id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
charsiu/libriphrase_meta | charsiu | 2023-10-27T18:33:50Z | 40 | 0 | null | [
"region:us"
] | 2023-10-27T18:33:50Z | 2023-10-27T18:30:29.000Z | 2023-10-27T18:30:29 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: anchor
dtype: string
- name: anchor_spk
dtype: int64
- name: anchor_text
dtype: string
- name: anchor_dur
dtype: float64
- name: comparison
dtype: string
- name: comparison_spk
dtype: int64
- name: comparison_text
dtype: string
- name: comparison_dur
dtype: float64
- name: type
dtype: string
- name: target
dtype: int64
- name: class
dtype: int64
- name: anchor_phone
dtype: string
- name: comparison_phone
dtype: string
splits:
- name: train
num_bytes: 53970720
num_examples: 203013
download_size: 8382220
dataset_size: 53970720
---
# Dataset Card for "libriphrase_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.35992351174354553,
-0.2675364017486572,
0.4148982763290405,
0.031053833663463593,
-0.25862935185432434,
-0.03867526352405548,
0.2413831502199173,
-0.019139137119054794,
0.8892353773117065,
0.5554897785186768,
-0.763922929763794,
-0.834837794303894,
-0.5488250851631165,
-0.04956544563174... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Ka4on/mri | Ka4on | 2023-10-28T01:03:33Z | 40 | 0 | null | [
"region:us"
] | 2023-10-28T01:03:33Z | 2023-10-28T01:02:50.000Z | 2023-10-28T01:02:50 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
automated-research-group/gpt2-winogrande | automated-research-group | 2023-10-28T06:48:10Z | 40 | 0 | null | [
"region:us"
] | 2023-10-28T06:48:10Z | 2023-10-28T06:48:09.000Z | 2023-10-28T06:48:09 | ---
dataset_info:
features:
- name: answer
dtype: string
- name: id
dtype: string
- name: question
dtype: string
- name: input_perplexity
dtype: float64
- name: input_likelihood
dtype: float64
- name: output_perplexity
dtype: float64
- name: output_likelihood
dtype: float64
splits:
- name: validation
num_bytes: 357232
num_examples: 1267
download_size: 162550
dataset_size: 357232
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "gpt2-winogrande"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.41015559434890747,
-0.17709650099277496,
0.29955539107322693,
0.138153538107872,
-0.3233467638492584,
-0.05001383274793625,
0.15053588151931763,
-0.2849363684654236,
0.513012707233429,
0.20205198228359222,
-0.6006694436073303,
-0.585280179977417,
-0.817493736743927,
-0.5743923783302307,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ayoub999/dataset_for_orange_factures | ayoub999 | 2023-11-09T13:59:48Z | 40 | 0 | null | [
"region:us"
] | 2023-11-09T13:59:48Z | 2023-10-30T15:15:47.000Z | 2023-10-30T15:15:47 | ---
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
sequence:
class_label:
names:
'0': O
'1': Ref
'2': NumFa
'3': Fourniss
'4': DateFa
'5': DateLim
'6': TotalHT
'7': TVA
'8': TotalTTc
'9': unitP
'10': Qt
'11': TVAP
'12': Désignation
'13': Adresse
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 2942860.8
num_examples: 12
- name: test
num_bytes: 735715.2
num_examples: 3
download_size: 2799104
dataset_size: 3678576.0
---
# Dataset Card for "dataset_for_orange_factures"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5826767683029175,
-0.1584644615650177,
0.2013721466064453,
0.4803481996059418,
-0.1977083683013916,
-0.2482631802558899,
0.25698891282081604,
-0.2779044806957245,
0.938926100730896,
0.630354642868042,
-0.562171220779419,
-0.8030178546905518,
-0.45039695501327515,
-0.09437257796525955,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yuvalkirstain/task_prediction_train2 | yuvalkirstain | 2023-10-31T18:48:49Z | 40 | 0 | null | [
"region:us"
] | 2023-10-31T18:48:49Z | 2023-10-31T18:48:28.000Z | 2023-10-31T18:48:28 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: path
dtype: string
- name: text
dtype: string
- name: task_name
dtype: string
splits:
- name: train
num_bytes: 659890949
num_examples: 5663600
- name: validation
num_bytes: 7823929
num_examples: 60002
download_size: 148156628
dataset_size: 667714878
---
# Dataset Card for "task_prediction_train2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.22719773650169373,
-0.04342102259397507,
0.19181403517723083,
0.3092663586139679,
-0.05620702728629112,
-0.20392447710037231,
0.17041121423244476,
-0.3135908842086792,
0.496336430311203,
0.35913708806037903,
-0.8980210423469543,
-0.45277833938598633,
-0.8460240364074707,
-0.518759369850... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
btt-mining-coalation/red_pajama_random_5000 | btt-mining-coalation | 2023-11-09T14:45:49Z | 40 | 0 | null | [
"region:us"
] | 2023-11-09T14:45:49Z | 2023-11-05T01:42:27.000Z | 2023-11-05T01:42:27 | ---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: reward_dpo
dtype: float64
splits:
- name: train
num_bytes: 304835574
num_examples: 5000
download_size: 137390511
dataset_size: 304835574
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "red_pajama_random_5000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.6282620429992676,
-0.21327221393585205,
0.14692799746990204,
0.6847658753395081,
-0.36777690052986145,
-0.33240270614624023,
0.1714601218700409,
-0.24441415071487427,
1.0220221281051636,
0.6109746098518372,
-0.9150915145874023,
-0.6857317090034485,
-0.6623834371566772,
-0.01580510474741... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ostapeno/qa-openai_batched_long_icl0_clen512_maxD-1_maxC2000_08000_cleaned_train | ostapeno | 2023-11-06T14:50:24Z | 40 | 0 | null | [
"region:us"
] | 2023-11-06T14:50:24Z | 2023-11-06T14:45:47.000Z | 2023-11-06T14:45:47 | ---
configs:
- config_name: default
data_files:
- split: abstract_algebra
path: data/abstract_algebra-*
- split: college_biology
path: data/college_biology-*
- split: formal_logic
path: data/formal_logic-*
- split: global_facts
path: data/global_facts-*
- split: high_school_government_and_politics
path: data/high_school_government_and_politics-*
- split: high_school_physics
path: data/high_school_physics-*
- split: machine_learning
path: data/machine_learning-*
- split: prehistory
