id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
OpenLLMAI/anthropic_hh_oasst1_split | 2023-10-25T02:15:43.000Z | [
"region:us"
] | OpenLLMAI | null | null | 0 | 28 | 2023-10-25T02:12:08 | Entry not found | 15 | [
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onuralp/open-otter | 2023-10-31T10:45:59.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"doi:10.57967/hf/1270",
"region:us"
] | onuralp | null | null | 1 | 28 | 2023-10-26T07:53:53 | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: data_source
dtype: string
splits:
- name: train
num_bytes: 48901728
num_examples: 162864
download_size: 22256986
dataset_size: 48901728
task_categories:
- multiple-choice
- question-answering
language:
- en
pretty_name: open-otter
size_categories:
- 100K<n<1M
---
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Getting Started](#getting-started)
**Disclaimer: this dataset is curated for NeurIPS 2023 LLM efficiency challange, and currently work in progress. Please use at your own risk.**
<a name="dataset-summary"></a>
# Dataset Summary
We curated this dataset to finetune open source base models as part of [NeurIPS 2023 LLM Efficiency Challenge](https://llm-efficiency-challenge.github.io/) (1 LLM + 1 GPU + 1 Day). This challenge requires participants to use open source models and datasets with permissible licenses to encourage wider adoption, use and dissemination of open source contributions in generative AI space. Additionally, LLM generated datasets such as Alpaca and Orca datasets are not allowed.
**Open-Otter** combines the non-LLM generated subset of [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) datasets with other datasets, and is used for finetuning Llama-2-7b, Llama-2-13b and Mistral-7b-v0.1 base models to perform reasonably well in a suit of reasoning tasks selected by the organizers. Please visit the challenge website for more detailed information on the rules.
<a name="dataset-attribution"></a>
# Dataset Attribution
<a name="languages"></a>
# Languages
Evaluation for the challenge includes only English text. Therefore, Open-Otter includes data sources only in English.
_Note: we are not aware of any compelling literature demonstrating the value of finetuning on multilingual datasets (over datasets in target language). Please leave a comment if you come across any relevant work addressing this question._
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-fields"></a>
## Data Fields
Data fields follow Alpaca style formatting.
The fields are:
1) 'input', an optional field for providing additional context for response type
2) 'output', response, answer or solution to the corresponding instruction (e.g., a multiple choice question)
3) 'instruction', required field including the question and multiple choice options (when applicable)
4) 'data_source', original dataset and split for the data instance
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
TODO: NeurIPS 2023 LLM Efficiency Challenge
<a name="source-data"></a>
## Source Data
We have combined the non-LLM-generated subset of Open-Platypus dataset with 4 additional datasets:
- [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) dataset
- Excluding [airoboros-gpt4-1.4.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1) and [PRM800K](https://github.com/openai/prm800k)
- [ARC](https://allenai.org/data/arc) (Allen AI Reasoning Challenge)
- [CommonsenseQA](https://huggingface.co/datasets/commonsense_qa)
- [WinoGrande, debiased](https://huggingface.co/datasets/winogrande)
- [MedMCQA](https://huggingface.co/datasets/medmcqa)
Notably, train, validation and test splits were all included for each dataset. If answer key is not provided, test set is excluded.
<a name="dataset-use"></a>
# Dataset Use
## Getting Started
You can use Hugging Face datasets library to load this dataset.
```python
from datasets import load_dataset
dataset = load_dataset("onuralp/open-otter")
```
# Citation
If you find this dataset useful for your own work and are interested in acknowledging, please use the citation below.
```bibtex
@misc {onuralp2023,
author = { {Onuralp Soylemez} },
title = { open-otter (Revision 17db84f) },
year = 2023,
url = { https://huggingface.co/datasets/onuralp/open-otter },
doi = { 10.57967/hf/1270 },
publisher = { Hugging Face }
}
```
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kuanhuggingface/amazon_tts_encodec | 2023-10-27T02:41:22.000Z | [
"region:us"
] | kuanhuggingface | null | null | 0 | 28 | 2023-10-27T02:41:04 | ---
dataset_info:
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sequence: int64
- name: tgt_encodec_5
sequence: int64
- name: tgt_encodec_6
sequence: int64
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sequence: int64
splits:
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- name: validation
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num_examples: 957
- name: test
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download_size: 108942534
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---
# Dataset Card for "amazon_tts_encodec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,284 | [
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atmallen/qm_bob_grader_first_1.0e_0.0p_finetuning | 2023-10-27T05:38:10.000Z | [
"region:us"
] | atmallen | null | null | 0 | 28 | 2023-10-27T05:38:06 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: string
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sequence: string
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class_label:
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'1': 'True'
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dtype: bool
splits:
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num_examples: 20000
- name: test
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num_examples: 20000
download_size: 3522565
dataset_size: 17459516
---
# Dataset Card for "qm_bob_grader_first_1.0e_0.0p_finetuning"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 877 | [
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orkidea/palabrero-guc-draft | 2023-10-28T18:57:13.000Z | [
"region:us"
] | orkidea | null | null | 0 | 28 | 2023-10-27T21:19:21 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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dtype: audio
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dtype: string
splits:
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num_bytes: 62556423.0
num_examples: 17
download_size: 60689485
dataset_size: 62556423.0
---
# Dataset Card for "palabrero-guc-draft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 494 | [
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Deojoandco/capstone_forgpt_without_gold | 2023-10-27T22:29:35.000Z | [
"region:us"
] | Deojoandco | null | null | 0 | 28 | 2023-10-27T22:29:32 | ---
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "capstone_forgpt_without_gold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 527 | [
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georgeyw/dsir-pile-100k | 2023-10-28T01:34:19.000Z | [
"region:us"
] | georgeyw | null | null | 0 | 28 | 2023-10-28T01:33:32 | rows 4.9m to 5m in the pile | 27 | [
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xu3kev/proof-pile-2-proofsteps | 2023-10-28T21:45:17.000Z | [
"arxiv:2310.10631",
"region:us"
] | xu3kev | null | null | 0 | 28 | 2023-10-28T20:03:34 | ---
configs:
- config_name: default
data_files:
- split: lean_proofsteps
path: "lean_proofsteps/*.parquet"
- split: isa_proofsteps
path: "isa_proofsteps/*.parquet"
---
Proofsteps data from Proof-Pile-2
includes proofsteps for Lean and Isabelle
```python
from datasets import load_dataset
ds = load_dataset(
"xu3kev/proof-pile-2-proofsteps"
)
ds
DatasetDict({
lean_proofsteps: Dataset({
features: ['text', 'meta'],
num_rows: 3432
})
isa_proofsteps: Dataset({
features: ['text', 'meta'],
num_rows: 260726
})
})
```
Quoting from appendix of [LLEMMA: AN OPEN LANGUAGE MODEL FOR MATHEMATICS](https://arxiv.org/pdf/2310.10631.pdf)
```
B.1.2 LEAN PROOFSTEPS
We extract a dataset of (tactic state, next tactic) pairs from Mathlib 4 (mathlib Community, 2020)
using the lean-training-data (Morrison, 2023) tool. We use Mathlib 4 commit c779bd5,
which was created on August 20th 2023.
B.1.3 ISABELLE PROOFSTEPS
We construct a dataset of Isabelle proofs, building upon the PISA dataset Jiang et al. (2021). Isabelle
Proofsteps comprises proofs from the Archive of Formal Proofs and Isabelle Standard Library, scraped
with PISA Jiang et al. (2021). Each entry in the dataset includes the theorem statement, the proof
states and the proof steps, separated by specific tags. To maintain the integrity of evaluations using
the PISA test set, we decontaminate Isabelle Proofsteps by removing theorems whose names overlap
with those in the PISA test set. Although this approach results in a strict filtering – removing more
than 10,000 theorems although there are only 3600 in the PISA test set – we consider it acceptable in
order to mitigate data contamination. After filtering, Isabelle Proofsteps contains 251,000 theorems.
```
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jmelsbach/leichte-sprache-definitionen | 2023-10-30T15:08:24.000Z | [
"region:us"
] | jmelsbach | null | null | 0 | 28 | 2023-10-30T15:08:20 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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download_size: 417716
dataset_size: 663115.0
---
# Dataset Card for "leichte-sprache-definitionen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 694 | [
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slplab/QoLT_apacAnd5Sentences | 2023-11-02T12:36:57.000Z | [
"region:us"
] | slplab | null | null | 0 | 28 | 2023-11-02T12:36:07 | ---
dataset_info:
features:
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dtype: string
- name: audio
struct:
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sequence: float32
- name: path
dtype: string
- name: sampling_rate
dtype: int64
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dtype: string
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dtype: int64
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num_bytes: 507067812
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num_bytes: 62855037
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- name: test
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download_size: 278294583
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---
# Dataset Card for "QoLT_apacAnd5Sentences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 721 | [
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midas/semeval2010 | 2022-03-05T03:24:16.000Z | [
"arxiv:1910.08840",
"region:us"
] | midas | \ | @inproceedings{10.5555/1859664.1859668,
author = {Kim, Su Nam and Medelyan, Olena and Kan, Min-Yen and Baldwin, Timothy},
title = {SemEval-2010 Task 5: Automatic Keyphrase Extraction from Scientific Articles},
year = {2010},
publisher = {Association for Computational Linguistics},
address = {USA},
abstract = {This paper describes Task 5 of the Workshop on Semantic Evaluation 2010 (SemEval-2010). Systems are to automatically assign keyphrases or keywords to given scientific articles. The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones. We present the overall ranking of the submitted systems and discuss our findings to suggest future directions for this task.},
booktitle = {Proceedings of the 5th International Workshop on Semantic Evaluation},
pages = {21–26},
numpages = {6},
location = {Los Angeles, California},
series = {SemEval '10}
} | 0 | 27 | 2022-03-02T23:29:22 |
A dataset for benchmarking keyphrase extraction and generation techniques from long document english scientific articles. For more details about the dataset please refer the original paper - [https://dl.acm.org/doi/10.5555/1859664.1859668](https://dl.acm.org/doi/10.5555/1859664.1859668)
Original source of the data - [https://github.com/boudinfl/semeval-2010-pre](https://github.com/boudinfl/semeval-2010-pre)
## Dataset Summary
The Semeval-2010 dataset was originally proposed by *Su Nam Kim et al* in the paper titled - [SemEval-2010 Task 5: Automatic Keyphrase Extraction from Scientific Articles](https://aclanthology.org/S10-1004.pdf) in the year 2010. The dataset consists of a set of 284 English scientific papers from the ACM Digital Library (conference and work-shop papers). The selected articles belong to the
following four 1998 ACM classifications: C2.4 (Distributed Systems), H3.3 (Information Search and Retrieval), I2.11 (Distributed Artificial Intelligence – Multiagent Systems) and J4 (Socialand Behavioral Sciences – Economics). Each paper has two sets of keyphrases annotated by readers and author. The original dataset was divided into trail, training and test splits, evenly distributed across four domains. The trial, training and test splits had 40, 144 and 100 articles respectively, and the trial split was a subset of training split. We provide test and train splits with 100 and 144 articles respectively.
The dataset shared over here categorizes the keyphrases into *extractive* and *abstractive*. **Extractive keyphrases** are those that could be found in the input text and the **abstractive keyphrases** are those that are not present in the input text. In order to get all the meta-data about the documents and keyphrases please refer to the [original source](https://github.com/boudinfl/semeval-2010-pre) from which the dataset was taken from. The main motivation behind making this dataset available in the form as presented over here is to make it easy for the researchers to programmatically download it and evaluate their models for the tasks of keyphrase extraction and generation. As keyphrase extraction by treating it as a sequence tagging task and using contextual language models has become popular - [Keyphrase extraction from scholarly articles as sequence labeling using contextualized embeddings](https://arxiv.org/pdf/1910.08840.pdf), we have also made the token tags available in the BIO tagging format.
## Dataset Structure
### Data Fields
- **id**: unique identifier of the document.
- **document**: Whitespace separated list of words in the document.
- **doc_bio_tags**: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| #datapoints |
|--|--|
| Test | 100 |
| Train | 144 |
Train
- Percentage of keyphrases that are named entities: 63.01% (named entities detected using scispacy - en-core-sci-lg model)
- Percentage of keyphrases that are noun phrases: 82.50% (noun phrases detected using spacy en-core-web-lg after removing determiners)
Test
- Percentage of keyphrases that are named entities: 62.06% (named entities detected using scispacy - en-core-sci-lg model)
- Percentage of keyphrases that are noun phrases: 78.36% (noun phrases detected using spacy after removing determiners)
## Usage
### Full Dataset
```python
from datasets import load_dataset
# get entire dataset
dataset = load_dataset("midas/semeval2010", "raw")
# sample from the train split
print("Sample from train dataset split")
test_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test dataset split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
Sample from train dataset split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['HITS', 'on', 'the', 'Web:', 'How', 'does', 'it', 'Compare?', 'Marc', 'Najork', 'Microsoft', 'Research', '1065', 'La', 'Avenida', 'Mountain', 'View,', 'CA,', 'USA', 'najork@microsoft.com', 'Hugo', 'Zaragoza', '∗', 'Yahoo!', 'Research', 'Barcelona', 'Ocata', '1', 'Barcelona', '08003,', 'Spain', 'hugoz@es.yahoo-inc.com', 'Michael', 'Taylor', 'Microsoft', 'Research', '7', 'J', 'J', 'Thompson', 'Ave', 'Cambridge', 'CB3', '0FB,', 'UK', 'mitaylor@microsoft.com', 'ABSTRACT', 'This', 'paper', 'describes', 'a', 'large-scale', 'evaluation', 'of', 'the', 'effectiveness', 'of', 'HITS', 'in', 'comparison', 'with', 'other', 'link-based', 'ranking', 'algorithms,', 'when', 'used', 'in', 'combination', 'with', 'a', 'state-ofthe-art', 'text', 'retrieval', 'algorithm', 'exploiting', 'anchor', 'text.', 'We', 'quantified', 'their', 'effectiveness', 'using', 'three', 'common', 'performance', 'measures:', 'the', 'mean', 'reciprocal', 'rank,', 'the', 'mean', 'average', 'precision,', 'and', 'the', 'normalized', 'discounted', 'cumulative', 'gain', 'measurements.', 'The', 'evaluation', 'is', 'based', 'on', 'two', 'large', 'data', 'sets:', 'a', 'breadth-first', 'search', 'crawl', 'of', '463', 'million', 'web', 'pages', 'containing', '17.6', 'billion', 'hyperlinks', 'and', 'referencing', '2.9', 'billion', 'distinct', 'URLs;', 'and', 'a', 'set', 'of', '28,043', 'queries', 'sampled', 'from', 'a', 'query', 'log,', 'each', 'query', 'having', 'on', 'average', '2,383', 'results,', 'about', '17', 'of', 'which', 'were', 'labeled', 'by', 'judges.', 'We', 'found', 'that', 'HITS', 'outperforms', 'PageRank,', 'but', 'is', 'about', 'as', 'effective', 'as', 'web-page', 'in-degree.', 'The', 'same', 'holds', 'true', 'when', 'any', 'of', 'the', 'link-based', 'features', 'are', 'combined', 'with', 'the', 'text', 'retrieval', 'algorithm.', 'Finally,', 'we', 'studied', 'the', 'relationship', 'between', 'query', 'specificity', 'and', 'the', 'effectiveness', 'of', 'selected', 'features,', 'and', 'found', 'that', 'link-based', 'features', 'perform', 'better', 'for', 'general', 'queries,', 'whereas', 'BM25F', 'performs', 'better', 'for', 'specific', 'queries.', 'Categories', 'and', 'Subject', 'Descriptors', 'H.3.3', '[Information', 'Search', 'and', 'Retrieval]:', 'Information', 'Storage', 'and', 'Retrieval-search', 'process,', 'selection', 'process', 'General', 'Terms', 'Algorithms,', 'Measurement,', 'Experimentation', '1.', 'INTRODUCTION', 'Link', 'graph', 'features', 'such', 'as', 'in-degree', 'and', 'PageRank', 'have', 'been', 'shown', 'to', 'significantly', 'improve', 'the', 'performance', 'of', 'text', 'retrieval', 'algorithms', 'on', 'the', 'web.', 'The', 'HITS', 'algorithm', 'is', 'also', 'believed', 'to', 'be', 'of', 'interest', 'for', 'web', 'search;', 'to', 'some', 'degree,', 'one', 'may', 'expect', 'HITS', 'to', 'be', 'more', 'informative', 'that', 'other', 'link-based', 'features', 'because', 'it', 'is', 'query-dependent:', 'it', 'tries', 'to', 'measure', 'the', 'interest', 'of', 'pages', 'with', 'respect', 'to', 'a', 'given', 'query.', 'However,', 'it', 'remains', 'unclear', 'today', 'whether', 'there', 'are', 'practical', 'benefits', 'of', 'HITS', 'over', 'other', 'link', 'graph', 'measures.', 'This', 'is', 'even', 'more', 'true', 'when', 'we', 'consider', 'that', 'modern', 'retrieval', 'algorithms', 'used', 'on', 'the', 'web', 'use', 'a', 'document', 'representation', 'which', 'incorporates', 'the', 'document"s', 'anchor', 'text,', 'i.e.', 'the', 'text', 'of', 'incoming', 'links.', 'This,', 'at', 'least', 'to', 'some', 'degree,', 'takes', 'the', 'link', 'graph', 'into', 'account,', 'in', 'a', 'query-dependent', 'manner.', 'Comparing', 'HITS', 'to', 'PageRank', 'or', 'in-degree', 'empirically', 'is', 'no', 'easy', 'task.', 'There', 'are', 'two', 'main', 'difficulties:', 'scale', 'and', 'relevance.', 'Scale', 