DESC\n)ORDER BY\n [Average Question Score] DESC',
'CreationDate':'2010-05-27 20:08:16',
'validated':true
}
```
### Data Fields
- QuerySetId: a unique ID coming from the Stack Exchange Data Explorer.
- Title: utterance title.
- Description: utterance description (might be empty).
- QueryBody: the underlying SQL query.
- CreationDate: when this sample was created.
- validated: `true` if this sample was validated to be in gold quality by humans.
### Data Splits
The data is split into a training, validation and test set. The validation and test set contain only samples that were validated by humans to be in gold quality.
Train Valid Test
10309 857 857
## Dataset Creation
### Curation Rationale
Most available semantic parsing datasets, comprising of pairs of natural utterances and logical forms, were collected solely for the purpose of training and evaluation of natural language understanding systems. As a result, they do not contain any of the richness and variety of natural-occurring utterances, where humans ask about data they need or are curious about. SEDE contains a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset. There is a large gap between the performance on SEDE compared to other common datasets, which leaves a room for future research for generalisation of Text-to-SQL models.
### Source Data
#### Initial Data Collection and Normalization
To introduce a realistic Text-to-SQL benchmark, we gather SQL queries together with their titles and descriptions from a naturally occurring dataset: the Stack Exchange Data Explorer. Stack Exchange is an online question & answers community, with over 3 million questions asked. However in its raw form many of the rows are duplicated or contain unusable queries or titles. The reason for this large difference between the original data size and the cleaned version is that any time that the author of the query executes it, an entry is saved to the log. To alleviate these issues, we write rule-based filters that remove bad queries/descriptions pairs with high precision. For example, we filter out examples with numbers in the description, if these numbers do not appear in the query (refer to the preprocessing script in the repository for the complete list of filters and the number of examples each of them filter). Whenever a query has multiple versions due to multiple executions, we take the last executed query which passed all filters. After this filtering step, we are left with 12,309 examples. Using these filters cleans most of the noise, but not all of it. To complete the cleaning process, we manually go over the examples in the validation and test sets, and either filter-out wrong examples or perform minimal changes to either the utterances or the queries (for example, fix a wrong textual value) to ensure that models are evaluated with correct data. The final number of all training, validation and test examples is 12,023.
#### Who are the source language producers?
The language producers are Stack Exchange Data Explorer (https://data.stackexchange.com/) users.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
All the data in the dataset is for public use.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that the release of this challenging dataset will encourage research on improving generalisation for real-world SQL prediction that will help non technical business users acquire the data they need from their company's database.
### Discussion of Biases
[N/A]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Moshe Hazoom, Vibhor Malik and Ben Bogin, during work done at Ruper.
### Licensing Information
Apache-2.0 License
### Citation Information
```
@misc{hazoom2021texttosql,
title={Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data},
author={Moshe Hazoom and Vibhor Malik and Ben Bogin},
year={2021},
eprint={2106.05006},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Hazoom](https://github.com/Hazoom) for adding this dataset. | [
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0n1xus/pytorrent-standalone | 0n1xus | 2021-12-02T06:13:15Z | 304 | 0 | null | [
"region:us"
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Abirate/code_net_dataset | Abirate | 2021-12-11T17:41:32Z | 304 | 2 | null | [
"region:us"
] | 2021-12-11T17:41:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Abirate/code_net_test_final_dataset | Abirate | 2022-01-27T10:15:52Z | 304 | 1 | null | [
"region:us"
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CodedotAI/code-clippy-tfrecords | CodedotAI | 2021-12-07T21:40:32Z | 304 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/multi_language | Nexdata | 2023-11-22T09:43:08Z | 304 | 3 | null | [
"task_categories:automatic-speech-recognition",
"language:en",
"language:de",
"language:fr",
"language:it",
"language:es",
"language:ko",
"language:ja",
"region:us"
] | 2023-11-22T09:43:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
task_categories:
- automatic-speech-recognition
language:
- en
- de
- fr
- it
- es
- ko
- ja
---
# Dataset Card for multi_language
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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://nexdata.ai/?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset contains 25,000 hours of multi-language reading speech data. It's recorded by native speakers, covering English, French, German, Russian, Spanish, Portuguese, Italian, Japanese, Korean, Hindi, Vietnamese, Tagalog, Thai etc.The recording is rich in content, covering multiple categories such as economy, entertainment, news, oral language, numbers, and letters. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%.
For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
English, French, German, Russian, Spanish, Portuguese, Italian, Japanese, Korean, Hindi, Vietnamese, Tagalog, Thai etc.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commercial License
### Citation Information
[More Information Needed]
### Contributions | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DelgadoPanadero/Pokemon | DelgadoPanadero | 2022-01-03T10:10:40Z | 304 | 3 | null | [
"region:us"
] | 2022-01-03T10:10:40Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Pokemon Dataset
This dataset contains a text representation of more that 10k pokemon sprites from different pokemon videogames (red, yellow, gold, ruby,...). The original images are from 40 to 96 pixel of size and every pixel is represented with an ASCII character depending to its color.