path: data/prehistory-*
- split: security_studies
path: data/security_studies-*
- split: sociology
path: data/sociology-*
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: docno
dtype: string
- name: subject
dtype: string
- name: icl_examples
dtype: 'null'
- name: author_instr
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: abstract_algebra
num_bytes: 23879722.12837838
num_examples: 8000
- name: college_biology
num_bytes: 25235647.10957722
num_examples: 8000
- name: formal_logic
num_bytes: 24553218.679549113
num_examples: 8000
- name: global_facts
num_bytes: 24477439.56043956
num_examples: 8000
- name: high_school_government_and_politics
num_bytes: 25633829.363360763
num_examples: 8000
- name: high_school_physics
num_bytes: 26191171.15244726
num_examples: 8000
- name: machine_learning
num_bytes: 26083439.57671958
num_examples: 8000
- name: prehistory
num_bytes: 25412395.782298435
num_examples: 8000
- name: security_studies
num_bytes: 25639530.37483843
num_examples: 8000
- name: sociology
num_bytes: 24402985.15227051
num_examples: 8000
download_size: 140394435
dataset_size: 251509378.8798793
---
# Dataset Card for "qa-openai_batched_long_icl0_clen512_maxD-1_maxC2000_08000_cleaned_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.586467444896698,
0.029071280732750893,
0.15121541917324066,
0.3275112211704254,
-0.4351518452167511,
-0.344635009765625,
-0.021098937839269638,
-0.0950879231095314,
0.5700687766075134,
0.6171809434890747,
-0.8575785160064697,
-0.5152838826179504,
-0.33041974902153015,
-0.055025629699230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
evanfrick/lichess | evanfrick | 2023-11-08T08:36:24Z | 40 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-08T08:36:24Z | 2023-11-08T05:48:31.000Z | 2023-11-08T05:48:31 | ---
license: mit
---
| [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rjaiswal/tubogas-dataset | rjaiswal | 2023-11-20T09:42:33Z | 40 | 0 | null | [
"region:us"
] | 2023-11-20T09:42:33Z | 2023-11-16T15:57:04.000Z | 2023-11-16T15:57:04 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4389802.0
num_examples: 42
download_size: 2186125
dataset_size: 4389802.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tubogas-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7161089777946472,
-0.19296574592590332,
0.11274369806051254,
0.2646186351776123,
-0.2646429240703583,
-0.009472347795963287,
0.3281655013561249,
0.1251978576183319,
0.8259877562522888,
0.5093401670455933,
-0.6648954749107361,
-0.7734971046447754,
-0.6830518245697021,
-0.1351934671401977... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hodgesz/llama2-sql-create-context4 | hodgesz | 2023-11-18T01:13:21Z | 40 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-18T01:13:21Z | 2023-11-18T01:12:52.000Z | 2023-11-18T01:12:52 | ---
license: apache-2.0
---
| [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ngoxuanphong/zalo | ngoxuanphong | 2023-11-21T10:00:50Z | 40 | 0 | null | [
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-11-21T10:00:50Z | 2023-11-20T04:46:01.000Z | 2023-11-20T04:46:01 | ---
license: apache-2.0
language:
- en
size_categories:
- 1K<n<10K
--- | [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SUSTech/mt_bench_judge | SUSTech | 2023-11-23T10:18:55Z | 40 | 0 | null | [
"region:us"
] | 2023-11-23T10:18:55Z | 2023-11-23T10:03:09.000Z | 2023-11-23T10:03:09 | ---
dataset_info:
features:
- name: question_id
dtype: int64
- name: model
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
- name: turn
dtype: int64
- name: judge
sequence: string
- name: user_prompt
dtype: string
- name: judgment
dtype: string
- name: score
dtype: float64
- name: tstamp
dtype: float64
- name: category
dtype: string
- name: reference
sequence: string
splits:
- name: train
num_bytes: 4409406
num_examples: 800
download_size: 949262
dataset_size: 4409406
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mt_bench_judge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5070008635520935,
-0.13040265440940857,
0.5846325159072876,
0.24061515927314758,
-0.3629312813282013,
-0.3571460545063019,
0.254354327917099,
0.10948209464550018,
0.6192311644554138,
0.6882535815238953,
-0.6617558002471924,
-0.786194384098053,
-0.786971390247345,
-0.4348928928375244,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jashmehta3300/social-injection-bold | jashmehta3300 | 2023-11-26T04:32:23Z | 40 | 0 | null | [
"region:us"
] | 2023-11-26T04:32:23Z | 2023-11-25T18:23:38.000Z | 2023-11-25T18:23:38 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lavis-nlp/german_legal_sentences | lavis-nlp | 2022-10-20T18:34:19Z | 39 | 3 | null | [
"task_categories:text-retrieval",
"task_ids:semantic-similarity-scoring",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n>1M",
"source_datasets:original",
"language:de",
"license:unknown",
"arxiv:2005.13342",
"arxiv:2010.1025... | 2022-10-20T18:34:19Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- de
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n>1M
source_datasets:
- original
task_categories:
- text-retrieval
- text-scoring
task_ids:
- semantic-similarity-scoring
- text-retrieval-other-example-based-retrieval
---
# Dataset Card for German Legal Sentences
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://lavis-nlp.github.io/german_legal_sentences/
- **Repository:** https://github.com/lavis-nlp/german_legal_sentences
- **Paper:** coming soon
- **Leaderboard:**
- **Point of Contact:** [Marco Wrzalik](mailto:marco.wrzalik@hs-rm.de)
### Dataset Summary
German Legal Sentences (GLS) is an automatically generated training dataset for semantic sentence matching and citation recommendation in the domain in german legal documents. It follows the concept of weak supervision, where imperfect labels are generated using multiple heuristics. For this purpose we use a combination of legal citation matching and BM25 similarity. The contained sentences and their citations are parsed from real judicial decisions provided by [Open Legal Data](http://openlegaldata.io/) (https://arxiv.org/abs/2005.13342).