'is', 'important', 'because', 'link-based', 'features', 'are', 'known', 'to', 'improve', 'in', 'quality', 'as', 'the', 'document', 'graph', 'grows.', 'If', 'we', 'carry', 'out', 'a', 'small', 'experiment,', 'our', 'conclusions', 'won"t', 'carry', 'over', 'to', 'large', 'graphs', 'such', 'as', 'the', 'web.', 'However,', 'computing', 'HITS', 'efficiently', 'on', 'a', 'graph', 'the', 'size', 'of', 'a', 'realistic', 'web', 'crawl', 'is', 'extraordinarily', 'difficult.', 'Relevance', 'is', 'also', 'crucial', 'because', 'we', 'cannot', 'measure', 'the', 'performance', 'of', 'a', 'feature', 'in', 'the', 'absence', 'of', 'human', 'judgments:', 'what', 'is', 'crucial', 'is', 'ranking', 'at', 'the', 'top', 'of', 'the', 'ten', 'or', 'so', 'documents', 'that', 'a', 'user', 'will', 'peruse.', 'To', 'our', 'knowledge,', 'this', 'paper', 'is', 'the', 'first', 'attempt', 'to', 'evaluate', 'HITS', 'at', 'a', 'large', 'scale', 'and', 'compare', 'it', 'to', 'other', 'link-based', 'features', 'with', 'respect', 'to', 'human', 'evaluated', 'judgment.', 'Our', 'results', 'confirm', 'many', 'of', 'the', 'intuitions', 'we', 'have', 'about', 'link-based', 'features', 'and', 'their', 'relationship', 'to', 'text', 'retrieval', 'methods', 'exploiting', 'anchor', 'text.', 'This', 'is', 'reassuring:', 'in', 'the', 'absence', 'of', 'a', 'theoretical', 'model', 'capable', 'of', 'tying', 'these', 'measures', 'with', 'relevance,', 'the', 'only', 'way', 'to', 'validate', 'our', 'intuitions', 'is', 'to', 'carry', 'out', 'realistic', 'experiments.', 'However,', 'we', 'were', 'quite', 'surprised', 'to', 'find', 'that', 'HITS,', 'a', 'query-dependent', 'feature,', 'is', 'about', 'as', 'effective', 'as', 'web', 'page', 'in-degree,', 'the', 'most', 'simpleminded', 'query-independent', 'link-based', 'feature.', 'This', 'continues', 'to', 'be', 'true', 'when', 'the', 'link-based', 'features', 'are', 'combined', 'with', 'a', 'text', 'retrieval', 'algorithm', 'exploiting', 'anchor', 'text.', 'The', 'remainder', 'of', 'this', 'paper', 'is', 'structured', 'as', 'follows:', 'Section', '2', 'surveys', 'related', 'work.', 'Section', '3', 'describes', 'the', 'data', 'sets', 'we', 'used', 'in', 'our', 'study.', 'Section', '4', 'reviews', 'the', 'performance', 'measures', 'we', 'used.', 'Sections', '5', 'and', '6', 'describe', 'the', 'PageRank', 'and', 'HITS', 'algorithms', 'in', 'more', 'detail,', 'and', 'sketch', 'the', 'computational', 'infrastructure', 'we', 'employed', 'to', 'carry', 'out', 'large', 'scale', 'experiments.', 'Section', '7', 'presents', 'the', 'results', 'of', 'our', 'evaluations,', 'and', 'Section', '8', 'offers', 'concluding', 'remarks.', '2.', 'RELATED', 'WORK', 'The', 'idea', 'of', 'using', 'hyperlink', 'analysis', 'for', 'ranking', 'web', 'search', 'results', 'arose', 'around', '1997,', 'and', 'manifested', 'itself', 'in', 'the', 'HITS', '[16,', '17]', 'and', 'PageRank', '[5,', '21]', 'algorithms.', 'The', 'popularity', 'of', 'these', 'two', 'algorithms', 'and', 'the', 'phenomenal', 'success', 'of', 'the', 'Google', 'search', 'engine,', 'which', 'uses', 'PageRank,', 'have', 'spawned', 'a', 'large', 'amount', 'of', 'subsequent', 'research.', 'There', 'are', 'numerous', 'attempts', 'at', 'improving', 'the', 'effectiveness', 'of', 'HITS', 'and', 'PageRank.', 'Query-dependent', 'link-based', 'ranking', 'algorithms', 'inspired', 'by', 'HITS', 'include', 'SALSA', '[19],', 'Randomized', 'HITS', '[20],', 'and', 'PHITS', '[7],', 'to', 'name', 'a', 'few.', 'Query-independent', 'link-based', 'ranking', 'algorithms', 'inspired', 'by', 'PageRank', 'include', 'TrafficRank', '[22],', 'BlockRank', '[14],', 'and', 'TrustRank', '[11],', 'and', 'many', 'others.', 'Another', 'line', 'of', 'research', 'is', 'concerned', 'with', 'analyzing', 'the', 'mathematical', 'properties', 'of', 'HITS', 'and', 'PageRank.', 'For', 'example,', 'Borodin', 'et', 'al.', '[3]', 'investigated', 'various', 'theoretical', 'properties', 'of', 'PageRank,', 'HITS,', 'SALSA,', 'and', 'PHITS,', 'including', 'their', 'similarity', 'and', 'stability,', 'while', 'Bianchini', 'et', 'al.', '[2]', 'studied', 'the', 'relationship', 'between', 'the', 'structure', 'of', 'the', 'web', 'graph', 'and', 'the', 'distribution', 'of', 'PageRank', 'scores,', 'and', 'Langville', 'and', 'Meyer', 'examined', 'basic', 'properties', 'of', 'PageRank', 'such', 'as', 'existence', 'and', 'uniqueness', 'of', 'an', 'eigenvector', 'and', 'convergence', 'of', 'power', 'iteration', '[18].', 'Given', 'the', 'attention', 'that', 'has', 'been', 'paid', 'to', 'improving', 'the', 'effectiveness', 'of', 'PageRank', 'and', 'HITS,', 'and', 'the', 'thorough', 'studies', 'of', 'the', 'mathematical', 'properties', 'of', 'these', 'algorithms,', 'it', 'is', 'somewhat', 'surprising', 'that', 'very', 'few', 'evaluations', 'of', 'their', 'effectiveness', 'have', 'been', 'published.', 'We', 'are', 'aware', 'of', 'two', 'studies', 'that', 'have', 'attempted', 'to', 'formally', 'evaluate', 'the', 'effectiveness', 'of', 'HITS', 'and', 'of', 'PageRank.', 'Amento', 'et', 'al.', '[1]', 'employed', 'quantitative', 'measures,', 'but', 'based', 'their', 'experiments', 'on', 'the', 'result', 'sets', 'of', 'just', '5', 'queries', 'and', 'the', 'web-graph', 'induced', 'by', 'topical', 'crawls', 'around', 'the', 'result', 'set', 'of', 'each', 'query.', 'A', 'more', 'recent', 'study', 'by', 'Borodin', 'et', 'al.', '[4]', 'is', 'based', 'on', '34', 'queries,', 'result', 'sets', 'of', '200', 'pages', 'per', 'query', 'obtained', 'from', 'Google,', 'and', 'a', 'neighborhood', 'graph', 'derived', 'by', 'retrieving', '50', 'in-links', 'per', 'result', 'from', 'Google.', 'By']
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'O', 'O', 'O', 'O']
Extractive/present Keyphrases: ['ranking', 'pagerank', 'mean reciprocal rank', 'mean average precision', 'query specificity', 'link graph', 'scale and relevance', 'hyperlink analysis', 'rank', 'bm25f', 'mrr', 'map', 'ndcg']
Abstractive/absent Keyphrases: ['normalized discounted cumulative gain measurement', 'breadth-first search crawl', 'feature selection', 'link-based feature', 'quantitative measure', 'crawled web page', 'hit']
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Sample from test dataset split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['Live', 'Data', 'Center', 'Migration', 'across', 'WANs:', 'A', 'Robust', 'Cooperative', 'Context', 'Aware', 'Approach', 'K.K.', 'Ramakrishnan,', 'Prashant', 'Shenoy', ',', 'Jacobus', 'Van', 'der', 'Merwe', 'AT&T', 'Labs-Research', '/', 'University', 'of', 'Massachusetts', 'ABSTRACT', 'A', 'significant', 'concern', 'for', 'Internet-based', 'service', 'providers', 'is', 'the', 'continued', 'operation', 'and', 'availability', 'of', 'services', 'in', 'the', 'face', 'of', 'outages,', 'whether', 'planned', 'or', 'unplanned.', 'In', 'this', 'paper', 'we', 'advocate', 'a', 'cooperative,', 'context-aware', 'approach', 'to', 'data', 'center', 'migration', 'across', 'WANs', 'to', 'deal', 'with', 'outages', 'in', 'a', 'non-disruptive', 'manner.', 'We', 'specifically', 'seek', 'to', 'achieve', 'high', 'availability', 'of', 'data', 'center', 'services', 'in', 'the', 'face', 'of', 'both', 'planned', 'and', 'unanticipated', 'outages', 'of', 'data', 'center', 'facilities.', 'We', 'make', 'use', 'of', 'server', 'virtualization', 'technologies', 'to', 'enable', 'the', 'replication', 'and', 'migration', 'of', 'server', 'functions.', 'We', 'propose', 'new', 'network', 'functions', 'to', 'enable', 'server', 'migration', 'and', 'replication', 'across', 'wide', 'area', 'networks', '(e.g.,', 'the', 'Internet),', 'and', 'finally', 'show', 'the', 'utility', 'of', 'intelligent', 'and', 'dynamic', 'storage', 'replication', 'technology', 'to', 'ensure', 'applications', 'have', 'access', 'to', 'data', 'in', 'the', 'face', 'of', 'outages', 'with', 'very', 'tight', 'recovery', 'point', 'objectives.', 'Categories', 'and', 'Subject', 'Descriptors', 'C.2.4', '[Computer-Communication', 'Networks]:', 'Distributed', 'Systems', 'General', 'Terms', 'Design,', 'Reliability', '1.', 'INTRODUCTION', 'A', 'significant', 'concern', 'for', 'Internet-based', 'service', 'providers', 'is', 'the', 'continued', 'operation', 'and', 'availability', 'of', 'services', 'in', 'the', 'face', 'of', 'outages,', 'whether', 'planned', 'or', 'unplanned.', 'These', 'concerns', 'are', 'exacerbated', 'by', 'the', 'increased', 'use', 'of', 'the', 'Internet', 'for', 'mission', 'critical', 'business', 'and', 'real-time', 'entertainment', 'applications.', 'A', 'relatively', 'minor', 'outage', 'can', 'disrupt', 'and', 'inconvenience', 'a', 'large', 'number', 'of', 'users.', 'Today', 'these', 'services', 'are', 'almost', 'exclusively', 'hosted', 'in', 'data', 'centers.', 'Recent', 'advances', 'in', 'server', 'virtualization', 'technologies', '[8,', '14,', '22]', 'allow', 'for', 'the', 'live', 'migration', 'of', 'services', 'within', 'a', 'local', 'area', 'network', '(LAN)', 'environment.', 'In', 'the', 'LAN', 'environment,', 'these', 'technologies', 'have', 'proven', 'to', 'be', 'a', 'very', 'effective', 'tool', 'to', 'enable', 'data', 'center', 'management', 'in', 'a', 'non-disruptive', 'fashion.', 'Not', 'only', 'can', 'it', 'support', 'planned', 'maintenance', 'events', '[8],', 'but', 'it', 'can', 'also', 'be', 'used', 'in', 'a', 'more', 'dynamic', 'fashion', 'to', 'automatically', 'balance', 'load', 'between', 'the', 'physical', 'servers', 'in', 'a', 'data', 'center', '[22].', 'When', 'using', 'these', 'technologies', 'in', 'a', 'LAN', 'environment,', 'services', 'execute', 'in', 'a', 'virtual', 'server,', 'and', 'the', 'migration', 'services', 'provided', 'by', 'the', 'underlying', 'virtualization', 'framework', 'allows', 'for', 'a', 'virtual', 'server', 'to', 'be', 'migrated', 'from', 'one', 'physical', 'server', 'to', 'another,', 'without', 'any', 'significant', 'downtime', 'for', 'the', 'service', 'or', 'application.', 'In', 'particular,', 'since', 'the', 'virtual', 'server', 'retains', 'the', 'same', 'network', 'address', 'as', 'before,', 'any', 'ongoing', 'network', 'level', 'interactions', 'are', 'not', 'disrupted.', 'Similarly,', 'in', 'a', 'LAN', 'environment,', 'storage', 'requirements', 'are', 'normally', 'met', 'via', 'either', 'network', 'attached', 'storage', '(NAS)', 'or', 'via', 'a', 'storage', 'area', 'network', '(SAN)', 'which', 'is', 'still', 'reachable', 'from', 'the', 'new', 'physical', 'server', 'location', 'to', 'allow', 'for', 'continued', 'storage', 'access.', 'Unfortunately', 'in', 'a', 'wide', 'area', 'environment', '(WAN),', 'live', 'server', 'migration', 'is', 'not', 'as', 'easily', 'achievable', 'for', 'two', 'reasons:', 'First,', 'live', 'migration', 'requires', 'the', 'virtual', 'server', 'to', 'maintain', 'the', 'same', 'network', 'address', 'so', 'that', 'from', 'a', 'network', 'connectivity', 'viewpoint', 'the', 'migrated', 'server', 'is', 'indistinguishable', 'from', 'the', 'original.', 'While', 'this', 'is', 'fairly', 'easily', 'achieved', 'in', 'a', 'shared', 'LAN', 'environment,', 'no', 'current', 'mechanisms', 'are', 'available', 'to', 'efficiently', 'achieve', 'the', 'same', 'feat', 'in', 'a', 'WAN', 'environment.', 'Second,', 'while', 'fairly', 'sophisticated', 'remote', 'replication', 'mechanisms', 'have', 'been', 'developed', 'in', 'the', 'context', 'of', 'disaster', 'recovery', '[20,', '7,', '11],', 'these', 'mechanisms', 'are', 'ill', 'suited', 'to', 'live', 'data', 'center', 'migration,', 'because', 'in', 'general', 'the', 'available', 'technologies', 'are', 'unaware', 'of', 'application/service', 'level', 'semantics.', 'In', 'this', 'paper', 'we', 'outline', 'a', 'design', 'for', 'live', 'service', 'migration', 'across', 'WANs.', 'Our', 'design', 'makes', 'use', 'of', 'existing', 'server', 'virtualization', 'technologies', 'and', 'propose', 'network', 'and', 'storage', 'mechanisms', 'to', 'facilitate', 'migration', 'across', 'a', 'WAN.', 'The', 'essence', 'of', 'our', 'approach', 'is', 'cooperative,', 'context', 'aware', 'migration,', 'where', 'a', 'migration', 'management', 'system', 'orchestrates', 'the', 'data', 'center', 'migration', 'across', 'all', 'three', 'subsystems', 'involved,', 'namely', 'the', 'server', 'platforms,', 'the', 'wide', 'area', 'network', 'and', 'the', 'disk', 'storage', 'system.', 'While', 'conceptually', 'similar', 'in', 'nature', 'to', 'the', 'LAN', 'based', 'work', 'described', 'above,', 'using', 'migration', 'technologies', 'across', 'a', 'wide', 'area', 'network', 'presents', 'unique', 'challenges', 'and', 'has', 'to', 'our', 'knowledge', 'not', 'been', 'achieved.', 'Our', 'main', 'contribution', 'is', 'the', 'design', 'of', 'a', 'framework', 'that', 'will', 'allow', 'the', 'migration', 'across', 'a', 'WAN', 'of', 'all', 'subsystems', 'involved', 'with', 'enabling', 'data', 'center', 'services.', 'We', 'describe', 'new', 'mechanisms', 'as', 'well', 'as', 'extensions', 'to', 'existing', 'technologies', 'to', 'enable', 'this', 'and', 'outline', 'the', 'cooperative,', 'context', 'aware', 'functionality', 'needed', 'across', 'the', 'different', 'subsystems', 'to', 'enable', 'this.', '262', '2.', 'LIVE', 'DATA', 'CENTER', 'MIGRATION', 'ACROSS', 'WANS', 'Three', 'essential', 'subsystems', 'are', 'involved', 'with', 'hosting', 'services', 'in', 'a', 'data', 'center:', 'First,', 'the', 'servers', 'host', 'the', 'application', 'or', 'service', 'logic.', 'Second,', 'services', 'are', 'normally', 'hosted', 'in', 'a', 'data', 'center', 'to', 'provide', 'shared', 'access', 'through', 'a', 'network,', 'either', 'the', 'Internet', 'or', 'virtual', 'private', 'networks', '(VPNs).', 'Finally,', 'most', 'applications', 'require', 'disk', 'storage', 'for', 'storing', 'data', 'and', 'the', 'amount', 'of', 'disk', 'space', 'and', 'the', 'frequency', 'of', 'access', 'varies', 'greatly', 'between', 'different', 'services/applications.', 'Disruptions,', 'failures,', 'or', 'in', 'general,', 'outages', 'of', 'any', 'kind', 'of', 'any', 'of', 'these', 'components', 'will', 'cause', 'service', 'disruption.', 'For', 'this', 'reason,', 'prior', 'work', 'and', 'current', 'practices', 'have', 'addressed', 'the', 'robustness', 'of', 'individual', 'components.', 'For', 'example,', 'data', 'centers', 'typically', 'have', 'multiple', 'network', 'connections', 'and', 'redundant', 'LAN', 'devices', 'to', 'ensure', 'redundancy', 'at', 'the', 'networking', 'level.', 'Similarly,', 'physical', 'servers', 'are', 'being', 'designed', 'with', 'redundant', 'hot-swappable', 'components', '(disks,', 'processor', 'blades,', 'power', 'supplies', 'etc).', 'Finally,', 'redundancy', 'at', 'the', 'storage', 'level', 'can', 'be', 'provided', 'through', 'sophisticated', 'data', 'mirroring', 'technologies.', 'The', 'focus', 'of', 'our', 'work,', 'however,', 'is', 'on', 'the', 'case', 'where', 'such', 'local', 'redundancy', 'mechanisms', 'are', 'not', 'sufficient.', 'Specifically,', 'we', 'are', 'interested', 'in', 'providing', 'service', 'availability', 'when', 'the', 'data', 'center', 'as', 'a', 'whole', 'becomes', 'unavailable,', 'for', 'example', 'because', 'of', 'data', 'center', 'wide', 'maintenance', 'operations,', 'or', 'because', 'of', 'catastrophic', 'events.', 'As', 'such,', 'our', 'basic', 'approach', 'is', 'to', 'migrate', 'services', 'between', 'data', 'centers', 'across', 'the', 'wide', 'are', 'network', '(WAN).', 'By', 'necessity,', 'moving', 'or', 'migrating', 'services', 'from', 'one', 'data', 'center', 'to', 'another', 'needs', 'to', 'consider', 'all', 'three', 'of', 'these', 'components.', 'Historically,', 'such', 'migration', 'has', 'been', 'disruptive', 'in', 'nature,', 'requiring', 'downtime', 'of', 'the', 'actual', 'services', 'involved,', 'or', 'requiring', 'heavy', 'weight', 'replication', 'techniques.', 'In', 'the', 'latter', 'case', 'concurrently', 'running', 'replicas', 'of', 'a', 'service', 'can', 'be', 'made', 'available', 'thus', 'allowing', 'a', 'subset', 'of', 'the', 'service', 'to', 'be', 'migrated', 'or', 'maintained', 'without', 'impacting', 'the', 'service']
Document BIO Tags: ['O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases: ['data center migration', 'wan', 'lan', 'virtual server', 'storage replication', 'synchronous replication', 'asynchronous replication', 'network support', 'storage', 'voip']
Abstractive/absent Keyphrases: ['internet-based service', 'voice-over-ip', 'database']
-----------
```
### Keyphrase Extraction
```python
from datasets import load_dataset
# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/semeval2010", "extraction")
print("Samples for Keyphrase Extraction")
# sample from the train split
print("Sample from train data split")
test_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")
```
### Keyphrase Generation
```python
# get the dataset only for keyphrase generation
dataset = load_dataset("midas/semeval2010", "generation")
print("Samples for Keyphrase Generation")
# sample from the train split
print("Sample from train data split")
test_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
## Citation Information
```
@inproceedings{10.5555/1859664.1859668,
author = {Kim, Su Nam and Medelyan, Olena and Kan, Min-Yen and Baldwin, Timothy},
title = {SemEval-2010 Task 5: Automatic Keyphrase Extraction from Scientific Articles},
year = {2010},
publisher = {Association for Computational Linguistics},
address = {USA},
abstract = {This paper describes Task 5 of the Workshop on Semantic Evaluation 2010 (SemEval-2010). Systems are to automatically assign keyphrases or keywords to given scientific articles. The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones. We present the overall ranking of the submitted systems and discuss our findings to suggest future directions for this task.},
booktitle = {Proceedings of the 5th International Workshop on Semantic Evaluation},
pages = {21–26},
numpages = {6},
location = {Los Angeles, California},
series = {SemEval '10}
}
```
## Contributions
Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset
| 38,057 | [
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persiannlp/parsinlu_query_paraphrasing | 2022-10-22T15:13:22.000Z | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|quora|google",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] | persiannlp | A Persian query paraphrasing task (paraphrase or not, given two questions).
The questions are partly mined using Google auto-complete, and partly translated from Quora paraphrasing dataset. | @article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
} | 0 | 27 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- fa
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|quora|google
task_categories:
- query-paraphrasing
task_ids:
- query-paraphrasing
---
# Dataset Card for PersiNLU (Query Paraphrasing)
## Table of Contents
- [Dataset Card for PersiNLU (Query Paraphrasing)](#dataset-card-for-persi_nlu_query_paraphrasing)
- [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:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** d.khashabi@gmail.com
### Dataset Summary
A Persian query paraphrasng task (deciding whether two questions are paraphrases of each other).