# Supported Tasks
* Text Generation
# Languages
* ASCII colo representation
# Data Fields
```
{'pokemon': pokemon sprite in ASCII representation
'game': videogame in witch the sprite appears
'size': pixel size
'number': number of the pokemon}
```
# License
* All the creative right are property of Nintendo
# Preview
```
00 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
01 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
02 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
03 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
04 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
05 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
06 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
07 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
08 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
09 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
10 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
11 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P P P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; P P ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
12 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P P P F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; P P P P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
13 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P P J J ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F J P P P P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
14 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; J J J F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J P P P ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
15 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F J J J J F P ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
16 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ A J J J J J J J ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
17 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J F F ; F F F F F F F A A J J J J J J J F ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
18 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F F F F Z Z Z Z Z J J F J J J J J J F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
19 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F Z Z Z Z Z Z Z Z J J J J J J F F ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
20 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J Z Z Z Z Z Z Z Z J J J J J F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F Z J A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
21 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A J J J J Z Z Z Z Z J F ; ; F J J F A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F Z J J J A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
22 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F ; ; J J J J J J J ; ~ ; ; J J F F ; ~ ~ ~ ~ ~ ~ ~ ~ F Z J J J F A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
23 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J ; ~ ; J J J J J J J ; ; P ; J J F F ; ~ ~ ~ ~ ~ ~ F F Z J J F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~
24 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A J ; ; P J J J J J J J F ; ; F J J F F A ~ ~ ~ ~ ~ F Z Z J F F F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~
25 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F J F ; F J J A F J J J J J J J > > F F F ; ~ ~ F F J J J F F F F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~
26 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; R J J J J F J J J J J F J J J > > > = F F ; A A J J F F F F F F F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~
27 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; > F J J J J F = = = = F J J J > > > = F A A Z F A F F F F F F F F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~
28 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A ~ ; = J J J J J = = R R J J J J > > = = A Z F J Z Z A F F F F F F F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~
29 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A Z A A = J J J J J = R R = J J J J J = = F A J J J J F A F F F F F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
30 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A Z J J F A J J J J J J = = J J J J J J J F A J J J J J J ; F F F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
31 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J F F A J J J J J J J J J J J J J J A J J J J F F ; F F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
32 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F F F F J J J J J J J J J J J J J J J J J F F F ; F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
33 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; F F F F J J J J J J J J J J J J J J J J J F F F ; A F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
34 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; F F F J J J J J J J J J J J J J J J J F F F ; ~ ~ A F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
35 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; A J J J J J J J J J J J J J J J J F F F F ; ~ ~ ~ A F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
36 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J J J J J J J J J J J J J J F F F A ~ ~ A A F F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
37 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A J J J J J J J J J J J J J J J J J F F F A ; ; J F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
38 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J J J J J J J J J J J J J J J F F F A J F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
39 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A J J J J J J J J J J J J J J J J F F F F A F F F F ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
40 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A A A J J J J J J J J J J J J J J F F F F F ; F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
41 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A Z Z F A J J J J J J J J J J J J J F F F F F ; ; F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
42 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F J F J A J J J J J J J J J J J F F F F F F ; ~ ; F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
43 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F F F J J J J J J J J J F F F F F F F F ; ; A A A F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
44 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F F F F A F F J J J J F F F F F F F F F F A A A A ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
45 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F A F F F F F F F F F F F F F F F A A ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
46 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; F F F ; F F F F F F F F F F F F F F F ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
47 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A ; ; F F F F F F F F F F F F F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
48 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ; A F F F F F F F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
49 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; A F F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
50 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; F F ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
51 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
52 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F A F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
53 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
54 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
55 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
56 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
57 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
58 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
59 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
60 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
61 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
62 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
63 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
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Doohae/klue-mrc-bm25 | Doohae | 2022-02-09T08:10:52Z | 304 | 0 | null | [
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DrishtiSharma/as_opus100_processed | DrishtiSharma | 2022-02-09T17:36:03Z | 304 | 0 | null | [
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DrishtiSharma/bg_opus100_processed | DrishtiSharma | 2022-02-09T07:33:17Z | 304 | 0 | null | [
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DrishtiSharma/br_opus100_processed | DrishtiSharma | 2022-02-10T01:50:05Z | 304 | 0 | null | [
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DrishtiSharma/hi_opus100_processed | DrishtiSharma | 2022-02-09T18:34:07Z | 304 | 0 | null | [
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DrishtiSharma/kk_opus100_processed | DrishtiSharma | 2022-02-09T08:23:36Z | 304 | 0 | null | [
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DrishtiSharma/or_opus100_processed | DrishtiSharma | 2022-02-10T03:12:01Z | 304 | 0 | null | [
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DrishtiSharma/sl_opus100_processed | DrishtiSharma | 2022-02-09T03:53:47Z | 304 | 0 | null | [
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DrishtiSharma/sr_opus100_processed | DrishtiSharma | 2022-02-08T17:32:38Z | 304 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Gabriel/quora_swe | Gabriel | 2022-10-22T09:39:38Z | 304 | 0 | null | [
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"size_categories:10K<n<100K",
"language:sv",
"license:mit",
"question-pairing",
"semantic-search",
"region:us"
] | 2022-10-22T09:39:38Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- sv
license:
- mit
size_categories:
- 10K<n<100K
task_categories:
- text-retrieval
- text-classification
task_ids:
- semantic-similarity-classification
tags:
- question-pairing
- semantic-search
---
# Dataset Card for "quora_swe"
The dataset quora_swe is a subset of the automatically translated (MNT) Swedish Semantic Textual Similarity dataset: quora-deduplicates .
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
HHousen/ParaSCI | HHousen | 2021-11-24T03:38:25Z | 304 | 1 | null | [
"arxiv:2101.08382",
"region:us"
] | 2021-11-24T03:38:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Reformatted version of the ParaSCI dataset from [ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation](https://arxiv.org/abs/2101.08382). Data retrieved from [dqxiu/ParaSCI](https://github.com/dqxiu/ParaSCI). | [
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LysandreJik/demo4 | LysandreJik | 2021-09-25T20:02:48Z | 304 | 0 | null | [
"region:us"
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LysandreJik/test-16344361893586 | LysandreJik | 2021-10-17T02:03:11Z | 304 | 0 | null | [
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khondoker/SentNoB | khondoker | 2023-04-23T10:32:36Z | 304 | 0 | sentnob | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:bn",
"region:us"
] | 2023-04-23T10:32:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- bn
task_categories:
- text-classification
pretty_name: SentNoB
task_ids:
- sentiment-classification
annotations_creators:
- expert-generated
language_creators:
- expert-generated
paperswithcode_id: sentnob
---
# Dataset Card for "SentNoB"
### Dataset Summary
Social Media User Comments' Sentiment Analysis Dataset. Each user comments are labeled with either positive (1), negative (2), or neutral (0).