### Supported Tasks and Leaderboards
The main associated task is *Semantic Similarity Ranking*. We propose to use the *Mean Reciprocal Rank* (MRR) cut at the tenth position as well as MAP and Recall on Rankings of size 200. As baselines we provide the follows:
| Method | MRR@10 | MAP@200 | Recall@200 |
|-----------------------------------|---------:|-----------:|------------:|
| BM25 - default `(k1=1.2; b=0.75)` | 25.7 | 17.6 | 42.9 |
| BM25 - tuned `(k1=0.47; b=0.97)` | 26.2 | 18.1 | 43.3 |
| [CoRT](https://arxiv.org/abs/2010.10252) | 31.2 | 21.4 | 56.2 |
| [CoRT + BM25](https://arxiv.org/abs/2010.10252) | 32.1 | 22.1 | 67.1 |
In addition, we want to support a *Citation Recommendation* task in the future.
If you wish to contribute evaluation measures or give any suggestion or critique, please write an [e-mail](mailto:marco.wrzalik@hs-rm.de).
### Languages
This dataset contains texts from the specific domain of German court decisions.
## Dataset Structure
### Data Instances
```
{'query.doc_id': 28860,
'query.ref_ids': [6215, 248, 248],
'query.sent_id': 304863,
'query.text': 'Zudem ist zu berücksichtigen , dass die Vollverzinsung nach '
'[REF] i. V. m. [REF] gleichermaßen zugunsten wie zulasten des '
'Steuerpflichtigen wirkt , sodass bei einer Überzahlung durch '
'den Steuerpflichtigen der Staat dem Steuerpflichtigen neben '
'der Erstattung ebenfalls den entstandenen potentiellen Zins- '
'und Liquiditätsnachteil in der pauschalierten Höhe des [REF] '
'zu ersetzen hat , unabhängig davon , in welcher Höhe dem '
'Berechtigten tatsächlich Zinsen entgangen sind .',
'related.doc_id': 56348,
'related.ref_ids': [248, 6215, 62375],
'related.sent_id': 558646,
'related.text': 'Ferner ist zu berücksichtigen , dass der Zinssatz des [REF] '
'im Rahmen des [REF] sowohl für Steuernachforderung wie auch '
'für Steuererstattungen und damit gleichermaßen zugunsten wie '
'zulasten des Steuerpflichtigen wirkt , Vgl. BVerfG , '
'Nichtannahmebeschluss vom [DATE] [REF] , juris , mit der '
'Folge , dass auch Erstattungsansprüche unabhängig davon , ob '
'und in welcher Höhe dem Berechtigten tatsächlich Zinsen '
'entgangen sind , mit monatlich 0,0 % verzinst werden .'}
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The documents we take from [Open Legal Data](http://openlegaldata.io/) (https://arxiv.org/abs/2005.13342) are first preprocessed by removing line breaks, enumeration characters and headings. Afterwards we parse legal citations using hand-crafted regular expressions. Each citation is split into it components and normalized, thus different variants of the same citation are matched together. For instance, "§211 Absatz 1 des Strafgesetzbuches" is normalized to "§ 211 Abs. 1 StGB". Every time we discover an unknown citation, we assign an unique id to it. We use these ids to replace parsed citations in the document text with a simple reference tag containing this id (e.g `[REF321]`). At the same time we parse dates and replace them with the date tag `[DATE]`. Both remove dots which can may be confused with the end of a sentence, which makes the next stage easier.
We use [SoMaJo](https://github.com/tsproisl/SoMaJo) to perform sentence tokenizing on the pre-processed documents. Each sentence that does not contain at least one legal citation is discarded. For the rest we assign sentence ids, remove all reference ids from them as well as any contents in braces (braces often contain large enumerations of citations and their sources). At the same time we keep track of the corresponding document from which a sentence originates and which references occur in it.