The questions are partially generated from Google auto-complete, and partially translated from the Quora paraphrasing dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```json
{
"q1": "اعمال حج تمتع از چه روزی شروع میشود؟",
"q2": "ویار از چه روزی شروع میشود؟",
"label": "0",
"category": "natural"
}
```
### Data Fields
- `q1`: the first question.
- `q2`: the second question.
- `category`: whether the questions are mined from Quora (`qqp`) or they're extracted from Google auto-complete (`natural`).
- `label`: `1` if the questions are paraphrases; `0` otherwise.
### Data Splits
The train/dev/test splits contains 1830/898/1916 samples.
## Dataset Creation
### Curation Rationale
For details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
| 4,474 | [
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0.02... |
qanastek/WMT-16-PubMed | 2022-10-22T15:20:12.000Z | [
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:bg",
"language:cs",
"language:da",
"language:de",
"lan... | qanastek | WMT'16 Biomedical Translation Task - PubMed parallel datasets
http://www.statmt.org/wmt16/biomedical-translation-task.html | @inproceedings{bojar-etal-2016-findings,
title = Findings of the 2016 Conference on Machine Translation,
author = {
Bojar, Ondrej and
Chatterjee, Rajen and
Federmann, Christian and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Koehn, Philipp and
Logacheva, Varvara and
Monz, Christof and
Negri, Matteo and
Neveol, Aurelie and
Neves, Mariana and
Popel, Martin and
Post, Matt and
Rubino, Raphael and
Scarton, Carolina and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin and
Zampieri, Marcos
},
booktitle = Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers,
month = aug,
year = 2016,
address = Berlin, Germany,
publisher = Association for Computational Linguistics,
url = https://aclanthology.org/W16-2301,
doi = 10.18653/v1/W16-2301,
pages = 131--198,
} | 2 | 27 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
multilinguality:
- multilingual
pretty_name: WMT-16-PubMed
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- translation
- machine-translation
task_ids:
- translation
- machine-translation
---
# WMT-16-PubMed : European parallel translation corpus from the European Medicines Agency
## Table of Contents
- [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://www.statmt.org/wmt16/biomedical-translation-task.html
- **Repository:** https://github.com/biomedical-translation-corpora/corpora
- **Paper:** https://aclanthology.org/W16-2301/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Yanis Labrak](mailto:yanis.labrak@univ-avignon.fr)
### Dataset Summary
`WMT-16-PubMed` is a parallel corpus for neural machine translation collected and aligned for ACL 2016 during the [WMT'16 Shared Task: Biomedical Translation Task](https://www.statmt.org/wmt16/biomedical-translation-task.html).
### Supported Tasks and Leaderboards
`translation`: The dataset can be used to train a model for translation.
### Languages
The corpora consists of a pair of source and target sentences for all 4 different languages :
**List of languages :** `English (en)`,`Spanish (es)`,`French (fr)`,`Portuguese (pt)`.
## Load the dataset with HuggingFace
```python
from datasets import load_dataset
dataset = load_dataset("qanastek/WMT-16-PubMed", split='train', download_mode='force_redownload')
print(dataset)
print(dataset[0])
```
## Dataset Structure
### Data Instances
```plain
lang doc_id workshop publisher source_text target_text
0 en-fr 26839447 WMT'16 Biomedical Translation Task - PubMed pubmed Global Health: Where Do Physiotherapy and Reha... La place des cheveux et des poils dans les rit...
1 en-fr 26837117 WMT'16 Biomedical Translation Task - PubMed pubmed Carabin Les Carabins
2 en-fr 26837116 WMT'16 Biomedical Translation Task - PubMed pubmed In Process Citation Le laboratoire d'Anatomie, Biomécanique et Org...
3 en-fr 26837115 WMT'16 Biomedical Translation Task - PubMed pubmed Comment on the misappropriation of bibliograph... Du détournement des références bibliographique...
4 en-fr 26837114 WMT'16 Biomedical Translation Task - PubMed pubmed Anti-aging medicine, a science-based, essentia... La médecine anti-âge, une médecine scientifiqu...
... ... ... ... ... ... ...
973972 en-pt 20274330 WMT'16 Biomedical Translation Task - PubMed pubmed Myocardial infarction, diagnosis and treatment Infarto do miocárdio; diagnóstico e tratamento
973973 en-pt 20274329 WMT'16 Biomedical Translation Task - PubMed pubmed The health areas politics A política dos campos de saúde
973974 en-pt 20274328 WMT'16 Biomedical Translation Task - PubMed pubmed The role in tissue edema and liquid exchanges ... O papel dos tecidos nos edemas e nas trocas lí...
973975 en-pt 20274327 WMT'16 Biomedical Translation Task - PubMed pubmed About suppuration of the wound after thoracopl... Sôbre as supurações da ferida operatória após ...
973976 en-pt 20274326 WMT'16 Biomedical Translation Task - PubMed pubmed Experimental study of liver lesions in the tre... Estudo experimental das lesões hepáticas no tr...
```
### Data Fields
**lang** : The pair of source and target language of type `String`.
**source_text** : The source text of type `String`.
**target_text** : The target text of type `String`.
### Data Splits
`en-es` : 285,584
`en-fr` : 614,093
`en-pt` : 74,300
## Dataset Creation
### Curation Rationale
For details, check the corresponding [pages](https://www.statmt.org/wmt16/biomedical-translation-task.html).
### Source Data
<!-- #### Initial Data Collection and Normalization
ddd -->
#### Who are the source language producers?
The shared task as been organized by :
* Antonio Jimeno Yepes (IBM Research Australia)
* Aurélie Névéol (LIMSI, CNRS, France)
* Mariana Neves (Hasso-Plattner Institute, Germany)
* Karin Verspoor (University of Melbourne, Australia)
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Considerations for Using the Data
### Other Known Limitations
The nature of the task introduce a variability in the quality of the target translations.
## Additional Information
### Dataset Curators
__Hugging Face WMT-16-PubMed__: Labrak Yanis, Dufour Richard (Not affiliated with the original corpus)
__WMT'16 Shared Task: Biomedical Translation Task__:
* Antonio Jimeno Yepes (IBM Research Australia)
* Aurélie Névéol (LIMSI, CNRS, France)
* Mariana Neves (Hasso-Plattner Institute, Germany)
* Karin Verspoor (University of Melbourne, Australia)
<!-- ### Licensing Information
ddd -->
### Citation Information
Please cite the following paper when using this dataset.
```latex
@inproceedings{bojar-etal-2016-findings,
title = Findings of the 2016 Conference on Machine Translation,
author = {
Bojar, Ondrej and
Chatterjee, Rajen and
Federmann, Christian and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Koehn, Philipp and
Logacheva, Varvara and
Monz, Christof and
Negri, Matteo and
Neveol, Aurelie and
Neves, Mariana and
Popel, Martin and
Post, Matt and
Rubino, Raphael and
Scarton, Carolina and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin and
Zampieri, Marcos,
},
booktitle = Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers,
month = aug,
year = 2016,
address = Berlin, Germany,
publisher = Association for Computational Linguistics,
url = https://aclanthology.org/W16-2301,
doi = 10.18653/v1/W16-2301,
pages = 131--198,
}
```
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warwickai/financial_phrasebank_mirror | 2022-01-17T00:19:04.000Z | [
"region:us"
] | warwickai | null | null | 0 | 27 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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Filippo/osdg_cd | 2023-10-08T09:57:13.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"region:us"
] | Filippo | The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs). | @dataset{osdg_2023_8397907,
author = {OSDG and
UNDP IICPSD SDG AI Lab and
PPMI},
title = {OSDG Community Dataset (OSDG-CD)},
month = oct,
year = 2023,
note = {{This CSV file uses UTF-8 character encoding. For
easy access on MS Excel, open the file using Data
→ From Text/CSV. Please split CSV data into
different columns by using a TAB delimiter.}},
publisher = {Zenodo},
version = {2023.10},
doi = {10.5281/zenodo.8397907},
url = {https://doi.org/10.5281/zenodo.8397907}
} | 1 | 27 | 2022-04-30T21:54:04 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: OSDG Community Dataset (OSDG-CD)
dataset_info:
config_name: main_config
features:
- name: doi
dtype: string
- name: text_id
dtype: string
- name: text
dtype: string
- name: sdg
dtype: uint16
- name: label
dtype:
class_label:
names:
'0': SDG 1
'1': SDG 2
'2': SDG 3
'3': SDG 4
'4': SDG 5
'5': SDG 6
'6': SDG 7
'7': SDG 8
'8': SDG 9
'9': SDG 10
'10': SDG 11
'11': SDG 12
'12': SDG 13
'13': SDG 14
'14': SDG 15
'15': SDG 16
- name: labels_negative
dtype: uint16
- name: labels_positive
dtype: uint16
- name: agreement
dtype: float32
splits:
- name: train
num_bytes: 30151244
num_examples: 42355
download_size: 29770590
dataset_size: 30151244
---
# Dataset Card for OSDG-CD
## 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:** [OSDG-CD homepage](https://zenodo.org/record/8397907)
### Dataset Summary
The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs).
> NOTES
>
> * There are currently no examples for SDGs 16 and 17. See [this GitHub issue](https://github.com/osdg-ai/osdg-data/issues/3).
> * As of July 2023, there areexamples also for SDG 16.
### Supported Tasks and Leaderboards
TBD
### Languages
The language of the dataset is English.
## Dataset Structure
### Data Instances
For each instance, there is a string for the text, a string for the SDG, and an integer for the label.
```
{'text': 'Each section states the economic principle, reviews international good practice and discusses the situation in Brazil.',
'label': 5}
```
The average token count for the premises and hypotheses are given below:
| Feature | Mean Token Count |
| ---------- | ---------------- |
| Premise | 14.1 |
| Hypothesis | 8.3 |
### Data Fields
- `doi`: Digital Object Identifier of the original document
- `text_id`: unique text identifier
- `text`: text excerpt from the document
- `sdg`: the SDG the text is validated against
- `label`: an integer from `0` to `17` which corresponds to the `sdg` field
- `labels_negative`: the number of volunteers who rejected the suggested SDG label
- `labels_positive`: the number of volunteers who accepted the suggested SDG label
- `agreement`: agreement score based on the formula
### Data Splits
The OSDG-CD dataset has 1 splits: _train_.
| Dataset Split | Number of Instances in Split |
| ------------- |----------------------------- |
| Train | 32,327 |
## Dataset Creation
### Curation Rationale
The [The OSDG Community Dataset (OSDG-CD)](https://zenodo.org/record/8397907) was developed as a benchmark for ...
with the goal of producing a dataset large enough to train models using neural methodologies.
### Source Data
#### Initial Data Collection and Normalization
TBD
#### Who are the source language producers?
TBD
### Annotations
#### Annotation process
TBD
#### Who are the annotators?
TBD
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers.
## Considerations for Using the Data
### Social Impact of Dataset
TBD
## Additional Information
TBD
### Dataset Curators
TBD
### Licensing Information
The OSDG Community Dataset (OSDG-CD) is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@dataset{osdg_2023_8397907,
author = {OSDG and
UNDP IICPSD SDG AI Lab and
PPMI},
title = {OSDG Community Dataset (OSDG-CD)},
month = oct,
year = 2023,
note = {{This CSV file uses UTF-8 character encoding. For
easy access on MS Excel, open the file using Data
→ From Text/CSV. Please split CSV data into
different columns by using a TAB delimiter.}},
publisher = {Zenodo},
version = {2023.10},
doi = {10.5281/zenodo.8397907},
url = {https://doi.org/10.5281/zenodo.8397907}
}
```
### Contributions
TBD
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bigscience-data/roots_zh_du_reader | 2022-12-12T11:17:03.000Z | [
"language:zh",
"license:apache-2.0",
"region:us"
] | bigscience-data | null | null | 4 | 27 | 2022-05-18T09:19:50 | ---
language: zh
license: apache-2.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_zh_du_reader
# DuReader
- Dataset uid: `du_reader`
### Description
DuReader is a large-scale real-world Chinese dataset for Machine Reading Comprehension (MRC) and Question Answering (QA).
### Homepage
https://ai.baidu.com/broad/introduction?dataset=dureader
### Licensing
- copyright - all rights reserved
- apache-2.0: Apache License 2.0
Copyright 2017 Baidu.com, Inc. All Rights Reserved
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
### Speaker Locations
- China
### Sizes
- 0.1771 % of total
- 0.6194 % of zh
### BigScience processing steps
#### Filters applied to: zh
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
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rjac/all-the-news-2-1-Component-one | 2022-07-28T21:01:39.000Z | [
"annotations_creators:Andrew Thompson",
"annotations_creators:components.one",
"language:en",
"region:us"
] | rjac | null | null | 0 | 27 | 2022-06-19T22:35:47 | ---
annotations_creators:
- Andrew Thompson
- components.one
language:
- en
---
# 2.7 million news articles and essays
## Table of Contents
- [Dataset Description](#dataset-description)
## Dataset Description
2.7 million news articles and essays from 27 American publications. Includes date, title, publication, article text, publication name, year, month, and URL (for some). Articles mostly span from 2016 to early 2020.
- Type: CSV
- Size: 3.4 GB compressed, 8.8 GB uncompressed
- Created by: Andrew Thompson
- Date added: 4/3/2020
- Date modified: 4/3/2020
- source: [Component one Datasets 2.7 Millions](https://components.one/datasets/all-the-news-2-news-articles-dataset)
- Date of Download and processed: 19/6/2022
- Header was modified with the respective columns
- Row number 2,324,812 was removed | 814 | [
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jakartaresearch/indonews | 2022-08-07T04:27:54.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"news",
"news-classifcation",
"indon... | jakartaresearch | This dataset is built as a playground for beginner to make a use case for creating sentiment analysis model. | null | 2 | 27 | 2022-08-07T04:03:02 | ---
annotations_creators:
- found
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Indonews
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- news
- news-classifcation
- indonesia
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Indonesian News Categorization
## 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
Indonews: Multiclass News Categorization scrapped popular news portals in Indonesia.
### 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 [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. | 2,871 | [
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-0.060028076171875,
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0.03793... |
imodels/diabetes-readmission | 2022-08-14T15:38:59.000Z | [
"task_categories:tabular-classification",
"size_categories:100K<n<1M",
"interpretability",
"fairness",
"medicine",
"region:us"
] | imodels | null | null | 1 | 27 | 2022-08-14T15:19:27 | ---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: diabetes-readmission
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- interpretability
- fairness
- medicine
task_categories:
- tabular-classification
task_ids: []
---
Port of the diabetes-readmission dataset from UCI (link [here](https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008)). See details there and use carefully.
Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb).
The target is the binary outcome `readmitted`.
### Sample usage
Load the data:
```
from datasets import load_dataset
dataset = load_dataset("imodels/diabetes-readmission")
df = pd.DataFrame(dataset['train'])
X = df.drop(columns=['readmitted'])
y = df['readmitted'].values
```
Fit a model:
```
import imodels
import numpy as np
m = imodels.FIGSClassifier(max_rules=5)
m.fit(X, y)
print(m)
```
Evaluate:
```
df_test = pd.DataFrame(dataset['test'])
X_test = df.drop(columns=['readmitted'])
y_test = df['readmitted'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))
``` | 1,271 | [
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tomekkorbak/detoxify-pile-chunk3-300000-350000 | 2022-10-03T18:38:38.000Z | [
"region:us"
] | tomekkorbak | null | null | 0 | 27 | 2022-10-03T18:38:31 | Entry not found | 15 | [
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0.037... |
daspartho/stable-diffusion-prompts | 2023-08-25T14:33:31.000Z | [
"language:en",
"region:us"
] | daspartho | null | null | 15 | 27 | 2022-11-17T17:25:56 | ---
language: en
dataset_info:
features:
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 284636288
num_examples: 1819808
download_size: 101931289
dataset_size: 284636288
---
Subset dataset of [diffusiondb](https://huggingface.co/datasets/poloclub/diffusiondb) consisting of just unique prompts.
Created this subset dataset for the [Prompt Extend](https://github.com/daspartho/prompt-extend) project. | 439 | [
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-0.01152801513671875,
0.0064... |
stacked-summaries/stacked-samsum-1024 | 2023-05-29T00:30:18.000Z | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"source_datasets:samsum",
"language:en",
"license:apache-2.0",
"stacked summaries",
"region:us"
] | stacked-summaries | null | null | 4 | 27 | 2022-12-04T12:22:28 | ---
license: apache-2.0
source_datasets:
- samsum
task_categories:
- summarization
language:
- en
tags:
- stacked summaries
pretty_name: Stacked Samsum - 1024
size_categories:
- 10K<n<100K
---
# stacked samsum 1024
Created with the `stacked-booksum` repo version v0.25. It contains:
1. Original Dataset: copy of the base dataset
2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:
- Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.
- Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.
3. Special Token: The dataset utilizes the `[NEXT_CONCEPT]` token to indicate a new topic **within** the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.
## stats
## dataset details
Default (train):
```python
[2022-12-04 13:19:32] INFO:root:{'num_columns': 4,
'num_rows': 14732,
'num_unique_target': 14730,
'num_unique_text': 14265,
'summary - average chars': 110.13,
'summary - average tokens': 28.693727939180015,
'text input - average chars': 511.22,
'text input - average tokens': 148.88759163725223}
```
stacked (train)
```python
[2022-12-05 00:49:04] INFO:root:stacked 14730 rows, 2 rows were ineligible
[2022-12-05 00:49:04] INFO:root:dropped 20 duplicate rows, 29442 rows remain
[2022-12-05 00:49:04] INFO:root:shuffling output with seed 182
[2022-12-05 00:49:04] INFO:root:STACKED - basic stats - train
[2022-12-05 00:49:04] INFO:root:{'num_columns': 5,
'num_rows': 29442,
'num_unique_chapters': 28975,
'num_unique_summaries': 29441,
'summary - average chars': 452.8,
'summary - average tokens': 106.46820868147545,
'text input - average chars': 1814.09,
'text input - average tokens': 528.665579783982}
``` | 2,008 | [
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... |
Whispering-GPT/lex-fridman-podcast | 2023-07-13T14:47:17.000Z | [
"task_categories:automatic-speech-recognition",
"language:en",
"whisper",
"whispering",
"medium",
"region:us"
] | Whispering-GPT | null | null | 3 | 27 | 2022-12-12T09:09:49 | ---
language: en
task_categories:
- automatic-speech-recognition
dataset_info:
features:
- name: id
dtype: string
- name: channel
dtype: string
- name: channel_id
dtype: string
- name: title
dtype: string
- name: categories
sequence: string
- name: tags
sequence: string
- name: description
dtype: string
- name: text
dtype: string
- name: segments
list:
- name: start
dtype: float64
- name: end
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 102530760
num_examples: 346
download_size: 57264732
dataset_size: 102530760
tags:
- whisper
- whispering
- medium
---
# Dataset Card for "lexFridmanPodcast-transcript-audio"
## Table of Contents
- [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)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Whispering-GPT](https://github.com/matallanas/whisper_gpt_pipeline)
- **Repository:** [whisper_gpt_pipeline](https://github.com/matallanas/whisper_gpt_pipeline)
- **Paper:** [whisper](https://cdn.openai.com/papers/whisper.pdf) and [gpt](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
- **Point of Contact:** [Whispering-GPT organization](https://huggingface.co/Whispering-GPT)
### Dataset Summary
This dataset is created by applying whisper to the videos of the Youtube channel [Lex Fridman Podcast](https://www.youtube.com/watch?v=FhfmGM6hswI&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4&ab_channel=LexFridman). The dataset was created a medium size whisper model.
### Languages
- **Language**: English
## Dataset Structure
The dataset contains all the transcripts plus the audio of the different videos of Lex Fridman Podcast.
### Data Fields
The dataset is composed by:
- **id**: Id of the youtube video.
- **channel**: Name of the channel.
- **channel\_id**: Id of the youtube channel.
- **title**: Title given to the video.
- **categories**: Category of the video.
- **description**: Description added by the author.
- **text**: Whole transcript of the video.
- **segments**: A list with the time and transcription of the video.