### Citation Information
```
@inproceedings{islam2021sentnob,
title={SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts},
author={Islam, Khondoker Ittehadul and Kar, Sudipta and Islam, Md Saiful and Amin, Mohammad Ruhul},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021},
pages={3265--3271},
year={2021}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Kira-Asimov/gender_clinical_trial | Kira-Asimov | 2022-02-10T10:16:03Z | 304 | 2 | null | [
"region:us"
] | 2022-02-10T10:16:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Gender classification from Clinical Trial Public Data
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NahedAbdelgaber/evaluating-student-writing | NahedAbdelgaber | 2021-12-30T05:48:09Z | 304 | 0 | null | [
"region:us"
] | 2021-12-30T05:48:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TRoboto/names | TRoboto | 2022-01-29T16:33:25Z | 304 | 1 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-01-29T16:33:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
project: Maha
license: cc-by-4.0
---
## Dataset Summary
It includes list of Arabic names with meaning and origin of most names
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YuAnthony/chid | YuAnthony | 2022-02-23T05:19:14Z | 304 | 1 | null | [
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abidlabs/test-audio-1 | abidlabs | 2022-01-19T16:26:19Z | 304 | 0 | null | [
"region:us"
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abidlabs/test-translation-dataset | abidlabs | 2022-02-01T23:15:18Z | 304 | 0 | null | [
"region:us"
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anzorq/kbd-ru-1.67M-temp | anzorq | 2022-01-14T12:00:11Z | 304 | 0 | null | [
"region:us"
] | 2022-01-14T12:00:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | kbd: web sites dump deduplicated latin script – 835K sentences
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anzorq/kbd-ru-jsonl-tmp | anzorq | 2022-01-14T01:19:45Z | 304 | 0 | null | [
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anzorq/kbd-ru-temp | anzorq | 2022-01-06T23:24:50Z | 304 | 0 | null | [
"region:us"
] | 2022-01-06T23:24:50Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | test | [
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caltonji/harrypotter_squad_v2 | caltonji | 2021-12-31T19:11:29Z | 304 | 0 | null | [
"region:us"
] | 2021-12-31T19:11:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sagawa/ZINC-canonicalized | sagawa | 2022-09-04T02:21:08Z | 304 | 0 | null | [
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"license:apache-2.0",
"ZINC",
"chemical",
"SMILES",
"region:us"
] | 2022-09-04T02:21:08Z | 2022-09-03T06:01:18.000Z | 2022-09-03T06:01:18 | ---
annotations_creators: []
language: []
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: canonicalized ZINC
size_categories:
- 10M<n<100M
source_datasets:
- original
tags:
- ZINC
- chemical
- SMILES
task_categories: []
task_ids: []
---
### dataset description
We downloaded ZINC dataset from [here](https://zinc15.docking.org/) and canonicalized it.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
```python:
from rdkit import Chem
def canonicalize(mol):
mol = Chem.MolToSmiles(Chem.MolFromSmiles(mol),True)
return mol
```
We randomly split the preprocessed data into train and validation. The ratio is 9 : 1. | [
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jamescalam/youtube-transcriptions | jamescalam | 2022-10-22T01:20:07Z | 304 | 19 | null | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_categories:visual-question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"task_ids:document-retrieval",
"task_ids:visual-question-answering",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:afl-3.0",
"youtube",
"technical",
"speech to text",
"speech",
"video",
"video search",
"audio",
"audio search",
"region:us"
] | 2022-10-22T01:20:07Z | 2022-10-13T20:31:27.000Z | 2022-10-13T20:31:27 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Youtube Transcriptions
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- technical
- speech to text
- speech
- video
- video search
- audio
- audio search
task_categories:
- conversational
- question-answering
- text-retrieval
- visual-question-answering
task_ids:
- open-domain-qa
- extractive-qa
- document-retrieval
- visual-question-answering
---
The YouTube transcriptions dataset contains technical tutorials (currently from [James Briggs](https://www.youtube.com/c/jamesbriggs), [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ), and [AI Coffee Break](https://www.youtube.com/c/aicoffeebreak)) transcribed using [OpenAI's Whisper](https://huggingface.co/openai/whisper-large) (large). Each row represents roughly a sentence-length chunk of text alongside the video URL and timestamp.
Note that each item in the dataset contains just a short chunk of text. For most use cases you will likely need to merge multiple rows to create more substantial chunks of text, if you need to do that, this code snippet will help:
```python
from datasets import load_dataset
# first download the dataset
data = load_dataset(
'jamescalam/youtube-transcriptions',
split='train'
)
new_data = [] # this will store adjusted data
window = 6 # number of sentences to combine
stride = 3 # number of sentences to 'stride' over, used to create overlap
for i in range(0, len(data), stride):
i_end = min(len(data)-1, i+window)
if data[i]['title'] != data[i_end]['title']:
# in this case we skip this entry as we have start/end of two videos
continue
# create larger text chunk
text = ' '.join(data[i:i_end]['text'])
# add to adjusted data list
new_data.append({
'start': data[i]['start'],
'end': data[i_end]['end'],
'title': data[i]['title'],
'text': text,
'id': data[i]['id'],
'url': data[i]['url'],
'published': data[i]['published']
})
``` | [
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bswac | null | 2022-11-03T16:15:55Z | 303 | 0 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:bs",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-11-03T16:15:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- bs
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: BsWac
dataset_info:
features:
- name: sentence
dtype: string
config_name: bswac
splits:
- name: train
num_bytes: 9156258478
num_examples: 354581267
download_size: 1988514951
dataset_size: 9156258478
---
# Dataset Card for BsWac
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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:** http://nlp.ffzg.hr/resources/corpora/bswac/
- **Repository:** https://www.clarin.si/repository/xmlui/handle/11356/1062
- **Paper:** http://nlp.ffzg.hr/data/publications/nljubesi/ljubesic14-bs.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr)
### Dataset Summary
The Bosnian web corpus bsWaC was built by crawling the .ba top-level domain in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Bosnian vs. Croatian vs. Serbian).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is monolingual in Bosnian language.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@misc{11356/1062,
title = {Bosnian web corpus {bsWaC} 1.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1062},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
year = {2016} }
```
### Contributions
Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset. | [
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coached_conv_pref | null | 2023-01-25T14:28:17Z | 303 | 2 | coached-conversational-preference-elicitation | [
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:token-classification",
"task_ids:dialogue-modeling",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"Conversational Recommendation",
"region:us"
] | 2023-01-25T14:28:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
- token-classification
task_ids:
- dialogue-modeling
- parsing
paperswithcode_id: coached-conversational-preference-elicitation
pretty_name: Coached Conversational Preference Elicitation
tags:
- Conversational Recommendation
dataset_info:
features:
- name: conversationId
dtype: string
- name: utterances
sequence:
- name: index
dtype: int32
- name: speaker
dtype:
class_label:
names:
'0': USER
'1': ASSISTANT
- name: text
dtype: string
- name: segments
sequence:
- name: startIndex
dtype: int32
- name: endIndex
dtype: int32
- name: text
dtype: string
- name: annotations
sequence:
- name: annotationType
dtype:
class_label:
names:
'0': ENTITY_NAME
'1': ENTITY_PREFERENCE
'2': ENTITY_DESCRIPTION
'3': ENTITY_OTHER
- name: entityType
dtype:
class_label:
names:
'0': MOVIE_GENRE_OR_CATEGORY
'1': MOVIE_OR_SERIES
'2': PERSON
'3': SOMETHING_ELSE
config_name: coached_conv_pref
splits:
- name: train
num_bytes: 2295579
num_examples: 502
download_size: 5191959
dataset_size: 2295579
---
# Dataset Card for Coached Conversational Preference Elicitation
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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:** [Coached Conversational Preference Elicitation Homepage](https://research.google/tools/datasets/coached-conversational-preference-elicitation/)
- **Repository:** [Coached Conversational Preference Elicitation Repository](https://github.com/google-research-datasets/ccpe)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/W19-5941/)
### Dataset Summary
A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers, where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits the 'user’s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The assistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her preferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of entities.
### Supported Tasks and Leaderboards
* `other-other-Conversational Recommendation`: The dataset can be used to train a model for Conversational recommendation, which consists in Coached Conversation Preference Elicitation.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point comprises of a series of utterances between the 'assistant' and the 'user'. Each such utterance is annotated into categories mentioned in data fields.
