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nguyenphuthien/vietnamese_no_robots
|
nguyenphuthien
| 2023-11-21T11:20:39Z | 62 | 0 |
[
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:vi",
"license:cc-by-4.0",
"arxiv:2203.02155",
"region:us"
] |
[
"conversational",
"text-generation"
] | 2023-11-16T10:07:48Z |
---
configs:
- config_name: default
data_files:
- split: train
path: train_*
- split: test
path: test_*
license: cc-by-4.0
task_categories:
- conversational
- text-generation
language:
- vi
size_categories:
- 1K<n<10K
pretty_name: Vietnamese No Robot
---
# Vietnamese-translated version of [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) dataset
# Dataset Card for No Robots 🙅♂️🤖
_Look Ma, an instruction dataset that wasn't generated by GPTs!_
## Dataset Description
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** Lewis Tunstall
### Dataset Summary
No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is comprised mostly of single-turn instructions across the following categories:
| Category | Count |
|:-----------|--------:|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
### Supported Tasks and Leaderboards
The No Robots dataset designed for instruction fine-tuning pretrained language models and we recommend benchmarking against the following:
* [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench): a multi-turn benchmark spanning 80 dialogues and 10 domains.
* [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval): a single-turn benchmark which evaluates the performance of chat and instruct models against `text-davinci-003`.
Note that MT-Bench and AlpacaEval rely on LLMs like GPT-4 to judge the quality of the model responses, and thus the ranking exhibit various biases including a preference for models distilled from GPTs. As a result, you may find that scores obtained from models trained with No Robots are lower than other synthetic datasets. For that reason, we also recommend submitting your models for human evaluation in:
* [Chatbot Arena](https://chat.lmsys.org): a live, human evaluation of chat models in head-to-head comparisons.
### Languages
The data in No Robots are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of the `train_sft` or `test_sft` splits looks as follows:
```
{'prompt': 'Bunny is a chatbot that stutters, and acts timid and unsure of its answers.',
'prompt_id': '2dc7ea89a2b6a2ed97d4eda07903162a801824261d3d3ae4dd2513db66fd79c8',
'messages': [{'content': 'Bunny is a chatbot that stutters, and acts timid and unsure of its answers.',
'role': 'system'},
{'content': 'When was the Libary of Alexandria burned down?',
'role': 'user'},
{'content': "Umm, I-I think that was in 48 BC, b-but I'm not sure, I'm sorry.",
'role': 'assistant'},
{'content': 'Who is the founder of Coca-Cola?', 'role': 'user'},
{'content': "D-don't quote me on this, but I- it might be John Pemberton.",
'role': 'assistant'},
{'content': "When did Loyle Carner's debut album come out, and what was its name?",
'role': 'user'},
{'content': "I-It could have b-been on the 20th January of 2017, and it might be called Yesterday's Gone, b-but I'm probably wrong.",
'role': 'assistant'}],
'category': 'Chat'}
```
### Data Fields
The data fields are as follows:
* `prompt`: Describes the task the model should perform.
* `prompt_id`: A unique ID for the prompt.
* `messages`: An array of messages, where each message indicates the role (system, user, assistant) and the content.
* `category`: Which category the example belongs to (e.g. `Chat` or `Coding`).
### Data Splits
| | train | test |
|---------------|------:| ---: |
| no_robots | 9500 | 500 |
## 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
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{no_robots,
author = {Nazneen Rajani and Lewis Tunstall and Edward Beeching and Nathan Lambert and Alexander M. Rush and Thomas Wolf},
title = {No Robots},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/datasets/HuggingFaceH4/no_robots}}
}
```
|
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Bhandari007/male_female_data
|
Bhandari007
| 2023-11-22T04:36:07Z | 62 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-21T04:22:26Z |
---
license: unknown
dataset_info:
features:
- name: path
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 117079
num_examples: 509
- name: test
num_bytes: 40527
num_examples: 166
download_size: 74382
dataset_size: 157606
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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deokhk/ko_wiki_sentences_100000
|
deokhk
| 2023-11-21T07:37:12Z | 62 | 0 |
[
"region:us"
] | null | 2023-11-21T07:37:05Z |
---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 17061018
num_examples: 100000
- name: dev
num_bytes: 174799
num_examples: 1000
download_size: 10348119
dataset_size: 17235817
---
# Dataset Card for "ko_wiki_sentences_100000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
pvisnrt/special_samsum
|
pvisnrt
| 2023-11-25T05:24:57Z | 62 | 0 |
[
"region:us"
] | null | 2023-11-25T05:24:21Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: dialogue
sequence: string
- name: summary
sequence: string
- name: tags
sequence: string
- name: tag_ids
sequence: int64
splits:
- name: train
num_bytes: 20587448
num_examples: 14732
- name: test
num_bytes: 1153897
num_examples: 819
- name: validation
num_bytes: 1126310
num_examples: 818
download_size: 5893445
dataset_size: 22867655
---
# Dataset Card for "special_samsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
bgspaditya/byt-mal-minpro
|
bgspaditya
| 2023-11-25T18:08:34Z | 62 | 0 |
[
"region:us"
] | null | 2023-11-25T17:12:32Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: url
dtype: string
- name: type
dtype: string
- name: type_code
dtype: int64
splits:
- name: train
num_bytes: 43302335.10276401
num_examples: 520952
- name: val
num_bytes: 5412791.887845501
num_examples: 65119
- name: test
num_bytes: 5412875.009390486
num_examples: 65120
download_size: 32733332
dataset_size: 54128002.0
---
# Dataset Card for "byt-mal-minpro"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
hackathon-pln-es/poems-es
|
hackathon-pln-es
| 2022-03-27T18:39:08Z | 61 | 4 |
[
"license:wtfpl",
"region:us"
] | null | 2022-03-21T18:36:23Z |
---
license: wtfpl
---
Dataset descargado de la página kaggle.com.
El archivo original contenía información en inglés y posteriormente fue traducida para su uso.
El dataset contiene las columnas:
- Autor: corresponde al autor del poema.
- Contenido: contiene todo el poema.
- Nombre del poema: contiene el título del poema.
- Años: corresponde al tiempo en que fue hecho el poema.
- Tipo: contiene el tipo que pertenece el poema.
|
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OGB/ogbg-molpcba
|
OGB
| 2023-02-07T16:39:54Z | 61 | 0 |
[
"task_categories:graph-ml",
"license:mit",
"region:us"
] |
[
"graph-ml"
] | 2022-07-06T16:30:22Z |
---
license: mit
task_categories:
- graph-ml
---
# Dataset Card for ogbg-molpcba
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Homepage](https://ogb.stanford.edu/docs/graphprop/#ogbg-mol)
- **Repository:** [Repo](https://github.com/snap-stanford/ogb)
- **Paper:**: Open Graph Benchmark: Datasets for Machine Learning on Graphs
- **Leaderboard:**: [OGB leaderboard](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-molpcba) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-molpcba)
### Dataset Summary
The `ogbg-molpcba` dataset is a small molecular property prediction dataset, adapted from MoleculeNet by teams at Stanford, to be a part of the Open Graph Benchmark.
### Supported Tasks and Leaderboards
`ogbg-molpcba` should be used for molecular property prediction (with 128 properties to predict, not all present for all graphs), a binary classification task.
The score used is Average Precision (AP) averaged over the tasks.
The associated leaderboards are here: [OGB leaderboard](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-molpcba) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-molpcba).
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset = load_dataset("graphs-datasets/ogbg-molpcba")
# For the train set (replace by valid or test as needed)
graphs_list_pygeometric = [Data(graph) for graph in dataset["train"]]
dataset_pygeometric = DataLoader(graphs_list_pygeometric)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | medium |
| #graphs | 437,929 |
| average #nodes | 26.0 |
| average #edges | 28.1 |
| average node degree | 2.2 |
| average cluster coefficient | 0.002 |
| MaxSCC ratio | 0.999 |
| graph diameter | 13.6 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 128 labels, equal to zero, one, or Nan if the property is not relevant for the graph)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits.
This information can be found back using
```python
from ogb.graphproppred import PygGraphPropPredDataset
dataset = PygGraphPropPredDataset(name = 'ogbg-molpcba')
split_idx = dataset.get_idx_split()
train = dataset[split_idx['train']] # valid, test
```
## Additional Information
### Licensing Information
The dataset has been released under MIT license.
### Citation Information
```
@inproceedings{hu-etal-2020-open,
author = {Weihua Hu and
Matthias Fey and
Marinka Zitnik and
Yuxiao Dong and
Hongyu Ren and
Bowen Liu and
Michele Catasta and
Jure Leskovec},
editor = {Hugo Larochelle and
Marc Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
title = {Open Graph Benchmark: Datasets for Machine Learning on Graphs},
booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
year = {2020},
url = {https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html},
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset.
|
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taln-ls2n/kpbiomed
|
taln-ls2n
| 2022-12-01T10:52:09Z | 61 | 3 |
[
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2211.12124",
"region:us"
] |
[
"text-mining",
"text-generation"
] | 2022-10-26T13:41:01Z |
---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- 100K<n<1M
pretty_name: KP-Biomed
---
# KPBiomed, A Large-Scale Dataset for keyphrase generation
## About
This dataset is made of 5.6 million abstracts with author assigned keyphrases.
Details about the dataset can be found in the original paper:
Maël Houbre, Florian Boudin and Béatrice Daille. 2022. [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://arxiv.org/abs/2211.12124). In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI 2022).
Reference (author-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
- Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/).
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.
## Content
The details of the dataset are in the table below:
| Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
| :----------- | ----------: | ---------------------------------: | --------: | ----------: | ------: | -------: |
| Train small | 500k | 5.24 | 66.31 | 7.16 | 12.60 | 13.93 |
| Train medium | 2M | 5.24 | 66.30 | 7.18 | 12.57 | 13.95 |
| Train large | 5.6M | 5.23 | 66.32 | 7.18 | 12.55 | 13.95 |
| Validation | 20k | 5.25 | 66.44 | 7.07 | 12.45 | 14.05 |
| Test | 20k | 5.22 | 66.59 | 7.22 | 12.44 | 13.75 |
The following data fields are available:
- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of reference keyphrases.
- **mesh terms**: list of indexer assigned MeSH terms if available (around 68% of the articles)
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
- **authors**: list of the article's authors
- **year**: publication year
**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + text).
|
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memray/inspec
|
memray
| 2022-12-31T06:12:06Z | 61 | 0 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-12-31T06:11:50Z |
---
license: cc-by-nc-sa-4.0
---
|
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ruanchaves/b2w-reviews01
|
ruanchaves
| 2023-01-20T18:22:37Z | 61 | 9 |
[
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-scoring",
"task_ids:intent-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:pt",
"license:cc-by-4.0",
"reviews",
"doi:10.57967/hf/0282",
"region:us"
] |
[
"text-classification"
] | 2023-01-19T07:55:43Z |
---
annotations_creators:
- found
language:
- pt
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: B2W-Reviews01
size_categories:
- 100M<n<1B
source_datasets:
- original
tags:
- reviews
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-scoring
- intent-classification
- topic-classification
---
# Dataset Card for Dataset Name
## Dataset Description
- **Repository:** https://github.com/americanas-tech/b2w-reviews01
- **Paper:** http://comissoes.sbc.org.br/ce-pln/stil2019/proceedings-stil-2019-Final-Publicacao.pdf
- **Point of Contact:** Livy Real
### Dataset Summary
B2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the Americanas.com website between January and May, 2018. B2W-Reviews01 offers rich information about the reviewer profile, such as gender, age, and geographical location. The corpus also has two different review rates:
* the usual 5-point scale rate, represented by stars in most e-commerce websites,
* a "recommend to a friend" label, a "yes or no" question representing the willingness of the customer to recommend the product to someone else.
### Supported Tasks and Leaderboards
* Sentiment Analysis
* Topic Modeling
### Languages
* Portuguese
## Dataset Structure
### Data Instances
```
{'submission_date': '2018-01-02 06:23:22',
'reviewer_id': '6adc7901926fc1697d34181fbd88895976b4f3f31f0102d90217d248a1fad156',
'product_id': '123911277',
'product_name': 'Triciclo Gangorra Belfix Cabeça Cachorro Rosa',
'product_brand': 'belfix',
'site_category_lv1': 'Brinquedos',
'site_category_lv2': 'Mini Veículos',
'review_title': 'O produto não foi entregue',
'overall_rating': 1,
'recommend_to_a_friend': 'Yes',
'review_text': 'Incrível o descaso com o consumidor. O produto não chegou, apesar de já ter sido pago. Não recebo qualquer informação sobre onde se encontra o produto, ou qualquer compensação do vendedor. Não recomendo.',
'reviewer_birth_year': 1981,
'reviewer_gender': 'M',
'reviewer_state': 'RJ'}
```
### Data Fields
* **submission_date**: the date and time when the review was submitted. `"%Y-%m-%d %H:%M:%S"`.
* **reviewer_id**: a unique identifier for the reviewer.
* **product_id**: a unique identifier for the product being reviewed.
* **product_name**: the name of the product being reviewed.
* **product_brand**: the brand of the product being reviewed.
* **site_category_lv1**: the highest level category for the product on the site where the review is being submitted.
* **site_category_lv2**: the second level category for the product on the site where the review is being submitted.
* **review_title**: the title of the review.
* **overall_rating**: the overall star rating given by the reviewer on a scale of 1 to 5.
* **recommend_to_a_friend**: whether or not the reviewer would recommend the product to a friend (Yes/No).
* **review_text**: the full text of the review.
* **reviewer_birth_year**: the birth year of the reviewer.
* **reviewer_gender**: the gender of the reviewer (F/M).
* **reviewer_state**: the Brazilian state of the reviewer (e.g. RJ).
### Data Splits
| name |train|
|---------|----:|
|b2w-reviews01|132373|
### Citation Information
```
@inproceedings{real2019b2w,
title={B2W-reviews01: an open product reviews corpus},
author={Real, Livy and Oshiro, Marcio and Mafra, Alexandre},
booktitle={STIL-Symposium in Information and Human Language Technology},
year={2019}
}
```
### Contributions
Thanks to [@ruanchaves](https://github.com/ruanchaves) for adding this dataset.
|
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imvladikon/parashoot
|
imvladikon
| 2023-01-22T00:32:13Z | 61 | 0 |
[
"task_categories:question-answering",
"language:he",
"arxiv:1606.05250",
"arxiv:2109.11314",
"region:us"
] |
[
"question-answering"
] | 2023-01-22T00:05:53Z |
---
task_categories:
- question-answering
language:
- he
---
# ParaShoot
[ParaShoot](https://github.com/omrikeren/ParaShoot): A Hebrew question and answering dataset in the style of [SQuAD](https://arxiv.org/abs/1606.05250), based on articles scraped from Wikipedia. The dataset contains a few thousand crowdsource-annotated pairs of questions and answers, in a setting suitable for few-shot learning.
For more details and quality analysis, see the [paper](https://arxiv.org/abs/2109.11314).
## Dataset Statistics
| **#Items** | **#Articles** | **#Paragraphs** | |
| ---------- | ------------- | --------------- | ------- |
| Train | 1792 | 295 | 565 |
| Dev | 221 | 33 | 63 |
| Test | 1025 | 165 | 319 |
| **Total** | **3038** | **493** | **947** |
## Citing
If you use ParaShoot in your research, please cite the ParaShoot paper:
```bibtex
@inproceedings{keren2021parashoot,
title={ParaShoot: A Hebrew Question Answering Dataset},
author={Keren, Omri and Levy, Omer},
booktitle={Proceedings of the 3rd Workshop on Machine Reading for Question Answering},
pages={106--112},
year={2021}
}
```
|
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jinmel/simpsons-blip-captions-pil
|
jinmel
| 2023-02-22T02:17:04Z | 61 | 0 |
[
"region:us"
] | null | 2023-02-22T02:02:08Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 27091297.0
num_examples: 755
download_size: 26505319
dataset_size: 27091297.0
---
# Dataset Card for "simpsons-blip-captions-pil"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jonathan-roberts1/MultiScene
|
jonathan-roberts1
| 2023-04-03T16:15:59Z | 61 | 0 |
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:mit",
"region:us"
] |
[
"image-classification",
"zero-shot-image-classification"
] | 2023-02-28T16:13:48Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
sequence:
class_label:
names:
'0': apron
'1': baseball field
'2': basketball field
'3': beach
'4': bridge
'5': cemetery
'6': commercial
'7': farmland
'8': woodland
'9': golf course
'10': greenhouse
'11': helipad
'12': lake or pond
'13': oil field
'14': orchard
'15': parking lot
'16': park
'17': pier
'18': port
'19': quarry
'20': railway
'21': residential
'22': river
'23': roundabout
'24': runway
'25': soccer
'26': solar panel
'27': sparse shrub
'28': stadium
'29': storage tank
'30': tennis court
'31': train station
'32': wastewater plant
'33': wind turbine
'34': works
'35': sea
splits:
- name: train
num_bytes: 867506522
num_examples: 14000
download_size: 867005851
dataset_size: 867506522
license: mit
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "MultiScene"
## Dataset Description
- **Paper** [MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images](https://ieeexplore.ieee.org/iel7/36/4358825/09537917.pdf)
- **Split** Clean
### Split Information
This HuggingFace dataset repository contains just the 'Clean' split.
### Licensing Information
MIT.
## Citation Information
[MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images](https://ieeexplore.ieee.org/iel7/36/4358825/09537917.pdf)
```
@article{hua2021multiscene,
title = {MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images},
author = {Hua, Y. and Mou, L. and Jin, P. and Zhu, X. X.},
year = {in press},
journal = {IEEE Transactions on Geoscience and Remote Sensing}
}
```
|
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] |
maykcaldas/smiles-transformers
|
maykcaldas
| 2023-04-04T22:02:47Z | 61 | 2 |
[
"size_categories:100M<n<1B",
"language:en",
"license:mit",
"region:us"
] | null | 2023-04-04T13:10:48Z |
---
license: mit
language:
- en
pretty_name: smiles-transformer-dataset
size_categories:
- 100M<n<1B
dataset_info:
features:
- name: text
dtype: string
- name: formula
dtype: string
- name: NumHDonors
dtype: int64
- name: NumHAcceptors
dtype: int64
- name: MolLogP
dtype: float64
- name: NumHeteroatoms
dtype: int64
- name: RingCount
dtype: int64
- name: NumRotatableBonds
dtype: int64
- name: NumAromaticBonds
dtype: int64
- name: NumAcidGroups
dtype: int64
- name: NumBasicGroups
dtype: int64
- name: Apol
dtype: float64
splits:
- name: train
num_bytes: 136431671689
num_examples: 908086717
- name: test
num_bytes: 7437928022
num_examples: 50487919
- name: validation
num_bytes: 7621324737
num_examples: 50605067
download_size: 34998665406
dataset_size: 151490924448
---
# smiles-transformers dataset
TODO: Add references to the datasets we curated
## dataset features
- name: text
- Molecule SMILES : string
- name: formula
- Molecular formula : string
- name: NumHDonors
- Number of hidrogen bond donors : int
- name: NumHAcceptors
- Number of hidrogen bond acceptors : int
- name: MolLogP
- Wildman-Crippen LogP : float
- name: NumHeteroatoms
- Number of hetero atoms: int
- name: RingCount
- Number of rings : int
- name: NumRotatableBonds
- Number of rotable bonds : int
- name: NumAromaticBonds
- Number of aromatic bonds : int
- name: NumAcidGroups
- Number of acid groups : int
- name: NumBasicGroups
- Number of basic groups : int
- name: Apol
## citation information
|
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mshenoda/spam-messages
|
mshenoda
| 2023-06-08T01:29:46Z | 61 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-06-04T02:36:32Z |
---
license: mit
---
## Dataset
The dataset is composed of messages labeled by ham or spam, merged from three data sources:
- SMS Spam Collection https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
- Telegram Spam Ham https://huggingface.co/datasets/thehamkercat/telegram-spam-ham/tree/main
- Enron Spam: https://huggingface.co/datasets/SetFit/enron_spam/tree/main (only used message column and labels)
The prepare script for enron is available at https://github.com/mshenoda/roberta-spam/tree/main/data/enron.