- **start**: When started the trancription.
- **end**: When the transcription ends.
- **text**: The text of the transcription.
### Data Splits
- Train split.
## Dataset Creation
### Source Data
The transcriptions are from the videos of [Lex Fridman Podcast](https://www.youtube.com/watch?v=FhfmGM6hswI&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4&ab_channel=LexFridman)
### Contributions
Thanks to [Whispering-GPT](https://huggingface.co/Whispering-GPT) organization for adding this dataset. | 3,474 | [
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0.0274658203125,
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-0.08062744140625,
-0.07354736328125,
-0.053985595703125,
-... |
keremberke/pothole-segmentation | 2023-01-15T18:38:49.000Z | [
"task_categories:image-segmentation",
"roboflow",
"roboflow2huggingface",
"Construction",
"Self Driving",
"Transportation",
"Damage Risk",
"region:us"
] | keremberke | null | @misc{ pothole-detection-irkz9_dataset,
title = { Pothole Detection Dataset },
type = { Open Source Dataset },
author = { IMACS Pothole Detection },
howpublished = { \\url{ https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 } },
url = { https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jan },
note = { visited on 2023-01-15 },
} | 2 | 27 | 2023-01-15T18:38:37 | ---
task_categories:
- image-segmentation
tags:
- roboflow
- roboflow2huggingface
- Construction
- Self Driving
- Transportation
- Damage Risk
---
<div align="center">
<img width="640" alt="keremberke/pothole-segmentation" src="https://huggingface.co/datasets/keremberke/pothole-segmentation/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['pothole']
```
### Number of Images
```json
{'test': 5, 'train': 80, 'valid': 5}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/pothole-segmentation", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9/dataset/4](https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9/dataset/4?ref=roboflow2huggingface)
### Citation
```
@misc{ pothole-detection-irkz9_dataset,
title = { Pothole Detection Dataset },
type = { Open Source Dataset },
author = { IMACS Pothole Detection },
howpublished = { \\url{ https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 } },
url = { https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jan },
note = { visited on 2023-01-15 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on January 15, 2023 at 6:38 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 90 images.
Pothole are annotated in COCO format.
The following pre-processing was applied to each image:
No image augmentation techniques were applied.
| 2,375 | [
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-0.00318145751953125,
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0.00... |
ruanchaves/b2w-reviews01 | 2023-01-20T18:22:37.000Z | [
"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_datase... | ruanchaves | 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 | @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}
} | 9 | 27 | 2023-01-19T07:55:43 | ---
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. | 3,628 | [
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0.016... |
Cohere/miracl-ja-queries-22-12 | 2023-02-06T11:57:00.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:ja",
"license:apache-2.0",
"region:us"
] | Cohere | null | null | 1 | 27 | 2023-01-31T09:20:40 | ---
annotations_creators:
- expert-generated
language:
- ja
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# MIRACL (ja) embedded with cohere.ai `multilingual-22-12` encoder
We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
The query embeddings can be found in [Cohere/miracl-ja-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ja-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-ja-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ja-corpus-22-12).
For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
Dataset info:
> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Loading the dataset
In [miracl-ja-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ja-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-ja-corpus-22-12", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-ja-corpus-22-12", split="train", streaming=True)
for doc in docs:
docid = doc['docid']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
Have a look at [miracl-ja-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ja-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
To search in the documents, you must use **dot-product**.
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB
from datasets import load_dataset
import torch
#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-ja-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-ja-queries-22-12", split="dev")
# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```
You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```
## Performance
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |
| 6,103 | [
[
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0.... |
theblackcat102/instruction_translations | 2023-03-05T06:36:37.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"ChatGPT",
"SimpleAI",
"Detection",
"doi:10.57967/hf/0423",
"region:us"
] | theblackcat102 | Translation of Instruction dataset | \ | 5 | 27 | 2023-03-04T00:51:47 | ---
task_categories:
- text-generation
language:
- en
tags:
- ChatGPT
- SimpleAI
- Detection
size_categories:
- 10K<n<100K
license: mit
---
# Translations for Instruction dataset
Translations were generated by [M2M 12B](https://huggingface.co/facebook/m2m100-12B-avg-5-ckpt) and the output generations were limited at 512 tokens due to VRAM limit (40G).
| 361 | [
[
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climatebert/tcfd_recommendations | 2023-04-19T07:08:06.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | climatebert | null | null | 1 | 27 | 2023-04-11T13:10:27 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: TCFDRecommendations
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': metrics
'2': strategy
'3': risk
'4': governance
splits:
- name: train
num_bytes: 638487
num_examples: 1300
- name: test
num_bytes: 222330
num_examples: 400
download_size: 492631
dataset_size: 860817
---
# Dataset Card for tcfd_recommendations
## Dataset Description
- **Homepage:** [climatebert.ai](https://climatebert.ai)
- **Repository:**
- **Paper:** [papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435)
- **Leaderboard:**
- **Point of Contact:** [Nicolas Webersinke](mailto:nicolas.webersinke@fau.de)
### Dataset Summary
We introduce an expert-annotated dataset for classifying the TCFD recommendation categories ([fsb-tcfd.org](https://www.fsb-tcfd.org)) of paragraphs in corporate disclosures.
### Supported Tasks and Leaderboards
The dataset supports a multiclass classification task of paragraphs into the four TCFD recommendation categories (governance, strategy, risk management, metrics and targets) and the non-climate-related class.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
```
{
'text': '− Scope 3: Optional scope that includes indirect emissions associated with the goods and services supply chain produced outside the organization. Included are emissions from the transport of products from our logistics centres to stores (downstream) performed by external logistics operators (air, land and sea transport) as well as the emissions associated with electricity consumption in franchise stores.',
'label': 1
}
```
### Data Fields
- text: a paragraph extracted from corporate annual reports and sustainability reports
- label: the label (0 -> none (i.e., not climate-related), 1 -> metrics, 2 -> strategy, 3 -> risk, 4 -> governance)
### Data Splits
The dataset is split into:
- train: 1,300
- test: 400
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Our dataset contains paragraphs extracted from financial disclosures by firms. We collect text from corporate annual reports and sustainability reports.
For more information regarding our sample selection, please refer to the Appendix of our paper (see [citation](#citation-information)).
#### Who are the source language producers?
Mainly large listed companies.
### Annotations
#### Annotation process
For more information on our annotation process and annotation guidelines, please refer to the Appendix of our paper (see [citation](#citation-information)).
#### Who are the annotators?
The authors and students at Universität Zürich and Friedrich-Alexander-Universität Erlangen-Nürnberg with majors in finance and sustainable finance.
### Personal and Sensitive Information
Since our text sources contain public information, no personal and sensitive information should be included.
## 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
- Julia Anna Bingler
- Mathias Kraus
- Markus Leippold
- Nicolas Webersinke
### Licensing Information
This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit [creativecommons.org/licenses/by-nc-sa/4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
If you are interested in commercial use of the dataset, please contact [markus.leippold@bf.uzh.ch](mailto:markus.leippold@bf.uzh.ch).
### Citation Information
```bibtex
@techreport{bingler2023cheaptalk,
title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk},
author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
type={Working paper},
institution={Available at SSRN 3998435},
year={2023}
}
```
### Contributions
Thanks to [@webersni](https://github.com/webersni) for adding this dataset. | 4,635 | [
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kuleshov/alpaca-data | 2023-04-13T23:05:16.000Z | [
"region:us"
] | kuleshov | null | null | 0 | 27 | 2023-04-13T23:04:15 | Entry not found | 15 | [
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NicolaiSivesind/human-vs-machine | 2023-05-11T13:03:54.000Z | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc",
"chatgpt",
"gpt",
"research abstracts",
"wikipedia introductions",
"region:us"
] | NicolaiSivesind | This dataset contains labeled data with human-produced and machine-generated texts based on various
domains: Wikipedia introductions and academic articles. | @InProceedings{human-vs-machine:dataset,
title = {Human vs Machine dataset collection},
author={Nicolai Sivesind & Andreas Bentzen Winje},
year={2023}
} | 5 | 27 | 2023-04-14T12:24:29 | ---
license: cc
task_categories:
- text-classification
pretty_name: Human vs Machine - Labled text segments produced by humans and LLMs
size_categories:
- 100K<n<1M
language:
- en
tags:
- chatgpt
- gpt
- research abstracts
- wikipedia introductions
---
# Human-vs-Machine
This is a dataset collection created in relation to a bachelor thesis written by Nicolai Thorer Sivesind and Andreas Bentzen Winje. It contains human-produced and machine-generated text samples from two domains: Wikipedia introducions and Scientific research abstracts.
Each of the two domains are already exisitng datasets reformatted for text-classification:
[GPT-wiki-intros:](https://huggingface.co/datasets/aadityaubhat/GPT-wiki-intro)
+ Generated samples are produced using the GPT-3 model, _text-curie-001_
+ Target content set by title of real wikipedia introduction and a starter sentence.
+ Target word count of 200 words each.
+ Contains 150k data points of each class.
+ Created by Aaditya Bhat
[ChatGPT-Research-Abstracts](https://huggingface.co/datasets/NicolaiSivesind/ChatGPT-Research-Abstracts):
+ Generated samples are produced using the GPT-3.5 model, _GPT-3.5-turbo-0301_ (Snapshot of the model used in ChatGPT 1st of March, 2023).
+ Target content set by title of real abstract.
+ Target word count equal to the human-produced abstract
+ Contains 10k data points of each class.
+ Created by Nicolai Thorer Sivesind
### Credits
+ [GPT-wiki-intro](https://huggingface.co/datasets/aadityaubhat/GPT-wiki-intro), by Aaditya Bhat
### Citation
Please use the following citation:
```
@misc {sivesind_2023,
author = { {Nicolai Thorer Sivesind}, {Andreas Bentzen Winje}},
title = { Human-vs-Machine },
year = 2023,
publisher = { Hugging Face }
}
```
More information about the dataset will be added once the thesis is finished (end of may 2023). | 1,883 | [
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CCOM/vi_backbones | 2023-10-12T15:17:20.000Z | [
"task_categories:image-classification",
"task_categories:feature-extraction",
"size_categories:n<1K",
"language:en",
"license:mit",
"code",
"region:us"
] | CCOM | null | null | 1 | 27 | 2023-04-27T17:42:10 | ---
license: mit
task_categories:
- image-classification
- feature-extraction
language:
- en
tags:
- code
pretty_name: Vi-Backbones
size_categories:
- n<1K
---
# Dataset Card for "CCOM/vi_backbones"
## Usage
```
from datasets import load_dataset
backbones_on_in1k_v1 = load_dataset("CCOM/vi_backbones", split="IMAGENET1K_V1")
backbones_on_in1k_v2 = load_dataset("CCOM/vi_backbones", split="IMAGENET1K_V2")
for weights in backbones_on_in1k_v1:
print(weights)
for weights in backbones_on_in1k_v2:
print(weights)
```
## Maintenance
```
git clone git@hf.co:datasets/CCOM/vi_backbones
```
## Reference
```
https://pytorch.org/vision/main/_modules
``` | 659 | [
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whu9/multi_xsciene_postprocess | 2023-06-02T23:24:12.000Z | [
"region:us"
] | whu9 | null | null | 0 | 27 | 2023-06-02T23:24:08 | ---
dataset_info:
features:
- name: source
dtype: string
- name: summary
dtype: string
- name: source_num_tokens
dtype: int64
- name: summary_num_tokens
dtype: int64
splits:
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num_bytes: 165217234
num_examples: 30351
- name: test
num_bytes: 27275286
num_examples: 5090
- name: validation
num_bytes: 27471336
num_examples: 5061
download_size: 101726627
dataset_size: 219963856
---
# Dataset Card for "multi_xsciene_postprocess"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 633 | [
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d0rj/synthetic-instruct-gptj-pairwise-ru | 2023-06-05T14:01:42.000Z | [
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:Dahoas/synthetic-instruct-gptj-pairwise",
"language:ru",
"license:mit",
"human-feedback",
"ChatGPT",
"reward",
"region:us"
] | d0rj | null | null | 1 | 27 | 2023-06-05T13:56:21 | ---
language_creators:
- translated
language:
- ru
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- Dahoas/synthetic-instruct-gptj-pairwise
license: mit
tags:
- human-feedback
- ChatGPT
- reward
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 59332942.0
num_examples: 33143
download_size: 28805604
dataset_size: 59332942.0
---
# Dataset Card for "synthetic-instruct-gptj-pairwise-ru"
This is translated version of [Dahoas/synthetic-instruct-gptj-pairwise dataset](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) into Russian. | 722 | [
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Cubpaw/voxelgym_5c_42x42_100000 | 2023-06-11T21:06:32.000Z | [
"region:us"
] | Cubpaw | null | null | 0 | 27 | 2023-06-11T21:05:00 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
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dtype: image
- name: path_label
dtype: image
- name: path_rgb_label
dtype: image
splits:
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num_bytes: 73637120.0
num_examples: 80000
- name: validation
num_bytes: 18495820.0
num_examples: 20000
download_size: 70037769
dataset_size: 92132940.0
---
# Dataset Card for "voxelgym_5c_42x42_100000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 595 | [
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KaiLv/UDR_DART | 2023-06-21T12:36:09.000Z | [
"region:us"
] | KaiLv | null | null | 0 | 27 | 2023-06-21T12:35:55 | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: question
dtype: string
- name: target
dtype: string
- name: references
dtype: string
- name: len_question
dtype: int64
- name: len_target
dtype: int64
splits:
- name: train
num_bytes: 8360993
num_examples: 30123
- name: validation
num_bytes: 1657570
num_examples: 2718
- name: test
num_bytes: 2532366
num_examples: 4159
- name: debug
num_bytes: 1396342
num_examples: 5000
download_size: 4740566
dataset_size: 13947271
---
# Dataset Card for "UDR_DART"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 729 | [
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KaiLv/UDR_Yelp | 2023-06-21T12:52:51.000Z | [
"region:us"
] | KaiLv | null | null | 0 | 27 | 2023-06-21T12:52:34 | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: label
dtype: int64
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 22696875
num_examples: 30000
- name: test
num_bytes: 2261177
num_examples: 3000
- name: debug
num_bytes: 3745338
num_examples: 5000
download_size: 18407788
dataset_size: 28703390
---
# Dataset Card for "UDR_Yelp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 545 | [
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causal-lm/finance | 2023-06-25T02:49:02.000Z | [
"region:us"
] | causal-lm | null | null | 6 | 27 | 2023-06-25T02:48:28 | Entry not found | 15 | [
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FreedomIntelligence/alpaca-gpt4-arabic | 2023-08-06T08:07:51.000Z | [
"license:apache-2.0",
"region:us"
] | FreedomIntelligence | null | null | 2 | 27 | 2023-06-26T08:17:14 | ---
license: apache-2.0
---
The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT). | 152 | [
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-0.012969970703125,
... |
ILSUM/ILSUM-1.0 | 2023-07-26T13:05:11.000Z | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"language:hi",
"language:gu",
"language:en",
"region:us"
] | ILSUM | null | null | 0 | 27 | 2023-07-26T10:19:54 | ---
task_categories:
- summarization
language:
- hi
- gu
- en
configs:
- config_name: Hindi
data_files:
- split: train
path: Hindi/train.csv
- split: test
path: Hindi/test.csv
- split: validation
path: Hindi/val.csv
default: true
- config_name: Gujarati
data_files:
- split: train
path: Gujarati/train.csv
- split: test
path: Gujarati/test.csv
- split: validation
path: Gujarati/val.csv
- config_name: English
data_files:
- split: train
path: English/train.csv
- split: test
path: English/test.csv
- split: validation
path: English/val.csv
config_names:
- English
- Hindi
- Gujarati
size_categories:
- 1K<n<10K
- 10K<n<100K
---
# Dataset Card for "ILSUM-1.0"
### Dataset Summary
Automatic text summarization for Indian languages has received surprisingly little attention from the NLP research community. While large scale datasets exist for a number of languages like English, Chinese, French, German, Spanish, etc. no such datasets exist for any Indian languages. Most existing datasets are either not public, or are too small to be useful. Through this shared task we aim to bridge the existing gap by creating reusable corpora for Indian Language Summarization. In the first edition we cover two major indian languages Hindi and Gujarati, which have over 350 million and over 50 million speakers respectively. Apart from this we also include Indian English, a widely regonized dialect which can be substantially different from English spoken elsewhere.
The dataset for this task is built using articles and headline pairs from several leading newspapers of the country. We provide ~10,000 news articles for each language. The task is to generate a meaningful fixed length summary, either extractive or abstractive, for each article. While several previous works in other languages use news artciles - headlines pair, the current dataset poses a unique challenge of code-mixing and script mixing. It is very common for news articles to borrow phrases from english, even if the article itself is written in an Indian Language.
Examples like these are a common occurence both in the headlines as well as in the articles.
~~~
- "IND vs SA, 5મી T20 તસવીરોમાં: વરસાદે વિલન બની મજા બગાડી" (India vs SA, 5th T20 in pictures: rain spoils the match)
- "LIC के IPO में पैसा लगाने वालों का टूटा दिल, आई एक और नुकसानदेह खबर" (Investors of LIC IPO left broken hearted, yet another bad news).
~~~
### Languages
- Hindi
- Gujarati
- English
### Data Fields
~~~
- id: Unique id of each datapoint
- Article: Entire News article
- Headline: Headline of News Article
- Summary: Summary of News Article
~~~
### Data Splits
Data for all three languages is divided into three splits train, validation and test.
### Load dataset using hf-dataset class
```python
from datasets import load_dataset
dataset = load_dataset("ILSUM/ILSUM-1.0", "Hindi")
# you can use any of the following config names as a second argument:
# "English", "Hindi", "Gujarati"
```
### Citation Information
If you are using the dataset or the models please cite the following paper
~~~
@article{satapara2022findings,
title={Findings of the first shared task on indian language summarization (ilsum): Approaches, challenges and the path ahead},
author={Satapara, Shrey and Modha, Bhavan and Modha, Sandip and Mehta, Parth},
journal={Working Notes of FIRE},
pages={9--13},
year={2022}
}
~~~
### Contributions
- Bhavan Modha, University Of Texas at Dallas, USA
- Shrey Satapara, Indian Institute Of Technology, Hyderabad, India
- Sandip Modha, LDRP-ITR, Gandhinagar, India
- Parth Mehta, Parmonic, USA
<!--## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Supported Tasks and Leaderboards
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
[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]
| 4,619 | [
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amitysolution/Pantip_QA_200000_20220220 | 2023-08-09T03:27:57.000Z | [
"region:us"
] | amitysolution | null | null | 0 | 27 | 2023-08-09T03:24:29 | Entry not found | 15 | [
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0.037... |
SUFE-AIFLM-Lab/FinEval | 2023-08-22T01:27:07.000Z | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:zh",
"license:cc-by-nc-sa-4.0",
"arxiv:2308.09975",
"region:us"
] | SUFE-AIFLM-Lab | null | null | 6 | 27 | 2023-08-13T07:34:20 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- multiple-choice
- question-answering
language:
- zh
pretty_name: FinEval
size_categories:
- 1K<n<10K
viewer: false
---
<p><h1> The FinEval Dataset </h1></p>
<a name="dataset-announcement"></a>
FinEval is a collection of high-quality multiple-choice questions covering various domains such as finance, economics, accounting, and certifications. It consists of 4,661 questions spanning across 34 distinct academic subjects. To ensure a comprehensive assessment of model performance, FinEval employs various methods including zero-shot, few-shot, answer-only, and chain-of-thought prompts. Evaluating state-of-the-art large language models in both Chinese and English on FinEval reveals that only GPT-4 achieves an accuracy of 60% across different prompt settings, highlighting substantial growth potential of large language models in financial domain knowledge. Our work provides a more comprehensive benchmark for evaluating financial knowledge, utilizing simulated exam data and encompassing a wide range of large language model assessments.
Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy.
# Language
The language of the data is Chinese.