An example from the Coached Conversational Preference Elicitation dataset looks as follows:
```
{'conversationId': 'CCPE-6faee',
'utterances': {'index': [0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15],
'segments': [{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [0], 'entityType': [0]},
{'annotationType': [1], 'entityType': [0]}],
'endIndex': [20, 27],
'startIndex': [14, 0],
'text': ['comedy', 'I really like comedy movies']},
{'annotations': [{'annotationType': [0], 'entityType': [0]}],
'endIndex': [24],
'startIndex': [16],
'text': ['comedies']},
{'annotations': [{'annotationType': [1], 'entityType': [0]}],
'endIndex': [15],
'startIndex': [0],
'text': ['I love to laugh']},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [0], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]}],
'endIndex': [21, 21],
'startIndex': [8, 0],
'text': ['Step Brothers', 'I liked Step Brothers']},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [1], 'entityType': [1]}],
'endIndex': [32],
'startIndex': [0],
'text': ['Had some amazing one-liners that']},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [0], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]}],
'endIndex': [15, 15],
'startIndex': [13, 0],
'text': ['RV', "I don't like RV"]},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [1], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]}],
'endIndex': [48, 66],
'startIndex': [18, 50],
'text': ['It was just so slow and boring', "I didn't like it"]},
{'annotations': [{'annotationType': [0], 'entityType': [1]}],
'endIndex': [63],
'startIndex': [33],
'text': ['Jurassic World: Fallen Kingdom']},
{'annotations': [{'annotationType': [0], 'entityType': [1]},
{'annotationType': [3], 'entityType': [1]}],
'endIndex': [52, 52],
'startIndex': [22, 0],
'text': ['Jurassic World: Fallen Kingdom',
'I have seen the movie Jurassic World: Fallen Kingdom']},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [1], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]}],
'endIndex': [24, 125, 161],
'startIndex': [0, 95, 135],
'text': ['I really like the actors',
'I just really like the scenery',
'the dinosaurs were awesome']}],
'speaker': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
'text': ['What kinds of movies do you like?',
'I really like comedy movies.',
'Why do you like comedies?',
"I love to laugh and comedy movies, that's their whole purpose. Make you laugh.",
'Alright, how about a movie you liked?',
'I liked Step Brothers.',
'Why did you like that movie?',
'Had some amazing one-liners that still get used today even though the movie was made awhile ago.',
'Well, is there a movie you did not like?',
"I don't like RV.",
'Why not?',
"And I just didn't It was just so slow and boring. I didn't like it.",
'Ok, then have you seen the movie Jurassic World: Fallen Kingdom',
'I have seen the movie Jurassic World: Fallen Kingdom.',
'What is it about these kinds of movies that you like or dislike?',
'I really like the actors. I feel like they were doing their best to make the movie better. And I just really like the scenery, and the the dinosaurs were awesome.']}}
```
### Data Fields
Each conversation has the following fields:
* `conversationId`: A unique random ID for the conversation. The ID has no meaning.
* `utterances`: An array of utterances by the workers.
Each utterance has the following fields:
* `index`: A 0-based index indicating the order of the utterances in the conversation.
* `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
* `text`: The raw text as written by the ASSISTANT, or transcribed from the spoken recording of USER.
* `segments`: An array of semantic annotations of spans in the text.
Each semantic annotation segment has the following fields:
* `startIndex`: The position of the start of the annotation in the utterance text.
* `endIndex`: The position of the end of the annotation in the utterance text.
* `text`: The raw text that has been annotated.
* `annotations`: An array of annotation details for this segment.
Each annotation has two fields:
* `annotationType`: The class of annotation (see ontology below).
* `entityType`: The class of the entity to which the text refers (see ontology below).
**EXPLANATION OF ONTOLOGY**
In the corpus, preferences and the entities that these preferences refer to are annotated with an annotation type as well as an entity type.
Annotation types fall into four categories:
* `ENTITY_NAME` (0): These mark the names of relevant entities mentioned.
* `ENTITY_PREFERENCE` (1): These are defined as statements indicating that the dialog participant does or does not like the relevant entity in general, or that they do or do not like some aspect of the entity. This may also be thought of the participant having some sentiment about what is being discussed.
* `ENTITY_DESCRIPTION` (2): Neutral descriptions that describe an entity but do not convey an explicit liking or disliking.
* `ENTITY_OTHER` (3): Other relevant statements about an entity that convey relevant information of how the participant relates to the entity but do not provide a sentiment. Most often, these relate to whether a participant has seen a particular movie, or knows a lot about a given entity.
Entity types are marked as belonging to one of four categories:
* `MOVIE_GENRE_OR_CATEGORY` (0): For genres or general descriptions that capture a particular type or style of movie.
* `MOVIE_OR_SERIES` (1): For the full or partial name of a movie or series of movies.
* `PERSON` (2): For the full or partial name of an actual person.
* `SOMETHING_ELSE ` (3): For other important proper nouns, such as the names of characters or locations.
### Data Splits
There is a single split of the dataset named 'train' which contains the whole datset.