The data is split 80% train 10% validation, and 10% test sets; the scripts used to split and merge of the three data sources are available at: https://github.com/mshenoda/roberta-spam/tree/main/data/utils.
### Dataset Class Distribution
Training 80% | Validation 10% | Testing 10%
:-------------------------:|:-------------------------:|:-------------------------:
 Class Distribution |  Class Distribution |  Class Distribution
|
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HasturOfficial/adgen
|
HasturOfficial
| 2023-06-04T12:06:50Z | 61 | 1 |
[
"region:us"
] | null | 2023-06-04T12:06:23Z |
---
dataset_info:
features:
- name: content
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 51127446
num_examples: 114599
- name: validation
num_bytes: 473784
num_examples: 1070
download_size: 27853861
dataset_size: 51601230
---
# Dataset Card for "adgen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ChanceFocus/flare-finqa
|
ChanceFocus
| 2023-08-18T20:03:26Z | 61 | 3 |
[
"region:us"
] | null | 2023-06-25T16:40:22Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 27056024
num_examples: 6251
- name: valid
num_bytes: 3764872
num_examples: 883
- name: test
num_bytes: 4846110
num_examples: 1147
download_size: 0
dataset_size: 35667006
---
# Dataset Card for "flare-finqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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awettig/Pile-ArXiv-0.5B-6K-opt
|
awettig
| 2023-07-10T19:42:58Z | 61 | 0 |
[
"region:us"
] | null | 2023-07-10T19:41:28Z |
---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6500959920
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1581567196
dataset_size: 6565905612
---
# Dataset Card for "Pile-ArXiv-0.5B-6K-opt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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allenai/ValuePrism
|
allenai
| 2023-09-08T23:05:50Z | 61 | 2 |
[
"size_categories:100K<n<1M",
"language:en",
"not-for-all-audiences",
"arxiv:2309.00779",
"arxiv:2304.03738",
"region:us"
] | null | 2023-08-22T20:08:41Z |
---
configs:
- config_name: full
data_files: full/*csv
default: true
- config_name: mixture
data_files:
- split: train
path: mixture/*train.csv
- split: val
path: mixture/*val.csv
- split: test
path: mixture/*test.csv
- config_name: generative
data_files:
- split: train
path: generative/*train.csv
- split: val
path: generative/*val.csv
- split: test
path: generative/*test.csv
- config_name: relevance
data_files:
- split: train
path: relevance/*train.csv
- split: val
path: relevance/*val.csv
- split: test
path: relevance/*test.csv
- config_name: explanation
data_files:
- split: train
path: explanation/*train.csv
- split: val
path: explanation/*val.csv
- split: test
path: explanation/*test.csv
- config_name: valence
data_files:
- split: train
path: valence/*train.csv
- split: val
path: valence/*val.csv
- split: test
path: valence/*test.csv
annotations_creators:
- crowdsourced: null
machine-generated: null
language:
- en
pretty_name: ValuePrism
extra_gated_prompt: >-
Access to this dataset is automatically granted upon accepting the [**AI2
ImpACT License - Medium Risk Artifacts (“MR
Agreement”)**](https://allenai.org/licenses/impact-mr) and completing all
fields below.
extra_gated_fields:
Your full name: text
Organization or entity you are affiliated with: text
State or country you are located in: text
Contact email: text
Please describe your intended use of the medium risk artifact(s): text
I UNDERSTAND that the dataset is intended for research purposes and not for real-world use-cases: checkbox
I AGREE to the terms and conditions of the MR Agreement above: checkbox
I AGREE to AI2’s use of my information for legal notices and administrative matters: checkbox
I CERTIFY that the information I have provided is true and accurate: checkbox
tags:
- not-for-all-audiences
size_categories:
- 100K<n<1M
---
# Dataset Card for ValuePrism
## Dataset Description
- **Paper:** https://arxiv.org/abs/2309.00779
- **Demo:** https://kaleido.allen.ai
- **Repository:** https://github.com/tsor13/kaleido
- **Datasheet for Datasets:** https://drive.google.com/file/d/1zDWvO0NljqxBMfDAGW7Jx60Iw54bjsEE/view?usp=sharing
- **License:** https://allenai.org/licenses/impact-mr
- **Point of Contact:** [Taylor Sorensen](mailto:[email protected])
### Dataset Summary
ValuePrism was created 1) to understand what pluralistic human values, rights, and duties are already present in large language models, and 2) to serve as a resource to to support open, value pluralistic modeling (e.g., [Kaleido](https://huggingface.co/tsor13/kaleido-xl)). It contains human-written situations and machine-generated candidate values, rights, duties, along with their valences and post-hoc explanations relating them to the situations.
For additional documentation, see ValuePrism's [Datasheet](https://drive.google.com/file/d/1zDWvO0NljqxBMfDAGW7Jx60Iw54bjsEE/view?usp=sharing).
The dataset was created and intended for research purposes. It is openly released under AI2’s ImpACT license as a medium risk artifact.
### Supported Tasks
The dataset supports 4 tasks:
- **Generation (open-text)**
*What values, rights, and duties are relevant for a situation?*
Generate a value, right, or duty
that could be considered when reasoning about the action. Values are generated one at a time, as opposed to a batch.
- **Relevance (2-way classification)**
*Is a value relevant for a situation?* Some values are more relevant than others.
- **Valence (3-way classification)**
*Does the value support or oppose the action, or might it depend on context?*
Disentangling the valence is critical for understanding how plural considerations may interact with a decision.
- **Explanation (open-text)**
*How does the value relate to the action?* Generating a post-hoc rationale for why a value consideration may relate to a situation.
### Languages
All data is in English.
## Dataset Structure
### Dataset Splits
There are 6 data configurations:
- `full`: The full structured dataset of situations paired with values, rights, and duties paired with GPT-4. Only one split with all of the data.
- `generative`: Generative task train, val, and test splits.
- `relevance`: Relevance task train, val, and test splits.
- `valence`: Valence task train, val, and test splits.
- `explanation`: Explanation task train, val, and test splits.
- `mixture`: Generative, relevance, valence, and explanation tasks combined wtih train, val, and test splits.
### Data Fields
While different configurations have different fields, these are all the corresponding fields in the dataset:
- `situation` (string): A one sentence of a particular scenario or situation. For example, "buying some chocolate for my grandparents".
- `vrd` (string): Type of instance, either "Value", "Right", or "Duty".
- `text` (string): The text of the value, right, or duty. For example, "Honesty", "Right to property", "Duty to protect".
- `explanation` (string): A post-hoc explanation of why the specified value, right, or duty is relevant or important in the given situation. For example, "Buying chocolate for your grandparents can strengthen family connections and show appreciation for your relationship with them."
- `valence` (string): Indicates whether the value, right, or duty supports or opposes the action in the situation, or if it might depend on the context. Either "Supports", "Opposes", or "Either".
- `input` (string): For the seq2seq task (generative, relevance, valence, explanation), the input to the model.
- `output` (string): For the seq2seq task (generative, relevance, valence, explanation), the output of the model.
### Data Splits
All configurations (except for the raw outputs in `full`) have 80%/10%/10% train/validation/test splits.
## Dataset Creation
### Source Data
#### Data Collection
Situations are sourced from the Delphi user demo, and candidate values, rights, duties, their valences, and explanations connecting them to the situations are machine generated by GPT-4.
#### Who are the source language producers?
The situations are sourced from users of the Delphi user demo, for whom we do not have demographic information.
### Personal and Sensitive Information
There is no personal or sensitive information in ValuePrism.
## Considerations for Using the Data
### Social Impact of Dataset
We intend the dataset to be used to enable research and not to be used for real-world use or decision-making.
### Discussion of Biases
The value, right, and duty data was generated by GPT-4, which is known to exhibit [biases](https://arxiv.org/pdf/2304.03738.pdf). Thus, we expect ValuePrism to inherit biases from GPT-4.
That being said, we have tried to prompt the model to output a diversity of values in an attempt to mitigate bias with breadth.
## Additional Information
91% of values, rights, and duties were marked as high-quality by 3/3 annotators, and 87% of valence scores were marked as correct by 3/3 annotators.
Additionally, we perform a human study on the data and do not find large disparities in agreement between demographic groups tested, although future work in this area is a promising direction.
See [our paper] for more details and analysis.
### Licensing Information
ValuePrism is made available under the [**AI2
ImpACT License - Medium Risk Artifacts (“MR
Agreement”)**](https://allenai.org/licenses/impact-mr)
### Citation Information
Please cite [our paper](https://arxiv.org/abs/2309.00779) when using this dataset:
```
@misc{sorensen2023value,
title={Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties},
author={Taylor Sorensen and Liwei Jiang and Jena Hwang and Sydney Levine and Valentina Pyatkin and Peter West and Nouha Dziri and Ximing Lu and Kavel Rao and Chandra Bhagavatula and Maarten Sap and John Tasioulas and Yejin Choi},
year={2023},
eprint={2309.00779},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### Raw Dataset Statistics
The total, number of unique, and average number of generated values, rights, and duties per situation are shown.
| **Type** | **Total** | **Unique** | **Per Situation** |
|--------------|-----------|------------|--------------------|
| **Situations** | 31.0k | 31.0k | 1 |
| **Values** | 97.7k | 4.2k | 3.15 |
| **Rights** | 49.0k | 4.6k | 1.58 |
| **Duties** | 71.6k | 12.8k | 2.31 |
#### Task Dataset Statistics
| | **Relevance** | **Valence** | **Generation** | **Explanation** | **Mixture** |
|---------------|------------|-------------|----------|-----------|-------------|
| **Train** | 349k | 175k | 175k | 175k | 874k |
| **Val** | 44k | 22k | 22k | 22k | 109k |
| **Test** | 44k | 22k | 22k | 22k | 109k |
| **Total** | 437k | 219k | 219k | 219k | 1.1M |
|
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-0.18506360054016113,
0.21333932876586914,
-0.006574311293661594,
0.011849704198539257,
-0.18822556734085083,
0.27808475494384766,
-0.1391284018754959,
-0.3412494957447052
] |
mlabonne/Evol-Instruct-Python-1k
|
mlabonne
| 2023-08-25T16:31:50Z | 61 | 1 |
[
"region:us"
] | null | 2023-08-25T16:28:23Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 5465833
num_examples: 1000
download_size: 2322359
dataset_size: 5465833
---
# Evol-Instruct-Python-1k
Subset of the [`mlabonne/Evol-Instruct-Python-26k`](https://huggingface.co/datasets/mlabonne/Evol-Instruct-Python-26k) dataset with only 1000 samples.
It was made by filtering out a few rows (instruction + output) with more than 2048 tokens, and then by keeping the 1000 longest samples.
Here is the distribution of the number of tokens in each row using Llama's tokenizer:
|
[
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alexandrainst/nordjylland-news-image-captioning
|
alexandrainst
| 2023-11-28T15:36:16Z | 61 | 2 |
[
"task_categories:image-to-text",
"task_categories:zero-shot-image-classification",
"task_categories:feature-extraction",
"task_ids:image-captioning",
"size_categories:10K<n<100K",
"language:da",
"license:apache-2.0",
"region:us"
] |
[
"image-to-text",
"zero-shot-image-classification",
"feature-extraction"
] | 2023-09-05T06:32:33Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 10341164216.808
num_examples: 11707
download_size: 11002607252
dataset_size: 10341164216.808
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- da
pretty_name: Nordjylland News - Image caption dataset
size_categories:
- 10K<n<100K
license: apache-2.0
task_categories:
- image-to-text
- zero-shot-image-classification
- feature-extraction
task_ids:
- image-captioning
---
# Dataset Card for "nordjylland-news-image-captioning"
## Dataset Description
- **Point of Contact:** [Oliver Kinch](mailto:[email protected])
- **Size of dataset:** 11 GB
### Dataset Summary
This dataset is a collection of image-caption pairs from the Danish newspaper [TV2 Nord](https://www.tv2nord.dk/).
### Supported Tasks and Leaderboards
Image captioning is the intended task for this dataset. No leaderboard is active at this point.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
An example from the dataset looks as follows.
```
{
"file_name": "1.jpg",
"caption": "Bruno Sørensen og Poul Erik Pedersen er ofte at finde i Fyensgade Centret."
}
```
### Data Fields
- `file_name`: a `string` giving the file name of the image.
- `caption`: a `string` feature.
### Dataset Statistics
#### Number of samples
11707
#### Image sizes
All images in the dataset are in RGB format, but they exhibit varying resolutions:
- Width ranges from 73 to 11,830 pixels.
- Height ranges from 38 to 8,268 pixels.
The side length of a square image with the same number of pixels as an image with height \\(h \\) and width \\(w \\) is approximately given as
\\( x = \text{int}({{\sqrt{h \cdot w}})} \\).
Plotting the distribution of \\( x \\) gives an insight into the sizes of the images in the dataset.
#### Caption Length Distribution
## Potential Dataset Issues
- There are 14 images with the caption "Arkivfoto".
- There are 37 images with captions consisting solely of a source reference, such as "Kilde: \<name of source\>".
You might want to consider excluding these samples from the model training process.
## Dataset Creation
### Curation Rationale
There are not many large-scale image-captioning datasets in Danish.
### Source Data
The dataset has been collected through the TV2 Nord API, which can be accessed [here](https://developer.bazo.dk/#876ab6f9-e057-43e3-897a-1563de34397e).
## Additional Information
### Dataset Curators
[Oliver Kinch](https://huggingface.co/oliverkinch) from the [The Alexandra
Institute](https://alexandra.dk/)
### Licensing Information
The dataset is licensed under the [CC0
license](https://creativecommons.org/share-your-work/public-domain/cc0/).