# Performance Leaderboard
We divide the evaluation into Answer Only and Chain of Thought. For examples of prompts for both methods, please refer to zero-shot for Answer Only, few-shot for Answer Only, and Chain of Thought.
Below is the average accuracy(%) on the test split. We report the average accuracy over the subjects within each category. "Average" column indicates the average accuracy over all the subjects. Notably, we only report the results from each model under the best setting, which is determined by the highest average accuracy achieved among four settings (i.e., zero- and few-shot learning with and without CoT):
| Model | Size | Finance | Economy | Accounting | Certificate | Average |
|------------------------|---------|:-------:|:-------:|:----------:|:-----------:|:-------:|
| GPT-4 | unknown | 71.0 | 74.5 | 59.3 | 70.4 | 68.6 |
| ChatGPT | 175B | 59.3 | 61.6 | 45.2 | 55.1 | 55.0 |
| Qwen-7B | 7B | 54.5 | 54.4 | 50.3 | 55.8 | 53.8 |
| Qwen-Chat-7B | 7B | 51.5 | 52.1 | 44.5 | 53.6 | 50.5 |
| Baichuan-13B-Base | 13B | 52.6 | 50.2 | 43.4 | 53.5 | 50.1 |
| Baichuan-13B-Chat | 13B | 51.6 | 51.1 | 41.7 | 52.8 | 49.4 |
| ChatGLM2-6B | 6B | 46.5 | 46.4 | 44.5 | 51.5 | 47.4 |
| InternLM-7B | 7B | 49.0 | 49.2 | 40.5 | 49.4 | 47.1 |
| InternLM-Chat-7B | 7B | 48.4 | 49.1 | 40.8 | 49.5 | 47.0 |
| LLaMA-2-Chat-70B | 70B | 47.1 | 46.7 | 41.5 | 45.7 | 45.2 |
| Falcon-40B | 40B | 45.4 | 43.2 | 35.8 | 44.8 | 42.4 |
| Baichuan-7B | 7B | 44.9 | 41.5 | 34.9 | 45.6 | 42.0 |
| LLaMA-2-Chat-13B | 13B | 41.6 | 38.4 | 34.1 | 42.1 | 39.3 |
| Ziya-LLaMA-13B-v1 | 13B | 43.3 | 36.9 | 34.3 | 41.2 | 39.3 |
| Bloomz-7b1-mt | 7B | 41.4 | 42.1 | 32.5 | 39.7 | 38.8 |
| LLaMA-2-13B | 13B | 39.5 | 38.6 | 31.6 | 39.6 | 37.4 |
| ChatGLM-6B | 6B | 38.8 | 36.2 | 33.8 | 39.1 | 37.2 |
| Chinese-Llama-2-7B | 7B | 37.8 | 37.8 | 31.4 | 36.7 | 35.9 |
| Chinese-Alpaca-Plus-7B | 7B | 30.5 | 33.4 | 32.7 | 38.5 | 34.0 |
| moss-moon-003-sft | 16B | 35.6 | 34.3 | 28.7 | 35.6 | 33.7 |
| LLaMA-2-Chat-7B | 7B | 35.6 | 31.8 | 31.9 | 34.0 | 33.5 |
| LLaMA-2-7B | 7B | 34.9 | 36.4 | 31.4 | 31.6 | 33.4 |
| AquilaChat-7B | 7B | 34.2 | 31.3 | 29.8 | 36.2 | 33.1 |
| moss-moon-003-base | 16B | 32.2 | 33.1 | 29.2 | 30.7 | 31.2 |
| Aquila-7B | 7B | 27.1 | 31.6 | 32.4 | 33.6 | 31.2 |
| LLaMA-13B | 13B | 33.1 | 29.7 | 27.2 | 33.6 | 31.1 |
| Falcon-7B | 7B | 28.5 | 28.2 | 27.5 | 27.4 | 27.9 |
# Load the data
```python
from datasets import load_dataset
dataset=load_dataset(r"SUFE-AIFLM-Lab/FinEval",name="finance")
```
Please cite our paper if you use our dataset.
```
@misc{2308.09975,
Author = {Liwen Zhang and Weige Cai and Zhaowei Liu and Zhi Yang and Wei Dai and Yujie Liao and Qianru Qin and Yifei Li and Xingyu Liu and Zhiqiang Liu and Zhoufan Zhu and Anbo Wu and Xin Guo and Yun Chen},
Title = {FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models},
Year = {2023},
Eprint = {arXiv:2308.09975},
}
``` | 5,585 | [
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pianoroll/maestro-do-storage | 2023-08-13T08:27:40.000Z | [
"region:us"
] | pianoroll | null | null | 0 | 27 | 2023-08-13T08:09:34 | ---
dataset_info:
features:
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dtype: string
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dtype: string
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dtype: string
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- name: split
dtype: string
splits:
- name: train
num_bytes: 419735
num_examples: 1276
download_size: 89454
dataset_size: 419735
---
# Dataset Card for "maestro-do-storage"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 548 | [
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grammarly/coedit | 2023-10-21T01:49:43.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"arxiv:2305.09857",
"region:us"
] | grammarly | null | null | 12 | 27 | 2023-08-15T00:01:51 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: coedit
size_categories:
- 10K<n<100K
---
# Dataset Card for CoEdIT: Text Editing via Instruction Tuning
## Paper: [CoEdIT: Text Editing by Task-Specific Instruction Tuning](https://arxiv.org/abs/2305.09857)
## Authors: Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang
## Project Repo: [https://github.com/vipulraheja/coedit](https://github.com/vipulraheja/coedit)
## Dataset Summary
This is the dataset that was used to train the CoEdIT text editing models. Full details of the dataset can be found in our paper.
# Dataset Structure
The dataset is in JSON format.
## Data Instances
```
{
'_id': 1,
'task': "gec",
'src': "Improve the grammaticality: As the number of people grows, the need of habitable environment is unquestionably essential.",
'tgt': "As the number of people grows, the need for a habitable environment is unquestionably increasing."
}
```
## Data Fields
* `_id`:
* `task`: Text editing task for this instance
* `src`: input text (formatted as `instruction: input_text`)
* `tgt`: output text
## Considerations for Using the Data
Please note that this dataset contains 69k instances (as opposed to the 82k instances we used in the paper). This is because this public release includes only the instances that were acquired and curated from publicly available datasets. Specifically, it is missing roughly 13k instances in training and 1.5k instances in validation data from Simplification and Formality Transfer tasks due to licensing restrictions.
# Citation
```
@article{raheja2023coedit,
title={CoEdIT: Text Editing by Task-Specific Instruction Tuning},
author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang},
year={2023},
eprint={2305.09857},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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Healthy13/Text2SQL | 2023-08-31T08:59:06.000Z | [
"region:us"
] | Healthy13 | null | null | 8 | 27 | 2023-08-31T08:53:12 | Entry not found | 15 | [
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BadreddineHug/LayoutLMv3_dataset_5 | 2023-09-04T13:36:22.000Z | [
"region:us"
] | BadreddineHug | null | null | 0 | 27 | 2023-09-04T13:36:12 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
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class_label:
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'0': O
'1': NumFa
'2': Fourniss
'3': DateFa
'4': TotalHT
'5': TVA
'6': TotalTTc
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sequence: string
splits:
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num_examples: 77
- name: test
num_bytes: 5236905.56701031
num_examples: 20
download_size: 20802412
dataset_size: 25398992.0
---
# Dataset Card for "LayoutLMv3_dataset_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 905 | [
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hdeldar/Persian-Text-llama2-1k | 2023-09-17T14:53:05.000Z | [
"region:us"
] | hdeldar | null | null | 0 | 27 | 2023-09-17T14:47:31 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1830325
num_examples: 1000
download_size: 1841325
dataset_size: 1830325
dataset_name: json
configs:
- config_name: default
data_files:
- split: train
path: data/data-*
---
# Persian-Text-QA: Lazy Llama 2 Formatting
This is a subset (1k samples) of the [`SeyedAli/Persian-Text-QA`](https://huggingface.co/datasets/SeyedAli/Persian-Text-QA) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing).
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.
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johannes-garstenauer/embeddings_from_distilbert_class_heaps_and_eval1perc | 2023-10-03T10:31:49.000Z | [
"region:us"
] | johannes-garstenauer | null | null | 0 | 27 | 2023-10-03T10:26:52 | ---
dataset_info:
features:
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sequence: float32
splits:
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num_bytes: 4263758393
num_examples: 2691
download_size: 4185738962
dataset_size: 4263758393
---
# Dataset Card for "embeddings_from_distilbert_class_heaps_and_eval1perc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 668 | [
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librarian-bots/dataset-abstracts | 2023-10-04T16:57:41.000Z | [
"size_categories:n<1K",
"language:en",
"region:us"
] | librarian-bots | null | null | 0 | 27 | 2023-10-04T16:41:47 | ---
language:
- en
size_categories:
- n<1K
configs:
- config_name: abstracts
data_files:
- split: train
path: abstracts/train-*
- split: test
path: abstracts/test-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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features:
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dtype: string
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download_size: 104180
dataset_size: 96806
---
# Dataset Card for "dataset-abstracts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 2,508 | [
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minh21/COVID-QA-testset-biencoder-data-75_25 | 2023-10-06T07:38:26.000Z | [
"region:us"
] | minh21 | null | null | 0 | 27 | 2023-10-06T07:38:23 | ---
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---
# Dataset Card for "COVID-QA-testset-biencoder-data-75_25"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 656 | [
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Sajjad-Sh33/train_ds | 2023-10-07T10:01:02.000Z | [
"region:us"
] | Sajjad-Sh33 | null | null | 0 | 27 | 2023-10-07T06:38:39 | ---
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---
# Dataset Card for "train_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 486 | [
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johannes-garstenauer/embeddings_from_distilbert_class_heaps_and_eval_part1 | 2023-10-07T17:27:39.000Z | [
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] | johannes-garstenauer | null | null | 0 | 27 | 2023-10-07T17:25:48 | ---
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# Dataset Card for "embeddings_from_distilbert_class_heaps_and_eval_part1"
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arifzanko/test_chat_summarization | 2023-10-10T01:19:22.000Z | [
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ariesta/forensic-datasets | 2023-10-09T21:28:13.000Z | [
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bh8648/reports-kor-43 | 2023-10-13T11:12:59.000Z | [
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] | bh8648 | null | null | 0 | 27 | 2023-10-13T11:12:53 | ---
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---
# Dataset Card for "reports-kor-43"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 522 | [
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Sharka/CIVQA_easyocr_encode_train_extra | 2023-10-14T20:51:17.000Z | [
"region:us"
] | Sharka | null | null | 0 | 27 | 2023-10-14T20:48:08 | ---
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---
# Dataset Card for "CIVQA_easyocr_encode_train_extra"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 818 | [
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vgoldberg/6k-longform-summ | 2023-10-16T23:33:47.000Z | [
"region:us"
] | vgoldberg | null | null | 0 | 27 | 2023-10-16T23:33:39 | ---
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---
# Dataset Card for "6k-longform-summ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 500 | [
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zmusaddique/Pathfinders_instruction_30k | 2023-10-17T15:27:38.000Z | [
"region:us"
] | zmusaddique | null | null | 0 | 27 | 2023-10-17T14:44:48 | ---
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---
# Dataset Card for "Pathfinders_instruction_30k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 458 | [
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jay401521/datasets | 2023-10-21T09:26:11.000Z | [
"region:us"
] | jay401521 | null | null | 0 | 27 | 2023-10-20T10:35:13 | ---
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---
# Dataset Card for "datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 482 | [
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tianyang/repobench_ablation_8k | 2023-10-25T02:25:45.000Z | [
"region:us"
] | tianyang | null | null | 0 | 27 | 2023-10-25T02:21:48 | ---
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---
# Dataset Card for "repobench_ablation_8k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,169 | [
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19kmunz/iot-23-preprocessed-minimumcolumns | 2023-10-27T16:28:27.000Z | [
"region:us"
] | 19kmunz | null | null | 0 | 27 | 2023-10-27T16:26:59 | ---
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---
# Dataset Card for "iot-23-preprocessed-minimumcolumns"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 684 | [
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thanhduycao/soict_private_test_v2 | 2023-10-28T06:52:08.000Z | [
"region:us"
] | thanhduycao | null | null | 0 | 27 | 2023-10-28T06:51:47 | ---
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# Dataset Card for "soict_private_test_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 605 | [
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dajor85570/invoices-and-receipts_ocr_v1 | 2023-10-28T13:33:00.000Z | [
"region:us"
] | dajor85570 | null | null | 0 | 27 | 2023-10-28T13:26:27 | ---
configs:
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# Dataset Card for "invoices-and-receipts_ocr_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 782 | [
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peixian/equity_evaluation_corpus | 2022-10-20T23:35:15.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"gender-classification",
"region:us"
] | peixian | Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems and resources. Further, there is a lack of benchmark datasets for examining inappropriate biases in system predictions. Here, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We used the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 ‘Affect in Tweets’. We found that several of the systems showed statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available, and encourage its use to evaluate biases in sentiment and other NLP tasks. | @article{DBLP:journals/corr/abs-1805-04508,
author = {Svetlana Kiritchenko and
Saif M. Mohammad},
title = {Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems},
journal = {CoRR},
volume = {abs/1805.04508},
year = {2018},
url = {http://arxiv.org/abs/1805.04508},
archivePrefix = {arXiv},
eprint = {1805.04508},
timestamp = {Mon, 13 Aug 2018 16:47:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1805-04508.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 3 | 26 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
tags:
- gender-classification
---
# Dataset Card for equity-evaluation-corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems and resources. Further, there is a lack of benchmark datasets for examining inappropriate biases in system predictions. Here, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We used the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 Affect in Tweets. We found that several of the systems showed statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available, and encourage its use to evaluate biases in sentiment and other NLP tasks.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
- `sentence`: a `string` feature.
- `template`: a `string` feature.
- `person`: a `string` feature.
- `race`: a `string` feature.
- `emotion`: a `string` feature.
- `emotion word`: a `string` feature.
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
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vocab-transformers/wiki-en-passages-20210101 | 2022-02-24T17:09:32.000Z | [
"region:us"
] | vocab-transformers | null | null | 0 | 26 | 2022-03-02T23:29:22 | # wiki-en-passages-20210101
This is a processed dump of the English Wikipedia from 2021-01-01. Each page has been splitted into paragraphs as they appear in the text. Lists, tables and headlines had been removed. In total it has 38,080,804 passages.
Further, each article contain meta-data on the number of languages this article exists in and on the number of views this article received over a 1 year period.
The articles are sorted from most popular (most languages available, most views) to least popular. | 516 | [
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... |
sasha/wino_bias_prompt2 | 2022-06-23T12:57:52.000Z | [
"region:us"
] | sasha | null | null | 0 | 26 | 2022-06-23T12:57:00 | Entry not found | 15 | [
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0.0379... |
tomekkorbak/detoxify-pile-chunk3-350000-400000 | 2022-10-03T18:43:48.000Z | [
"region:us"
] | tomekkorbak | null | null | 0 | 26 | 2022-10-03T18:43:39 | Entry not found | 15 | [
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0.03790... |
tomekkorbak/detoxify-pile-chunk3-400000-450000 | 2022-10-03T18:51:21.000Z | [
"region:us"
] | tomekkorbak | null | null | 0 | 26 | 2022-10-03T18:51:13 | Entry not found | 15 | [
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tomekkorbak/detoxify-pile-chunk3-450000-500000 | 2022-10-03T19:48:41.000Z | [
"region:us"
] | tomekkorbak | null | null | 0 | 26 | 2022-10-03T19:48:33 | Entry not found | 15 | [
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0.03790... |
mrmoor/cyber-threat-intelligence-splited | 2022-10-25T08:29:32.000Z | [
"region:us"
] | mrmoor | null | null | 1 | 26 | 2022-10-25T07:30:48 | Entry not found | 15 | [
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0.03790... |
bigbio/lll | 2022-12-22T15:44:52.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | The LLL05 challenge task is to learn rules to extract protein/gene interactions from biology abstracts from the Medline
bibliography database. The goal of the challenge is to test the ability of the participating IE systems to identify the
interactions and the gene/proteins that interact. The participants will test their IE patterns on a test set with the
aim of extracting the correct agent and target.The challenge focuses on information extraction of gene interactions in
Bacillus subtilis. Extracting gene interaction is the most popular event IE task in biology. Bacillus subtilis (Bs) is
a model bacterium and many papers have been published on direct gene interactions involved in sporulation. The gene
interactions are generally mentioned in the abstract and the full text of the paper is not needed. Extracting gene
interaction means, extracting the agent (proteins) and the target (genes) of all couples of genic interactions from
sentences. | @article{article,
author = {Nédellec, C.},
year = {2005},
month = {01},
pages = {},
title = {Learning Language in Logic - Genic Interaction Extraction Challenge},
journal = {Proceedings of the Learning Language in Logic 2005 Workshop at the International Conference on Machine Learning}
} | 2 | 26 | 2022-11-13T22:09:11 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: LLL05
homepage: http://genome.jouy.inra.fr/texte/LLLchallenge
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
---
# Dataset Card for LLL05
## Dataset Description
- **Homepage:** http://genome.jouy.inra.fr/texte/LLLchallenge
- **Pubmed:** True
- **Public:** True
- **Tasks:** RE
The LLL05 challenge task is to learn rules to extract protein/gene interactions from biology abstracts from the Medline
bibliography database. The goal of the challenge is to test the ability of the participating IE systems to identify the
interactions and the gene/proteins that interact. The participants will test their IE patterns on a test set with the
aim of extracting the correct agent and target.The challenge focuses on information extraction of gene interactions in
Bacillus subtilis. Extracting gene interaction is the most popular event IE task in biology. Bacillus subtilis (Bs) is
a model bacterium and many papers have been published on direct gene interactions involved in sporulation. The gene
interactions are generally mentioned in the abstract and the full text of the paper is not needed. Extracting gene
interaction means, extracting the agent (proteins) and the target (genes) of all couples of genic interactions from
sentences.
## Citation Information
```
@article{article,
author = {Nédellec, C.},
year = {2005},
month = {01},
pages = {},
title = {Learning Language in Logic - Genic Interaction Extraction Challenge},
journal = {Proceedings of the Learning Language in Logic 2005 Workshop at the International Conference on Machine Learning}
}
```
| 1,771 | [
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0.0057830810546875,
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-0.04510498046875,
0.04733... |
bigbio/meddialog | 2022-12-22T15:45:13.000Z | [
"multilinguality:multilingual",
"language:en",
"language:zh",
"license:unknown",
"arxiv:2004.03329",
"region:us"
] | bigbio | The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com.
All copyrights of the data belong to healthcaremagic.com and icliniq.com. | @article{DBLP:journals/corr/abs-2004-03329,
author = {Shu Chen and
Zeqian Ju and
Xiangyu Dong and
Hongchao Fang and
Sicheng Wang and
Yue Yang and
Jiaqi Zeng and
Ruisi Zhang and
Ruoyu Zhang and
Meng Zhou and
Penghui Zhu and
Pengtao Xie},
title = {MedDialog: {A} Large-scale Medical Dialogue Dataset},
journal = {CoRR},
volume = {abs/2004.03329},
year = {2020},
url = {https://arxiv.org/abs/2004.03329},
eprinttype = {arXiv},
eprint = {2004.03329},
biburl = {https://dblp.org/rec/journals/corr/abs-2004-03329.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 4 | 26 | 2022-11-13T22:09:25 |
---
language:
- en
- zh
bigbio_language:
- English
- Chinese
license: unknown
multilinguality: multilingual
bigbio_license_shortname: UNKNOWN
pretty_name: MedDialog
homepage: https://github.com/UCSD-AI4H/Medical-Dialogue-System
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- TEXT_CLASSIFICATION
---
# Dataset Card for MedDialog
## Dataset Description
- **Homepage:** https://github.com/UCSD-AI4H/Medical-Dialogue-System
- **Pubmed:** False
- **Public:** True
- **Tasks:** TXTCLASS
The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com.