| | Train |
| ------------------- | ----- |
| Input Conversations | 502 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{radlinski-etal-2019-ccpe,
title = {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences},
author = {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi},
booktitle = {Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue ({SIGDIAL})},
year = 2019
}
```
### Contributions
Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset. | [
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tashkeela | null | 2022-11-03T16:07:53Z | 303 | 1 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ar",
"license:gpl-2.0",
"diacritics-prediction",
"region:us"
] | 2022-11-03T16:07:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- gpl-2.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Tashkeela
tags:
- diacritics-prediction
dataset_info:
features:
- name: text
dtype: string
- name: book
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 1081110249
num_examples: 97
download_size: 183393530
dataset_size: 1081110249
---
# Dataset Card for Tashkeela
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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:** [Tashkeela](https://sourceforge.net/projects/tashkeela/)
- **Repository:** [Tashkeela](https://sourceforge.net/projects/tashkeela/)
- **Paper:** [Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems](https://www.sciencedirect.com/science/article/pii/S2352340917300112)
- **Point of Contact:** [Taha Zerrouki](mailto:t_zerrouki@esi.dz)
### Dataset Summary
It contains 75 million of fully vocalized words mainly
97 books from classical and modern Arabic language.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
```
{'book': 'zip://Tashkeela-arabic-diacritized-text-utf8-0.3/texts.txt/msa/al-kalema.org/أشكال-التجارب-في-مَثَل-الزارع.htm.txt::https://sourceforge.net/projects/tashkeela/files/latest/download',
'text': 'الكلمة\n\n\nصفحه اصلی\nاشترك\nالكتاب المقدس\nجميع المقالات\nالترتيب بالموضوع\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nهذا المقال على نسخة PDF\n\n\nأشكال التجارب في مَثَل الزارع\n\n\tقد رأينا في مقال " \nوسائل واشكال التجارب" الأشكال التي من الممكن أن تتخذها التجارب (وخاصة الاختبارات التي تأتي من خلال الآلام والاضطهاد وأشراك إطاعة شهوات الإنسان العتيق، الجسد)، نستطيع أيضاً أن نرى هذه الأقسام عاملة في مثال الزارع. هناك مجموعتين في مثال الزارع أنه برغم من سماعهم واستقبالهم للكلمة، إلا أنهم لم يجلبوا ثماراً. والسؤال هو لماذا؟\n\n1. التجارب في القسم الثاني من مثال الزارع\n\nفيما يخص القسم الثاني من مثال الزارع، تخبرنا عنها متى 13: 20- 21 ولوقا 8: 13 \nمتى 13: 20- 21\n" وَالْمَزْرُوعُ عَلَى الأَمَاكِنِ الْمُحْجِرَةِ هُوَ الَّذِي يَسْمَعُ الْكَلِمَةَ، وَحَالاً يَقْبَلُهَا بِفَرَحٍ، وَلكِنْ لَيْسَ لَهُ أَصْلٌ فِي ذَاتِهِ، بَلْ هُوَ إِلَى حِينٍ. فَإِذَا حَدَثَ ضِيقٌ أَوِ اضْطِهَادٌ مِنْ أَجْلِ الْكَلِمَةِ فَحَالاً يَعْثُرُ."\nلوقا 8: 13\n" وَالَّذِينَ عَلَى الصَّخْرِ هُمُ الَّذِينَ مَتَى سَمِعُوا يَقْبَلُونَ الْكَلِمَةَ بِفَرَحٍ، وَهؤُلاَءِ لَيْسَ لَهُمْ أَصْلٌ، فَيُؤْمِنُونَ إِلَى حِينٍ، وَفِي وَقْتِ التَّجْرِبَةِ يَرْتَدُّونَ."\n\nكما نرى، الناس في هذا القسم سمعوا الكلمة وحالاً قبلوها بفرح! بمعنى آخر، لقد كانوا متحمسين جداً تجاه الكلمة. ثم جاءت التجارب والاختبارات في شكل ضيق واضطهاد من أجل الكلمة، أي أنه بسبب الكلمة، اضطهد هؤلاء الناس. وعندئذ توقفوا. عوضاً عن أن يحفظوا ويتمسكوا بالكلمة التي قد حدث واستقبلوها بفرح، تراجعوا وسقطوا بعيداً، إن كنت مؤمناً صغيراً مليء بالحماسة تجاه الله، وبالرغم من أنه قد يبدو أنه لا يوجد شيطان من حولك، فهذا لن يستمر إلى الأبد. فالتجارب والاختبارات آتية. ستحتاج إلى أن تحفظ وتتمسك بالإيمان وبالكلمة التي قد حدث واستقبلتها بفرح. كما تقول لنا الكلمة:\nعبرانيين 10: 35- 39\n" فَلاَ تَطْرَحُوا ثِقَتَكُمُ الَّتِي لَهَا مُجَازَاةٌ عَظِيمَةٌ. لأَنَّكُمْ تَحْتَاجُونَ إِلَى الصَّبْرِ، حَتَّى إِذَا صَنَعْتُمْ مَشِيئَةَ اللهِ تَنَالُونَ الْمَوْعِدَ. لأَنَّهُ بَعْدَ قَلِيل جِدًّا «سَيَأْتِي الآتِي وَلاَ يُبْطِئُ. أَمَّا الْبَارُّ فَبِالإِيمَانِ يَحْيَا، وَإِنِ ارْتَدَّ لاَ تُسَرُّ بِهِ نَفْسِي». وَأَمَّا نَحْنُ فَلَسْنَا مِنَ الارْتِدَادِ لِلْهَلاَكِ، بَلْ مِنَ الإِيمَانِ لاقْتِنَاءِ النَّفْسِ."\n\nوالضيق قد يأخذ أشكالاً عديدة. رأيت أناساً يسقطون، تاركين الإيمان لأن آبائهم أو أقاربهم وأصدقائهم قد عارضوهم ورفضوهم بسبب إيمانهم. بالطبع قد يأخذ الاضطهاد أشكالاً أكثر من ذلك أيضاً، مثل أن تلقى في سجن أو أن تعذب لأجل إيمانك. قد يسبب الموت كذلك، كما حدث مع اسطفانوس ويعقوب أخو يوحنا. وتقول الكلمة من أجلك ومن أجل كل الذين حوكموا:\nرومية 16: 19- 20\n" لأَنَّ طَاعَتَكُمْ ذَاعَتْ إِلَى الْجَمِيعِ، فَأَفْرَحُ أَنَا بِكُمْ، وَأُرِيدُ أَنْ تَكُونُوا حُكَمَاءَ لِلْخَيْرِ وَبُسَطَاءَ لِلشَّرِّ. وَإِلهُ السَّلاَمِ سَيَسْحَقُ الشَّيْطَانَ تَحْتَ أَرْجُلِكُمْ سَرِيعًا."\nو بطرس الأولى 5: 8- 10\n" اُصْحُوا وَاسْهَرُوا. لأَنَّ إِبْلِيسَ خَصْمَكُمْ كَأَسَدٍ زَائِرٍ، يَجُولُ مُلْتَمِسًا مَنْ يَبْتَلِعُهُ هُوَ. فَقَاوِمُوهُ، رَاسِخِينَ فِي الإِيمَانِ، عَالِمِينَ أَنَّ نَفْسَ هذِهِ الآلاَمِ تُجْرَى عَلَى إِخْوَتِكُمُ الَّذِينَ فِي الْعَالَمِ. وَإِلهُ كُلِّ نِعْمَةٍ الَّذِي دَعَانَا إِلَى مَجْدِهِ الأَبَدِيِّ فِي الْمَسِيحِ يَسُوعَ، بَعْدَمَا تَأَلَّمْتُمْ يَسِيرًا، هُوَ يُكَمِّلُكُمْ، وَيُثَبِّتُكُمْ، وَيُقَوِّيكُمْ، وَيُمَكِّنُكُمْ."