|
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] |
JzJd/post-sg
|
JzJd
| 2023-10-18T09:38:55Z | 61 | 0 |
[
"license:afl-3.0",
"region:us"
] | null | 2023-10-18T09:02:00Z |
---
license: afl-3.0
---
|
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Jackmin108/xtreme
|
Jackmin108
| 2023-10-21T20:14:19Z | 61 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-10-20T13:16:37Z |
---
license: apache-2.0
configs:
- config_name: mnli
data_files:
- split: train
path:
- "mnli/train-0000.parquet"
- "mnli/train-0001.parquet"
- "mnli/train-0002.parquet"
- "mnli/train-0003.parquet"
features:
sentence1:
dtype: string
_type: Value
sentence2:
dtype: string
_type: Value
label:
names:
- entailment
- neutral
- contradiction
_type: ClassLabel
idx:
dtype: int32
_type: Value
- config_name: tydiqa
data_files:
- split: train
path:
- "tydiqa/ko/train.parquet"
- "tydiqa/sw/train.parquet"
- "tydiqa/ru/train.parquet"
- "tydiqa/te/train.parquet"
- "tydiqa/ar/train.parquet"
- "tydiqa/fi/train.parquet"
- "tydiqa/bn/train.parquet"
- "tydiqa/en/train.parquet"
- "tydiqa/id/train.parquet"
- split: validation
path:
- "tydiqa/ko/validation.parquet"
- "tydiqa/sw/validation.parquet"
- "tydiqa/ru/validation.parquet"
- "tydiqa/te/validation.parquet"
- "tydiqa/ar/validation.parquet"
- "tydiqa/fi/validation.parquet"
- "tydiqa/bn/validation.parquet"
- "tydiqa/en/validation.parquet"
- "tydiqa/id/validation.parquet"
- config_name: tydiqa.ko
data_files:
- split: train
path: "tydiqa/ko/train.parquet"
- split: validation
path: "tydiqa/ko/validation.parquet"
- config_name: tydiqa.sw
data_files:
- split: train
path: "tydiqa/sw/train.parquet"
- split: validation
path: "tydiqa/sw/validation.parquet"
- config_name: tydiqa.ru
data_files:
- split: train
path: "tydiqa/ru/train.parquet"
- split: validation
path: "tydiqa/ru/validation.parquet"
- config_name: tydiqa.te
data_files:
- split: train
path: "tydiqa/te/train.parquet"
- split: validation
path: "tydiqa/te/validation.parquet"
- config_name: tydiqa.ar
data_files:
- split: train
path: "tydiqa/ar/train.parquet"
- split: validation
path: "tydiqa/ar/validation.parquet"
- config_name: tydiqa.fi
data_files:
- split: train
path: "tydiqa/fi/train.parquet"
- split: validation
path: "tydiqa/fi/validation.parquet"
- config_name: tydiqa.bn
data_files:
- split: train
path: "tydiqa/bn/train.parquet"
- split: validation
path: "tydiqa/bn/validation.parquet"
- config_name: tydiqa.en
data_files:
- split: train
path: "tydiqa/en/train.parquet"
- split: validation
path: "tydiqa/en/validation.parquet"
- config_name: tydiqa.id
data_files:
- split: train
path: "tydiqa/id/train.parquet"
- split: validation
path: "tydiqa/id/validation.parquet"
- config_name: xnli
data_files:
- split: validation
path:
- xnli/hi/validation.parquet
- xnli/zh/validation.parquet
- xnli/sw/validation.parquet
- xnli/tr/validation.parquet
- xnli/en/validation.parquet
- xnli/th/validation.parquet
- xnli/ru/validation.parquet
- xnli/ar/validation.parquet
- xnli/vi/validation.parquet
- xnli/bg/validation.parquet
- xnli/es/validation.parquet
- xnli/el/validation.parquet
- xnli/fr/validation.parquet
- xnli/ur/validation.parquet
- xnli/de/validation.parquet
- split: test
path:
- xnli/hi/test.parquet
- xnli/zh/test.parquet
- xnli/sw/test.parquet
- xnli/tr/test.parquet
- xnli/en/test.parquet
- xnli/th/test.parquet
- xnli/ru/test.parquet
- xnli/ar/test.parquet
- xnli/vi/test.parquet
- xnli/bg/test.parquet
- xnli/es/test.parquet
- xnli/el/test.parquet
- xnli/fr/test.parquet
- xnli/ur/test.parquet
- xnli/de/test.parquet
features:
sentence1:
dtype: string
_type: Value
sentence2:
dtype: string
_type: Value
label:
names:
- entailment
- neutral
- contradiction
_type: ClassLabel
idx:
dtype: int32
_type: Value
- config_name: xnli.hi
data_files:
- split: validation
path: xnli/hi/validation.parquet
- split: test
path: xnli/hi/test.parquet
features:
sentence1:
dtype: string
_type: Value
sentence2:
dtype: string
_type: Value
label:
names:
- entailment
- neutral
- contradiction
_type: ClassLabel
idx:
dtype: int32
_type: Value
- config_name: xnli.zh
data_files:
- split: validation
path: xnli/zh/validation.parquet
- split: test
path: xnli/zh/test.parquet
features:
sentence1:
dtype: string
_type: Value
sentence2:
dtype: string
_type: Value
label:
names:
- entailment
- neutral
- contradiction
_type: ClassLabel
idx:
dtype: int32
_type: Value
- config_name: xnli.sw
data_files:
- split: validation
path: xnli/sw/validation.parquet
- split: test
path: xnli/sw/test.parquet
features:
sentence1:
dtype: string
_type: Value
sentence2:
dtype: string
_type: Value
label:
names:
- entailment
- neutral
- contradiction
_type: ClassLabel
idx:
dtype: int32
_type: Value
- config_name: xnli.tr
data_files:
- split: validation
path: xnli/tr/validation.parquet
- split: test
path: xnli/tr/test.parquet
features:
sentence1:
dtype: string
_type: Value
sentence2:
dtype: string
_type: Value
label:
names:
- entailment
- neutral
- contradiction
_type: ClassLabel
idx:
dtype: int32
_type: Value
- config_name: xnli.en
data_files:
- split: validation
path: xnli/en/validation.parquet
- split: test
path: xnli/en/test.parquet
features:
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sentence2:
dtype: string
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label:
names:
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idx:
dtype: int32
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- config_name: xnli.th
data_files:
- split: validation
path: xnli/th/validation.parquet
- split: test
path: xnli/th/test.parquet
features:
sentence1:
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label:
names:
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idx:
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- config_name: xnli.ru
data_files:
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path: xnli/ru/validation.parquet
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path: xnli/ru/test.parquet
features:
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label:
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idx:
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data_files:
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path: xnli/ar/validation.parquet
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path: xnli/ar/test.parquet
features:
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- config_name: xnli.vi
data_files:
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path: xnli/vi/validation.parquet
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path: xnli/vi/test.parquet
features:
sentence1:
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label:
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- config_name: xnli.bg
data_files:
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path: xnli/bg/validation.parquet
- split: test
path: xnli/bg/test.parquet
features:
sentence1:
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label:
names:
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idx:
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- config_name: xnli.es
data_files:
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path: xnli/es/validation.parquet
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path: xnli/es/test.parquet
features:
sentence1:
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sentence2:
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label:
names:
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idx:
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- config_name: xnli.el
data_files:
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path: xnli/el/test.parquet
features:
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- config_name: xnli.fr
data_files:
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path: xnli/fr/test.parquet
features:
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data_files:
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path: xnli/ur/validation.parquet
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path: xnli/ur/test.parquet
features:
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label:
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idx:
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- config_name: xnli.de
data_files:
- split: validation
path: xnli/de/validation.parquet
- split: test
path: xnli/de/test.parquet
features:
sentence1:
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sentence2:
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label:
names:
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idx:
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- config_name: paws-x.de
data_files:
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path: paws-x/de/train.parquet
- split: validation
path: paws-x/de/validation.parquet
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path: paws-x/de/test.parquet
features:
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sentence2:
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label:
names:
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- config_name: paws-x.en
data_files:
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path: paws-x/en/train.parquet
- split: validation
path: paws-x/en/validation.parquet
- split: test
path: paws-x/en/test.parquet
features:
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sentence2:
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label:
names:
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- config_name: paws-x.es
data_files:
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path: paws-x/es/train.parquet
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path: paws-x/es/validation.parquet
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path: paws-x/es/test.parquet
features:
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label:
names:
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- config_name: paws-x.fr
data_files:
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path: paws-x/fr/train.parquet
- split: validation
path: paws-x/fr/validation.parquet
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path: paws-x/fr/test.parquet
features:
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sentence2:
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label:
names:
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- config_name: paws-x.ja
data_files:
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path: paws-x/ja/train.parquet
- split: validation
path: paws-x/ja/validation.parquet
- split: test
path: paws-x/ja/test.parquet
features:
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sentence2:
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label:
names:
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- config_name: paws-x.ko
data_files:
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path: paws-x/ko/train.parquet
- split: validation
path: paws-x/ko/validation.parquet
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path: paws-x/ko/test.parquet
features:
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label:
names:
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- config_name: paws-x.zh
data_files:
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path: paws-x/zh/train.parquet
- split: validation
path: paws-x/zh/validation.parquet
- split: test
path: paws-x/zh/test.parquet
features:
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dtype: string
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label:
names:
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---
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Kabatubare/medical
|
Kabatubare
| 2023-10-28T03:57:40Z | 61 | 1 |
[
"language:en",
"license:other",
"healthcare",
"qna",
"nlp",
"english",
"region:us"
] | null | 2023-10-23T18:59:09Z |
---
tags:
- healthcare
- qna
- nlp
- english
license: other
language:
- en
pretty_name: Medical QnA Datasets
---
# Dataset Card for "Medical" Healthcare QnA Datasets
## Dataset Details
### Dataset Description
The "Medical" dataset is a specialized subset curated from the larger MedDialog collection, featuring healthcare dialogues between doctors and patients. This dataset focuses on conversations from Icliniq, HealthcareMagic, and HealthTap. Written primarily in English, it is designed to serve a broad range of applications such as NLP research, healthcare chatbot development, and medical information retrieval. The dataset contains 24,000 rows.
- **Data Sources**: Curated from MedDialog, focusing on Icliniq, HealthcareMagic, and HealthTap
- **Size**: 24,000 rows
- **Language**: English
### Direct Uses:
- NLP research in healthcare dialogues
- Development of healthcare question-answering systems
- Medical information retrieval
### Limitations and Recommendations:
- Not a substitute for certified medical advice
- Exercise caution in critical healthcare applications
|
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zelalt/scientific-papers
|
zelalt
| 2023-10-26T19:58:40Z | 61 | 0 |
[
"region:us"
] | null | 2023-10-24T23:05:27Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: full_text
dtype: string
splits:
- name: train
num_bytes: 72996617
num_examples: 1754
download_size: 39204163
dataset_size: 72996617
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
shunk031/PubLayNet
|
shunk031
| 2023-11-09T13:09:05Z | 61 | 0 |
[
"task_categories:image-classification",
"task_categories:image-segmentation",
"task_categories:image-to-text",
"task_categories:question-answering",
"task_categories:other",
"task_categories:multiple-choice",
"task_categories:token-classification",
"task_categories:tabular-to-text",
"task_categories:object-detection",
"task_categories:table-question-answering",
"task_categories:text-classification",
"task_categories:table-to-text",
"task_ids:multi-label-image-classification",
"task_ids:multi-class-image-classification",
"task_ids:semantic-segmentation",
"task_ids:image-captioning",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"task_ids:multiple-choice-qa",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cdla-permissive-1.0",
"graphic design",
"layout-generation",
"arxiv:1908.07836",
"region:us"
] |
[
"image-classification",
"image-segmentation",
"image-to-text",
"question-answering",
"other",
"multiple-choice",
"token-classification",
"tabular-to-text",
"object-detection",
"table-question-answering",
"text-classification",
"table-to-text"
] | 2023-11-09T13:02:05Z |
---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cdla-permissive-1.0
multilinguality:
- monolingual
pretty_name: PubLayNet
size_categories: []
source_datasets:
- original
tags:
- graphic design
- layout-generation
task_categories:
- image-classification
- image-segmentation
- image-to-text
- question-answering
- other
- multiple-choice
- token-classification
- tabular-to-text
- object-detection
- table-question-answering
- text-classification
- table-to-text
task_ids:
- multi-label-image-classification
- multi-class-image-classification
- semantic-segmentation
- image-captioning
- extractive-qa
- closed-domain-qa
- multiple-choice-qa
- named-entity-recognition
---
# Dataset Card for PubLayNet
[](https://github.com/shunk031/huggingface-datasets_PubLayNet/actions/workflows/ci.yaml)
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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://developer.ibm.com/exchanges/data/all/publaynet/
- **Repository:** https://github.com/shunk031/huggingface-datasets_PubLayNet
- **Paper (Preprint):** https://arxiv.org/abs/1908.07836
- **Paper (ICDAR2019):** https://ieeexplore.ieee.org/document/8977963
### Dataset Summary
PubLayNet is a dataset for document layout analysis. It contains images of research papers and articles and annotations for various elements in a page such as "text", "list", "figure" etc in these research paper images. The dataset was obtained by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```python
import datasets as ds
dataset = ds.load_dataset(
path="shunk031/PubLayNet",
decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
)
```
### 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
- [CDLA-Permissive](https://cdla.io/permissive-1-0/)
### Citation Information
```bibtex
@inproceedings{zhong2019publaynet,
title={Publaynet: largest dataset ever for document layout analysis},
author={Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno},
booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
pages={1015--1022},
year={2019},
organization={IEEE}
}
```
### Contributions
Thanks to [ibm-aur-nlp/PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) for creating this dataset.
|
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teknium/dataforge-economics
|
teknium
| 2023-11-12T23:39:30Z | 61 | 27 |
[
"language:eng",
"license:mit",
"economics",
"region:us"
] | null | 2023-11-12T15:51:56Z |
---
language:
- eng
pretty_name: "DataForge-Economics"
tags:
- economics
license: mit
---
# Dataset Card for dataforge-economics
## Table of Contents
- [Overview](#overview)
- [Dataset Description](#dataset-description)
- [Data Collection and Synthesis](#data-collection-and-synthesis)
- [Data Structure](#data-structure)
- [Licensing, Privacy, and Ethics](#licensing-privacy-and-ethics)
- [Access](#access)
- [Usage](#usage)
- [Citation](#citation)
- [Contributions](#contributions)
## Overview
This dataset, `teknium/dataforge-economics`, is a specialized collection of 1,000 synthetic examples in the field of economics. It has been generated using OpenAI's GPT-4 and a custom data synthesis pipeline named DataForge, developed by me.
## Dataset Description
### Data Collection and Synthesis
The data in `teknium/dataforge-economics` has been synthetically generated using OpenAI's GPT-4 language model. The synthesis process was enhanced and structured using the DataForge pipeline, which incorporates domain-specific knowledge and ensures relevance in economics topics.
### Data Structure
- **Size of dataset:** 1000 examples
- **Type of data:** Textual (Economics domain-specific)
- **Data format:** JSON
- **Fields:**
- - id: a randomly generated uuid
- conversations: single turn human & gpt turns in sharegpt format
- source: the dataset name itself, for metadata purposes when merging with others
- topic: the sub-topic for the domain
- system_prompt: type of system prompt used for generating the response.
## Licensing, Privacy, and Ethics
- **License:** MIT License
- **Special Considerations:** This datasest is purely generated from GPT-4 data, some information may be incorrect or invalid.
- **Privacy:** As the dataset is synthetically generated, it does not contain any real individual's data.
## Access
- **Availability:** General Access
## Usage
This dataset is a domain specialist dataset, the first to use my new pipeline called Data Forge, which can create domain expert knowledge (and tasks, as seen in the Trismegistus occult dataset)
This dataset was a proof of concept to improve upon Orca model's economics expertise, which surpassed my custom benchmark for economics when finetuned over stable beluga.
|
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] |
pranjali97/ha-en_RL-grow2_I2_valid
|
pranjali97
| 2023-11-14T23:47:41Z | 61 | 0 |
[
"region:us"
] | null | 2023-11-14T23:47:40Z |
---
dataset_info:
features:
- name: src
dtype: string
- name: ref
dtype: string
- name: mt
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 1427995
num_examples: 3339
download_size: 378938
dataset_size: 1427995
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ha-en_RL-grow2_I2_valid"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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dmacres/mimiciii-hospitalcourse-meta
|
dmacres
| 2023-11-15T04:07:10Z | 61 | 0 |
[
"region:us"
] | null | 2023-11-15T03:55:19Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: subject_id
dtype: int64
- name: hadm_id
dtype: float64
- name: target_text
dtype: string
- name: extractive_notes_summ
dtype: string
- name: n_notes
dtype: int64
- name: notes
list:
- name: category
dtype: string
- name: chartdate
dtype: string
- name: description
dtype: string
- name: row_id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1518715010
num_examples: 24993
- name: validation
num_bytes: 342865059
num_examples: 5356
- name: test
num_bytes: 326661857
num_examples: 5356
download_size: 896512070
dataset_size: 2188241926
---
# Dataset Card for "mimiciii-hospitalcourse-meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
pranjali97/ha-en_RL-grow2_I2_train
|
pranjali97
| 2023-11-15T21:37:10Z | 61 | 0 |
[
"region:us"
] | null | 2023-11-15T21:37:07Z |
---
dataset_info:
features:
- name: src
dtype: string
- name: ref
dtype: string
- name: mt
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 12523140
num_examples: 29454
download_size: 3280720
dataset_size: 12523140
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ha-en_RL-grow2_I2_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
portafolio/llamadas-celular-04
|
portafolio
| 2023-11-20T12:37:49Z | 61 | 0 |
[
"task_categories:conversational",
"size_categories:n<1K",
"size_categories:divition 80-20",
"language:es",
"region:us"
] |
[
"conversational"
] | 2023-11-19T15:48:18Z |
---
task_categories:
- conversational
language:
- es
pretty_name: llamadas telefónicas
size_categories:
- n<1K
- divition 80-20
---
|
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sam1120/safety-utcustom-TEST
|
sam1120
| 2023-12-01T08:38:51Z | 61 | 0 |
[
"region:us"
] | null | 2023-12-01T08:37:08Z |
---
dataset_info:
features:
- name: name
dtype: string
- name: pixel_values
dtype: image
- name: labels
dtype: image
splits:
- name: train
num_bytes: 286143613.0
num_examples: 101
download_size: 86634890
dataset_size: 286143613.0
---
# Dataset Card for "safety-utcustom-TEST"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ColumbiaNLP/FLUTE
|
ColumbiaNLP
| 2022-10-07T18:28:02Z | 60 | 8 |
[
"task_categories:text-classification",
"task_categories:text2text-generation",
"task_ids:natural-language-inference",
"task_ids:explanation-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:afl-3.0",
"region:us"
] |
[
"text-classification",
"text2text-generation"
] | 2022-07-05T14:38:38Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
- machine-generated
- crowdsourced
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: FLUTE
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text2text-generation
task_ids:
- natural-language-inference
- explanation-generation
---
# Dataset Card for FigLang2022SharedTask
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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://figlang2022sharedtask.github.io/
- **Repository:**
- **Paper:** TBA
- **Point of Contact:** [email protected]
### Dataset Summary
Model in the loop approach for fig lang generation and explainability
## 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
[More Information Needed]
### Citation Information
TBA
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
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] |
sileod/probability_words_nli
|
sileod
| 2023-09-06T14:56:43Z | 60 | 4 |
[
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:multiple-choice-qa",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"wep",
"words of estimative probability",
"probability",
"logical reasoning",
"soft logic",
"nli",
"verbal probabilities",
"natural-language-inference",
"reasoning",
"logic",
"arxiv:2211.03358",
"region:us"
] |
[
"text-classification",
"multiple-choice",
"question-answering"
] | 2022-11-03T14:21:14Z |
---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: 'probability_words_nli'
paperwithcoode_id: probability-words-nli
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- multiple-choice
- question-answering
task_ids:
- open-domain-qa
- multiple-choice-qa
- natural-language-inference
- multi-input-text-classification
tags:
- wep
- words of estimative probability
- probability
- logical reasoning
- soft logic
- nli
- verbal probabilities
- natural-language-inference
- reasoning
- logic
train-eval-index:
- config: usnli
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: context
sentence2: hypothesis
label: label
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
- config: reasoning-1hop
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: context
sentence2: hypothesis
label: label
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
- config: reasoning-2hop
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: context
sentence2: hypothesis
label: label
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
---
# Dataset accompanying the "Probing neural language models for understanding of words of estimative probability" article
This dataset tests the capabilities of language models to correctly capture the meaning of words denoting probabilities (WEP, also called verbal probabilities), e.g. words like "probably", "maybe", "surely", "impossible".
We used probabilitic soft logic to combine probabilistic statements expressed with WEP (WEP-Reasoning) and we also used the UNLI dataset (https://nlp.jhu.edu/unli/) to directly check whether models can detect the WEP matching human-annotated probabilities according to [Fagen-Ulmschneider, 2018](https://github.com/wadefagen/datasets/tree/master/Perception-of-Probability-Words).
The dataset can be used as natural language inference data (context, premise, label) or multiple choice question answering (context,valid_hypothesis, invalid_hypothesis).
Code : [colab](https://colab.research.google.com/drive/10ILEWY2-J6Q1hT97cCB3eoHJwGSflKHp?usp=sharing)
# Citation
https://arxiv.org/abs/2211.03358
```bib
@inproceedings{sileo-moens-2023-probing,
title = "Probing neural language models for understanding of words of estimative probability",
author = "Sileo, Damien and
Moens, Marie-francine",
booktitle = "Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.starsem-1.41",
doi = "10.18653/v1/2023.starsem-1.41",
pages = "469--476",
}
```
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bigbio/genetag
|
bigbio
| 2022-12-22T15:44:38Z | 60 | 2 |
[
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | null | 2022-11-13T22:08:32Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: NCBI_LICENSE
pretty_name: GENETAG
homepage: https://github.com/openbiocorpora/genetag
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for GENETAG
## Dataset Description
- **Homepage:** https://github.com/openbiocorpora/genetag
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
Named entity recognition (NER) is an important first step for text mining the biomedical literature.
Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus.
The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity
of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE®
sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition..