#### Who are the source language producers?
The source language originates in the context of German court proceedings.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The annotations are machine-generated.
### Personal and Sensitive Information
The source documents are already public and anonymized.
## Considerations for Using the Data
### Social Impact of Dataset
With this dataset, we strive towards better accessibility of court decisions to the general public by accelerating research on semantic search technologies. We hope that emerging search technologies will enable the layperson to find relevant information without knowing the specific terms used by lawyers.
### 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
Coming soon!
### Contributions
Thanks to [@mwrzalik](https://github.com/mwrzalik) for adding this dataset. | [
-0.3053070306777954,
-0.6488118171691895,
0.5966860055923462,
0.05480438098311424,
-0.34128203988075256,
-0.3778536915779114,
-0.33543047308921814,
-0.22077585756778717,
0.36270809173583984,
0.4365076720714569,
-0.3717495799064636,
-0.9940224289894104,
-0.5817584991455078,
0.25738072395324... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
m-newhauser/senator-tweets | m-newhauser | 2022-03-07T16:37:44Z | 39 | 1 | null | [
"region:us"
] | 2022-03-07T16:37:44Z | 2022-03-07T16:37:35.000Z | 2022-03-07T16:37:35 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
taln-ls2n/kp20k | taln-ls2n | 2023-09-13T13:15:04Z | 39 | 1 | null | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:unknown",
"keyphrase-generation",
"keyphrase-extraction",
"text-mining",
"region:us"
] | 2023-09-13T13:15:04Z | 2022-04-14T09:00:02.000Z | 2022-04-14T09:00:02 | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- text-generation
task_ids: []
pretty_name: KP20k
tags:
- keyphrase-generation
- keyphrase-extraction
- text-mining
---
# KP20k Benchmark Dataset for Keyphrase Generation
## About
KP20k is a dataset for benchmarking keyphrase extraction and generation models.
The data is composed of 570 809 abstracts and their associated titles from scientific articles.
Details about the dataset can be found in the original paper:
- Meng et al 2017.
[Deep keyphrase Generation](https://aclanthology.org/P17-1054.pdf)
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 582–592
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
- Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/).
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.
## Content
The dataset is divided into the following three splits:
| Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----------: | --------: | ----------: | ------: | -------: |
| Train | 530 809 | 5.29 | 58.19 | 10.93 | 17.36 | 13.52 |
| Test | 20 000 | 5.28 | 58.40 | 10.84 | 17.20 | 13.56 |
| Validation | 20 000 | 5.27 | 58.20 | 10.94 | 17.26 | 13.61 |
The following data fields are available:
- **id**: unique identifier of the document. **NB** There were no ids in the original dataset. The ids were generated using the python module shortuuid (https://pypi.org/project/shortuuid/)
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of the author assigned keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + abstract). | [
-0.2397076040506363,
-0.27083244919776917,
0.4346559941768646,
0.24682028591632843,
-0.39116397500038147,
0.18899905681610107,
-0.06481043249368668,
-0.18186625838279724,
0.10181933641433716,
0.4054800570011139,
-0.5717522501945496,
-0.8443481922149658,
-0.5878648161888123,
0.5735462903976... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
KevinZ/oLMpics | KevinZ | 2022-04-19T18:08:06Z | 39 | 0 | null | [
"region:us"
] | 2022-04-19T18:08:06Z | 2022-04-18T02:14:53.000Z | 2022-04-18T02:14:53 | oLMpics README
| [
-0.5414970517158508,
-0.13784530758857727,
0.29386138916015625,
-0.061467669904232025,
-0.6916552186012268,
-0.16386118531227112,
0.08057012408971786,
-0.6702588200569153,
0.11930129677057266,
0.9155616164207458,
-0.8323282599449158,
-0.5169150233268738,
-0.31646761298179626,
-0.3293415009... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GroNLP/divemt | GroNLP | 2023-02-10T11:04:33Z | 39 | 2 | null | [
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:it",
"language:vi",
"language:nl",
"langu... | 2023-02-10T11:04:33Z | 2022-05-23T19:56:55.000Z | 2022-05-23T19:56:55 | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- en
- it
- vi
- nl
- uk
- tr
- ar
license:
- gpl-3.0
multilinguality:
- translation
pretty_name: divemt
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
---
# Dataset Card for DivEMT
*For more details on DivEMT, see our [EMNLP 2022 Paper](https://arxiv.org/abs/2205.12215) and our [Github repository](https://github.com/gsarti/divemt)*
## Dataset Description
- **Source:** [Github](https://github.com/gsarti/divemt)
- **Paper:** [Arxiv](https://arxiv.org/abs/2205.12215)
- **Point of Contact:** [Gabriele Sarti](mailto:g.sarti@rug.nl)
[Gabriele Sarti](https://gsarti.com) • [Arianna Bisazza](https://www.cs.rug.nl/~bisazza/) • [Ana Guerberof Arenas](https://scholar.google.com/citations?user=i6bqaTsAAAAJ) • [Antonio Toral](https://antoniotor.al/)
<img src="https://huggingface.co/datasets/GroNLP/divemt/resolve/main/divemt.png" alt="DivEMT annotation pipeline" width="600"/>
>We introduce DivEMT, the first publicly available post-editing study of Neural Machine Translation (NMT) over a typologically diverse set of target languages. Using a strictly controlled setup, 18 professional translators were instructed to translate or post-edit the same set of English documents into Arabic, Dutch, Italian, Turkish, Ukrainian, and Vietnamese. During the process, their edits, keystrokes, editing times and pauses were recorded, enabling an in-depth, cross-lingual evaluation of NMT quality and post-editing effectiveness. Using this new dataset, we assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity. We find that post-editing is consistently faster than translation from scratch. However, the magnitude of productivity gains varies widely across systems and languages, highlighting major disparities in post-editing effectiveness for languages at different degrees of typological relatedness to English, even when controlling for system architecture and training data size. We publicly release the complete dataset including all collected behavioral data, to foster new research on the translation capabilities of NMT systems for typologically diverse languages.