All copyrights of the data belong to healthcaremagic.com and icliniq.com.
## Citation Information
```
@article{DBLP:journals/corr/abs-2004-03329,
author = {Shu Chen and
Zeqian Ju and
Xiangyu Dong and
Hongchao Fang and
Sicheng Wang and
Yue Yang and
Jiaqi Zeng and
Ruisi Zhang and
Ruoyu Zhang and
Meng Zhou and
Penghui Zhu and
Pengtao Xie},
title = {MedDialog: {A} Large-scale Medical Dialogue Dataset},
journal = {CoRR},
volume = {abs/2004.03329},
year = {2020},
url = {https://arxiv.org/abs/2004.03329},
eprinttype = {arXiv},
eprint = {2004.03329},
biburl = {https://dblp.org/rec/journals/corr/abs-2004-03329.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
| 1,669 | [
[
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0.038116455078125,
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-0.0272979736328125,
0.012451171875,
0.039764404296875,
-0.057708740234375,
-0.053466796875,
-0.0263519287109375,
... |
bigbio/n2c2_2018_track1 | 2022-12-22T15:45:59.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | Track 1 of the 2018 National NLP Clinical Challenges shared tasks focused
on identifying which patients in a corpus of longitudinal medical records
meet and do not meet identified selection criteria.
This shared task aimed to determine whether NLP systems could be trained to identify if patients met or did not meet
a set of selection criteria taken from real clinical trials. The selected criteria required measurement detection (
“Any HbA1c value between 6.5 and 9.5%”), inference (“Use of aspirin to prevent myocardial infarction”),
temporal reasoning (“Diagnosis of ketoacidosis in the past year”), and expert judgment to assess (“Major
diabetes-related complication”). For the corpus, we used the dataset of American English, longitudinal clinical
narratives from the 2014 i2b2/UTHealth shared task 4.
The final selected 13 selection criteria are as follows:
1. DRUG-ABUSE: Drug abuse, current or past
2. ALCOHOL-ABUSE: Current alcohol use over weekly recommended limits
3. ENGLISH: Patient must speak English
4. MAKES-DECISIONS: Patient must make their own medical decisions
5. ABDOMINAL: History of intra-abdominal surgery, small or large intestine
resection, or small bowel obstruction.
6. MAJOR-DIABETES: Major diabetes-related complication. For the purposes of
this annotation, we define “major complication” (as opposed to “minor complication”)
as any of the following that are a result of (or strongly correlated with) uncontrolled diabetes:
a. Amputation
b. Kidney damage
c. Skin conditions
d. Retinopathy
e. nephropathy
f. neuropathy
7. ADVANCED-CAD: Advanced cardiovascular disease (CAD).
For the purposes of this annotation, we define “advanced” as having 2 or more of the following:
a. Taking 2 or more medications to treat CAD
b. History of myocardial infarction (MI)
c. Currently experiencing angina
d. Ischemia, past or present
8. MI-6MOS: MI in the past 6 months
9. KETO-1YR: Diagnosis of ketoacidosis in the past year
10. DIETSUPP-2MOS: Taken a dietary supplement (excluding vitamin D) in the past 2 months
11. ASP-FOR-MI: Use of aspirin to prevent MI
12. HBA1C: Any hemoglobin A1c (HbA1c) value between 6.5% and 9.5%
13. CREATININE: Serum creatinine > upper limit of normal
The training consists of 202 patient records with document-level annotations, 10 records
with textual spans indicating annotator’s evidence for their annotations while test set contains 86.
Note:
* The inter-annotator average agreement is 84.9%
* Whereabouts of 10 records with textual spans indicating annotator’s evidence are unknown.
However, author did a simple script based validation to check if any of the tags contained any text
in any of the training set and they do not, which confirms that atleast train and test do not
have any evidence tagged alongside corresponding tags. | @article{DBLP:journals/jamia/StubbsFSHU19,
author = {
Amber Stubbs and
Michele Filannino and
Ergin Soysal and
Samuel Henry and
Ozlem Uzuner
},
title = {Cohort selection for clinical trials: n2c2 2018 shared task track 1},
journal = {J. Am. Medical Informatics Assoc.},
volume = {26},
number = {11},
pages = {1163--1171},
year = {2019},
url = {https://doi.org/10.1093/jamia/ocz163},
doi = {10.1093/jamia/ocz163},
timestamp = {Mon, 15 Jun 2020 16:56:11 +0200},
biburl = {https://dblp.org/rec/journals/jamia/StubbsFSHU19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 1 | 26 | 2022-11-13T22:10:45 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: DUA
pretty_name: n2c2 2018 Selection Criteria
homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
bigbio_pubmed: False
bigbio_public: False
bigbio_tasks:
- TEXT_CLASSIFICATION
---
# Dataset Card for n2c2 2018 Selection Criteria
## Dataset Description
- **Homepage:** https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
- **Pubmed:** False
- **Public:** False
- **Tasks:** TXTCLASS
Track 1 of the 2018 National NLP Clinical Challenges shared tasks focused
on identifying which patients in a corpus of longitudinal medical records
meet and do not meet identified selection criteria.
This shared task aimed to determine whether NLP systems could be trained to identify if patients met or did not meet
a set of selection criteria taken from real clinical trials. The selected criteria required measurement detection (
“Any HbA1c value between 6.5 and 9.5%”), inference (“Use of aspirin to prevent myocardial infarction”),
temporal reasoning (“Diagnosis of ketoacidosis in the past year”), and expert judgment to assess (“Major
diabetes-related complication”). For the corpus, we used the dataset of American English, longitudinal clinical
narratives from the 2014 i2b2/UTHealth shared task 4.
The final selected 13 selection criteria are as follows:
1. DRUG-ABUSE: Drug abuse, current or past
2. ALCOHOL-ABUSE: Current alcohol use over weekly recommended limits
3. ENGLISH: Patient must speak English
4. MAKES-DECISIONS: Patient must make their own medical decisions
5. ABDOMINAL: History of intra-abdominal surgery, small or large intestine
resection, or small bowel obstruction.
6. MAJOR-DIABETES: Major diabetes-related complication. For the purposes of
this annotation, we define “major complication” (as opposed to “minor complication”)
as any of the following that are a result of (or strongly correlated with) uncontrolled diabetes:
a. Amputation
b. Kidney damage
c. Skin conditions
d. Retinopathy
e. nephropathy
f. neuropathy
7. ADVANCED-CAD: Advanced cardiovascular disease (CAD).
For the purposes of this annotation, we define “advanced” as having 2 or more of the following:
a. Taking 2 or more medications to treat CAD
b. History of myocardial infarction (MI)
c. Currently experiencing angina
d. Ischemia, past or present
8. MI-6MOS: MI in the past 6 months
9. KETO-1YR: Diagnosis of ketoacidosis in the past year
10. DIETSUPP-2MOS: Taken a dietary supplement (excluding vitamin D) in the past 2 months
11. ASP-FOR-MI: Use of aspirin to prevent MI
12. HBA1C: Any hemoglobin A1c (HbA1c) value between 6.5% and 9.5%
13. CREATININE: Serum creatinine > upper limit of normal
The training consists of 202 patient records with document-level annotations, 10 records
with textual spans indicating annotator’s evidence for their annotations while test set contains 86.
Note:
* The inter-annotator average agreement is 84.9%
* Whereabouts of 10 records with textual spans indicating annotator’s evidence are unknown.
However, author did a simple script based validation to check if any of the tags contained any text
in any of the training set and they do not, which confirms that atleast train and test do not
have any evidence tagged alongside corresponding tags.
## Citation Information
```
@article{DBLP:journals/jamia/StubbsFSHU19,
author = {
Amber Stubbs and
Michele Filannino and
Ergin Soysal and
Samuel Henry and
Ozlem Uzuner
},
title = {Cohort selection for clinical trials: n2c2 2018 shared task track 1},
journal = {J. Am. Medical Informatics Assoc.},
volume = {26},
number = {11},
pages = {1163--1171},
year = {2019},
url = {https://doi.org/10.1093/jamia/ocz163},
doi = {10.1093/jamia/ocz163},
timestamp = {Mon, 15 Jun 2020 16:56:11 +0200},
biburl = {https://dblp.org/rec/journals/jamia/StubbsFSHU19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
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rcds/MultiLegalSBD | 2023-10-23T06:36:36.000Z | [
"task_categories:token-classification",
"size_categories:100K<n<1M",
"language:en",
"language:es",
"language:de",
"language:it",
"language:pt",
"language:fr",
"region:us"
] | rcds | null | null | 3 | 26 | 2023-01-10T15:17:41 | ---
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task_categories:
- token-classification
language:
- en
- es
- de
- it
- pt
- fr
pretty_name: 'MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset'
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a multilingual dataset containing ~130k annotated sentence boundaries. It contains laws and court decision in 6 different languages.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English, French, Italian, German, Portuguese, Spanish
## Dataset Structure
It is structured in the following format: {language}\_{type}\_{shard}.jsonl.xz
type is one of the following:
- laws
- judgements
Use the the dataset like this:
```
from datasets import load_dataset
config = 'fr_laws' #{language}_{type} | to load all languages and/or all types, use 'all_all'
dataset = load_dataset('rdcs/MultiLegalSBD', config)
```
### Data Instances
[More Information Needed]
### Data Fields
- text: the original text
- spans:
- start: offset of the first character
- end: offset of the last character
- label: One label only -> Sentence
- token_start: id of the first token
- token_end: id of the last token
- tokens:
- text: token text
- start: offset of the first character
- end: offset of the last character
- id: token id
- ws: whether the token is followed by whitespace
### Data Splits
There is only one split available
## 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{10.1145/3594536.3595132,
author = {Brugger, Tobias and St\"{u}rmer, Matthias and Niklaus, Joel},
title = {MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset},
year = {2023},
isbn = {9798400701979},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3594536.3595132},
doi = {10.1145/3594536.3595132},
abstract = {Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks. It is a challenging task for algorithms, especially in the legal domain, considering the complex and different sentence structures used. In this work, we curated a diverse multilingual legal dataset consisting of over 130'000 annotated sentences in 6 languages. Our experimental results indicate that the performance of existing SBD models is subpar on multilingual legal data. We trained and tested monolingual and multilingual models based on CRF, BiLSTM-CRF, and transformers, demonstrating state-of-the-art performance. We also show that our multilingual models outperform all baselines in the zero-shot setting on a Portuguese test set. To encourage further research and development by the community, we have made our dataset, models, and code publicly available.},
booktitle = {Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law},
pages = {42–51},
numpages = {10},
keywords = {Natural Language Processing, Sentence Boundary Detection, Text Annotation, Legal Document Analysis, Multilingual},
location = {Braga, Portugal},
series = {ICAIL '23}
}
```
### Contributions
[More Information Needed] | 24,078 | [
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rcds/swiss_legislation | 2023-07-20T07:36:07.000Z | [
"task_categories:text-classification",
"task_categories:translation",
"size_categories:100K<n<1M",
"language:de",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"region:us"
] | rcds | This dataset contains Swiss law articles | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | 5 | 26 | 2023-01-22T20:02:28 | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
- translation
language:
- de
- fr
- it
pretty_name: Swiss Legislation
size_categories:
- 100K<n<1M
---
# Dataset Card for Swiss Legislation
## 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
Swiss Legislation is a multilingual, diachronic dataset of 36K Swiss laws. This dataset is part of a challenging Information Retreival task.
### Supported Tasks and Leaderboards
### Languages
The total number of texts in the dataset is 35,698. The dataset is saved in _lexfind_v2.jsonl_ format.
Switzerland has four official languages German, French, Italian and Romanch with some additional English laws being represenated. Laws are written by legal experts.
36K & 18K & 11K & 6K & 534 & 207
| Language | Subset | Number of Documents |
|------------|------------|----------------------|
| German | **de** | 18K |
| French | **fr** | 11K |
| Italian | **it** | 6K |
| Romanch | **rm** | 534 |
| English | **en** | 207 |
## Dataset Structure
### Data Fields
Each entry in the dataset is a dictionary with the following keys:
- `canton`: the canton of origin of the legislation
- example: "ag"
- `language`: the language of the legislation
- example: "de"
- `uuid`: a unique identifier for the legislation
- example: "ec312f57-05fe-4552-ba50-8c9c269e0f3b"
- `title`: the title of the legislation
- example: "Gesetz über die Geoinformation im Kanton Aargau"
- `short`: a short description of the legislation
- example: "Kantonales Geoinformationsgesetz"
- `abbreviation`: an abbreviation for the legislation
- example: "KGeoIG"
- `sr_number`: a reference number for the legislation
- example: "740.100"
- `is_active`: whether the legislation is currently in force
- example: true
- `version_active_since`: the date since when the legislation's current version is active
- example: "2021-09-01"
- `family_active_since`: the date since when the legislation's current version's family is active
- example: "2011-05-24"
- `version_inactive_since`: the date since when the legislation's current version is inactive
- example: null
- `version_found_at`: the date the legislation's current version was found
- example: "2021-09-01"
- `pdf_url`: a link to the legislation's pdf
- example: "https://www.lexfind.ch/tol/1557/de"
- `html_url`: a link to the legislation's html
- example: "https://gesetzessammlungen.ag.ch/app/de/texts_of_law/740.100")_
- `pdf_content`: the legislation's pdf content
- example: "740.100 - Gesetz über..."
- `html_content`: the legislation's html content
- example: ""
- `changes`: a list of changes made to the legislation
- example: []
- `history`: a list of the legislation's history
- example: []
- `quotes`: a list of quotes from the legislation
- example: []
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
1. 'ch': Switzerland (Federal) - 15840
2. 'fr': Fribourg - 1633
3. 'be': Bern - 1344
4. 'vs': Valais - 1328
5. 'gr': Graubünden - 1205
6. 'ne': Neuchâtel - 1115
7. 'zh': Zurich - 974
8. 'bs': Basel-Stadt - 899
9. 'bl': Basel-Landschaft - 863
10. 'vd': Vaud - 870
11. 'ge': Geneva - 837
12. 'sg': St. Gallen - 764
13. 'ju': Jura - 804
14. 'zg': Zug - 632
15. 'ti': Ticino - 627
16. 'lu': Lucerne - 584
17. 'so': Solothurn - 547
18. 'ow': Obwalden - 513
19. 'ik': Interkantonal - 510
20. 'sh': Schaffhausen - 469
21. 'gl': Glarus - 467
22. 'tg': Thurgau - 453
23. 'sz': Schwyz - 423
24. 'ai': Appenzell Innerrhoden - 416
25. 'ag': Aargau - 483
26. 'ar': Appenzell Ausserrhoden - 330
27. 'nw': Nidwalden - 401
28. 'ur': Uri - 367
29.
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| 7,275 | [
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dirtycomputer/OCNLI | 2023-02-17T02:20:20.000Z | [
"region:us"
] | dirtycomputer | null | null | 1 | 26 | 2023-02-17T02:17:06 | Entry not found | 15 | [
[
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hazemessam/squad_v2 | 2023-03-06T15:18:07.000Z | [
"license:cc-by-4.0",
"region:us"
] | hazemessam | null | null | 0 | 26 | 2023-03-06T15:03:51 | ---
license: cc-by-4.0
---
[SQuAD V2.0](https://rajpurkar.github.io/SQuAD-explorer/).
It contains the following:
* Train and Validation sets.
* The end position of each answer. | 178 | [
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-... |
bigcode/the-stack-march-sample-special-tokens-stripped | 2023-03-08T15:23:31.000Z | [
"region:us"
] | bigcode | null | null | 0 | 26 | 2023-03-08T15:07:56 | ---
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 3034084423
num_examples: 746856
download_size: 1107347598
dataset_size: 3034084423
---
# Dataset Card for "the-stack-march-sample-special-tokens-stripped"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 402 | [
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musabg/wikipedia-oscar-tr | 2023-05-10T08:57:22.000Z | [
"region:us"
] | musabg | null | null | 0 | 26 | 2023-03-09T15:49:57 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 74636783061.0
num_examples: 13847707
download_size: 41512074295
dataset_size: 74636783061.0
---
# Wikipedia and OSCAR Turkish Dataset
👋 Welcome to the "Wikipedia and OSCAR Turkish" Huggingface Repo!
📚 This repo contains a Turkish language dataset generated by merging Wikipedia and OSCAR cleaned Common Crawl. The dataset contains over 13 million examples with a single feature - text.
🔍 This dataset can be useful for natural language processing tasks in Turkish language.
📥 To download the dataset, you can use the Hugging Face Datasets library. Here's some sample code to get started:
from datasets import load_dataset
dataset = load_dataset("musabg/wikipedia-oscar-tr")
🤖 Have fun exploring this dataset and training language models on it! | 870 | [
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0.02433... |
Den4ikAI/russian_instructions_2 | 2023-06-11T05:20:17.000Z | [
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
] | Den4ikAI | null | null | 5 | 26 | 2023-03-11T10:10:28 | ---
license: mit
language:
- ru
size_categories:
- 100K<n<1M
---
June 10:
1. Почищены криво переведенные примеры кода
2. Добавлено >50000 человеческих примеров QA и инструкций
Обновленная версия русского датасета инструкций и QA.
Улучшения:
```
1. Увеличен размер с 40 мегабайт до 130 (60к сэмплов - 200к)
2. Улучшено качество перевода.
```
Структура датасета:
```
{
"sample":[
"Как я могу улучшить свою связь между телом и разумом?",
"Начните с разработки регулярной практики осознанности. 2. Обязательно практикуйте баланс на нескольких уровнях: физическом, эмоциональном, умственном и духовном. 3. Свяжитесь с природой, когда это возможно - идите на прогулки или бегайте на улице, или просто сидите в парке и наблюдайте природу вокруг вас. 4. Развивайте позитивные утверждения, которые вы можете использовать, чтобы помочь поддерживать оптимизм и сильные отношения с вашим телом."