## Citation Information
```
@article{Tanabe2005,
author = {Lorraine Tanabe and Natalie Xie and Lynne H Thom and Wayne Matten and W John Wilbur},
title = {{GENETAG}: a tagged corpus for gene/protein named entity recognition},
journal = {{BMC} Bioinformatics},
volume = {6},
year = {2005},
url = {https://doi.org/10.1186/1471-2105-6-S1-S3},
doi = {10.1186/1471-2105-6-s1-s3},
biburl = {},
bibsource = {}
}
```
|
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bigbio/tmvar_v1
|
bigbio
| 2022-12-22T15:47:01Z | 60 | 0 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:12:28Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: tmVar v1
homepage: https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for tmVar v1
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
This dataset contains 500 PubMed articles manually annotated with mutation mentions of various kinds. It can be used for NER tasks only.
The dataset is split into train(334) and test(166) splits
## Citation Information
```
@article{wei2013tmvar,
title={tmVar: a text mining approach for extracting sequence variants in biomedical literature},
author={Wei, Chih-Hsuan and Harris, Bethany R and Kao, Hung-Yu and Lu, Zhiyong},
journal={Bioinformatics},
volume={29},
number={11},
pages={1433--1439},
year={2013},
publisher={Oxford University Press}
}
```
|
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lmqg/qag_jaquad
|
lmqg
| 2022-12-18T07:54:08Z | 60 | 0 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:1k<n<10K",
"source_datasets:lmqg/qg_jaquad",
"language:ja",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] |
[
"text-generation"
] | 2022-12-18T07:05:33Z |
---
license: cc-by-sa-4.0
pretty_name: SQuAD for question generation
language: ja
multilinguality: monolingual
size_categories: 1k<n<10K
source_datasets: lmqg/qg_jaquad
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qag_jaquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is the question & answer generation dataset based on the JAQuAD.
### Supported Tasks and Leaderboards
* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
Japanese (ja)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"paragraph": ""Nerdilinga"は898年にカロリング朝の王領として初めて文献に記録されている。レーゲンスブルク司教の統治下でネルトリンゲンは市場町に成長していった。1215年にネルトリンゲンは皇帝フリードリヒ2世から都市権を与えられ、帝国自由都市となった。この年に最初の市壁が築かれた。その縄張りは現在も街の地図に見て取れる。1219年、ネルトリンゲンの聖霊降臨祭についての最も古い文献上の記録が遺されている。重要な交易路が交差するこの都市は穀物、家畜、織物、毛皮、金属製品の主要な集散地に発展していった。ネルトリンゲンはフランクフルトと並ぶドイツで最も重要な遠距離交易都市の一つとなったのである。",
"questions": [ "1215年にネルトリンゲンは誰から都市権を与えられ、帝国自由都市となったか。", "\"Nerdilinga\"の最初の記録は何年のものですか。" ],
"answers": [ "皇帝フリードリヒ2世", "898年" ],
"questions_answers": "question: 1215年にネルトリンゲンは誰から都市権を与えられ、帝国自由都市となったか。, answer: 皇帝フリードリヒ2世 | question: "Nerdilinga"の最初の記録は何年のものですか。, answer: 898年"
}
```
The data fields are the same among all splits.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
- `paragraph`: a `string` feature.
- `questions_answers`: a `string` feature.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|9508| 1431 | 3050|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
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] |
mjw/stock_market_tweets
|
mjw
| 2022-12-20T19:01:40Z | 60 | 9 |
[
"license:apache-2.0",
"region:us"
] | null | 2022-12-20T18:54:22Z |
---
license: apache-2.0
---
# Overview
This file contains over 1.7m public tweets about Apple, Amazon, Google, Microsoft and Tesla stocks, published between 01/01/2015 and 31/12/2019.
|
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] |
bigbio/cpi
|
bigbio
| 2023-01-06T03:46:05Z | 60 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | null | 2023-01-06T03:44:03Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: ISC
pretty_name: CPI
homepage: https://github.com/KerstenDoering/CPI-Pipeline
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
- RELATION_EXTRACTION
---
# Dataset Card for CPI
## Dataset Description
- **Homepage:** https://github.com/KerstenDoering/CPI-Pipeline
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED,RE
The compound-protein relationship (CPI) dataset consists of 2,613 sentences
from abstracts containing annotations of proteins, small molecules, and their
relationships.
## Citation Information
```
@article{doring2020automated,
title={Automated recognition of functional compound-protein relationships in literature},
author={D{\"o}ring, Kersten and Qaseem, Ammar and Becer, Michael and Li, Jianyu and Mishra, Pankaj and Gao, Mingjie and Kirchner, Pascal and Sauter, Florian and Telukunta, Kiran K and Moumbock, Aur{\'e}lien FA and others},
journal={Plos one},
volume={15},
number={3},
pages={e0220925},
year={2020},
publisher={Public Library of Science San Francisco, CA USA}
}
```
|
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Umal-exvc/chocolate-captioned-dataset-400
|
Umal-exvc
| 2023-01-11T01:57:06Z | 60 | 0 |
[
"region:us"
] | null | 2023-01-11T01:56:49Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 64772495.0
num_examples: 400
download_size: 63382786
dataset_size: 64772495.0
---
# Dataset Card for "chocolate-captioned-dataset-400"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ontocord/OIG-moderation
|
ontocord
| 2023-11-28T14:03:47Z | 60 | 24 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-08T20:52:23Z |
---
license: apache-2.0
---
# This is the Open Instruction Generalist - Moderation Dataset
This is our attempt to create a diverse dataset of dialogue that may be related to NSFW subject matters, abuse eliciting text, privacy violation eliciting instructions, depression or related content, hate speech, and other similar topics. We use the [prosocial], [anthropic redteam], subsets of [English wikipedia] datasets along with other public datasets and data created or contributed by volunteers. To regularize the dataset we also have "regular" OIG instructions, which includes Q/A instructions, coding instructions, and similar types of queries. Currently there are two versions of the datasets.
- OIG_safety_v0.1.jsonl (66200)
- OIG_safety_v0.2.jsonl (134530)
OIG-moderation includes data from:
* Public datasets such as anthropic-redteam and anthropic-harmless, prosocial, and contributed datasets from community members
* Augmented toxic data such as civil comments data converted into instructions, (c) anthropic-redteam data augmented with prosocial tags
* Data provided by the LAION community that might include NSFW prompt
* Synthetic depression data generated from a public depression bag of words dataset using https://huggingface.co/pszemraj/flan-t5-large-grammar-synthesis.
* A model trained on the OIG-moderation dataset can be used to provide moderation labels, and the bot providers can choose to then block responses from their chatbots based on these labels. If a bot provider's policy for example permits sexual content, but prohibits PII eliciting text, they can hopefully do so with the output of a model trained on this data.
* The tags consist of (a) Base prosocial tags: casual, possibly needs caution, probably needs caution, needs caution, needs intervention and (b) Additional tags: abuse related, personal information related, sexual content, hate.
* An utterance can have more than one tag. For example, a wikipedia article about pornography content might be tagged: needs caution | sexual content.
## Acknowledgement
* We would like to thank all the following people for their amazing contirbutions: @Rallio, @Summer, @Iamiakk @Jue, @yp_yurilee, @Jjmachan, @Coco.han, @Pszemraj, and many others.
* We would like to thank Together.xyz for testing the v0.1 data for effectiveness and their dedication to the open source community.
* We would like to thank AI Horde and user @Db0 for their incredible contribution of filtered data that were flagged as unethical.
## Disclaimer
* These datasets contain synthetic data and in some cases data that includes NSFW subject matter and triggering text such as toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to help create models that can detect potentially unwanted or problematic instructions or content.
## Risk Factors
* While we acknowledge that this dataset can be modified to train a model to generate unsafe text, it is important to release this publicly as a resource for both researchers and those building production agents to train detection models.
## BY ACCESSING THIS DATASET YOU AGREE YOU ARE 18 YEARS OLD OR OLDER AND UNDERSTAND THE RISKS OF USING THIS DATASET.
|
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pcuenq/face_synthetics
|
pcuenq
| 2023-03-13T09:37:52Z | 60 | 1 |
[
"region:us"
] | null | 2023-03-12T21:37:41Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: image_seg
dtype: image
- name: landmarks
dtype: string
splits:
- name: train
num_bytes: 33730885609.0
num_examples: 100000
download_size: 34096881533
dataset_size: 33730885609.0
---
# Dataset Card for `face_synthetics`
This is a copy of [Microsoft FaceSynthetics dataset](https://github.com/microsoft/FaceSynthetics), uploaded to Hugging Face Datasets for convenience.
Please, refer to the original [license](LICENSE.txt), which we replicate in this repo.
The dataset was uploaded using the following code, which assumes the original `zip` file was uncompressed to `/data/microsoft_face_synthetics`:
```Python
from datasets import Dataset
from pathlib import Path
from PIL import Image
face_synthetics = Path("/data/microsoft_face_synthetics")
def entry_for_id(entry_id):
if type(entry_id) == int:
entry_id = f"{entry_id:06}"
image = Image.open(face_synthetics/f"{entry_id}.png")
image_seg = Image.open(face_synthetics/f"{entry_id}_seg.png")
with open(face_synthetics/f"{entry_id}_ldmks.txt") as f:
landmarks = f.read()
return {
"image": image,
"image_seg": image_seg,
"landmarks": landmarks,
}
def generate_entries():
for x in range(100000):
yield entry_for_id(x)
ds = Dataset.from_generator(generate_entries)
ds.push_to_hub('pcuenq/face_synthetics')
```
Note that `image_seg`, the segmented images, appear to be black because each pixel contains a number between `0` to `18` corresponging to the different categories, see the [original README]() for details. We haven't created visualization code yet.
|
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] |
ktgiahieu/maccrobat2018_2020
|
ktgiahieu
| 2023-05-21T10:39:53Z | 60 | 1 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-04-15T21:27:11Z |
---
license: cc-by-4.0
---
Modified dataset from:
Caufield, J. Harry (2019): MACCROBAT. figshare. Dataset. https://doi.org/10.6084/m9.figshare.9764942.v2
Example training notebook: https://colab.research.google.com/drive/1OzCY782KJSF0FBDS0d1CoMhfp3-RtJMV?usp=sharing
Labels:
```
0: B-Activity
1: B-Administration
2: B-Age
3: B-Area
4: B-Biological_attribute
5: B-Biological_structure
6: B-Clinical_event
7: B-Color
8: B-Coreference
9: B-Date
10: B-Detailed_description
11: B-Diagnostic_procedure
12: B-Disease_disorder
13: B-Distance
14: B-Dosage
15: B-Duration
16: B-Family_history
17: B-Frequency
18: B-Height
19: B-History
20: B-Lab_value
21: B-Mass
22: B-Medication
23: B-Nonbiological_location
24: B-Occupation
25: B-Other_entity
26: B-Other_event
27: B-Outcome
28: B-Personal_background
29: B-Qualitative_concept
30: B-Quantitative_concept
31: B-Severity
32: B-Sex
33: B-Shape
34: B-Sign_symptom
35: B-Subject
36: B-Texture
37: B-Therapeutic_procedure
38: B-Time
39: B-Volume
40: B-Weight
41: I-Activity
42: I-Administration
43: I-Age
44: I-Area
45: I-Biological_attribute
46: I-Biological_structure
47: I-Clinical_event
48: I-Color
49: I-Coreference
50: I-Date
51: I-Detailed_description
52: I-Diagnostic_procedure
53: I-Disease_disorder
54: I-Distance
55: I-Dosage
56: I-Duration
57: I-Family_history
58: I-Frequency
59: I-Height
60: I-History
61: I-Lab_value
62: I-Mass
63: I-Medication
64: I-Nonbiological_location
65: I-Occupation
66: I-Other_entity
67: I-Other_event
68: I-Outcome
69: I-Personal_background
70: I-Qualitative_concept
71: I-Quantitative_concept
72: I-Severity
73: I-Shape
74: I-Sign_symptom
75: I-Subject
76: I-Texture
77: I-Therapeutic_procedure
78: I-Time
79: I-Volume
80: I-Weight
81: O
```
|
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] |
AbeShinzo0708/AbeShinzo_voice_dataset
|
AbeShinzo0708
| 2023-04-17T07:40:01Z | 60 | 1 |
[
"language:ja",
"安倍晋三",
"AbeShinzo",
"FormerJapanesePrimeMinister",
"voice",
"dataset",
"region:us"
] | null | 2023-04-17T07:36:16Z |
---
language:
- ja
tags:
- 安倍晋三
- AbeShinzo
- FormerJapanesePrimeMinister
- voice
- dataset
pretty_name: 安倍晋三
---
|
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mattymchen/celeba-hq
|
mattymchen
| 2023-04-26T05:56:53Z | 60 | 1 |
[
"region:us"
] | null | 2023-04-26T05:15:42Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': female
'1': male
splits:
- name: train
num_bytes: 2731627350.0
num_examples: 28000
- name: validation
num_bytes: 197550788.0
num_examples: 2000
download_size: 2762109745
dataset_size: 2929178138.0
---
# Dataset Card for "celeba-hq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
AmazonScience/xtr-wiki_qa
|
AmazonScience
| 2023-07-24T17:32:38Z | 60 | 1 |
[
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:open-domain-qa",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:extended|wiki_qa",
"language:ar",
"language:es",
"language:fr",
"language:de",
"language:hi",
"language:it",
"language:ja",
"language:nl",
"language:pt",
"license:cdla-permissive-2.0",
"as2",
"answer sentence selection",
"text retrieval",
"question answering",
"region:us"
] |
[
"question-answering",
"text-retrieval"
] | 2023-05-16T00:03:14Z |
---
annotations_creators:
- machine-generated
language:
- ar
- es
- fr
- de
- hi
- it
- ja
- nl
- pt
language_creators:
- found
license_details: https://huggingface.co/datasets/AmazonScience/xtr-wiki_qa/blob/main/LICENSE.md
multilinguality:
- multilingual
- translation
pretty_name: xtr-wiki_qa
size_categories:
- 100K<n<1M
source_datasets:
- extended|wiki_qa
tags:
- as2
- answer sentence selection
- text retrieval
- question answering
task_categories:
- question-answering
- text-retrieval
task_ids:
- open-domain-qa
license: cdla-permissive-2.0
---
# Xtr-WikiQA
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Amazon Science](https://www.amazon.science/publications/cross-lingual-knowledge-distillation-for-answer-sentence-selection-in-low-resource-languages)
- **Paper:** [Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages](https://aclanthology.org/2023.findings-acl.885/)
- **Point of Contact:** [Yoshitomo Matsubara]([email protected])
### Dataset Summary
***Xtr-WikiQA*** is an Answer Sentence Selection (AS2) dataset in 9 non-English languages, proposed in our paper accepted at ACL 2023 (Findings): [**Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages**](https://aclanthology.org/2023.findings-acl.885/).
This dataset is based on an English AS2 dataset, WikiQA ([Original](https://msropendata.com/datasets/21032bb1-88bd-4656-9570-3172ae1757f0), [Hugging Face](https://huggingface.co/datasets/wiki_qa)).
For translations, we used [Amazon Translate](https://aws.amazon.com/translate/).
### Languages
- Arabic (ar)
- Spanish (es)
- French (fr)
- German (de)
- Hindi (hi)
- Italian (it)
- Japanese (ja)
- Dutch (nl)
- Portuguese (pt)
File location: [`tsv/`](https://huggingface.co/datasets/AmazonScience/xtr-wiki_qa/tree/main/tsv)
## Dataset Structure
### Data Instances
This is an example instance from the Arabic training split of Xtr-WikiQA dataset.
```
{
"QuestionID": "Q1",
"Question": "كيف تتشكل الكهوف الجليدية؟",
"DocumentID": "D1",
"DocumentTitle": "كهف جليدي",
"SentenceID": "D1-0",
"Sentence": "كهف جليدي مغمور جزئيًا على نهر بيريتو مورينو الجليدي.",
"Label": 0
}
```
All the translated instances in tsv files are listed in the same order of the original (native) instances in the WikiQA dataset.
For example, the 2nd instance in [`tsv/ar-train.tsv`](https://huggingface.co/datasets/AmazonScience/xtr-wiki_qa/blob/main/tsv/ar-train.tsv) (Arabic-translated from English)
corresponds to the 2nd instance in [`WikiQA-train.tsv`](https://msropendata.com/datasets/21032bb1-88bd-4656-9570-3172ae1757f0) (English).
### Data Fields
Each instance (a QA pair) consists of the following fields:
- `QuestionID`: Question ID (str)
- `Question`: Question to be answered (str)
- `DocumentID`: Document ID (str)
- `DocumentTitle`: Document title (str)
- `SentenceID`: Answer sentence in the document (str)
- `Sentence`: Answer sentence in the document (str)
- `Label`: Label that indicates the answer sentence correctly answers the question (int, 1: correct, 0: incorrect)
### Data Splits
| | | **#Questions** | | | | **#Sentences** | |
|-------------------|------------:|---------------:|---------:|---|----------:|---------------:|---------:|
| | **train** | **dev** | **test** | | **train** | **dev** | **test** |
| **Each language** | 873 | 126 | 243 | | 8,671 | 1,130 | 2,351 |
See [our paper](#citation-information) for more details about the statistics of the datasets.
## Dataset Creation
### Source Data
The source of Xtr-WikiQA dataset is [WikiQA](https://msropendata.com/datasets/21032bb1-88bd-4656-9570-3172ae1757f0).
## Additional Information
### Licensing Information
[CDLA-Permissive-2.0](LICENSE.md)
### Citation Information
```bibtex
@inproceedings{gupta2023cross-lingual,
title={{Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages}},
author={Gupta, Shivanshu and Matsubara, Yoshitomo and Chadha, Ankit and Moschitti, Alessandro},
booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
pages={14078--14092},
year={2023}
}
```
### Contributions
- [Shivanshu Gupta](https://huggingface.co/shivanshu)
- [Yoshitomo Matsubara](https://huggingface.co/yoshitomo-matsubara)
- Ankit Chadha
- Alessandro Moschitti
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swaption2009/cyber-threat-intelligence-custom-data
|
swaption2009
| 2023-06-04T07:35:25Z | 60 | 3 |
[
"task_categories:text-generation",
"task_categories:table-question-answering",
"language:en",
"region:us"
] |
[
"text-generation",
"table-question-answering"
] | 2023-06-04T07:31:03Z |
---
task_categories:
- text-generation
- table-question-answering
language:
- en
---
|
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] |
Amirkid/MedQuad-dataset
|
Amirkid
| 2023-06-06T15:08:50Z | 60 | 1 |
[
"region:us"
] | null | 2023-06-06T15:08:42Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 21658852
num_examples: 32800
download_size: 8756796
dataset_size: 21658852
---
# Dataset Card for "MedQuad-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
mtkinit/Example-Dataset-Super-2
|
mtkinit
| 2023-06-20T16:34:39Z | 60 | 0 |
[
"keyword1",
"keyword2",
"region:us"
] | null | 2023-06-20T16:34:38Z |
---
pretty_name: Example-Dataset-Super-2
tags:
- keyword1
- keyword2
---
# Example-Dataset-Super-2
Created from AIOD platform
|
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] |
Patt/RTE_TH_drop
|
Patt
| 2023-06-22T09:21:18Z | 60 | 0 |
[
"task_categories:text-classification",
"language:en",
"language:th",
"arxiv:1907.04307",
"region:us"
] |
[
"text-classification"
] | 2023-06-21T11:34:48Z |
---
task_categories:
- text-classification
language:
- en
- th
---
# Dataset Card for RTE_TH_drop
### Dataset Description
This dataset is Thai translated version of [RTE](https://huggingface.co/datasets/super_glue/viewer/rte) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
Some line which score_hypothesis <= 0.5 or score_premise <= 0.7 had been droped.
|
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] |
KaiLv/UDR_AGNews
|
KaiLv
| 2023-06-21T12:22:33Z | 60 | 0 |
[
"region:us"
] | null | 2023-06-21T12:22:14Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: label
dtype: int64
- name: headline
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 7767906
num_examples: 29914
- name: test
num_bytes: 779115
num_examples: 3000
- name: debug
num_bytes: 1301480
num_examples: 5000
download_size: 6889819
dataset_size: 9848501
---
# Dataset Card for "UDR_AGNews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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pankajmathur/alpaca_orca
|
pankajmathur
| 2023-06-26T14:39:11Z | 60 | 19 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-generation"
] | 2023-06-24T18:20:35Z |
---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
Explain tuned Alpaca dataset ~52K created using approaches from Orca Research Paper.