\n\nتمسك بالإيمان حتى النهاية. ضع حياتك ووضعك بين يدي الله وكن مستعداً لمواجهة أي شيء قد يحدث، أجل وحتى السخرية والعذاب. الله معك، سيقويك وسيعينك تماماً مثلما فعل مع يسوع في بستان جسثيماني. وتماماً مثلما فعل مع بولس في السجن عندما اضطهد من قِبَل اليهود (أعمال الرسل 23: 11). وكما قال بولس في كورنثوس الثانية 1: 7:" عَالِمِينَ أَنَّكُمْ كَمَا أَنْتُمْ شُرَكَاءُ فِي الآلاَمِ، كَذلِكَ فِي التَّعْزِيَةِ أَيْضًا." فالعزاء الآتي من الله يوازن أي سخرية أو أي عذاب قد يأتي إلينا من أي إنسان.\n\n2. التجارب في القسم الثالث من مثال الزارع\n\nبخصوص القسم الثالث من مثال الزارع، فنقرأ عنه في مرقس 4: 18- 19\n\n" وَهؤُلاَءِ هُمُ الَّذِينَ زُرِعُوا بَيْنَ الشَّوْكِ: هؤُلاَءِ هُمُ الَّذِينَ يَسْمَعُونَ الْكَلِمَةَ، وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ تَدْخُلُ وَتَخْنُقُ الْكَلِمَةَ فَتَصِيرُ بِلاَ ثَمَرٍ."\nو لوقا 8: 14\n" وَالَّذِي سَقَطَ بَيْنَ الشَّوْكِ هُمُ الَّذِينَ يَسْمَعُونَ، ثُمَّ يَذْهَبُونَ فَيَخْتَنِقُونَ مِنْ هُمُومِ الْحَيَاةِ وَغِنَاهَا وَلَذَّاتِهَا، وَلاَ يُنْضِجُونَ ثَمَرًا."\n\nهؤلاء قد سمعوا الكلمة وفهموها ولكنهم صاروا بلا ثمر، وما هو السبب؟ السبب هو لأنهم تركوا أبواب قلوبهم مفتوحة لأشواك " وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ" (مرقس 4: 19)، والتي تدخل فتخنق الكلمة، كما رأينا يعقوب دائماً ما يقول:\nيعقوب 1: 13- 15\n" لاَ يَقُلْ أَحَدٌ إِذَا جُرِّبَ: «إِنِّي أُجَرَّبُ مِنْ قِبَلِ اللهِ»، لأَنَّ اللهَ غَيْرُ مُجَرَّبٍ بِالشُّرُورِ، وَهُوَ لاَ يُجَرِّبُ أَحَدًا. وَلكِنَّ كُلَّ وَاحِدٍ يُجَرَّبُ إِذَا انْجَذَبَ وَانْخَدَعَ مِنْ شَهْوَتِهِ. ثُمَّ الشَّهْوَةُ إِذَا حَبِلَتْ تَلِدُ خَطِيَّةً، وَالْخَطِيَّةُ إِذَا كَمَلَتْ تُنْتِجُ مَوْتًا."\nوتيموثاوس الأولى 6: 9 تقول لنا\n" وَأَمَّا الَّذِينَ يُرِيدُونَ أَنْ يَكُونُوا أَغْنِيَاءَ، فَيَسْقُطُونَ فِي تَجْرِبَةٍ وَفَخٍّ وَشَهَوَاتٍ كَثِيرَةٍ غَبِيَّةٍ وَمُضِرَّةٍ، تُغَرِّقُ النَّاسَ فِي الْعَطَبِ وَالْهَلاَكِ."\n\nيجب أن نلاحظ شيئاً هنا: أن تأثير هموم الحياة هو نفس التأثير الذي لتجارب الغنى وشهوات الأشياء الأخرى. فهموم الحياة أيضاً لا تجلب الثمار، إذاً فإن اردت أن تكون مسيحياً مثمراً، أي مسيحي حقيقي وليس فقط مسيحي اسمي، فيجب عليك أن تزيل أشواك الهموم والغنى وملذات الحياة وأن تمنعهم من العودة مرة أخرى. تحتاج إلى أن تفعل شيئاً، تحتاج إلى أن تتغير والله سيعينك في هذا إن كنت حقاً تريده. التجارب في القسم الثالث من مثال الزارع لا تأتي من خلال الاضطهاد والآلام عن طريق الشيطان. ولكن هنا تأخذ التجارب صوراً أكثر مكراً والتي مع هذا تتطلب مقاومتنا. الاهتمام بما يهتم به هذا العالم ("هموم هذا العالم")، الرغبة في الغنى أو اشتهاء الأشياء الأخرى هي أمور خطيرة جداً. إنها أشواك يجب إزالتها. كما رأينا بولس يقول:\nرومية 13: 14\n" بَلِ الْبَسُوا الرَّبَّ يَسُوعَ الْمَسِيحَ، وَلاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ لأَجْلِ الشَّهَوَاتِ."\n\n" لاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ" والتي تعني أنه يجب علينا أن لا نهتم بالجسد وشهواته. ولكن عوضاً عن ذلك ينبغي لنا أن نطعم أنفسنا بلبن الكلمة الصافي الذي ننمو بواستطه (بطرس الأولى 2: 2).\n\n\nتاسوس كيولاشوجلو'}
```
### Data Fields
- `book` (str): Book filename.
- `text` (str): Text of the book.
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
The Modern Standard Arabic texts crawled from the Internet.
#### Who are the source language producers?
Websites.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[GNU General Public License, version 2 (GPLv2)](https://opensource.org/licenses/GPL-2.0).
### Citation Information
The dataset was published on this [paper](https://www.sciencedirect.com/science/article/pii/S2352340917300112#!):
```
@article{zerrouki2017tashkeela,
title={Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems},
author={Zerrouki, Taha and Balla, Amar},
journal={Data in brief},
volume={11},
pages={147},
year={2017},
publisher={Elsevier}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. | [
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AHussain0418/day4data | AHussain0418 | 2022-01-07T16:26:39Z | 303 | 0 | null | [
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AlekseyKorshuk/comedy-scripts | AlekseyKorshuk | 2022-02-11T14:50:39Z | 303 | 2 | null | [
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AlexMaclean/wikipedia-deletion-compressions | AlexMaclean | 2021-12-07T00:27:21Z | 303 | 1 | null | [
"region:us"
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AndrewMcDowell/de_corpora_parliament_processed | AndrewMcDowell | 2022-02-04T15:45:27Z | 303 | 0 | null | [
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Annabelleabbott/real-fake-news-workshop | Annabelleabbott | 2022-01-07T00:45:18Z | 303 | 1 | null | [
"region:us"
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Bosio/pacman | Bosio | 2021-09-28T16:00:06Z | 303 | 0 | null | [
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-0.17026884853839874,
0.26989835500717163,
-0.45591992139816284,
-0.02893286943435669,
-0.057962339371442795,
0.31015974283218384,
-1.0262155532836914
] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DeskDown/ALTDataset | DeskDown | 2022-02-13T17:03:25Z | 303 | 0 | null | [
"region:us"
] | 2022-02-13T17:03:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Asian Language Treebank (ALT)
This is a **subset** of ALT dataset published by Riza et al.