### Dataset Summary
This dataset contains the processed `warmup` and `main` splits of the DivEMT dataset. A sample of documents extracted from the Flores-101 corpus were either translated from scratch or post-edited from an existing automatic translation by a total of 18 professional translators across six typologically diverse languages (Arabic, Dutch, Italian, Turkish, Ukrainian, Vietnamese). During the translation, behavioral data (keystrokes, pauses, editing times) were collected using the [PET](https://github.com/wilkeraziz/PET) platform.
We publicly release the processed dataset including all collected behavioural data, to foster new research on the ability of state-of-the-art NMT systems to generate text in typologically diverse languages.
### News 🎉
**February, 2023**: The DivEMT dataset now contains linguistic annotations (`*_annotations` fields) computed with Stanza and word-level quality estimation tags (`src_wmt22_qe`, `mt_wmt22_qe`) obtained using the same scripts adopted for the WMT22 QE Task 2.
### Languages
The language data of DivEMT is in English (BCP-47 `en`), Italian (BCP-47 `it`), Dutch (BCP-47 `nl`), Arabic (BCP-47 `ar`), Turkish (BCP-47 `tr`), Ukrainian (BCP-47 `uk`) and Vietnamese (BCP-47 `vi`)
## Dataset Structure
### Data Instances
The dataset contains two configurations: `main` and `warmup`. `main` contains the full data collected during the main task and analyzed during our experiments. `warmup` contains the data collected in the verification phase, before the main task begins.
### Data Fields
The following fields are contained in the training set:
|Field|Description|
|-----|-----------|
|`unit_id` | The full entry identifier. Format: `flores101-{config}-{lang}-{doc_id}-{modality}-{sent_in_doc_num}` |
|`flores_id` | Index of the sentence in the original [Flores-101](https://huggingface.co/datasets/gsarti/flores_101) dataset |
|`item_id` | The sentence identifier. The first digits of the number represent the document containing the sentence, while the last digit of the number represents the sentence position inside the document. Documents can contain from 3 to 5 contiguous sentences each. |
|`subject_id` | The identifier for the translator performing the translation from scratch or post-editing task. Values: `t1`, `t2` or `t3`. |
|`lang_id` | Language identifier for the sentence, using Flores-101 three-letter format (e.g. `ara`, `nld`)|
|`doc_id` | Document identifier for the sentence |
|`task_type` | The modality of the translation task. Values: `ht` (translation from scratch), `pe1` (post-editing Google Translate translations), `pe2` (post-editing [mBART 1-to-50](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) translations). |
|`translation_type` | Either `ht` for from scratch or `pe` for post-editing |
|`src_len_chr` | Length of the English source text in number of characters |
|`mt_len_chr` | Length of the machine translation in number of characters (NaN for ht) |
|`tgt_len_chr` | Length of the target text in number of characters |
|`src_len_wrd` | Length of the English source text in number of words |
|`mt_len_wrd` | Length of the machine translation in number of words (NaN for ht) |
|`tgt_len_wrd` | Length of the target text in number of words |
|`edit_time` | Total editing time for the translation in seconds. |
|`k_total` | Total number of keystrokes for the translation. |
|`k_letter` | Total number of letter keystrokes for the translation. |
|`k_digit` | Total number of digit keystrokes for the translation. |
|`k_white` | Total number of whitespace keystrokes for the translation. |
|`k_symbol` | Total number of symbol (punctuation, etc.) keystrokes for the translation. |
|`k_nav` | Total number of navigation keystrokes (left-right arrows, mouse clicks) for the translation. |
|`k_erase` | Total number of erase keystrokes (backspace, cancel) for the translation. |
|`k_copy` | Total number of copy (Ctrl + C) actions during the translation. |
|`k_cut` | Total number of cut (Ctrl + X) actions during the translation. |
|`k_paste` | Total number of paste (Ctrl + V) actions during the translation. |
|`k_do` | Total number of Enter actions during the translation. |
|`n_pause_geq_300` | Number of pauses of 300ms or more during the translation. |
|`len_pause_geq_300` | Total duration of pauses of 300ms or more, in milliseconds. |
|`n_pause_geq_1000` | Number of pauses of 1s or more during the translation. |
|`len_pause_geq_1000` | Total duration of pauses of 1000ms or more, in milliseconds. |
|`event_time` | Total time summed across all translation events, should be comparable to `edit_time` in most cases. |
|`num_annotations` | Number of times the translator focused the textbox for performing the translation of the sentence during the translation session. E.g. 1 means the translation was performed once and never revised. |
|`n_insert` | Number of post-editing insertions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`n_delete` | Number of post-editing deletions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`n_substitute` | Number of post-editing substitutions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`n_shift` | Number of post-editing shifts (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`tot_shifted_words` | Total amount of shifted words from all shifts present in the sentence. |
|`tot_edits` | Total of all edit types for the sentence. |
|`hter` | Human-mediated Translation Edit Rate score computed between MT and post-edited TGT (empty for modality `ht`) using the [tercom](https://github.com/jhclark/tercom) library. |
|`cer` | Character-level HTER score computed between MT and post-edited TGT (empty for modality `ht`) using [CharacTER](https://github.com/rwth-i6/CharacTER).