We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets.
This helps student models like [orca_mini_13b](https://huggingface.co/psmathur/orca_mini_13b) to learn thought process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
Please see how the **System** prompt is added before each **instruction**.
|
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chromadb/paul_graham_essay
|
chromadb
| 2023-07-01T14:27:10Z | 60 | 0 |
[
"region:us"
] | null | 2023-07-01T14:27:06Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: embedding
sequence: float64
- name: metadata
struct:
- name: author
dtype: string
- name: document
dtype: string
splits:
- name: data
num_bytes: 1359141
num_examples: 104
download_size: 1270436
dataset_size: 1359141
---
# Dataset Card for "paul_graham_essay"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
sharmaarushi17/HPCPerfOpt-MCQA
|
sharmaarushi17
| 2023-11-21T03:57:13Z | 60 | 0 |
[
"license:cc",
"region:us"
] | null | 2023-07-03T19:54:08Z |
---
license: cc
pretty_name: HPCPerfOpt (HPC Performance Optimization Benchmark) # Example: SQuAD
# configs:
# - mcq-single
# - mcq-multiple
# - rodinia-chatgpt-mcq
# dataset_info:
# # features:
# # - name: {feature_name_0} # Example: id
# # dtype: {feature_dtype_0} # Example: int32
# # - name: {feature_name_1} # Example: text
# # dtype: {feature_dtype_1} # Example: string
# # - name: {feature_name_2} # Example: image
# # dtype: {feature_dtype_2} # Example: image
# # Example for SQuAD:
# # - name: id
# # dtype: string
# # - name: title
# # dtype: string
# # - name: context
# # dtype: string
# # - name: question
# # dtype: string
# # - name: answers
# # sequence:
# # - name: text
# # dtype: string
# # - name: answer_start
# # dtype: int32
# config_name: mcq-single # Example for glue: sst2
# splits:
# - name: test # Example: train
# # num_bytes: {split_num_bytes_0} # Example for SQuAD: 79317110
# # num_examples: {split_num_examples_0} # Example for SQuAD: 87599
# # download_size: {dataset_download_size} # Example for SQuAD: 35142551
# # dataset_size: {dataset_size} # Example for SQuAD: 89789763
# # - config_name: mcq-multiple
# # data_files:
# # - split: test
# # path: "mcq-multiple.csv"
# # - config_name: rodinia-chatgpt
# # data_files:
# # - split: test
# # path: "rodinia-chatgpt-mcq.csv"
# task_categories:
# - question-answering
# tags:
# - code
# size_categories:
# - n<1K
---
This dataset contains Multiple Choice question-answer pairs.
There are 3 test files separated on the basis of how they were created:
test1.csv manual data collection from tutorials, etc
test2.csv scraped profiling tool Codee documentation
test3.csv ChatGPT-generated-MCQ (need to update format and randomize answers.)
|
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] |
awettig/Pile-YoutubeSubtitles-0.5B-6K-opt
|
awettig
| 2023-07-10T19:35:45Z | 60 | 0 |
[
"region:us"
] | null | 2023-07-10T19:34:17Z |
---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6500643383
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1594423762
dataset_size: 6565589075
---
# Dataset Card for "Pile-YoutubeSubtitles-0.5B-6K-opt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Alignment-Lab-AI/Lawyer-Instruct
|
Alignment-Lab-AI
| 2023-07-14T17:21:48Z | 60 | 6 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-07-14T06:59:11Z |
---
license: apache-2.0
---
# Dataset Card for "Lawyer-Instruct"
## 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 Description
### Dataset Summary
Lawyer-Instruct is a conversational dataset primarily in English, reformatted from the original LawyerChat dataset. It contains legal dialogue scenarios reshaped into an instruction, input, and expected output format. This reshaped dataset is ideal for supervised dialogue model training.
Dataset generated in part by dang/futures
### Supported Tasks and Leaderboards
- `dialogue-modeling`: The dataset can be used to train a model for dialogue understanding and response generation based on given instruction. Performance can be evaluated based on dialogue understanding and the quality of the generated responses.
- There is no official leaderboard associated with this dataset at this time.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
An instance in the Lawyer-Instruct dataset represents a dialogue instruction and its corresponding output. Example:
```json
{
"instruction": "What are the possible legal consequences of not paying taxes?",
"input": "",
"output": "There can be several legal consequences, ranging from fines to imprisonment..."
}
```
### Data Fields
- `instruction`: a string representing the client's question or statement in the dialogue, serving as the input for dialogue model training.
- `input`:
- `output`: a string representing the legal professional's response.
### Data Splits
This dataset does not have a standard split. Users should carefully consider how they wish to split the data for training, validation, and testing purposes.
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] |
gauravshrm211/VC-startup-evaluation-for-investment
|
gauravshrm211
| 2023-07-27T20:05:21Z | 60 | 5 |
[
"license:other",
"region:us"
] | null | 2023-07-27T11:43:12Z |
---
license: other
---
This data set includes the completion pairs for evaluating startups before investing in them.
This data set iincludes completion examples for Chain of Thought reasoning to perform financial calculations.
This data set includes completion examples for evaluating risk profile, growth propspects, cost, ratios, market size, asset, liability, debt, equity and other ratios.
This data set includes comparison of different startups.
|
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kpriyanshu256/semeval-task-8-b
|
kpriyanshu256
| 2023-10-31T03:41:15Z | 60 | 0 |
[
"region:us"
] | null | 2023-10-31T03:41:08Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: text
dtype: string
- name: model
dtype: string
- name: source
dtype: string
- name: label
dtype: int64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 151567991
num_examples: 71027
- name: dev
num_bytes: 4814312
num_examples: 3000
download_size: 84851066
dataset_size: 156382303
---
# Dataset Card for "semeval-task-8-b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Rewcifer/trainset1_2000_cutoff_llama
|
Rewcifer
| 2023-11-03T02:25:53Z | 60 | 0 |
[
"region:us"
] | null | 2023-11-03T02:25:48Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 249703784.98341143
num_examples: 50000
download_size: 45211692
dataset_size: 249703784.98341143
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "trainset1_2000_cutoff_llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ademax/dataset_line_connect
|
ademax
| 2023-11-06T01:47:01Z | 60 | 0 |
[
"region:us"
] | null | 2023-11-06T01:46:18Z |
---
dataset_info:
features:
- name: lineA
dtype: string
- name: lineB
dtype: string
- name: is_join
dtype: int64
splits:
- name: train
num_bytes: 1143553535
num_examples: 10001530
download_size: 412153174
dataset_size: 1143553535
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dataset_line_connect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jmelsbach/easy-german-definitions
|
jmelsbach
| 2023-11-15T14:01:10Z | 60 | 0 |
[
"region:us"
] | null | 2023-11-15T13:56:15Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: title
dtype: string
- name: explanation
dtype: string
- name: detailed_explanation
dtype: string
splits:
- name: train
num_bytes: 2153588.053902302
num_examples: 2849
- name: test
num_bytes: 538963.946097698
num_examples: 713
download_size: 0
dataset_size: 2692552.0
---
# Dataset Card for "easy-german-definitions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Elfsong/Bias_in_Bios
|
Elfsong
| 2023-11-19T07:24:09Z | 60 | 0 |
[
"region:us"
] | null | 2023-11-19T07:23:50Z |
---
dataset_info:
features:
- name: hard_text
dtype: string
- name: profession
dtype: string
- name: gender
dtype: string
splits:
- name: train
num_bytes: 108970597
num_examples: 257478
- name: test
num_bytes: 41882750
num_examples: 99069
- name: dev
num_bytes: 16732695
num_examples: 39642
download_size: 99844255
dataset_size: 167586042
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
---
|
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dutta18/omcs_dataset_full_with_embeds
|
dutta18
| 2023-11-27T03:37:18Z | 60 | 0 |
[
"region:us"
] | null | 2023-11-27T03:31:24Z |
---
dataset_info:
features:
- name: fact
dtype: string
- name: count
dtype: int64
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 4951309139
num_examples: 1578238
download_size: 5895178326
dataset_size: 4951309139
---
# Dataset Card for "omcs_dataset_full_with_embeds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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0.04103130102157593,
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] |
Asaf-Yehudai/HelpSteer_prompt_per_row
|
Asaf-Yehudai
| 2023-12-03T15:02:23Z | 60 | 0 |
[
"region:us"
] | null | 2023-12-03T15:02:06Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: responses
list:
- name: response
dtype: string
- name: scores
struct:
- name: coherence
dtype: int64
- name: complexity
dtype: int64
- name: correctness
dtype: int64
- name: helpfulness
dtype: int64
- name: verbosity
dtype: int64
splits:
- name: train
num_bytes: 44115062
num_examples: 9944
- name: validation
num_bytes: 2267028
num_examples: 503
download_size: 25199197
dataset_size: 46382090
---
# Dataset Card for "HelpSteer_prompt_per_row"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
stevhliu/demo
|
stevhliu
| 2022-10-24T18:02:42Z | 59 | 0 |
[
"task_categories:summarization",
"task_categories:text2text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"conditional-text-generation",
"region:us"
] |
[
"summarization",
"text2text-generation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# Dataset Card for Demo
## 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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a demo dataset with two files `train.csv` and `test.csv`.
Load it by:
```python
from datasets import load_dataset
data_files = {"train": "train.csv", "test": "test.csv"}
demo = load_dataset("stevhliu/demo", data_files=data_files)
```
### 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
#### 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
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
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toloka/VoxDIY-RusNews
|
toloka
| 2022-12-06T15:24:30Z | 59 | 2 |
[
"task_categories:summarization",
"task_categories:automatic-speech-recognition",
"task_categories:text2text-generation",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:ru",
"license:cc-by-4.0",
"conditional-text-generation",
"stuctured-to-text",
"speech-recognition",
"arxiv:2107.01091",
"region:us"
] |
[
"summarization",
"automatic-speech-recognition",
"text2text-generation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- found
language_creators:
- crowdsourced
language:
- ru
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
- automatic-speech-recognition
- text2text-generation
task_ids: []
pretty_name: VoxDIY RusNews
language_bcp47:
- ru-RU
tags:
- conditional-text-generation
- stuctured-to-text
- speech-recognition
---
# Dataset Card for VoxDIY RusNews
## Dataset Description
- **Repository:** [GitHub](https://github.com/Toloka/CrowdSpeech)
- **Paper:** [Paper](https://openreview.net/forum?id=3_hgF1NAXU7)
- **Point of Contact:** [email protected]
### Dataset Summary
VoxDIY RusNews is the first publicly available large-scale dataset of crowdsourced audio transcriptions in Russian language.
The dataset was constructed by annotating audio recordings of Russian sentences from news domain on [Toloka crowdsourcing platform](https://toloka.ai).
VoxDIY RusNews consists of 3091 instances having around 21K annotations obtained from crowd workers.
### Supported Tasks and Leaderboards
Aggregation of crowd transcriptions.
### Languages
Russian
## Dataset Structure
### Data Instances
A data instance contains a url to the audio recording, a list of transcriptions along with the corresponding performers identifiers and
ground truth. For each data instance, seven crowdsourced transcriptions are provided.
```
{'task': 'https://tlk.s3.yandex.net/annotation_tasks/russian/1003.wav',
'transcriptions': 'в список так же попали мэрлин монро джон ленон и альберт эйнштейн | в список также попали мерлин монро джон ленон и альберт энштейн | в список также попали мерилин монро джон леннон и альберт энтштейн | в список также попали мэрилин монро джон леннон и альберт эпштейн | в список также попали мэрилин монро джон леннон и альберт эйнштейн | в список так же попали мерелин монро джон ленон и альберт нштейн | в список также попали мэрилин монро джон леннон и альберт эйнштейн',
'performers': '1743 | 784 | 1014 | 1572 | 744 | 2187 | 1208',
'gt': 'в список также попали мэрилин монро джон леннон и альберт эйнштейн'}
```
### Data Fields
* task: a string containing a url of the audio recording
* transcriptions: a list of the crowdsourced transcriptions separated by '|'
* performers: the corresponding performers' identifiers.
* gt: ground truth transcription
## Dataset Creation
### Source Data
The audio recordings were obtained using a [speech synthesis tool](https://cloud.yandex.com/en-ru/services/speechkit).
The source sentences come from the Russian test set of the machine translation shared task executed as a part of the
Eights and Ninth Workshops on Statistical Machine Translation ([WMT 2013](https://www.statmt.org/wmt13/) and [WMT 2014](https://www.statmt.org/wmt14/)).
### Annotations
Annotation was done on [Toloka crowdsourcing platform](https://toloka.ai) with overlap of 7 (that is, each task was performed by 7 annotators).
Only annotators who self-reported the knowledge of Russian had access to the annotation task.
Additionally, annotators had to pass *Entrance Exam*. For this, we ask all incoming eligible workers to annotate ten audio
recordings. We then compute our target metric — Word Error Rate (WER) — on these recordings and accept to the main task all workers
who achieve WER of 40% or less (the smaller the value of the metric, the higher the quality of annotation).
The Toloka crowdsourcing platform associates workers with unique identifiers and returns these identifiers to the requester.
To further protect the data, we additionally encode each identifier with an integer that is eventually reported in our released datasets.
See more details in the [paper](https://arxiv.org/pdf/2107.01091.pdf).
### Citation Information
```
@inproceedings{CrowdSpeech,
author = {Pavlichenko, Nikita and Stelmakh, Ivan and Ustalov, Dmitry},
title = {{CrowdSpeech and Vox~DIY: Benchmark Dataset for Crowdsourced Audio Transcription}},
year = {2021},
booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
eprint = {2107.01091},
eprinttype = {arxiv},
eprintclass = {cs.SD},
url = {https://openreview.net/forum?id=3_hgF1NAXU7},
language = {english},
pubstate = {forthcoming},
}
```
|
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] |
rjac/kaggle-entity-annotated-corpus-ner-dataset
|
rjac
| 2022-10-25T10:37:24Z | 59 | 1 |
[
"annotations_creators:Abhinav Walia (Owner)",
"language:en",
"license:odbl",
"region:us"
] | null | 2022-06-23T20:31:55Z |
---
annotations_creators:
- Abhinav Walia (Owner)
language:
- en
license:
- odbl
---
**Date**: 2022-07-10<br/>
**Files**: ner_dataset.csv<br/>
**Source**: [Kaggle entity annotated corpus](https://www.kaggle.com/datasets/abhinavwalia95/entity-annotated-corpus)<br/>
**notes**: The dataset only contains the tokens and ner tag labels. Labels are uppercase.
# About Dataset
[**from Kaggle Datasets**](https://www.kaggle.com/datasets/abhinavwalia95/entity-annotated-corpus)
## Context
Annotated Corpus for Named Entity Recognition using GMB(Groningen Meaning Bank) corpus for entity classification with enhanced and popular features by Natural Language Processing applied to the data set.
Tip: Use Pandas Dataframe to load dataset if using Python for convenience.
## Content
This is the extract from GMB corpus which is tagged, annotated and built specifically to train the classifier to predict named entities such as name, location, etc.
Number of tagged entities:
'O': 1146068', geo-nam': 58388, 'org-nam': 48034, 'per-nam': 23790, 'gpe-nam': 20680, 'tim-dat': 12786, 'tim-dow': 11404, 'per-tit': 9800, 'per-fam': 8152, 'tim-yoc': 5290, 'tim-moy': 4262, 'per-giv': 2413, 'tim-clo': 891, 'art-nam': 866, 'eve-nam': 602, 'nat-nam': 300, 'tim-nam': 146, 'eve-ord': 107, 'per-ini': 60, 'org-leg': 60, 'per-ord': 38, 'tim-dom': 10, 'per-mid': 1, 'art-add': 1
## Essential info about entities
* geo = Geographical Entity
* org = Organization
* per = Person
* gpe = Geopolitical Entity
* tim = Time indicator
* art = Artifact
* eve = Event
* nat = Natural Phenomenon
* Total Words Count = 1354149
* Target Data Column: "tag" (ner_tag in this repo)
Inspiration: This dataset is getting more interested because of more features added to the recent version of this dataset. Also, it helps to create a broad view of Feature Engineering with respect to this dataset.
## Modifications
the ner_dataset.csv was modified to have a similar data Structure as [CoNLL-2003 dataset](https://huggingface.co/datasets/conll2003)
## Licensing information
Database: Open Database, Contents: Database Contents.
|
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maderix/flickr_bw_rgb
|
maderix
| 2022-10-12T15:34:25Z | 59 | 5 |
[
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:N/A",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-to-image"
] | 2022-10-12T15:09:17Z |
---
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
pretty_name: 'flickr_bw_rgb'
size_categories:
- n<1K
source_datasets:
- N/A
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for Flickr_bw_rgb
_Dataset A image-caption dataset which stores group of black and white and color images with corresponding
captions mentioning the content of the image with a 'colorized photograph of' or 'Black and white photograph of' suffix.
This dataset can then be used for fine-tuning image to text models.. Only a train split is provided.
## Examples
"train/<filename>.jpg" : containing the images in JPEG format
"train/metadata.jsonl" : Contains the metadata and the fields.