]
}
```
Старая версия: https://huggingface.co/datasets/Den4ikAI/russian_instructions
```
@MISC{russian_instructions,
author = {Denis Petrov},
title = {Russian instructions dataset for conversational agents},
url = {https://huggingface.co/datasets/Den4ikAI/russian_instructions_2},
year = 2023
}
``` | 1,230 | [
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Multimodal-Fatima/Food101_train | 2023-05-04T06:17:20.000Z | [
"region:us"
] | Multimodal-Fatima | null | null | 0 | 26 | 2023-03-22T01:07:54 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': apple pie
'1': baby back ribs
'2': baklava
'3': beef carpaccio
'4': beef tartare
'5': beet salad
'6': beignets
'7': bibimbap
'8': bread pudding
'9': breakfast burrito
'10': bruschetta
'11': caesar salad
'12': cannoli
'13': caprese salad
'14': carrot cake
'15': ceviche
'16': cheesecake
'17': cheese plate
'18': chicken curry
'19': chicken quesadilla
'20': chicken wings
'21': chocolate cake
'22': chocolate mousse
'23': churros
'24': clam chowder
'25': club sandwich
'26': crab cakes
'27': creme brulee
'28': croque madame
'29': cup cakes
'30': deviled eggs
'31': donuts
'32': dumplings
'33': edamame
'34': eggs benedict
'35': escargots
'36': falafel
'37': filet mignon
'38': fish and chips
'39': foie gras
'40': french fries
'41': french onion soup
'42': french toast
'43': fried calamari
'44': fried rice
'45': frozen yogurt
'46': garlic bread
'47': gnocchi
'48': greek salad
'49': grilled cheese sandwich
'50': grilled salmon
'51': guacamole
'52': gyoza
'53': hamburger
'54': hot and sour soup
'55': hot dog
'56': huevos rancheros
'57': hummus
'58': ice cream
'59': lasagna
'60': lobster bisque
'61': lobster roll sandwich
'62': macaroni and cheese
'63': macarons
'64': miso soup
'65': mussels
'66': nachos
'67': omelette
'68': onion rings
'69': oysters
'70': pad thai
'71': paella
'72': pancakes
'73': panna cotta
'74': peking duck
'75': pho
'76': pizza
'77': pork chop
'78': poutine
'79': prime rib
'80': pulled pork sandwich
'81': ramen
'82': ravioli
'83': red velvet cake
'84': risotto
'85': samosa
'86': sashimi
'87': scallops
'88': seaweed salad
'89': shrimp and grits
'90': spaghetti bolognese
'91': spaghetti carbonara
'92': spring rolls
'93': steak
'94': strawberry shortcake
'95': sushi
'96': tacos
'97': takoyaki
'98': tiramisu
'99': tuna tartare
'100': waffles
- name: id
dtype: int64
- name: clip_tags_ViT_L_14_with_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_wo_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_simple_specific
dtype: string
- name: clip_tags_ViT_L_14_ensemble_specific
dtype: string
- name: clip_tags_ViT_B_16_simple_specific
dtype: string
- name: clip_tags_ViT_B_16_ensemble_specific
dtype: string
- name: clip_tags_ViT_B_32_simple_specific
dtype: string
- name: clip_tags_ViT_B_32_ensemble_specific
dtype: string
- name: Attributes_ViT_L_14_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_simple_specific
dtype: string
- name: clip_tags_LAION_ViT_H_14_2B_ensemble_specific
dtype: string
splits:
- name: train
num_bytes: 3906035933.0
num_examples: 75750
download_size: 3802666577
dataset_size: 3906035933.0
---
# Dataset Card for "Food101_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 4,098 | [
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chiapudding/kaggle-financial-sentiment | 2023-04-06T11:26:09.000Z | [
"region:us"
] | chiapudding | null | null | 3 | 26 | 2023-03-24T16:21:00 | Entry not found | 15 | [
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0.03790... |
koutch/staqc | 2023-03-27T14:53:22.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"code",
"arxiv:1803.09371",
"region:us"
] | koutch | StaQC (Stack Overflow Question-Code pairs) is a dataset of around 148K Python and 120K SQL domain question-code pairs,
which are automatically mined from Stack Overflow using a Bi-View Hierarchical Neural Network,
as described in the paper "StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow" (WWW'18). | @inproceedings{yao2018staqc,
title={StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow},
author={Yao, Ziyu and Weld, Daniel S and Chen, Wei-Peng and Sun, Huan},
booktitle={Proceedings of the 2018 World Wide Web Conference on World Wide Web},
pages={1693--1703},
year={2018},
organization={International World Wide Web Conferences Steering Committee}
} | 3 | 26 | 2023-03-27T11:43:26 | ---
dataset_info:
- config_name: mca_python
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
sequence: string
splits:
- name: train
num_bytes: 23286786
num_examples: 40391
download_size: 72054260
dataset_size: 23286786
- config_name: mca_sql
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
sequence: string
splits:
- name: train
num_bytes: 15164206
num_examples: 26052
download_size: 50304531
dataset_size: 15164206
- config_name: sca_python
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
dtype: string
splits:
- name: train
num_bytes: 39678168
num_examples: 85294
download_size: 47378850
dataset_size: 39678168
- config_name: sca_sql
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
dtype: string
splits:
- name: train
num_bytes: 28656467
num_examples: 75637
download_size: 34194025
dataset_size: 28656467
- config_name: man_python
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
sequence:
- name: text
dtype: string
- name: is_sda
dtype: bool
splits:
- name: train
num_bytes: 1445103
num_examples: 2052
download_size: 71250225
dataset_size: 1445103
- config_name: man_sql
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
sequence:
- name: text
dtype: string
- name: is_sda
dtype: bool
splits:
- name: train
num_bytes: 1123721
num_examples: 1587
download_size: 49745860
dataset_size: 1123721
license: cc-by-4.0
task_categories:
- question-answering
language:
- en
tags:
- code
pretty_name: staqc
size_categories:
- 10K<n<100K
---
# Dataset Card for StaQC (A Systematically Mined Question-Code Dataset from Stack Overflow)
## Dataset Description
- **Homepage: [GitHub](https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset)**
- **Paper: [StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow](https://arxiv.org/abs/1803.09371)**
### Dataset Summary
StaQC (Stack Overflow Question-Code pairs) is a large dataset of around 148K Python and 120K SQL domain question-code pairs,
which are automatically mined from Stack Overflow using a Bi-View Hierarchical Neural Network. StaQC is collected from three sources: multi-code answer posts, single-code answer posts, and manual annotations on multi-code answer posts.
The dataset was originally released by the main authors on [GitHub](https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset). This version is a *non-modified* redistributed copy (under the [license](#licensing-information) permission) made available on the hub for easier access.
#### Standalone solutions
As noted in the paper, the authors *define a code snippet as a code solution when the
questioner can solve the problem solely based on it (also named as
“standalone” solution).*
#### Manual annotations
The manual annotations are the collection of multi-code answer posts for which each code snippet was annotated with a boolean indicating whether or not the snippet is a *standalone solution* to the question.
#### Multi-code answer posts
A *Multi-code answer post* is an (accepted) answer post that contains multiple code snippets,
some of which may not be a *standalone* code solution to the question (see Section 1 in [paper](http://web.cse.ohio-state.edu/~sun.397/docs/StaQC-www18.pdf)).
For example, in [this multi-code answer post](https://stackoverflow.com/a/5996949),
the third code snippet is not a code solution to the question "How to limit a number to be within a specified range? (Python)".
Note: the multi-code answer posts contain also the manual annotations.
#### Single-code answer posts
A *Single-code answer post* is an (accepted) answer post that contains only one code snippet.
We pair such code snippets with the question title as a question-code pair.
### Supported Tasks and Leaderboards
This dataset can be used for Natural Language to Code Generation tasks.
### Languages
Python, SQL, English
## Dataset Structure
### Data Instances
Each configuration correspond to one of the three parts, in a given programming language.
There are three parts for the dataset:
- mca (Multi-code answer posts)
- sca (Single-code answer posts)
- man (Manual annotations)
And two programming/query languages:
- python
- sql
One can obtain obtain a configuration as a combination of a part in a programing language. For instance, one can obtain the automatically mined multi-code answers in python using:
```python
dataset = load_dataset("koutch/staqc", 'mca_python')
DatasetDict({
train: Dataset({
features: ['id', 'question_id', 'question', 'snippet'],
num_rows: 40391
})
})
```
or the manual annotations using:
```python
dataset = load_dataset("koutch/staqc", 'man_sql')
DatasetDict({
train: Dataset({
features: ['id', 'question_id', 'question', 'snippet'],
num_rows: 1587
})
})
```
#### Manual annotations
The manual annotations contain, for a given stackoverflow questions, for each individual code block in the accepted answer of that post, information on whether or not the given code block is a *standalone* solution to the question asked (the question title).
```
{
'question_id': 5947137,
'question': 'How can I use a list comprehension to extend a list in python?',
'snippet': {'text': ['import itertools as it\n\nreturn sum(it.imap(doSomething, originalList), [])\n',
'return sum(map(doSomething, originalList), [])\n',
'return sum((doSomething(x) for x in originalList), [])\n',
'accumulationList = []\nfor x in originalList:\n accumulationList.extend(doSomething(x))\nreturn accumulationList\n'],
'is_sda': [True, True, True, True]}
}
```
#### Multi-code answer posts
```
{
'question_id': 35349290,
'question': 'Python: Generating YYMM string between two dates',
'snippet': ['start_year = 2005\nend_year = 2007\nstart_month = 3\nend_month = 2\nyymm = [(yy, mm) for yy in range(start_year, end_year + 1) for mm in range(1, 13)\n if (start_year, start_month) <= (yy, mm) <= (end_year, end_month)]\n',
"formatted_yymm = ['{:>02}{:>02}.mat'.format(yy % 100, mm) for yy, mm in yymm]\n"]
}
```
#### Single-code answer posts
```
{
'question_id': 19387200,
'question': 'Python: get OS language',
'snippet': "import locale\nloc = locale.getlocale() # get current locale\nlocale.getdefaultlocale() # Tries to determine the default locale settings and returns them as a tuple of the form (language code, encoding); e.g, ('en_US', 'UTF-8').\n"
}
```
### Data Fields
- `question_id`: id of the stackoverflow question
- `question`: title of the stackoverflow question repurposed as the natural language intent
- `snippet`: mined or annotated standalone solution(s) (potentially) answerring the question
- `is_sda`: for the manual annotations, whether or not the given code snippet is a standalone solution to the question.
### Data Splits
Each configuration of the dataset contains only a training split.
## Dataset Creation
### Source Data
StackOverflow data dump.
### Annotations
See section 2.3 "Annotating QC Pairs for Model Training" of the [paper](https://arxiv.org/abs/1803.09371)
## Additional Information
### Licensing Information
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
### Citation Information
If you use the dataset or the code in your research, please cite the following paper:
```
@inproceedings{yao2018staqc,
title={StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow},
author={Yao, Ziyu and Weld, Daniel S and Chen, Wei-Peng and Sun, Huan},
booktitle={Proceedings of the 2018 World Wide Web Conference on World Wide Web},
pages={1693--1703},
year={2018},
organization={International World Wide Web Conferences Steering Committee}
}
```
### Contributions information
I did *not* contribute to the *creation* of this dataset, only to the redistribution. All credits should be attributed to the original authors. | 8,778 | [
[
-0.036651611328125,
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0.016021728515625,
0.01248931884765625,
-0.0181732177734375,
-0.0175018310546875,
-0.02215576171875,
-0.0233612060546875,
0.032135009765625,
0.042449951171875,
-0.052490234375,
-0.037628173828125,
-0.019683837890625,
0... |
gligen/flickr_tsv | 2023-03-28T23:57:22.000Z | [
"region:us"
] | gligen | null | null | 0 | 26 | 2023-03-28T23:49:29 | Entry not found | 15 | [
[
-0.021392822265625,
-0.01494598388671875,
0.05718994140625,
0.028839111328125,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
lansinuote/diffsion_from_scratch | 2023-04-14T06:36:47.000Z | [
"region:us"
] | lansinuote | null | null | 0 | 26 | 2023-04-14T06:34:05 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 119417305.0
num_examples: 833
download_size: 99672356
dataset_size: 119417305.0
---
# Dataset Card for "diffsion_from_scratch"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 404 | [
[
-0.045745849609375,
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0.0262298583984375,
-0.0209197998046875,
0.013092041015625,
0.0268402099609375,
-0.015625,
0.052001953125,
0.0301513671875,
-0.066162109375,
-0.0303802490234375,
-0.057647705078125,
-0.01331329345703... |
Estwld/atomic2020-origin | 2023-04-26T07:18:11.000Z | [
"region:us"
] | Estwld | null | null | 0 | 26 | 2023-04-26T04:18:29 | ---
dataset_info:
features:
- name: knowledge_type
dtype: string
- name: event
dtype: string
- name: relation
dtype: string
- name: relation_description
dtype: string
- name: tail
sequence: string
splits:
- name: train
num_bytes: 60537042
num_examples: 300293
- name: validation
num_bytes: 5678541
num_examples: 29195
- name: test
num_bytes: 9184458
num_examples: 44664
download_size: 18270825
dataset_size: 75400041
---
# Dataset Card for "atomic2020-origin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 660 | [
[
-0.046417236328125,
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0.05303955078125,
0.0302581787109375,
-0.06787109375,
-0.05096435546875,
-0.0440673828125,
-0.00490570... |
joey234/mmlu-high_school_world_history-verbal-neg-prepend | 2023-04-27T03:20:12.000Z | [
"region:us"
] | joey234 | null | null | 0 | 26 | 2023-04-27T02:02:21 | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 428765
num_examples: 237
download_size: 231609
dataset_size: 428765
---
# Dataset Card for "mmlu-high_school_world_history-verbal-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 598 | [
[
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0.002685546875,
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... |
ewof/dolly-instruct-unfiltered-deduped | 2023-05-13T03:54:10.000Z | [
"region:us"
] | ewof | null | null | 0 | 26 | 2023-05-10T01:39:20 | This dataset is databricks/databricks-dolly-15k unfiltered and deduped, removing 640 instances of blatant alignment and 14 duplicates.
14357 instructions remain.
clean.py was first ran on https://huggingface.co/datasets/databricks/databricks-dolly-15k/blob/d72c16e4644a463b9c678c71d9440befd4594556/databricks-dolly-15k.jsonl and then dedupe.py was ran on it. renamed to .json not .jsonl
inspired by https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
All credit to anon8231489123 for the cleanup script that I adapted to wizardlm_clean.py, I then took this script and adapted it to clean.py
| 629 | [
[
-0.01422882080078125,
-0.03839111328125,
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0.0242767333984375,
0.002017974853515625,
0.0181732177734375,
0.0703125,
-0.044769287109375,
-0.0305633544921875,
-0.0377197265625,
... |
pszemraj/dolly_hhrlhf-text2text | 2023-05-18T20:07:42.000Z | [
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"source_datasets:mosaicml/dolly_hhrlhf",
"language:en",
"license:cc-by-sa-3.0",
"instruct",
"region:us"
] | pszemraj | null | null | 1 | 26 | 2023-05-18T19:44:11 | ---
license: cc-by-sa-3.0
task_categories:
- text2text-generation
language:
- en
tags:
- instruct
size_categories:
- 10K<n<100K
source_datasets: mosaicml/dolly_hhrlhf
---
# dolly_hhrlhf-text2text
This is `mosaicml/dolly_hhrlhf` with the following changes:
- clean up/adapt `prompt` column for the `text2text-generation` task (no need for a special template)
- split the original `train` set into a 95% train and an explicit validation set (5%)
- fixed extra spaces in puncuation (as this is not a French dataset)
details on extra spaces:
```
Original sentence 1: How can I be healthy ?
Fixed sentence 1: How can I be healthy?
``` | 634 | [
[
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-0.042633056640625,
0.0227508544921875,
0.04461669921875,
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0.0201263427734375,
0.0623779296875,
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... |
fnlp/moss-003-sft-data | 2023-07-09T15:09:50.000Z | [
"license:cc-by-4.0",
"region:us"
] | fnlp | null | null | 46 | 26 | 2023-05-20T13:07:50 | ---
license: cc-by-4.0
---
# moss-003-sft-data
## Conversation Without Plugins
### Categories
| Category | \# samples |
|----------------------|-----------:|
| Brainstorming | 99,162 |
| Complex Instruction | 95,574 |
| Code | 198,079 |
| Role Playing | 246,375 |
| Writing | 341,087 |
| Harmless | 74,573 |
| Others | 19,701 |
| Total | 1,074,551 |
**Others** contains two categories: **Continue**(9,839) and **Switching**(9,862).
The **Continue** category refers to instances in a conversation where the user asks the system to continue outputting the response from the previous round that was not completed.
The **Switching** category refers to instances in a conversation where the user switches the language they are using.
We remove the data for honesty because it contains private information.
| 933 | [
[
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0.049957275390625,
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0.0643310546875,
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-0.037109375,
-0.021942138671875,
-0.002... |
clarin-knext/nq-pl-qrels | 2023-06-07T08:23:58.000Z | [
"language:pl",
"license:cc-by-sa-4.0",
"arxiv:2305.19840",
"region:us"
] | clarin-knext | null | null | 0 | 26 | 2023-06-06T17:45:32 | ---
license: cc-by-sa-4.0
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: konrad.wojtasik@pwr.edu.pl | 223 | [
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0.0286102294921875,
-0.03826904296875,
-0.048126220703125,
-0.0290069580078125,
... |
PKU-Alignment/BeaverTails-Evaluation | 2023-07-20T15:31:24.000Z | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-4.0",
"safe",
"safety",
"ai-safety",
"moderation",
"rejection-sampling",
"llm",
"lm",
"human-feedback",
"arxiv:2307.04657",
"region:us"
] | PKU-Alignment | null | null | 0 | 26 | 2023-06-14T16:14:14 | ---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- safe
- safety
- ai-safety
- moderation
- rejection-sampling
- llm
- lm
- human-feedback
size_categories:
- n<1K
---
# Dataset Card for BeaverTails-Evaluation
BeaverTails is an AI safety-focused collection comprising a series of datasets.
This repository contains test prompts specifically designed for evaluating language model safety.
It is important to note that although each prompt can be connected to multiple categories, only one category is labeled for each prompt.
The 14 harm categories are defined as follows:
1. **Animal Abuse:** This involves any form of cruelty or harm inflicted on animals, including physical abuse, neglect, or any behavior that causes distress or harm to an animal. It also includes content that promotes such behavior.
1. **Child Abuse:** This encompasses any form of physical, emotional, or sexual abuse directed toward children. It can also include neglect, exploitation, or any behavior that harms a child or violates their rights. Content that promotes or glorifies such behavior also falls under this category.
1. **Controversial Topics, Politics:** This refers to discussions or statements related to controversial topics, political ideologies, or sensitive issues that could lead to disagreement or conflict. These topics can be divisive and could potentially cause harm if not handled respectfully and thoughtfully.
1. **Discrimination, Stereotype, Injustice:** Discrimination refers to treating people unfairly or unequally based on their personal characteristics or affiliations such as race, age, gender, religion, etc. Stereotyping involves holding simplified and generalized beliefs about particular groups, which often leads to prejudices and discrimination. Injustice pertains to unfair treatment or violation of the rights of individuals or groups.
1. **Drug Abuse, Weapons, Banned Substance:** This category includes the misuse or abuse of illegal drugs, prescription drugs, or any other banned substances. It also involves the unauthorized or illegal possession, use, or trade of weapons.
1. **Financial Crime, Property Crime, Theft:** This includes all forms of illegal activities that lead to financial loss such as fraud, embezzlement, money laundering, bribery, and other forms of corruption. Property crime and theft involve the act of taking someone's property without their consent.
1. **Hate Speech, Offensive Language:** This category encompasses any form of communication or content that expresses intense hostility or violence towards an individual or group based on aspects like race, religion, ethnicity, nationality, gender, sexual orientation, or any other defining characteristics. This also includes language that is considered vulgar, crude, or offensive.
1. **Misinformation Regarding ethics, laws, and safety:** This category involves the spreading of incorrect or misleading information related to ethical issues, laws, regulations, or any matter related to safety. Misinformation can cause harm by influencing people's beliefs or actions negatively.
1. **Non-Violent Unethical Behavior:** This refers to any behavior that is morally or ethically wrong, but does not involve violence. Examples can include dishonest behaviors like lying, cheating, deceiving, spreading rumors, betraying trust, or any other form of unethical conduct.
1. **Privacy Violation:** This refers to actions that invade an individual's privacy, such as unauthorized access, use, disclosure, or collection of personal data. It also includes stalking, doxing, or any other form of intrusion into someone's personal life without their consent.
1. **Self-Harm:** This includes any form of self-inflicted harm, whether physical or emotional, or any content that promotes, glorifies, or encourages self-harm. This can also include discussions of suicidal thoughts or plans.
1. **Sexually Explicit, Adult Content:** This category includes any content that depicts explicit sexual activities or adult themes that are considered inappropriate or offensive. This can include pornography, explicit sexual text or messages, and any other form of sexually explicit material.
1. **Terrorism, Organized Crime:** This pertains to any form of content or action related to terrorism or organized crime, including endorsing or promoting terrorist activities, participating in organized criminal activities, or spreading propaganda for such groups.
1. **Violence, Aiding and Abetting, Incitement:** This involves any form of physical harm, threat, or violent behavior towards individuals or groups. Aiding and abetting refers to the act of helping, supporting, or encouraging such violent behaviors or illegal activities. Incitement pertains to the act of provoking or stirring up harmful, violent, or illegal actions.
**Disclaimer**: The BeaverTails dataset and its family contain content that may be offensive or upsetting.
Topics covered in the dataset include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially distressing subject matter.
Please engage with the dataset responsibly and in accordance with your own personal risk tolerance.