|`bleu` | Sentence-level BLEU score between MT and post-edited TGT (empty for modality `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
|`chrf` | Sentence-level chrF score between MT and post-edited TGT (empty for modality `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
|`time_s` | Edit time expressed in seconds. |
|`time_m` | Edit time expressed in minutes. |
|`time_h` | Edit time expressed in hours. |
|`time_per_char` | Edit time per source character, expressed in seconds. |
|`time_per_word` | Edit time per source word, expressed in seconds. |
|`key_per_char` | Proportion of keys per character needed to perform the translation. |
|`words_per_hour` | Amount of source words translated or post-edited per hour. |
|`words_per_minute` | Amount of source words translated or post-edited per minute. |
|`per_subject_visit_order` | Id denoting the order in which the translator accessed documents. 1 correspond to the first accessed document. |
|`src_text` | The original source sentence extracted from Wikinews, wikibooks or wikivoyage. |
|`mt_text` | Missing if tasktype is `ht`. Otherwise, contains the automatically-translated sentence before post-editing. |
|`tgt_text` | Final sentence produced by the translator (either via translation from scratch of `sl_text` or post-editing `mt_text`) |
|`aligned_edit` | Aligned visual representation of REF (`mt_text`), HYP (`tl_text`) and edit operations (I = Insertion, D = Deletion, S = Substitution) performed on the field. Replace `\\n` with `\n` to show the three aligned rows.|
|`src_tokens` | List of tokens obtained tokenizing `src_text` with Stanza using default params. |
|`src_annotations` | List of lists (one per `src_tokens` token) containing dictionaries (one per word, >1 for mwt) with pos, ner and other info parsed by Stanza |
|`mt_tokens` | List of tokens obtained tokenizing `mt_text` with Stanza using default params. |
|`mt_annotations` | List of lists (one per `mt_tokens` token) containing dictionaries (one per word, >1 for mwt) with pos, ner and other info parsed by Stanza |
|`tgt_tokens` | List of tokens obtained tokenizing `tgt_text` with Stanza using default params. |
|`tgt_annotations` | List of lists (one per `tgt_tokens` token) containing dictionaries (one per word, >1 for mwt) with pos, ner and other info parsed by Stanza |
### Data Splits
| config | train|
|-------:|-----:|
|`main` | 7740 (107 docs i.e. 430 sents x 18 translators) |
|`warmup`| 360 (5 docs i.e. 20 sents x 18 translators) |
#### Train Split
The `train` split contains the totality of triplets (or pairs, when translation from scratch is performed) annotated with behavioral data produced during the translation.
The following is an example of the subject `t1` post-editing a machine translation produced by Google Translate (task_type `pe1`) taken from the `train` split for Turkish. The field `aligned_edit` is showed over three lines to provide a visual understanding of its contents.
```json
{
'unit_id': 'flores101-main-tur-46-pe1-3',
'flores_id': 871,
'item_id': 'flores101-main-463',
'subject_id': 'tur_t1',
'task_type': 'pe1',
'translation_type': 'pe',
'src_len_chr': 109,
'mt_len_chr': 129.0,
'tgt_len_chr': 120,
'src_len_wrd': 17,
'mt_len_wrd': 15.0,
'tgt_len_wrd': 13,
'edit_time': 11.762999534606934,
'k_total': 31,
'k_letter': 9,
'k_digit': 0,
'k_white': 0,
'k_symbol': 0,
'k_nav': 20,
'k_erase': 2,
'k_copy': 0,
'k_cut': 0,
'k_paste': 0,
'k_do': 0,
'n_pause_geq_300': 2,
'len_pause_geq_300': 4986,
'n_pause_geq_1000': 1,
'len_pause_geq_1000': 4490,
'event_time': 11763,
'num_annotations': 2,
'last_modification_time': 1643569484,
'n_insert': 0.0,
'n_delete': 2.0,
'n_substitute': 1.0,
'n_shift': 0.0,
'tot_shifted_words': 0.0,
'tot_edits': 3.0,
'hter': 20.0,
'cer': 0.10,
'bleu': 0.0,
'chrf': 2.569999933242798,
'lang_id': 'tur',
'doc_id': 46,
'time_s': 11.762999534606934,
'time_m': 0.1960500031709671,
'time_h': 0.0032675000838935375,
'time_per_char': 0.1079174280166626,
'time_per_word': 0.6919412016868591,
'key_per_char': 0.2844036817550659,
'words_per_hour': 5202.75439453125,
'words_per_minute': 86.71257019042969,
'per_subject_visit_order': 201,
'src_text': 'As one example, American citizens in the Middle East might face different situations from Europeans or Arabs.',
'mt_text': "Bir örnek olarak, Orta Doğu'daki Amerikan vatandaşları, Avrupalılardan veya Araplardan farklı durumlarla karşı karşıya kalabilir.",
'tgt_text': "Örneğin, Orta Doğu'daki Amerikan vatandaşları, Avrupalılardan veya Araplardan farklı durumlarla karşı karşıya kalabilir.",
'aligned_edit': "REF: bir örnek olarak, orta doğu'daki amerikan vatandaşları, avrupalılardan veya araplardan farklı durumlarla karşı karşıya kalabilir.\\n
HYP: *** ***** örneğin, orta doğu'daki amerikan vatandaşları, avrupalılardan veya araplardan farklı durumlarla karşı karşıya kalabilir.\\n
EVAL: D D S"
}
```
The text is provided as-is, without further preprocessing or tokenization.