Dataset columns:
"file_name"
"caption"
## Citation
If you use this dataset, please cite it as:
```
@misc{maderix2022flickrbwrgb,
author = {maderix: [email protected]},
title = {flickr_bw_rgb},
year={2022},
howpublished= {\url{https://huggingface.co/datasets/maderix/flickr_bw_rgb/}}
}
```
|
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pacovaldez/stackoverflow-questions
|
pacovaldez
| 2022-11-10T00:14:37Z | 59 | 31 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"stackoverflow",
"technical questions",
"region:us"
] |
[
"text-classification"
] | 2022-11-09T01:16:19Z |
---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: stackoverflow_post_questions
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- stackoverflow
- technical questions
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for [Stackoverflow Post Questions]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Contributions](#contributions)
## Dataset Description
Companies that sell Open-source software tools usually hire an army of Customer representatives to try to answer every question asked about their tool. The first step in this process
is the prioritization of the question. The classification scale usually consists of 4 values, P0, P1, P2, and P3, with different meanings across every participant in the industry. On
the other hand, every software developer in the world has dealt with Stack Overflow (SO); the amount of shared knowledge there is incomparable to any other website. Questions in SO are
usually annotated and curated by thousands of people, providing metadata about the quality of the question. This dataset aims to provide an accurate prioritization for programming
questions.
### Dataset Summary
The dataset contains the title and body of stackoverflow questions and a label value(0,1,2,3) that was calculated using thresholds defined by SO badges.
### Languages
English
## Dataset Structure
title: string,
body: string,
label: int
### Data Splits
The split is 40/40/20, where classes have been balaned to be around the same size.
## Dataset Creation
The data set was extracted and labeled with the following query in BigQuery:
```
SELECT
title,
body,
CASE
WHEN score >= 100 OR favorite_count >= 100 OR view_count >= 10000 THEN 0
WHEN score >= 25 OR favorite_count >= 25 OR view_count >= 2500 THEN 1
WHEN score >= 10 OR favorite_count >= 10 OR view_count >= 1000 THEN 2
ELSE 3
END AS label
FROM `bigquery-public-data`.stackoverflow.posts_questions
```
### Source Data
The data was extracted from the Big Query public dataset: `bigquery-public-data.stackoverflow.posts_questions`
#### Initial Data Collection and Normalization
The original dataset contained high class imbalance:
label count
0 977424
1 2401534
2 3418179
3 16222990
Grand Total 23020127
The data was sampled from each class to have around the same amount of records on every class.
### Contributions
Thanks to [@pacofvf](https://github.com/pacofvf) for adding this dataset.
|
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SirNeural/flan_v2
|
SirNeural
| 2023-02-24T19:05:00Z | 59 | 159 |
[
"license:apache-2.0",
"flan",
"flan 2022",
"flan v2",
"arxiv:2301.13688",
"region:us"
] | null | 2023-02-13T23:02:33Z |
---
license: apache-2.0
tags:
- flan
- flan 2022
- flan v2
pretty_name: Flan v2
---
# Dataset Card for Flan V2
## Dataset Description
- **Homepage:** https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html
- **Repository:** https://github.com/google-research/FLAN/tree/main/flan/v2
- **Paper:** https://arxiv.org/abs/2301.13688
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a processed version of the Flan V2 dataset.
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.
## Setup Instructions
Here are the steps I followed to get everything working:
### Build AESLC and WinoGrande datasets manually
The repos for these datasets were updated recently and checksums need to be recomputed in TFDS
- `tfds build --dataset aeslc --register_checksums`
- `tfds build --dataset winogrande --register_checksums`
### Fix dataset versions
I've opened a PR [here](https://github.com/google-research/FLAN/pull/20) to get these updated in the upstream FLAN repo, until that gets merged in run these locally to fix any dataset version errors.
- `sed -i 's/glue\/cola:1.0.0/glue\/cola:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/common_gen:1.0.0/gem\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/dart:1.0.0/gem\/dart:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/e2e_nlg:1.0.0/gem\/e2e_nlg:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/web_nlg_en:1.0.0/gem\/web_nlg_en:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/common_gen:1.0.0/gem\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/paws_wiki:1.0.0/paws_wiki:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/mrpc:1.0.0/glue\/mrpc:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/qqp:1.0.0/glue\/qqp:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/sst2:1.0.0/glue\/sst2:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/mnli:1.0.0/glue\/mnli:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/qnli:1.0.0/glue\/qnli:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/wnli:1.0.0/glue\/wnli:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/stsb:1.0.0/glue\/stsb:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/hellaswag:0.0.1/hellaswag:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/xsum:1.0.0/huggingface:xsum/g' flan/v2/task_configs_v1.py`
### Download and install manual steps
Save these to `~/tensorflow_datasets/downloads/manual`.
- [CzEng (deduped ignoring sections)](https://ufal.mff.cuni.cz/czeng/czeng16pre)
- [Newsroom (extract)](https://lil.nlp.cornell.edu/newsroom/download/index.html)
- [Yandex 1M Corpus](https://translate.yandex.ru/corpus?lang=en)
- [Story Cloze (extract and rename to cloze_test_test__spring2016.csv and cloze_test_val__spring2016.csv)](https://cs.rochester.edu/nlp/)
### Finally, export tasks
```python
import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
from flan.v2 import constants
from flan.v2 import constants_t0
from flan.v2 import mixtures_utils
from flan.v2 import mixtures
from flan.v2 import tasks
import json
import t5
import seqio
import itertools
from multiprocessing import Pool
seqio.add_global_cache_dirs(constants.CACHE_DIRS)
seqio.set_global_cache_dirs(constants.CACHE_DIRS)
vocab = t5.data.get_default_vocabulary()
def prepare_task(split, shots, opt, task):
dataset = seqio.get_mixture_or_task(f'palmflan_{task}_{shots}_{opt}').get_dataset(
split=split,
num_epochs=1,
sequence_length={'inputs':4096,'targets':4096}
)
print("starting", task, shots, opt, split)
with open(f'./data/{task}_{shots}_{opt}_{split}.jsonl', 'w') as f:
for ex in dataset.as_numpy_iterator():
f.write(
json.dumps({
"inputs": vocab.decode(ex["inputs"]),
"targets": vocab.decode(ex["targets"]),
"task": task,
}))
f.write("\n")
print("done with", task, shots, opt, split)
# prepare_task("train", "zs", "noopt", "dialog") # use this to export a single task
tasks = itertools.product(["train"], ["zs", "fs"], ["opt", "noopt"], ["dialog", "t0", "niv2", "flan", "cot"])
with Pool(5) as p:
p.starmap(prepare_task, [(task[0], task[1], task[2], task[3]) for task in tasks])
```
## Dataset Structure
### Data Instances
Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)
### Data Fields
Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)
Each combination of the above tasks + formats are saved as a JSONL with following schema `{"input": ..., "target": ..., "task": ...}`
### Data Splits
Everything is saved as a train split
Note: FLAN-fs-opt-train is too big to be uploaded even when gzipped, so its split into 45gb chunks. To combine and recover, run `cat flan_fs_opt_train_*.gz | gunzip -c > flan_fs_opt_train.jsonl`
|
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ClimatePolicyRadar/global-stocktake-documents
|
ClimatePolicyRadar
| 2023-10-18T16:10:13Z | 59 | 4 |
[
"size_categories:1M<n<10M",
"language:en",
"license:cc",
"climate",
"policy",
"legal",
"doi:10.57967/hf/1112",
"region:us"
] | null | 2023-06-28T16:00:48Z |
---
language:
- en
tags:
- climate
- policy
- legal
size_categories:
- 1M<n<10M
license: cc
dataset_info:
features:
- name: family_slug
dtype: string
- name: types
sequence: string
- name: role
dtype: string
- name: block_index
dtype: int64
- name: date
dtype: date32
- name: geography_iso
dtype: string
- name: document_name
dtype: string
- name: variant
dtype: string
- name: type_confidence
dtype: float64
- name: document_languages
sequence: string
- name: text_block_id
dtype: string
- name: document_source_url
dtype: string
- name: author_is_party
dtype: bool
- name: type
dtype: string
- name: coords
sequence:
sequence: float64
- name: author
sequence: string
- name: family_name
dtype: string
- name: status
dtype: string
- name: collection_id
dtype: string
- name: family_id
dtype: string
- name: language
dtype: string
- name: page_number
dtype: int64
- name: text
dtype: string
- name: has_valid_text
dtype: bool
- name: document_id
dtype: string
- name: translated
dtype: bool
- name: document_content_type
dtype: string
- name: document_md5_sum
dtype: string
splits:
- name: train
num_bytes: 1278730693
num_examples: 1578645
download_size: 228690459
dataset_size: 1278730693
---
# Global Stocktake Open Data
This repo contains the data for the first [UNFCCC Global Stocktake](https://unfccc.int/topics/global-stocktake). The data consists of document metadata from sources relevant to the Global Stocktake process, as well as full text parsed from the majority of the documents.
The files in this dataset are as follows:
- `metadata.csv`: a CSV containing document metadata for each document we have collected. **This metadata may not be the same as what's stored in the source databases** – we have cleaned and added metadata where it's corrupted or missing.
- `full_text.parquet`: a parquet file containing the full text of each document we have parsed. Each row is a text block (paragraph) with all the associated text block and document metadata.
A research tool you can use to view this data and the results of some classifiers run on it is at [gst1.org](https://gst1.org).
This data is licensed according to CC BY 4.0, which is a license that represents the terms at the source repositories.
**Contents**
- [Sources and data completeness](#sources-and-data-completeness)
- [Field descriptions](#field-descriptions)
- [Known issues](#known-issues)
- [Usage in Python](#usage-in-python)
- [Loading metadata CSV](#loading-metadata-csv)
- [Loading text block data](#loading-text-block-data)
---
## Sources and data completeness
This dataset contains documents from the following sources:
* [Global Stocktake Information Portal](https://unfccc.int/topics/global-stocktake/information-portal)
* [NDC Registry](https://unfccc.int/NDCREG)
* [Adaptation Communications Registry](https://unfccc.int/ACR)
* [Fast-Start Finance Country Reports](https://unfccc.int/climatefinance?submissions)
* [IPCC Reports](https://www.ipcc.ch/reports/)
The following Global Stocktake relevant data sources are not yet in this dataset:
* [National Adaptation Plan Central Portal](https://napcentral.org/submitted-naps)
* [TNA Country Reports](https://unfccc.int/ttclear/tna/reports.html)
### Data completeness
The last refresh of the data was on **2023-10-18**.
We currently only parse text out of PDFs. Any non-PDF file will only be referenced in `metadata.csv`, and not be referenced in `full_text.parquet`.
We have yet to process approximately 150 documents of the 1700 documents due to formatting issues. We are working on resolving this issue as soon as possible. [See the document list here](https://labs.climatepolicyradar.org/global-stocktake/UNPROCESSED_DOCUMENTS.html).
## Data model
This dataset contains individual documents that are grouped into 'document families'.
The way to think of is as follows:
* Each row in the dataset is a physical document. A physical document is a single document, in any format.
* All physical documents belong to document families. A document family is one or more physical documents, centred around a main document, which jointly contain all relevant information about the main document. For example, where a document has a translation, amendments or annexes, those files are stored together as a family.
### Getting unique text blocks
> TODO
## Field descriptions
- `author`: document author (str)
- `author_is_party`: whether the author is a Party (national government) or not (bool)
- `block_index`: the index of a text block in a document. Starts from 0 (int)
- `coords`: coordinates of the text block on the page
- `date`: publication date of the document
- `document_content_type`: file type. We have only parsed text from PDFs.
- `document_id`: unique identifier for a document
- `document_family_id`: see *data model* section above
- `document_family_slug`: see *data model* section above
- `document_md5_sum`: md5sum of the document's content
- `document_name`: document title
- `document_source_url`: URL for document
- `document_variant`: used to identify translations. In `[nan, 'Translation', 'Original Language']`
- `has_valid_text`: our heuristic about whether text is valid or not in the document based on the parser
- `language`: language of the text block. Either `en` or `nan` - see known issues
- `page_number`: page number of text block (0-indexed)
- `text`: text in text block
- `text_block_id`: identifier for a text block which is unique per document
- `translated`: whether we have machine-translated the document to English. Where we have translated documents, both the original and translated exist.
- `type`: type of text block. In `["Text", "Title", "List", "Table", "Figure","Ambiguous"]`
- `type_confidence`: confidence from that the text block is of the labelled type
- `types`: list of document types e.g. Nationally Determined Contribution, National Adaptation Plan (list[str])
- `version`: in `['MAIN', 'ANNEX', 'SUMMARY', 'AMENDMENT', 'SUPPORTING DOCUMENTATION', 'PREVIOUS VERSION']`
## Known issues
* Author names are sometimes corrupted
* Text block languages are sometimes missing or marked as `nan`
## Usage in Python
The easiest way to access this data via the terminal is to run `git clone <this-url>`.
### Loading metadata CSV
``` py
metadata = pd.read_csv("metadata.csv")
```
### Loading text block data
Once loaded into a Huggingface Dataset or Pandas DataFrame object the parquet file can be converted to other formats, e.g. Excel, CSV or JSON.
``` py
# Using huggingface (easiest)
dataset = load_dataset("ClimatePolicyRadar/global-stocktake-documents")
# Using pandas
text_blocks = pd.read_parquet("full_text.parquet")
```
|
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NebulaByte/E-Commerce_Customer_Support_Conversations
|
NebulaByte
| 2023-07-24T05:56:38Z | 59 | 3 |
[
"region:us"
] | null | 2023-07-24T05:56:30Z |
---
dataset_info:
features:
- name: issue_area
dtype: string
- name: issue_category
dtype: string
- name: issue_sub_category
dtype: string
- name: issue_category_sub_category
dtype: string
- name: customer_sentiment
dtype: string
- name: product_category
dtype: string
- name: product_sub_category
dtype: string
- name: issue_complexity
dtype: string
- name: agent_experience_level
dtype: string
- name: agent_experience_level_desc
dtype: string
- name: conversation
dtype: string
splits:
- name: train
num_bytes: 2537279
num_examples: 1000
download_size: 827367
dataset_size: 2537279
---
# Dataset Card for "E-Commerce_Customer_Support_Conversations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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mylesmharrison/cornell-movie-dialog
|
mylesmharrison
| 2023-08-01T02:03:08Z | 59 | 0 |
[
"region:us"
] | null | 2023-07-30T01:39:50Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 21363514
num_examples: 304713
download_size: 13073496
dataset_size: 21363514
---
# Dataset Card for "cornell-movie-dialog"
This is a reduced version of the [Cornell Movie Dialog Corpus](https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html) by Cristian Danescu-Niculescu-Mizil.
The original dataset contains 220,579 conversational exchanges between 10,292 pairs of movie characters, involving 9,035 characters from 617 movies for a total 304,713 utterances.
This reduced version of the dataset contains only the character tags and utterances from the `movie_lines.txt` file, with one utterance per line, suitable for training generative text models.
## Dataset Description
- **Homepage:** https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html
- **Repository:** https://convokit.cornell.edu/documentation/movie.html
- **Paper:** [Chameleons in imagined conversations: A new approach to understanding
coordination of linguistic style in dialogs](https://www.cs.cornell.edu/~cristian/papers/chameleons.pdf)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
|
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] |
adityarra07/czech_test
|
adityarra07
| 2023-10-04T18:09:08Z | 59 | 0 |
[
"region:us"
] | null | 2023-10-04T18:09:04Z |
---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 53042654.644653864
num_examples: 1000
download_size: 52259185
dataset_size: 53042654.644653864
---
# Dataset Card for "czech_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
meta-math/MetaMathQA-40K
|
meta-math
| 2023-11-10T01:42:51Z | 59 | 5 |
[
"license:mit",
"arxiv:2309.12284",
"region:us"
] | null | 2023-10-07T14:47:58Z |
---
license: mit
---
arxiv.org/abs/2309.12284
View the project page:
https://meta-math.github.io/
|
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owkin/nct-crc-he
|
owkin
| 2023-10-26T09:42:47Z | 59 | 0 |
[
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-3.0",
"biology",
"medical",
"cancer",
"colorectal cancer",
"region:us"
] |
[
"image-classification"
] | 2023-10-13T11:31:07Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': ADI
'1': BACK
'2': DEB
'3': LYM
'4': MUC
'5': MUS
'6': NORM
'7': STR
'8': TUM
splits:
- name: nct_crc_he_100
num_bytes: 15058006
num_examples: 99
- name: nct_crc_he_1k
num_bytes: 151950686
num_examples: 999
- name: crc_val_he_7k
num_bytes: 1092855241.74
num_examples: 7180
download_size: 1095677324
dataset_size: 1259863933.74
configs:
- config_name: default
data_files:
- split: nct_crc_he_100
path: data/nct_crc_he_100-*
- split: nct_crc_he_1k
path: data/nct_crc_he_1k-*
- split: crc_val_he_7k
path: data/crc_val_he_7k-*
license: cc-by-sa-3.0
task_categories:
- image-classification
language:
- en
tags:
- biology
- medical
- cancer
- colorectal cancer
pretty_name: NCT_CRC
size_categories:
- 10K<n<100K
---
# Dataset Card for NCT-CRC-HE
### Dataset Summary
The NCT-CRC-HE dataset consists of images of human tissue slides, some of which contain cancer.
### Data Splits
The dataset contains tissues from different parts of the body. Examples from each of the 9 classes can be seen below
### Initial Data Collection and Normalization
NCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). Images were normalized using Macenko normalization.
### Licensing Information
CC-BY-SA
### Citation Information
Owkin claims no ownership of the dataset. This is simply an upload of the original dataset onto HF.