It included following low-resource languages:
- fil
- vi
- id
- ms
- khm
- th
- hi
- my
It also includes ja and zh languages. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DeskDown/ALTDataset_en-to-fil-vi-id-ms-ja-khm | DeskDown | 2022-01-03T22:31:36Z | 303 | 0 | null | [
"region:us"
] | 2022-01-03T22:31:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | __Introduction__
The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques
through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY
as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016).
Then, it was developed under ASEAN IVO as described in this Web page. The process of building ALT
began with sampling about 20,000 sentences from English Wikinews, and then these sentences were
translated into the other languages. ALT now has 13 languages:
Bengali, English, Filipino, Hindi, Bahasa Indonesia, Japanese, Khmer, Lao, Malay, Myanmar (Burmese),
Thai, Vietnamese, Chinese (Simplified Chinese).
In this dataset you can find parallel corpus of fil, vi, id, ms, ja, khm languages.
Dataset is tokenized using mbart50-like tokenizer. (To be added soon)
Tokens are padded\truncated at a size of 128.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Fraser/wiki_sentences | Fraser | 2021-07-21T07:43:08Z | 303 | 0 | null | [
"region:us"
] | 2021-07-21T07:43:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Wiki Sentences
A dataset of all english sentences in Wikipedia.
Taken from the OPTIMUS project. https://github.com/ChunyuanLI/Optimus/blob/master/download_datasets.md
The dataset is 11.8GB so best to load it using streaming:
```python
from datasets import load_dataset
dataset = load_dataset("Fraser/wiki_sentences", split='train', streaming=True)
```
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GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049378 | GEM-submissions | 2022-02-28T11:56:24Z | 303 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-28T11:56:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049424 | GEM-submissions | 2022-02-28T11:57:10Z | 303 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-28T11:57:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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GEM-submissions/ratishsp | GEM-submissions | 2022-02-11T16:04:09Z | 303 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-11T16:04:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Template
---
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Iskaj/dutch_corpora_parliament_processed | Iskaj | 2022-01-27T11:42:10Z | 303 | 0 | null | [
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LysandreJik/test-16340052901609 | LysandreJik | 2021-10-12T02:21:31Z | 303 | 0 | null | [
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LysandreJik/test-16344368182003 | LysandreJik | 2021-10-17T02:13:40Z | 303 | 0 | null | [
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JonathanSum/en_corpora_parliament_processed | JonathanSum | 2022-02-22T17:18:03Z | 303 | 0 | null | [
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SaulLu/toy_struc_dataset | SaulLu | 2021-09-22T12:26:40Z | 303 | 0 | null | [
"region:us"
] | 2021-09-22T12:26:40Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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WillFerreiraSantos/halos | WillFerreiraSantos | 2022-01-13T03:25:41Z | 303 | 0 | null | [
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Yatoro/github-issues | Yatoro | 2021-11-19T01:26:49Z | 303 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alperbayram/Tweet_Siniflandirma | alperbayram | 2022-10-25T10:02:12Z | 303 | 0 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"size_categories:unknown",
"language:tr",
"region:us"
] | 2022-10-25T10:02:12Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
language:
- tr
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Turkish Sentiment Dataset
---
# References
- [alper bayram](https://github.com/alperbayram)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
animesh/autonlp-data-peptides | animesh | 2021-10-12T09:08:03Z | 303 | 0 | null | [
"region:us"
] | 2021-10-12T09:08:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | [Deep learning the collisional cross sections of the peptide universe from a million experimental values](https://www.nature.com/articles/s41467-021-21352-8)
[Data](http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD017703) generated from [MaxQuant](http://coxdocs.org/doku.php?id=maxquant:start) output
```
wget https://ftp.pride.ebi.ac.uk/pride/data/archive/2020/12/PXD017703/HeLa_200ng_Library_MaxQuant.zip
unzip HeLa_200ng_Library_MaxQuant.zip
awk -F '\t' '{print $1,",",$40}' evidence.txt > pepCCS.csv
wc pepCCS.csv
352111 1056333 12736697 pepCCS.csv
```
[Code](https://github.com/mannlabs/DeepCollisionalCrossSection)
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anushakamath/sv_corpora_parliament_processed_v0 | anushakamath | 2022-02-05T11:39:24Z | 303 | 0 | null | [
"region:us"
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arch-raven/MAMI | arch-raven | 2022-01-06T17:55:22Z | 303 | 0 | null | [
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cem/dnm | cem | 2021-12-24T10:48:27Z | 303 | 0 | null | [
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cestwc/conjnli | cestwc | 2022-02-15T15:23:38Z | 303 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hf-test/sv_corpora_parliament_processed | hf-test | 2022-01-10T10:17:51Z | 303 | 1 | null | [
"region:us"
] | 2022-01-10T10:17:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Swedish text corpus created by extracting the `"text"` from `dataset = load_dataset("europarl_bilingual", lang1="en", lang2="sv", split="train")` and processing it with:
```python
import re
def extract_text(batch):
text = batch["translation"]["sv"]
batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
return batch
``` | [
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mrmoor/cyber-threat-intelligence-splited | mrmoor | 2022-10-25T08:29:32Z | 303 | 1 | null | [
"region:us"
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yoruba_text_c3 | null | 2023-06-16T15:06:58Z | 302 | 1 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:yo",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-06-16T15:06:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- yo
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Yorùbá Text C3
dataset_info:
- config_name: plain_text
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 77094396
num_examples: 562238
download_size: 75407454
dataset_size: 77094396
- config_name: yoruba_text_c3
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 77094396
num_examples: 562238
download_size: 75407454
dataset_size: 77094396
---
# Dataset Card for Yorùbá Text C3
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#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
- **Repository:** https://github.com/ajesujoba/YorubaTwi-Embedding/
- **Paper:** https://aclanthology.org/2020.lrec-1.335/
- **Leaderboard:**
- **Point of Contact:** [Jesujoba Alabi](mailto:alabijesujoba@gmail.com)
### Dataset Summary
Yorùbá Text C3 was collected from various sources from the web (Bible, JW300, books, news articles, wikipedia, etc)
to compare pre-trained word embeddings (Fasttext and BERT) and embeddings and embeddings trained on curated Yorùbá Texts.
The dataset consists of clean texts (i.e texts with proper Yorùbá diacritics) like the Bible & JW300 and noisy texts (
with incorrect or absent diacritics)
from other online sources like Wikipedia, BBC Yorùbá, and VON Yorùbá
### Supported Tasks and Leaderboards
For training word embeddings and language models on Yoruba texts.
### Languages
The language supported is Yorùbá.
## Dataset Structure
### Data Instances
A data point is a sentence in each line.