### Dataset Creation
The dataset was parsed from PET XML files into CSV format using the scripts available in the [DivEMT Github repository](https://github.com/gsarti/divemt).
Those are adapted from the ones by [Antonio Toral](https://research.rug.nl/en/persons/antonio-toral-ruiz) found at the following link: [https://github.com/antot/postediting_novel_frontiers](https://github.com/antot/postediting_novel_frontiers).
## Additional Information
### Dataset Curators
For problems related to this 🤗 Datasets version, please contact me at [g.sarti@rug.nl](mailto:g.sarti@rug.nl).
### Citation Information
```bibtex
@inproceedings{sarti-etal-2022-divemt,
title = "{D}iv{EMT}: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages",
author = "Sarti, Gabriele and
Bisazza, Arianna and
Guerberof-Arenas, Ana and
Toral, Antonio",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.532",
pages = "7795--7816",
}
``` | [
-0.3049851953983307,
-0.6313626766204834,
0.44244933128356934,
0.30382850766181946,
-0.4271915853023529,
-0.10384097695350647,
-0.4326137602329254,
-0.24820920825004578,
0.37696701288223267,
0.39765429496765137,
-0.6339412927627563,
-0.881414532661438,
-0.6099082231521606,
0.48315957188606... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
taln-ls2n/pubmed | taln-ls2n | 2022-10-26T19:14:46Z | 39 | 1 | null | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:1k<n<10k",
"language:en",
"license:unknown",
"keyphrase-generation",
"keyphrase-extraction",
"text-mining",
"region:us"
] | 2022-10-26T19:14:46Z | 2022-05-24T08:34:08.000Z | 2022-05-24T08:34:08 | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1k<n<10k
task_categories:
- text-generation
task_ids: []
pretty_name: PubMed
tags:
- keyphrase-generation
- keyphrase-extraction
- text-mining
---
# Schutz 2008 PubMed dataset for keyphrase extraction
## About
This dataset is made of 1320 articles with full text and author assigned keyphrases.
Details about the dataset can be found in the original paper:
Keyphrase extraction from single documents in the open domain exploiting linguistic and statistical methods. Alexander Thorsten Schutz. Master's thesis, National University of Ireland (2008).
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
- Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/).
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.
## Content
The details of the dataset are in the table below:
| Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----------: | --------: | ----------: | ------: | -------: |
| Test | 1320 | 5.40 | 84.54 | 9.14 | 3.84 | 2.47 |
The following data fields are available:
- **id**: unique identifier of the document.
- **title**: title of the document.
- **text**: full article minus the title.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + text).
| [
-0.005555886309593916,
-0.2898070514202118,
0.48875126242637634,
0.24048380553722382,
-0.46108391880989075,
-0.09348160028457642,
-0.1663980484008789,
-0.06701760739088058,
0.2617139220237732,
0.5958781838417053,
-0.3619893193244934,
-0.7547374963760376,
-0.6398683786392212,
0.675595998764... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AhmedSSabir/Textual-Image-Caption-Dataset | AhmedSSabir | 2023-10-14T12:32:07Z | 39 | 5 | null | [
"task_categories:image-to-text",
"task_categories:image-classification",
"task_categories:visual-question-answering",
"task_categories:sentence-similarity",
"language:en",
"image captioning",
"language grounding",
"visual semantic",
"semantic similarity",
"arxiv:2301.08784",
"arxiv:1408.5882",
... | 2023-10-14T12:32:07Z | 2022-06-08T10:36:12.000Z | 2022-06-08T10:36:12 | ---
task_categories:
- image-to-text
- image-classification
- visual-question-answering
- sentence-similarity
language:
- en
tags:
- image captioning
- language grounding
- visual semantic
- semantic similarity
pretty_name: ' image captioning language grounding visual semantic '
---
#### Update: OCT-2023 ###
Add v2 with recent SoTA model **swinV2 classifier** for both soft/*hard-label* visual_caption_cosine_score_v2 with _person_ label (0.2, 0.3 and 0.4)
# Introduction
Modern image captaining relies heavily on extracting knowledge, from images such as objects,
to capture the concept of static story in the image. In this paper, we propose a textual visual context dataset
for captioning, where the publicly available dataset COCO caption (Lin et al., 2014) has been extended with information
about the scene (such as objects in the image). Since this information has textual form, it can be used to leverage any NLP task,
such as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach.
Please refer to [project page](https://sabirdvd.github.io/project_page/Dataset_2022/index.html) and [Github](https://github.com/ahmedssabir/Visual-Semantic-Relatedness-Dataset-for-Image-Captioning) for more information. [](https://arxiv.org/abs/2301.08784) [](https://ahmed.jp/project_page/Dataset_2022/index.html)
For quick start please have a look this [demo](https://github.com/ahmedssabir/Textual-Visual-Semantic-Dataset/blob/main/BERT_CNN_Visual_re_ranker_demo.ipynb) and [pre-trained model with th 0.2, 0.3, 0.4](https://huggingface.co/AhmedSSabir/BERT-CNN-Visual-Semantic)
# Overview
We enrich COCO-Caption with textual Visual Context information. We use ResNet152, CLIP,
and Faster R-CNN to extract object information for each image. We use three filter approaches
to ensure the quality of the dataset (1) Threshold: to filter out predictions where the object classifier
is not confident enough, and (2) semantic alignment with semantic similarity to remove duplicated objects.