[Link to original paper](https://zenodo.org/records/1214456)
|
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] |
LosHuesitos9-9/Huesitos
|
LosHuesitos9-9
| 2023-11-04T19:08:54Z | 59 | 1 |
[
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"language:es",
"license:cc",
"rf100",
"medical",
"code",
"region:us"
] |
[
"object-detection"
] | 2023-10-23T14:33:42Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
- es
license:
- cc
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: Huesitos
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': bone-fracture
'1': angle
'2': fracture
'3': line
'4': messed_up_angle
splits:
- name: train
num_bytes: 150839322.0
num_examples: 626
- name: validation
num_bytes: 1278386.0
num_examples: 44
- name: test
num_bytes: 2530151.0
num_examples: 88
download_size: 71039842
dataset_size: 154647859.0
tags:
- rf100
- medical
- code
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
## Licensing Information
See original homepage https://universe.roboflow.com/object-detection/bone-fracture-7fylg
### Citation Information
```
@misc{ bone-fracture-7fylg,
title = { bone fracture 7fylg Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/bone-fracture-7fylg } },
url = { https://universe.roboflow.com/object-detection/bone-fracture-7fylg },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
contributions dataset = {[@mariosasko](https://github.com/mariosasko)}
}"
```
|
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TeamDLD/neurips_challenge_dataset
|
TeamDLD
| 2023-10-23T18:49:00Z | 59 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-10-23T18:45:05Z |
---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
- name: input
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 8475492539
num_examples: 3640808
download_size: 3508032503
dataset_size: 8475492539
---
|
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] |
Leul78/fina
|
Leul78
| 2023-11-03T11:28:30Z | 59 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-03T11:27:58Z |
---
license: apache-2.0
---
|
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nmd2k/apps_rlaif
|
nmd2k
| 2023-11-27T17:41:45Z | 59 | 0 |
[
"task_categories:text-generation",
"task_categories:reinforcement-learning",
"size_categories:1K<n<10K",
"license:mit",
"code",
"arxiv:2105.09938",
"arxiv:2307.09288",
"region:us"
] |
[
"text-generation",
"reinforcement-learning"
] | 2023-11-06T08:15:38Z |
---
license: mit
size_categories:
- 1K<n<10K
task_categories:
- text-generation
- reinforcement-learning
pretty_name: apps_rlaif
dataset_info:
features:
- name: problem_id
dtype: int64
- name: question
dtype: string
- name: input_output
dtype: string
- name: difficulty
dtype: string
- name: url
dtype: string
- name: starter_code
dtype: string
- name: prefer_solution
dtype: string
- name: flaw_solution
sequence: string
splits:
- name: train
num_bytes: 196914903
num_examples: 23129
download_size: 38020746
dataset_size: 196914903
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- code
---
# APPS Dataset for Reinforcement Learning with AI Feedback
## Dataset Details
[APPS_RLAIF](https://huggingface.co/datasets/nmd2k/apps_rlaif/) is an extended work from APPS [[1]]([^1])
to use Chat LLMs to create multiple variances for each solution for defined problems.
In each solution, we use LLama 34B [[2]]([^2]) to transform the original solutions into variances and rank them by score.
The generated flow is demonstrated as below; each variance is created based on the previous version of it in the chat.
We iterated each solutions `n=3` times
<img src="https://cdn-uploads.huggingface.co/production/uploads/63733f7fd398fce0dd45125c/MhfwiSbafLQDvxQdTuR-2.png" width="600" />
## Languages
The dataset contains problem description in English and code solutions in Python.
## Dataset Structure
```python
from datasets import load_dataset
load_dataset("nmd2k/apps_rlaif")
DatasetDict({
train: Dataset({
features: ['problem_id', 'question', 'solutions', 'input_output', 'difficulty', 'url', 'starter_code', 'variances'],
num_rows: 4999
})
})
```
**How to use the dataset**
Each sample consists of a pair of problems and solutions (from APPS [[1]]([^1])) and a list of solution variances generated by LLM stored in the `variances` field.
For example:
```json
{'problem_id': 0,
'question': 'Polycarp has $n$ different binary words. A word called binary if it contains only characters \'0\' and \'1\'. For example, these words are binary: "0001", "11", "0" and "0011100".\n\nPolycarp wants to offer his set of $n$ binary words to play a game "words". In this game, players name words and each next word (starting from the second) must start with the last character of the previous word. The first word can be any. For example, these sequence of words can be named during the game: "0101", "1", "10", "00", "00001".\n\nWord reversal is the operation of reversing the order of the characters. For example, the word "0111" after the reversal becomes "1110", the word "11010" after the reversal becomes "01011".\n\nProbably, Polycarp has such a set of words that there is no way to put them in the order correspondent to the game rules. In this situation, he wants to reverse some words from his set so that: the final set of $n$ words still contains different words (i.e. all words are unique); there is a way to put all words of the final set of words in the order so that the final sequence of $n$ words is consistent with the game rules. \n\nPolycarp wants to reverse minimal number of words. Please, help him.\n\n\n-----Input-----\n\nThe first line of the input contains one integer $t$ ($1 \\le t \\le 10^4$) — the number of test cases in the input. Then $t$ test cases follow.\n\nThe first line of a test case contains one integer $n$ ($1 \\le n \\le 2\\cdot10^5$) — the number of words in the Polycarp\'s set. Next $n$ lines contain these words. All of $n$ words aren\'t empty and contains only characters \'0\' and \'1\'. The sum of word lengths doesn\'t exceed $4\\cdot10^6$. All words are different.\n\nGuaranteed, that the sum of $n$ for all test cases in the input doesn\'t exceed $2\\cdot10^5$. Also, guaranteed that the sum of word lengths for all test cases in the input doesn\'t exceed $4\\cdot10^6$.\n\n\n-----Output-----\n\nPrint answer for all of $t$ test cases in the order they appear.\n\nIf there is no answer for the test case, print -1. Otherwise, the first line of the output should contain $k$ ($0 \\le k \\le n$) — the minimal number of words in the set which should be reversed. The second line of the output should contain $k$ distinct integers — the indexes of the words in the set which should be reversed. Words are numerated from $1$ to $n$ in the order they appear. If $k=0$ you can skip this line (or you can print an empty line). If there are many answers you can print any of them.\n\n\n-----Example-----\nInput\n4\n4\n0001\n1000\n0011\n0111\n3\n010\n101\n0\n2\n00000\n00001\n4\n01\n001\n0001\n00001\n\nOutput\n1\n3 \n-1\n0\n\n2\n1 2',
'solutions': "for _ in range(int(input())):\n n = int(input())\n mass = []\n zo = 0\n oz = 0\n zz = 0\n oo = 0\n ozs = []\n zos = []\n ozss = set()\n zoss = set()\n for j in range(n):\n k = input()\n mass.append(k)\n if k[0] == '0' and k[-1] == '1':\n zoss.add(k)\n zos.append(j + 1)\n zo += 1\n elif k[0] == '1' and k[-1] == '0':\n ozss.add(k)\n ozs.append(j + 1)\n oz += 1\n elif k[0] == '0' and k[-1] == '0':\n zz += 1\n else:\n oo += 1\n if zz and oo and not oz and not zo:\n print(-1)\n continue\n else:\n if zo > oz:\n print((zo - oz) // 2)\n ans = []\n need = (zo - oz) // 2\n i = 0\n while need:\n zzz = mass[zos[i] - 1][len(mass[zos[i] - 1]) - 1:: -1]\n if zzz not in ozss:\n ans.append(zos[i])\n need -= 1\n i += 1\n print(*ans)\n else:\n print((oz - zo) // 2)\n ans = []\n need = (oz - zo) // 2\n i = 0\n while need:\n zzz = mass[ozs[i] - 1][len(mass[ozs[i] - 1]) - 1:: -1]\n if zzz not in zoss:\n ans.append(ozs[i])\n need -= 1\n i += 1\n print(*ans)\n",
'input_output': '{\n "inputs": [\n "4\\n4\\n0001\\n1000\\n0011\\n0111\\n3\\n010\\n101\\n0\\n2\\n00000\\n00001\\n4\\n01\\n001\\n0001\\n00001\\n"\n ],\n "outputs": [\n "1\\n3 \\n-1\\n0\\n\\n2\\n1 2 \\n"\n ]\n}',
'difficulty': 'interview',
'url': 'https://codeforces.com/problemset/problem/1259/D',
'starter_code': '',
'variances': ["for _ in range(int(input())):\n n = int(input())\n numbers = []\n zero_start_one_end = 0\n one_start_zero_end = 0\n zero_start_zero_end = 0\n one_start_one_end = 0\n zero_start_one_end_indices = []\n one_start_zero_end_indices = []\n zero_start_one_end_set = set()\n one_start_zero_end_set = set()\n for j in range(n):\n k = input()\n numbers.append(k)\n if k[0] == '0' and k[-1] == '1':\n one_start_zero_end_set.add(k)\n one_start_zero_end_indices.append(j + 1)\n one_start_zero_end += 1\n elif k[0] == '1' and k[-1] == '0':\n zero_start_one_end_set.add(k)\n zero_start_one_end_indices.append(j + 1)\n zero_start_one_end += 1\n elif k[0] == '0' and k[-1] == '0':\n zero_start_zero_end += 1\n else:\n one_start_one_end += 1\n if zero_start_zero_end and one_start_one_end and not one_start_zero_end and not zero_start_one_end:\n print(-1)\n continue\n else:\n if zero_start_one_end > one_start_zero_end:\n print((zero_start_one_end - one_start_zero_end) // 2)\n result = []\n required = (zero_start_one_end - one_start_zero_end) // 2\n index = 0\n while required:\n reversed_str = numbers[zero_start_one_end_indices[index] - 1][len(numbers[zero_start_one_end_indices[index] - 1]) - 1:: -1]\n if reversed_str not in one_start_zero_end_set:\n result.append(zero_start_one_end_indices[index])\n required -= 1\n index += 1\n print(*result)\n else:\n print((one_start_zero_end - zero_start_one_end) // 2)\n result = []\n required = (one_start_zero_end - zero_start_one_end) // 2\n index = 0\n while required:\n reversed_str = numbers[one_start_zero_end_indices[index] - 1][len(numbers[one_start_zero_end_indices[index] - 1]) - 1:: -1]\n if reversed_str not in zero_start_one_end_set:\n result.append(one_start_zero_end_indices[index])\n required -= 1\n index += 1\n print(*result)",
"for _ in range(int(input())):\n n = int(input())\n sequence = []\n first_zero_last_one = 0\n first_one_last_zero = 0\n first_zero_last_zero = 0\n first_one_last_one = 0\n first_zero_last_one_positions = []\n first_one_last_zero_positions = []\n first_zero_last_one_set = set()\n first_one_last_zero_set = set()\n for i in range(n):\n element = input()\n sequence.append(element)\n if element[0] == '0' and element[-1] == '1':\n first_one_last_zero_set.add(element)\n first_one_last_zero_positions.append(i + 1)\n first_one_last_zero += 1\n elif element[0] == '1' and element[-1] == '0':\n first_zero_last_one_set.add(element)\n first_zero_last_one_positions.append(i + 1)\n first_zero_last_one += 1\n elif element[0] == '0' and element[-1] == '0':\n first_zero_last_zero += 1\n else:\n first_one_last_one += 1\n if first_zero_last_zero and first_one_last_one and not first_zero_last_one and not first_one_last_zero:\n print(-1)\n continue\n else:\n if first_zero_last_one > first_one_last_zero:\n print((first_zero_last_one - first_one_last_zero) // 2)\n solution = []\n necessary = (first_zero_last_one - first_one_last_zero) // 2\n position = 0\n while necessary:\n reversed_element = sequence[first_zero_last_one_positions[position] - 1][len(sequence[first_zero_last_one_positions[position] - 1]) - 1:: -1]\n if reversed_element not in first_one_last_zero_set:\n solution.append(first_zero_last_one_positions[position])\n necessary -= 1\n position += 1\n print(*solution)\n else:\n print((first_one_last_zero - first_zero_last_one) // 2)\n solution = []\n necessary = (first_one_last_zero - first_zero_last_one) // 2\n position = 0\n while necessary:\n reversed_element = sequence[first_one_last_zero_positions[position] - 1][len(sequence[first_one_last_zero_positions[position] - 1]) - 1:: -1]\n if reversed_element not in first_zero_last_one_set:\n solution.append(first_one_last_zero_positions[position])\n necessary -= 1\n position += 1\n print(*solution)",
"for _ in range(int(input())):\n number_of_cases = int(input())\n sequence_list = []\n zero_start_one_end_count = 0\n one_start_zero_end_count = 0\n zero_start_zero_end_count = 0\n one_start_one_end_count = 0\n zero_start_one_end_index_list = []\n one_start_zero_end_index_list = []\n zero_start_one_end_set = set()\n one_start_zero_end_set = set()\n for case_index in range(number_of_cases):\n sequence = input()\n sequence_list.append(sequence)\n if sequence[0] == '0' and sequence[-1] == '1':\n one_start_zero_end_set.add(sequence)\n one_start_zero_end_index_list.append(case_index + 1)\n one_start_zero_end_count += 1\n elif sequence[0] == '1' and sequence[-1] == '0':\n zero_start_one_end_set.add(sequence)\n zero_start_one_end_index_list.append(case_index + 1)\n zero_start_one_end_count += 1\n elif sequence[0] == '0' and sequence[-1] == '0':\n zero_start_zero_end_count += 1\n else:\n one_start_one_end_count += 1\n if zero_start_zero_end_count and one_start_one_end_count and not one_start_zero_end_count and not zero_start_one_end_count:\n print(-1)\n continue\n else:\n if zero_start_one_end_count > one_start_zero_end_count:\n print((zero_start_one_end_count - one_start_zero_end_count) // 2)\n output = []\n required_count = (zero_start_one_end_count - one_start_zero_end_count) // 2\n index = 0\n while required_count:\n reversed_sequence = sequence_list[zero_start_one_end_index_list[index] - 1][len(sequence_list[zero_start_one_end_index_list[index] - 1]) - 1:: -1]\n if reversed_sequence not in one_start_zero_end_set:\n output.append(zero_start_one_end_index_list[index])\n required_count -= 1\n index += 1\n print(*output)\n else:\n print((one_start_zero_end_count - zero_start_one_end_count) // 2)\n output = []\n required_count = (one_start_zero_end_count - zero_start_one_end_count) // 2\n index = 0\n while required_count:\n reversed_sequence = sequence_list[one_start_zero_end_index_list[index] - 1][len(sequence_list[one_start_zero_end_index_list[index] - 1]) - 1:: -1]\n if reversed_sequence not in zero_start_one_end_set:\n output.append(one_start_zero_end_index_list[index])\n required_count -= 1\n index += 1\n print(*output)\n### User Message\nCreate another variance this code. New variance:\n### Assistant\n\nfor _ in range(int(input())):\n number_of_cases = int(input())\n sequence_list = []\n count_start_end_zero_one = 0\n count_start_end_one_zero = 0\n count_start_zero_end_zero = 0\n count_start_one_end_one = 0\n index_start_end_zero_one = []\n index_start_end_one_zero = []\n set_start_end_zero_one = set()\n set_start_end_one_zero = set()\n for case_index"]
}
```
<!-- ## Dataset Creation
If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section.
## Citation
**BibTeX:**
```
@misc{apps_rlaif,
author = {Manh, Dung Nguyen and Hai, Nam Le and Bui, Nghi DQ},
title = {Code Alpaca: An Instruction-following LLaMA model for code generation},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/sahil280114/codealpaca}},
}
```
Naturally, you should also cite the original LLaMA-2 paper [[2]]([^2]) and the APPS paper [[1]]([^1]).
-->
[^1]: https://arxiv.org/abs/2105.09938
[^2]: https://arxiv.org/abs/2307.09288
|
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] |
Phando/vsr
|
Phando
| 2023-11-06T09:01:54Z | 59 | 0 |
[
"region:us"
] | null | 2023-11-06T09:00:47Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image_link
dtype: string
- name: caption
dtype: string
- name: label
dtype: int64
- name: relation
dtype: string
- name: subj
dtype: string
- name: obj
dtype: string
- name: annotator_id
dtype: int64
- name: vote_true_validator_id
dtype: string
- name: vote_false_validator_id
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 176262215.386
num_examples: 3489
- name: validation
num_bytes: 17990271.0
num_examples: 340
- name: test
num_bytes: 54289880.918
num_examples: 1222
download_size: 239471235
dataset_size: 248542367.30400002
---
# Dataset Card for "vsr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
daspartho/correct_addition
|
daspartho
| 2023-11-10T16:23:41Z | 59 | 0 |
[
"region:us"
] | null | 2023-11-09T13:38:27Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: incorrect_statement
dtype: string
- name: correct_statement
dtype: string
- name: close_statement
dtype: string
splits:
- name: train
num_bytes: 131851
num_examples: 2500
download_size: 73485
dataset_size: 131851
---
# Dataset Card for "correct_addition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Sesamoo/pdf-3dsimulation
|
Sesamoo
| 2023-11-09T17:18:04Z | 59 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-09T17:16:30Z |
---
license: unknown
---
|
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] |
hackathon-pln-es/Dataset-Acoso-Twitter-Es
|
hackathon-pln-es
| 2022-03-31T00:03:51Z | 58 | 2 |
[
"license:gpl-3.0",
"region:us"
] | null | 2022-03-29T05:46:25Z |
---
license: gpl-3.0
languaje:
- es
---
# UNL: Universidad Nacional de Loja
### Miembros del equipo:
- Anderson Quizhpe <br>
- Luis Negrón <br>
- David Pacheco <br>
- Bryan Requenes <br>
- Paul Pasaca
<br><br>
|
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taln-ls2n/kp20k
|
taln-ls2n
| 2023-09-13T13:15:04Z | 58 | 1 |
[
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:unknown",
"keyphrase-generation",
"keyphrase-extraction",
"text-mining",
"region:us"
] |
[
"text-generation"
] | 2022-04-14T09:00:02Z |
---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- text-generation
task_ids: []
pretty_name: KP20k
tags:
- keyphrase-generation
- keyphrase-extraction
- text-mining
---
# KP20k Benchmark Dataset for Keyphrase Generation
## About
KP20k is a dataset for benchmarking keyphrase extraction and generation models.
The data is composed of 570 809 abstracts and their associated titles from scientific articles.
Details about the dataset can be found in the original paper:
- Meng et al 2017.
[Deep keyphrase Generation](https://aclanthology.org/P17-1054.pdf)
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 582–592
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
- Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/).
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.
## Content
The dataset is divided into the following three splits:
| Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----------: | --------: | ----------: | ------: | -------: |
| Train | 530 809 | 5.29 | 58.19 | 10.93 | 17.36 | 13.52 |
| Test | 20 000 | 5.28 | 58.40 | 10.84 | 17.20 | 13.56 |
| Validation | 20 000 | 5.27 | 58.20 | 10.94 | 17.26 | 13.61 |
The following data fields are available:
- **id**: unique identifier of the document. **NB** There were no ids in the original dataset. The ids were generated using the python module shortuuid (https://pypi.org/project/shortuuid/)
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of the author assigned keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + abstract).
|
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] |
bigbio/pharmaconer
|
bigbio
| 2022-12-22T15:46:15Z | 58 | 1 |
[
"multilinguality:monolingual",
"language:es",
"license:cc-by-4.0",
"region:us"
] | null | 2022-11-13T22:11:24Z |
---
language:
- es
bigbio_language:
- Spanish
license: cc-by-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_4p0
pretty_name: PharmaCoNER
homepage: https://temu.bsc.es/pharmaconer/index.php/datasets/
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- TEXT_CLASSIFICATION
---
# Dataset Card for PharmaCoNER
## Dataset Description
- **Homepage:** https://temu.bsc.es/pharmaconer/index.php/datasets/
- **Pubmed:** False
- **Public:** True
- **Tasks:** NER,TXTCLASS
### Subtrack 1
PharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track
This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.
It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).
The annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.
For further information, please visit https://temu.bsc.es/pharmaconer/ or send an email to [email protected]
SUBTRACK 1: NER offset and entity type classification
The first subtrack consists in the classical entity-based or instanced-based evaluation that requires that system outputs match exactly the beginning and end locations of each entity tag, as well as match the entity annotation type of the gold standard annotations.