The dataset is intended for research purposes, specifically for research aimed at creating safer and less harmful AI systems.
The views and opinions expressed in the dataset do not represent the views of the PKU-Alignment Team or any of its members.
It is important to emphasize that the dataset should not be used for training dialogue agents, as doing so may likely result in harmful model behavior.
The primary objective of this dataset is to facilitate research that could minimize or prevent the harm caused by AI systems.
## Usage
The code snippet below demonstrates how to load the evaluation dataset:
```python
from datasets import load_dataset
# Load the whole dataset
dataset = load_dataset('PKU-Alignment/BeaverTails-Evaluation')
# Load only the v1 dataset
round0_dataset = load_dataset('PKU-Alignment/BeaverTails', data_dir='v1')
```
## Paper
You can find more information in our paper.
- **Dataset Paper:** <https://arxiv.org/abs/2307.04657>
## Contact
The original authors host this dataset on GitHub here: https://github.com/PKU-Alignment/beavertails
## License
BeaverTails dataset and its family are released under the CC BY-NC 4.0 License.
| 6,414 | [
[
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0.0171051025390625,
0.030059814453125,
0.0038661956787109375,
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0.0087890625,
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0.01172637939453125,
0.04058837890625,
-0.05859375,
-0.03851318359375,
-0.050872802734375,
0.010... |
mikex86/stackoverflow-posts | 2023-08-01T01:31:12.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:code",
"language:en",
"license:other",
"code",
"region:us"
] | mikex86 | null | null | 13 | 26 | 2023-06-14T18:48:00 | ---
license: other
language:
- code
- en
task_categories:
- question-answering
- text-generation
- text2text-generation
tags:
- code
viewer: true
pretty_name: StackOverflow Posts Markdown
size_categories:
- 10M<n<100M
---
# StackOverflow Posts Markdown
## Dataset Summary
This dataset contains all posts submitted to StackOverflow before the 14th of June 2023 formatted as **Markdown text**.<br>
The dataset contains ~60 Million posts, totaling ~35GB in size and ~65 billion characters of text.<br>
The data is sourced from [Internet Archive StackExchange Data Dump](https://archive.org/download/stackexchange).
## Dataset Structure
Each record corresponds to one post of a particular type.
Original ordering from the data dump is not exactly preserved due to parallelism in the script used to process the data dump.
The markdown content of each post is contained in the `Body` field. The license for a particular post is contained in the `ContentLicense` field.
### Data Fields
```typescript
{
Id: long,
PostTypeId: long, // 1=Question, 2=Answer, 3=Orphaned tag wiki, 4=Tag wiki excerpt, 5=Tag wiki, 6=Moderator nomination, 7=Wiki Placeholder, 8=Privilige Wiki
AcceptedAnswerId: long | null, // only present if PostTypeId=1
ParentId: long | null, // only present if PostTypeId=2
Score: long,
ViewCount: long | null,
Body: string | null,
Title: string | null,
ContentLicense: string | null,
FavoriteCount: long | null,
CreationDate: string | null,
LastActivityDate: string | null,
LastEditDate: string | null,
LastEditorUserId: long | null,
OwnerUserId: long | null,
Tags: array<string> | null
}
```
Also consider the [StackExchange Datadump Schema Documentation](https://meta.stackexchange.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede), as all fields
have analogs in the original dump format.
## How to use?
```python
from datasets import load_dataset
# predownload full dataset
ds = load_dataset('mikex86/stackoverflow-posts', split='train')
# dataset streaming (will only download the data as needed)
ds = load_dataset('mikex86/stackoverflow-posts', split='train', streaming=True)
for sample in iter(ds): print(sample["Body"])
```
## How is the text stored?
The original Data Dump formats the "Body" field as HTML, using tags such as `<code>`, `<h1>`, `<ul>`, etc.
This HTML format has been converted to Markdown.
### Markdown format
For reference, [this post on StackOverflow](https://stackoverflow.com/questions/53253940/make-react-useeffect-hook-not-run-on-initial-render) is formatted as follows:
#### Title: Make React useEffect hook not run on initial render
```markdown
According to the docs:
> `componentDidUpdate()` is invoked immediately after updating occurs. This method is not called for the initial render.
We can use the new `useEffect()` hook to simulate `componentDidUpdate()`, but it seems like `useEffect()` is being ran after every render, even the first time. How do I get it to not run on initial render?
As you can see in the example below, `componentDidUpdateFunction` is printed during the initial render but `componentDidUpdateClass` was not printed during the initial render.
```
function ComponentDidUpdateFunction() {
const [count, setCount] = React.useState(0);
React.useEffect(() => {
console.log(""componentDidUpdateFunction"");
});
return (
<div>
<p>componentDidUpdateFunction: {count} times</p>
<button
onClick={() => {
setCount(count + 1);
}}
>
Click Me
</button>
</div>
);
}
```
rest of the post omitted for brevity
```
## Details on the HTML to Markdown conversion
Using Jsoup, the original Body field was converted into a Jsoup Document. The child **nodes** (has special meaning in context of Jsoup) of this document were recursively traversed in a depth-first order.
Jsoup defines `.text()` as follows:
> ... the normalized, combined text of this element and all its children. Whitespace is normalized and trimmed. For example, given HTML <code><p>Hello <b>there</b> now! </p><code>, p.text() returns "Hello there now!"
Jsoup defines a `Node` as follows:
> The base, abstract Node model. Elements, Documents, Comments etc are all Node instances.
Additionally the existence of the `TextNode` should be noted, which represents floating text inside an HTML document that is not itself an HTML element.
Thus this text tag `<p>Hello<code>World</code></p>` would have two Jsoup child nodes `TextNode(value="Hello")` and `Element(tag="code", value="World")`.
The value `field` of a `TextNode` contains the free standing text without any further treatment (no whitespace stripping, etc.)
### Traversing Rules
- When ecountering a html tag for which a rule exists, children are not further traversed, **unless explicitly stated otherwise**.
- When encountering an `<a>` tag, `[${element.text()}](${element.attr("href")})` is emitted.
- When encountering an `<h1>` tag, `\n# ${element.text()}\n\n` is emitted.
- When encountering an `<h2>` tag, `\n## ${element.text()}\n\n` is emitted.
- When encountering an `<h3>` tag, `\n### ${element.text()}\n\n` is emitted.
- When encountering an `<h4>` tag, `\n#### ${element.text()}\n\n` is emitted.
- When encountering an `<h5>` tag, `\n##### ${element.text()}\n\n` is emitted.
- When encountering an `<h6>` tag, `\n###### ${element.text()}\n\n` is emitted.
- When encountering a `<code>` tag, `` `${element.text()}` ``is emitted
- When encountering a `<pre>` tag and said element **has** a `<code>` child tag, `` ```\n${element.text()}`\n```\n`` is emitted.
- When encountering a `<pre>` tag and said element **does not** have a `<code>` child tag, **children are traversed further**.
- When encountering an `<li>` tag, `- ` is emitted and **children are traversed further**.
- When encountering a `<blockquote>` tag, `> ` is emitted and **children are traversed further**.
- When encountering an `<hr>` tag, `\n---\n\n` is emitted
- When encountering an `<img>` tag, `![${element.attr("alt")}](${element.attr("src")})` is emitted.
- When encountering a `<table>` tag
- `\n| ` is emitted
- For each element of `element.select("th")`
- `${element.text()} | ` is emitted
- After the loop `\n| ` is emitted
- For each element of `element.select("th")`
- For each character of the `th.text()`
- `-` is emitted
- After the loop over each character of th ` | ` is emitted
- `\n` is emitted
- For each element of `element.select("tr")` with more than one children of tag type `td`
- `| ` is emitted
- For each element of `element.select("td")`
- `${td.text()} | ` is emitted
- After the loop over `<td>` elements, `\n` is emitted
- After the loop over `<tr>` elements, `\n` is emitted
- When encountering a jsoup `TextNode`, `${node.attr(node.nodeName())}` (which is equivalent to accessing the private field `node.value`) is emitted. | 7,077 | [
[
-0.0236663818359375,
-0.032806396484375,
0.0345458984375,
0.0037841796875,
-0.02838134765625,
0.006206512451171875,
-0.00827789306640625,
-0.0199127197265625,
0.01251983642578125,
0.027862548828125,
-0.052703857421875,
-0.04998779296875,
-0.0294036865234375,
... |
yulongmannlp/dev_para | 2023-06-26T00:32:27.000Z | [
"region:us"
] | yulongmannlp | Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
} | 0 | 26 | 2023-06-26T00:30:52 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.014984130859375,
0.05718994140625,
0.0288543701171875,
-0.0350341796875,
0.046478271484375,
0.052520751953125,
0.005062103271484375,
0.051361083984375,
0.016998291015625,
-0.0521240234375,
-0.01496124267578125,
-0.0604248046875,
0.037... |
oooriii/solr_fine_tunning_ca | 2023-07-24T07:54:09.000Z | [
"task_categories:summarization",
"task_categories:translation",
"size_categories:10K<n<100K",
"language:ca",
"license:cc0-1.0",
"solr",
"translate",
"nl_2_solr",
"region:us"
] | oooriii | This dataset has some search antural language sentences in catalan and their solr search language translation.
This is the original dataset:
```
load_dataset("oooriii/solr_fine_tunning_ca", '3.0.0')
```
And this is the HuggingFace translation pipeline:
```
pipeline(
task='translation_en_to_nl',
model='Helsinki-NLP/opus-mt-en-nl',
tokenizer='Helsinki-NLP/opus-mt-en-nl')
``` | \ | 0 | 26 | 2023-07-05T11:07:08 | ---
license: cc0-1.0
task_categories:
- summarization
- translation
language:
- ca
tags:
- solr
- translate
- nl_2_solr
size_categories:
- 10K<n<100K
---
# dataset: dataset_final_20230705
`wc -l dataset_final_20230705.txt
15036 dataset_final_20230705.txt`
15035 rows and tab separated fields
Id Language Text Expected | 319 | [
[
-0.009552001953125,
-0.03082275390625,
0.019195556640625,
0.050567626953125,
-0.031585693359375,
0.0011882781982421875,
-0.0166473388671875,
-0.007205963134765625,
0.01122283935546875,
0.044708251953125,
-0.04180908203125,
-0.039154052734375,
-0.043121337890625,... |
Andyrasika/image_captioning | 2023-07-12T05:08:26.000Z | [
"region:us"
] | Andyrasika | null | null | 1 | 26 | 2023-07-12T04:54:47 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
elsaEU/ELSA1M_track1 | 2023-08-27T08:01:57.000Z | [
"license:cc-by-4.0",
"region:us"
] | elsaEU | null | null | 2 | 26 | 2023-07-18T16:50:36 | ---
elsaEU--ELSA1M_track1:
description: ''
citation: ''
homepage: ''
license: ''
features:
image:
decode: true
id: null
dtype: Image
id:
dtype: string
id: null
_type: Value
original_prompt:
dtype: string
id: null
_type: Value
positive_prompt:
dtype: string
id: null
_type: Value
negative_prompt:
dtype: string
id: null
_type: Value
model:
dtype: string
id: null
_type: Value
nsfw:
dtype: string
id: null
_type: Value
url_real_image:
dtype: string
id: null
_type: Value
filepath:
dtype: string
id: null
_type: Value
aspect_ratio:
feature:
dtype: int64
id: null
_type: Value
length: -1
id: null
_type: Sequence
post_processed: null
supervised_keys: null
task_templates: null
builder_name: imagefolder
config_name: default
version:
version_str: 0.0.0
description: null
major: 0
minor: 0
patch: 0
splits:
train:
name: train
num_bytes: 445926712527.43
num_examples: 992655
dataset_name: ELSA1M_track1
download_checksums: null
download_size: 223034360161
post_processing_size: null
dataset_size: 445926712527.43
size_in_bytes: 668961072688.4299
license: cc-by-4.0
---
# ELSA - Multimedia use case
**ELSA Multimedia is a large collection of Deep Fake images, generated using diffusion models**
### Dataset Summary
This dataset was developed as part of the EU project ELSA. Specifically for the Multimedia use-case.
Official webpage: https://benchmarks.elsa-ai.eu/
This dataset aims to develop effective solutions for detecting and mitigating the spread of deep fake images in multimedia content. Deep fake images, which are highly realistic and deceptive manipulations, pose significant risks to privacy, security, and trust in digital media. This dataset can be used to train robust and accurate models that can identify and flag instances of deep fake images.
### ELSA versions
| Name | Description | Link |
| ------------- | ------------- | ---------------------|
| ELSA1M_track1 | Dataset of 1M images generated using diffusion model | https://huggingface.co/datasets/elsaEU/ELSA1M_track1 |
| ELSA500k_track2 | Dataset of 500k images generated using diffusion model with diffusion attentive attribution maps [1] | https://huggingface.co/datasets/elsaEU/ELSA500k_track2 |
```python
from datasets import load_dataset
elsa_data = load_dataset("elsaEU/ELSA1M_track1", split="train", streaming=True)
for sample in elsa_data:
image = sample.pop("image")
metadata = sample
```
Using <a href="https://huggingface.co/docs/datasets/stream">streaming=True</a> lets you work with the dataset without downloading it.
## Dataset Structure
Each parquet file contains nearly 1k images and a JSON file with metadata.
The Metadata for generated images are:
- ID: Laion image ID
- original_prompt: Laion Prompt
- positive_prompt: positive prompt used for image generation
- negative_prompt: negative prompt used for image generation
- model: model used for the image generation
- nsfw: nsfw tag from Laion
- url_real_image: Url of the real image associated to the same prompt
- filepath: filepath of the fake image
- aspect_ratio: aspect ratio of the generated image
### Dataset Curators
- Leonardo Labs (rosario.dicarlo.ext@leonardo.com)
- UNIMORE (https://aimagelab.ing.unimore.it/imagelab/) | 3,653 | [
[
-0.037567138671875,
-0.048187255859375,
0.023162841796875,
0.01186370849609375,
-0.033782958984375,
-0.00962066650390625,
0.024169921875,
-0.0253448486328125,
0.0175018310546875,
0.03375244140625,
-0.04388427734375,
-0.043853759765625,
-0.042144775390625,
-0... |
qanastek/LLaMaInstructionsFrenchMedMCQA | 2023-07-21T23:45:31.000Z | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1k<n<10k",
"source_datasets:original",
"lan... | qanastek | FrenchMedMCQA | @unpublished{labrak:hal-03824241,
TITLE = {{FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain}},
AUTHOR = {Labrak, Yanis and Bazoge, Adrien and Dufour, Richard and Daille, Béatrice and Gourraud, Pierre-Antoine and Morin, Emmanuel and Rouvier, Mickael},
URL = {https://hal.archives-ouvertes.fr/hal-03824241},
NOTE = {working paper or preprint},
YEAR = {2022},
MONTH = Oct,
PDF = {https://hal.archives-ouvertes.fr/hal-03824241/file/LOUHI_2022___QA-3.pdf},
HAL_ID = {hal-03824241},
HAL_VERSION = {v1},
} | 0 | 26 | 2023-07-21T23:29:30 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- fr
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1k<n<10k
source_datasets:
- original
task_categories:
- question-answering
- multiple-choice
task_ids:
- multiple-choice-qa
- open-domain-qa
paperswithcode_id: frenchmedmcqa
pretty_name: FrenchMedMCQA
---
# Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain
## Table of Contents
- [Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain](#dataset-card-for-frenchmedmcqa--a-french-multiple-choice-question-answering-corpus-for-medical-domain)
- [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)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contact](#contact)
## Dataset Description
- **Homepage:** https://deft2023.univ-avignon.fr/
- **Repository:** https://deft2023.univ-avignon.fr/
- **Paper:** [FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain](https://hal.science/hal-03824241/document)
- **Leaderboard:** Coming soon
- **Point of Contact:** [Yanis LABRAK](mailto:yanis.labrak@univ-avignon.fr)
### Dataset Summary
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.
Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).
We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.
### Supported Tasks and Leaderboards
Multiple-Choice Question Answering (MCQA)
### Languages
The questions and answers are available in French.
## Dataset Structure
### Data Instances
```json
{
"id": "230bac49b0fe863b772410bc8d01a025f63c3c999065480131d6334abd2efeff",
"prompt": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: We are giving you a scientific question (easy level) and five answers options (associated to « A », « B », « C », « D », « E »). Your task is to find the correct(s) answer(s) based on scientific facts, knowledge and reasoning. Don't generate anything other than one of the following characters : 'A B C D E'. ### Input: Parmi les affirmations suivantes, une seule est fausse, indiquer laquelle: les particules alpha (A) Sont formées de noyaux d'hélium (B) Sont peu pénétrantes (C) Toute l'énergie qu'elles transportent est cédée au long d'un parcours de quelques centimètres dans l'air (D) Sont arrêtées par une feuille de papier (E) Sont peu ionisantes ### Response: E",
"prompt_no_answer": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: We are giving you a scientific question (easy level) and five answers options (associated to « A », « B », « C », « D », « E »). Your task is to find the correct(s) answer(s) based on scientific facts, knowledge and reasoning. Don't generate anything other than one of the following characters : 'A B C D E'. ### Input: Parmi les affirmations suivantes, une seule est fausse, indiquer laquelle: les particules alpha (A) Sont formées de noyaux d'hélium (B) Sont peu pénétrantes (C) Toute l'énergie qu'elles transportent est cédée au long d'un parcours de quelques centimètres dans l'air (D) Sont arrêtées par une feuille de papier (E) Sont peu ionisantes ### Response:",
"correct_answers": [4],
}
```
### Data Fields
- `id` : a string question identifier for each example
- `prompt` : prompt text formatted for LLaMa (a string)
- `correct_answers` : Correct options, i.e., A, D and E
### Data Splits
| # Answers | Training | Validation | Test | Total |
|:---------:|:--------:|:----------:|:----:|:-----:|
| 1 | 595 | 164 | 321 | 1,080 |
| 2 | 528 | 45 | 97 | 670 |
| 3 | 718 | 71 | 141 | 930 |
| 4 | 296 | 30 | 56 | 382 |
| 5 | 34 | 2 | 7 | 43 |
| Total | 2171 | 312 | 622 | 3,105 |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers.
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Additional Information
### Dataset Curators
The dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael.
### Licensing Information
Apache 2.0
### Citation Information
If you find this useful in your research, please consider citing the dataset paper :
```latex
@inproceedings{labrak-etal-2022-frenchmedmcqa,
title = "{F}rench{M}ed{MCQA}: A {F}rench Multiple-Choice Question Answering Dataset for Medical domain",
author = "Labrak, Yanis and
Bazoge, Adrien and
Dufour, Richard and
Daille, Beatrice and
Gourraud, Pierre-Antoine and
Morin, Emmanuel and
Rouvier, Mickael",
booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.louhi-1.5",
pages = "41--46",
abstract = "This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.",
}
```
### Contact
Thanks to contact [Yanis LABRAK](https://github.com/qanastek) for more information about this dataset.
| 8,783 | [
[
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0.04315185546875,
0.037994384765625,
-0.052642822265625,
-0.04449462890625,
-0.046600341796875,
0.... |
MaggiePai/SLUE-sqa5-CODE | 2023-08-20T17:10:38.000Z | [
"region:us"
] | MaggiePai | null | null | 0 | 26 | 2023-08-20T15:16:50 | Entry not found | 15 | [
[
-0.0214080810546875,
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0.016998291015625,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.0379... |
Linhz/qag_viquad | 2023-08-24T16:33:51.000Z | [
"region:us"
] | Linhz | null | null | 0 | 26 | 2023-08-22T17:11:23 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01494598388671875,
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0.016998291015625,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.0379... |
Linhz/qag_vinewsqa | 2023-08-24T16:36:48.000Z | [
"region:us"
] | Linhz | null | null | 0 | 26 | 2023-08-24T16:32:26 | Entry not found | 15 | [
[
-0.0214080810546875,
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0.016998291015625,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.0379... |
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