{
'text': 'lílo àkàbà — ǹjẹ́ o máa ń ṣe àyẹ̀wò wọ̀nyí tó lè dáàbò bò ẹ́'
}
### Data Fields
- `text`: a `string` feature.
a sentence text per line
### Data Splits
Contains only the training split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Yorùbá.
### Source Data
#### Initial Data Collection and Normalization
The dataset comes from various sources of the web like Bible, JW300, books, news articles, wikipedia, etc.
See Table 1 in the [paper](https://www.aclweb.org/anthology/2020.lrec-1.335/) for the summary of the dataset and statistics
#### Who are the source language producers?
[Jehovah Witness](https://www.jw.org/yo/) (JW300)
[Yorùbá Bible](http://www.bible.com/)
[Yorùbá Wikipedia](dumps.wikimedia.org/yowiki)
[BBC Yorùbá](bbc.com/yoruba)
[VON Yorùbá](https://von.gov.ng/)
[Global Voices Yorùbá]( yo.globalvoices.org)
And other sources, see https://www.aclweb.org/anthology/2020.lrec-1.335/
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
The dataset is biased to the religion domain (Christianity) because of the inclusion of JW300 and the Bible.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The data sets were curated by Jesujoba Alabi and David Adelani, students of Saarland University, Saarbrücken, Germany .
### Licensing Information
The data is under the [Creative Commons Attribution-NonCommercial 4.0 ](https://creativecommons.org/licenses/by-nc/4.0/legalcode)
### Citation Information
```
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\`u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\`u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. | [
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DELith/github-issues | DELith | 2021-11-21T15:58:45Z | 302 | 0 | null | [
"region:us"
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DSCI511G1/COP26_Energy_Transition_Tweets | DSCI511G1 | 2021-12-06T17:53:41Z | 302 | 2 | null | [
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GEM/Taskmaster | GEM | 2022-10-24T15:30:09Z | 302 | 1 | null | [
"task_categories:conversational",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"dialog-response-generation",
"arxiv:2012.12458",
"region:us"
] | 2022-10-24T15:30:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: Taskmaster
tags:
- dialog-response-generation
---
# Dataset Card for GEM/Taskmaster
## Dataset Description
- **Homepage:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020
- **Repository:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020
- **Paper:** https://arxiv.org/abs/2012.12458
- **Leaderboard:** N/A
- **Point of Contact:** Karthik Krishnamoorthi
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/Taskmaster).
### Dataset Summary
This is a large task-oriented dialog dataset in which a model has to produce the response. The input contains the context and a structured representation of what the model is supposed to generate. The input is already pre-formatted as string, turning this into a pure text-to-text problem.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/Taskmaster')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/Taskmaster).
#### website
[Github](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)
#### paper
[Arxiv](https://arxiv.org/abs/2012.12458)
#### authors
Google researchers
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Arxiv](https://arxiv.org/abs/2012.12458)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@article{byrne2020tickettalk,
title={TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems},
author={Byrne, Bill and Krishnamoorthi, Karthik and Ganesh, Saravanan and Kale, Mihir Sanjay},
journal={arXiv preprint arXiv:2012.12458},
year={2020}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Karthik Krishnamoorthi
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
krishnamoorthi@google.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
NA
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
NA
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-4.0: Creative Commons Attribution 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Dialogues
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
a movie ticketing dialog dataset with 23,789 annotated conversations.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`other`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
NA
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Google researchers
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Google
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Tosin Adewumi (Luleå University of Technology)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id`: The unique example id
- `context`: The context of the conversation
- `target`: A string representing the target
-`references`: A List representing the target(s)
-`conversation_id`: A unique ID of the conversation
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
NA
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
NA
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{'context': "<PR>get_movie_attribute<PRAN>rating.movie<PRAV>rated R<C><U>I wanna see a movie<A>where are you?<U>spring hills kansas<PN>find_theaters<PAN>location<PAV>spring hills kansas<PR>find_theaters<PRAN>name.theater<PRAV>AMC Holiday Theater<PRAV>Cinemark Downtown<A>there are 2 theaters near you, the AMC Holiday Theater and Cinemark Downtown. Did you know which movie you'd like to see?<U>funny one please<PN>find_movies<PAN>location<PAV>spring hills kansas<PR>find_movies<PRAN>name.movie<PRAV>Not My Problem<PRAV>Family Jewels<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>name.genre<PR>get_movie_attribute<PRAN>name.genre<PRAV>comedy<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Matt Damon<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Noah Schnapp<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>name.genre<PR>get_movie_attribute<PRAN>name.genre<PRAV>romantic comedy<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Melissa McCarthy<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Ryan Reynolds<A>There's the comedy film called Not My Problem starring Matt Damon and Noah Schnapp. There's also a romantic comedy called Family Jewels starring Melissa McCarthy and Ryan Reynolds.<U>what ratings are there?<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>rating.movie<PR>get_movie_attribute<PRAN>rating.movie<PRAV>rated PG-13<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>rating.movie",
'conversation_id': 'dlg-d1f52e7e-c34c-4e85-b406-85ed138b5068',
'gem_id': 'Taskmaster-train-0',
'references': ['Not My Problem is rated PG-13 and Family Jewels is rated R.'],
'target': 'Not My Problem is rated PG-13 and Family Jewels is rated R.'}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
-`train`: 187182 examples
-`dev`: 23406 examples
-`test`: 23316 examples
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
NA
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
NA
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
Dialogue generation that makes sense
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
NA
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
NA
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
gem_id field was added to the 3 data splits
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
NA
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
BLEU: 60
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
automatic evaluation
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
NA
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
NA
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
NA
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
a movie ticketing dialog dataset with 23,789 annotated conversations.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Participatory experiment`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
NA
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Ticketing
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
NA
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
It's based on ticketing without personal information
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
NA
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
NA
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
NA
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
NA
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
NA
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GEM-submissions/Leo__bart-large__1645784880 | GEM-submissions | 2022-02-25T10:28:03Z | 302 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-25T10:28:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: bart-large
---
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GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645800191 | GEM-submissions | 2022-02-25T14:43:14Z | 302 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-25T14:43:14Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049601 | GEM-submissions | 2022-02-28T12:00:08Z | 302 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-28T12:00:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049876 | GEM-submissions | 2022-02-28T12:04:41Z | 302 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-28T12:04:41Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646050898 | GEM-submissions | 2022-02-28T12:21:46Z | 302 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-28T12:21:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646051364 | GEM-submissions | 2022-02-28T12:29:31Z | 302 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-28T12:29:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM-submissions/lewtun__this-is-a-test__1646052811 | GEM-submissions | 2022-02-28T12:53:35Z | 302 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-02-28T12:53:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: This is a test
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test
| [
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