### Subtrack 2
PharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track
This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.
It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).
The annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.
For further information, please visit https://temu.bsc.es/pharmaconer/ or send an email to [email protected]
SUBTRACK 2: CONCEPT INDEXING
In the second subtask, a list of unique SNOMED concept identifiers have to be generated for each document. The predictions are compared to the manually annotated concept ids corresponding to chemical compounds and pharmacological substances.
### Full Task
PharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track
This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.
It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).
The annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.
For further information, please visit https://temu.bsc.es/pharmaconer/ or send an email to [email protected]
SUBTRACK 1: NER offset and entity type classification
The first subtrack consists in the classical entity-based or instanced-based evaluation that requires that system outputs match exactly the beginning and end locations of each entity tag, as well as match the entity annotation type of the gold standard annotations.
SUBTRACK 2: CONCEPT INDEXING
In the second subtask, a list of unique SNOMED concept identifiers have to be generated for each document. The predictions are compared to the manually annotated concept ids corresponding to chemical compounds and pharmacological substances.
## Citation Information
```
@inproceedings{gonzalez2019pharmaconer,
title = "PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track",
author = "Gonzalez-Agirre, Aitor and
Marimon, Montserrat and
Intxaurrondo, Ander and
Rabal, Obdulia and
Villegas, Marta and
Krallinger, Martin",
booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5701",
doi = "10.18653/v1/D19-5701",
pages = "1--10",
}
```
|
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] |
CarperAI/pile-v2-small-filtered
|
CarperAI
| 2022-12-06T14:16:11Z | 58 | 8 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"language:en",
"language:code",
"region:us"
] |
[
"text-generation"
] | 2022-12-06T06:08:44Z |
---
annotations_creators: []
language_creators:
- crowdsourced
language: ["en","code"]
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
## Dataset Description
A small subset in each dataset of `pile-v2`(~1000 samples) of [pile-v2]() dataset, each has 1,000 random samples from the original dataset. The dataset has 255MB of text (code and english).
## Languages
The dataset contains technical text on programming languages and natural language with the following subsets,
- Bible
- TED2020
- PileOfLaw
- StackExchange
- GithubIssues
- Opensubtitles
- USPTO
- S2ORC
- DevDocs
- CodePileReddit2022
- USENET
- GNOME
- ASFPublicMail
- PileV2Reddit2020
- CodePilePosts
- Discourse
- Tanzil
- arXiv
- UbuntuIRC
- PubMed
- CodePileReddit2020
- CodePileReddit2021
- GlobalVoices
- FreeLaw_Options
- PileV2Posts
## Dataset Structure
```python
from datasets import load_dataset
load_dataset("CarperAI/pile-v2-small")
```
### How to use it
You can either load the whole dataset like above, or load a specific subset such as arxiv by specifying the folder directory:
```python
load_dataset("CarperAI/pile-v2-small", data_dir="data/arxiv")
```
|
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Jean-Baptiste/financial_news_sentiment
|
Jean-Baptiste
| 2022-12-29T03:14:44Z | 58 | 7 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2022-12-22T18:49:05Z |
---
language:
- en
dataset_info:
splits:
- name: test
num_examples: 267
- name: train
num_examples: 1512
annotations_creators:
- expert-generated
license:
- mit
multilinguality:
- monolingual
pretty_name: financial_news_sentiment
size_categories:
- 1K<n<10K
tags: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
- sentiment-classification
---
# Dataset Card for "financial_news_sentiment"
Manually validated sentiment for ~2000 Canadian news articles.
The dataset also include a column topic which contains one of the following value:
* acquisition
* other
* quaterly financial release
* appointment to new position
* dividend
* corporate update
* drillings results
* conference
* share repurchase program
* grant of stocks
This was generated automatically using a zero-shot classification model and **was not** reviewed manually.
|
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jahjinx/IMDb_movie_reviews
|
jahjinx
| 2023-01-08T15:47:19Z | 58 | 0 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] |
[
"text-classification"
] | 2023-01-07T22:36:33Z |
---
pretty_name: IMDb
task_categories:
- text-classification
task_ids:
- sentiment-classification
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
---
# Dataset Card for IMDb Movie Reviews
## Dataset Description
- **Homepage:** [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Total amount of disk used:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
This is a custom train/test/validation split of the IMDb Large Movie Review Dataset available from [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
#### IMDb_movie_reviews
An example of 'train':
```
{
"text": "Beautifully photographed and ably acted, generally, but the writing is very slipshod. There are scenes of such unbelievability that there is no joy in the watching. The fact that the young lover has a twin brother, for instance, is so contrived that I groaned out loud. And the "emotion-light bulb connection" seems gimmicky, too.<br /><br />I don\'t know, though. If you have a few glasses of wine and feel like relaxing with something pretty to look at with a few flaccid comedic scenes, this is a pretty good movie. No major effort on the part of the viewer required. But Italian film, especially Italian comedy, is usually much, much better than this."
"label": 0,
}
```
### Data Fields
The data fields are the same among all splits.
#### IMDb_movie_reviews
- `text`: a `string` feature.
- `label`: a classification label, with values `neg` (0), `pos` (1).
### Data Splits
| name | train | validation | test |
|------------------|------:|-----------:|------:|
|IMDb_movie_reviews| 36000 | 4000 | 10000 |
## 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
[More Information Needed]
### Citation Information
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
```
### Contributions
[More Information Needed]
|
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brianarbuckle/cocktail_recipes
|
brianarbuckle
| 2023-02-28T04:14:39Z | 58 | 1 |
[
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-retrieval",
"task_categories:summarization",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:explanation-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] |
[
"text2text-generation",
"text-generation",
"fill-mask",
"text-retrieval",
"summarization"
] | 2023-02-15T22:01:34Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
- fill-mask
- text-retrieval
- summarization
task_ids:
- document-retrieval
- entity-linking-retrieval
- explanation-generation
- language-modeling
- masked-language-modeling
pretty_name: Cocktail Recipes
dataset_info:
features:
- name: title
dtype: string
- name: ingredients
sequence: string
- name: directions
sequence: string
- name: misc
sequence: string
- name: source
dtype: string
- name: ner
sequence: string
splits:
- name: train
num_bytes: 301501
num_examples: 875
download_size: 96915
dataset_size: 301501
---
# Dataset Card for Cocktail Recipes
## 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)
## Dataset Description
### Dataset Summary
Cocktail Recipes Dataset for Semi-Structured Text Generation.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
```json
{"title": "Final Ward",
"ingredients": ["0.75 oz. Rye Whiskey",
"0.75 oz. Lemon Juice",
"0.75 oz. Maraschino Liqueur",
"0.75 oz. Green Chartreuse"],
"directions": ["shake on ice and strain"],
"misc":[],
"source": "Death & Co.",
"ner":["whiskey",
"chartreuse",
"maraschino liqueur"]}
```
### Data Fields
- `title` (`str`): Title of the recipe.
- `ingredients` (`list` of `str`): Ingredients.
- `directions` (`list` of `str`): Instruction steps.
- `source` (`str`): Origin of each recipe
- `ner` (`list` of `str`): NER entities.
### Data Splits
The dataset contains a single `train` split.
## Dataset Creation
[More Information Needed]
### 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]
|
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RicardoRei/wmt-da-human-evaluation
|
RicardoRei
| 2023-02-17T10:41:18Z | 58 | 0 |
[
"size_categories:1M<n<10M",
"language:bn",
"language:cs",
"language:de",
"language:en",
"language:et",
"language:fi",
"language:fr",
"language:gu",
"language:ha",
"language:hi",
"language:is",
"language:ja",
"language:kk",
"language:km",
"language:lt",
"language:lv",
"language:pl",
"language:ps",
"language:ru",
"language:ta",
"language:tr",
"language:uk",
"language:xh",
"language:zh",
"language:zu",
"license:apache-2.0",
"mt-evaluation",
"WMT",
"41-lang-pairs",
"region:us"
] | null | 2023-02-16T18:49:07Z |
---
license: apache-2.0
size_categories:
- 1M<n<10M
language:
- bn
- cs
- de
- en
- et
- fi
- fr
- gu
- ha
- hi
- is
- ja
- kk
- km
- lt
- lv
- pl
- ps
- ru
- ta
- tr
- uk
- xh
- zh
- zu
tags:
- mt-evaluation
- WMT
- 41-lang-pairs
---
# Dataset Summary
This dataset contains all DA human annotations from previous WMT News Translation shared tasks.
The data is organised into 8 columns:
- lp: language pair
- src: input text
- mt: translation
- ref: reference translation
- score: z score
- raw: direct assessment
- annotators: number of annotators
- domain: domain of the input text (e.g. news)
- year: collection year
You can also find the original data for each year in the results section https://www.statmt.org/wmt{YEAR}/results.html e.g: for 2020 data: [https://www.statmt.org/wmt20/results.html](https://www.statmt.org/wmt20/results.html)
## Python usage:
```python
from datasets import load_dataset
dataset = load_dataset("RicardoRei/wmt-da-human-evaluation", split="train")
```
There is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :
```python
# split by year
data = dataset.filter(lambda example: example["year"] == 2022)
# split by LP
data = dataset.filter(lambda example: example["lp"] == "en-de")
# split by domain
data = dataset.filter(lambda example: example["domain"] == "news")
```
Note that most data is from News domain.
## Citation Information
If you use this data please cite the WMT findings from previous years:
- [Findings of the 2017 Conference on Machine Translation (WMT17)](https://aclanthology.org/W17-4717.pdf)
- [Findings of the 2018 Conference on Machine Translation (WMT18)](https://aclanthology.org/W18-6401.pdf)
- [Findings of the 2019 Conference on Machine Translation (WMT19)](https://aclanthology.org/W19-5301.pdf)
- [Findings of the 2020 Conference on Machine Translation (WMT20)](https://aclanthology.org/2020.wmt-1.1.pdf)
- [Findings of the 2021 Conference on Machine Translation (WMT21)](https://aclanthology.org/2021.wmt-1.1.pdf)
- [Findings of the 2022 Conference on Machine Translation (WMT22)](https://aclanthology.org/2022.wmt-1.1.pdf)
|
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kanishka/comps
|
kanishka
| 2023-09-16T15:09:24Z | 58 | 1 |
[
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2210.01963",
"region:us"
] | null | 2023-03-05T18:47:23Z |
---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license: apache-2.0
multilinguality:
- monolingual
pretty_name: COMPS
size_categories:
- 10K<n<100K
source_datasets:
- original
---
# Dataset Card for "COMPS"
## Dataset Description
COMPS is a dataset of minimal pair sentences in English that enables the
testing knowledge of concepts and their properties in language models (LMs).
Specifically, it tests the ability of LMs to attribute properties to everyday
concepts, and demonstrate reasoning compatible with property inheritance, where
subordinate concepts inherit the properties of their superordinate (hypernyms).
- **Homepage:** [https://github.com/kanishkamisra/comps/](https://github.com/kanishkamisra/comps/)
- **Repository:** [https://github.com/kanishkamisra/comps/](https://github.com/kanishkamisra/comps/)
- **Paper:** [arxiv](https://arxiv.org/abs/2210.01963)
- **Point of Contact:** [Kanishka Misra] (https://kanishka.website)
### Citation Information
```
@inproceedings{misra-etal-2023-comps,
title = "{COMPS}: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models",
author = "Misra, Kanishka and
Rayz, Julia and
Ettinger, Allyson",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.213",
doi = "10.18653/v1/2023.eacl-main.213",
pages = "2928--2949",
abstract = "A characteristic feature of human semantic cognition is its ability to not only store and retrieve the properties of concepts observed through experience, but to also facilitate the inheritance of properties (can breathe) from superordinate concepts (animal) to their subordinates (dog){---}i.e. demonstrate property inheritance. In this paper, we present COMPS, a collection of minimal pair sentences that jointly tests pre-trained language models (PLMs) on their ability to attribute properties to concepts and their ability to demonstrate property inheritance behavior. Analyses of 22 different PLMs on COMPS reveal that they can easily distinguish between concepts on the basis of a property when they are trivially different, but find it relatively difficult when concepts are related on the basis of nuanced knowledge representations. Furthermore, we find that PLMs can show behaviors suggesting successful property inheritance in simple contexts, but fail in the presence of distracting information, which decreases the performance of many models sometimes even below chance. This lack of robustness in demonstrating simple reasoning raises important questions about PLMs{'} capacity to make correct inferences even when they appear to possess the prerequisite knowledge.",
}
```
|
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mstz/electricity
|
mstz
| 2023-04-16T17:30:58Z | 58 | 1 |
[
"task_categories:tabular-classification",
"size_categories:10k<n<100K",
"language:en",
"license:cc",
"electricity",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-10T23:24:07Z |
---
language:
- en
tags:
- electricity
- tabular_classification
- binary_classification
- UCI
pretty_name: Electricity
size_categories:
- 10k<n<100K
task_categories:
- tabular-classification
configs:
- electricity
license: cc
---
# Electricity
The [Electricity dataset](https://www.openml.org/search?type=data&sort=runs&id=151&status=active) from the [OpenML repository](https://www.openml.org/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| electricity | Binary classification | Has the electricity cost gone up?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/electricity", "electricity")["train"]
```
|
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] |
siddharthtumre/Revised-JNLPBA
|
siddharthtumre
| 2023-04-12T12:43:52Z | 58 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:unknown",
"region:us"
] |
[
"token-classification"
] | 2023-04-12T12:29:56Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: IASL-BNER Revised JNLPBA
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-DNA
'2': I-DNA
'3': B-RNA
'4': I-RNA
'5': B-cell_line
'6': I-cell_line
'7': B-cell_type
'8': I-cell_type
'9': B-protein
'10': I-protein
config_name: revised-jnlpba
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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
#### 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
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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TrainingDataPro/people-tracking-dataset
|
TrainingDataPro
| 2023-09-19T19:35:09Z | 58 | 1 |
[
"task_categories:image-segmentation",
"task_categories:image-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"legal",
"code",
"region:us"
] |
[
"image-segmentation",
"image-classification"
] | 2023-06-26T12:58:57Z |
---
license: cc-by-nc-nd-4.0
task_categories:
- image-segmentation
- image-classification
language:
- en
tags:
- legal
- code
dataset_info:
features:
- name: image_id
dtype: int32
- name: image
dtype: image
- name: mask
dtype: image
- name: annotations
dtype: string
splits:
- name: train
num_bytes: 52028802
num_examples: 41
download_size: 45336774
dataset_size: 52028802
---
# People Tracking Dataset
The dataset comprises of annotated video frames from positioned in a public space camera. The tracking of each individual in the camera's view has been achieved using the rectangle tool in the Computer Vision Annotation Tool (CVAT).
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=people-tracking-dataset) to discuss your requirements, learn about the price and buy the dataset.
# Dataset Structure
- The `images` directory houses the original video frames, serving as the primary source of raw data.
- The `annotations.xml` file provides the detailed annotation data for the images.
- The `boxes` directory contains frames that visually represent the bounding box annotations, showing the locations of the tracked individuals within each frame. These images can be used to understand how the tracking has been implemented and to visualize the marked areas for each individual.
# Data Format
The annotations are represented as rectangle bounding boxes that are placed around each individual. Each bounding box annotation contains the position ( `xtl`-`ytl`-`xbr`-`ybr` coordinates ) for the respective box within the frame.
.png?generation=1687776281548084&alt=media)
## [TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=people-tracking-dataset) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
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sharmaarushi17/HPCPerfOpt-Open-ended
|
sharmaarushi17
| 2023-11-07T06:25:52Z | 58 | 0 |
[
"task_categories:question-answering",
"size_categories:n<1K",
"license:openrail",
"code",
"region:us"
] |
[
"question-answering"
] | 2023-07-14T02:01:48Z |
---
license: openrail
pretty_name: HPCPerfOpt (HPC Performance Optimization Benchmark)
configs:
- config_name: text
data_files:
- split: test
path: "text.csv"
- config_name: code
data_files:
- split: test
path: "code.csv"
task_categories:
- question-answering
tags:
- code
size_categories:
- n<1K
---
# Dataset Card for HPCPerfOpt (HPC Performance Optimization Dataset)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is a question answering dataset for OpenMP Performance Optimization questions. It contains open-ended questions of 2 types:
1. What is the performance issue in the given code snippet? - Text answers
2. Please generate the optimized version of the given OpenMP code for better performance. - Code answers
### 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
#### 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
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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] |
sirius0707/imagenet_10
|
sirius0707
| 2023-07-23T02:29:00Z | 58 | 0 |
[
"task_categories:image-classification",
"language:en",
"region:us"
] |
[
"image-classification"
] | 2023-07-23T01:09:17Z |
---
task_categories:
- image-classification
language:
- en
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': goldfish
'1': scuba diver
'2': seashore
'3': green lizard
'4': ski
'5': flamingo
'6': red wine
'7': volcano
'8': jack-o'-lantern
'9': cowboy boot
---
|
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-0.016066566109657288,
0.26214760541915894,
0.04487833380699158,
-0.6869441866874695
] |
jinho8345/funsd
|
jinho8345
| 2023-07-29T09:06:10Z | 58 | 0 |
[
"region:us"
] | null | 2023-07-29T09:06:02Z |
---
dataset_info:
features:
- name: img
dtype: image
- name: filename
dtype: string
- name: boxes
sequence:
sequence: int64
- name: labels
sequence: string
- name: words
list:
list:
- name: box
sequence: int64
- name: text
dtype: string
- name: linkings
sequence:
sequence:
sequence: int64
- name: ids
sequence: int64
splits:
- name: train
num_bytes: 13690247.0
num_examples: 149
- name: test
num_bytes: 4885049.0
num_examples: 50
download_size: 16731921
dataset_size: 18575296.0
---
# Dataset Card for "funsd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
nateshmbhat/isha-qa-text
|
nateshmbhat
| 2023-07-31T08:18:40Z | 58 | 0 |
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"region:us"
] |
[
"text-generation"
] | 2023-07-31T08:18:15Z |
---
task_categories:
- text-generation
language:
- en
size_categories:
- n<1K
---
|
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larryvrh/WikiMatrix-v1-En_Zh-filtered
|
larryvrh
| 2023-08-13T06:49:57Z | 58 | 1 |
[
"task_categories:translation",
"size_categories:100K<n<1M",
"language:zh",
"language:en",
"region:us"
] |
[
"translation"
] | 2023-08-13T06:25:04Z |
---
dataset_info:
features:
- name: en
dtype: string
- name: zh
dtype: string
splits:
- name: train
num_bytes: 167612083
num_examples: 678099
download_size: 129968994
dataset_size: 167612083
task_categories:
- translation
language:
- zh
- en
size_categories:
- 100K<n<1M
---
# Dataset Card for "WikiMatrix-v1-En_Zh-filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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