(3) semantic relatedness score as soft-label: to guarantee the visual context and caption have a strong
relation. In particular, we use Sentence-RoBERTa-sts via cosine similarity to give a soft score, and then
we use a threshold to annotate the final label (if th ≥ 0.2, 0.3, 0.4 then 1,0). Finally, to take advantage
of the visual overlap between caption and visual context, and to extract global information, we use BERT followed by a shallow 1D-CNN (Kim, 2014)
to estimate the visual relatedness score.
<!--
## Dataset
(<a href="https://arxiv.org/abs/1408.5882">Kim, 2014</a>)
### Sample
```
|---------------+--------------+---------+---------------------------------------------------|
| VC1 | VC2 | VC3 | human annoated caption |
| ------------- | ----------- | --------| ------------------------------------------------- |
| cheeseburger | plate | hotdog | a plate with a hamburger fries and tomatoes |
| bakery | dining table | website | a table having tea and a cake on it |
| gown | groom | apron | its time to cut the cake at this couples wedding |
|---------------+--------------+---------+---------------------------------------------------|
```
-->
### Download
0. [Dowload Raw data with ID and Visual context](https://www.dropbox.com/s/xuov24on8477zg8/All_Caption_ID.csv?dl=0) -> original dataset with related ID caption [train2014](https://cocodataset.org/#download)
1. [Downlod Data with cosine score](https://www.dropbox.com/s/55sit8ow9tems4u/visual_caption_cosine_score.zip?dl=0)-> soft cosine lable with **th** 0.2, 0.3, 0.4 and 0.5 and hardlabel [0,1]
2. [Dowload Overlaping visual with caption](https://www.dropbox.com/s/br8nhnlf4k2czo8/COCO_overlaping_dataset.txt?dl=0)-> Overlap visual context and the human annotated caption
3. [Download Dataset (tsv file)](https://www.dropbox.com/s/dh38xibtjpohbeg/train_all.zip?dl=0) 0.0-> raw data with hard lable without cosine similairty and with **th**reshold cosine sim degree of the relation beteween the visual and caption = 0.2, 0.3, 0.4
4. [Download Dataset GenderBias](https://www.dropbox.com/s/1wki0b0d21078mj/gender%20natural.zip?dl=0)-> man/woman replaced with person class label
For future work, we plan to extract the visual context from the caption (without using a visual classifier) and estimate the visual relatedness score by
employing unsupervised learning (i.e. contrastive learning). (work in progress)
1. [Download CC](https://www.dropbox.com/s/pc1uv2rf6nqdp57/CC_caption_40.txt.zip) -> Caption dataset from Conceptinal Caption (CC) 2M (2255927 captions)
2. [Download CC+wiki](https://www.dropbox.com/s/xuov24on8477zg8/All_Caption_ID.csv?dl=0) -> CC+1M-wiki 3M (3255928)
3. [Download CC+wiki+COCO](https://www.dropbox.com/s/k7oqwr9a1a0h8x1/CC_caption_40%2Bwiki%2BCOCO.txt.zip) -> CC+wiki+COCO-Caption 3.5M (366984)
4. [Download COCO-caption+wiki](https://www.dropbox.com/s/wc4k677wp24kzhh/COCO%2Bwiki.txt.zip) -> COCO-caption +wiki 1.4M (1413915)
5. [Download COCO-caption+wiki+CC+8Mwiki](https://www.dropbox.com/s/xhfx32sjy2z5bpa/11M_wiki_7M%2BCC%2BCOCO.txt.zip) -> COCO-caption+wiki+CC+8Mwiki 11M (11541667)
## Citation
The details of this repo are described in the following paper. If you find this repo useful, please kindly cite it:
```bibtex
@article{sabir2023visual,
title={Visual Semantic Relatedness Dataset for Image Captioning},
author={Sabir, Ahmed and Moreno-Noguer, Francesc and Padr{\'o}, Llu{\'\i}s},
journal={arXiv preprint arXiv:2301.08784},
year={2023}
}
``` | [
-0.7025397419929504,
-0.6237320303916931,
0.056404128670692444,
0.3135712444782257,
-0.5657690763473511,
-0.09819695353507996,
-0.18751709163188934,
-0.6892073154449463,
0.44647255539894104,
0.5506954789161682,
-0.7087982892990112,
-0.7152636647224426,
-0.4892575740814209,
0.27318418025970... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BeIR/trec-news-generated-queries | BeIR | 2022-10-23T06:13:54Z | 39 | 1 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:13:54Z | 2022-06-17T13:04:13.000Z | 2022-06-17T13:04:13 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## 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
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
-0.5227212905883789,
-0.5249219536781311,
0.14435674250125885,
0.04820423573255539,
0.055916160345077515,
0.0011022627586498857,
-0.1081070527434349,
-0.24874727427959442,
0.28598034381866455,
0.07840226590633392,
-0.45233607292175293,
-0.7186435461044312,
-0.347678542137146,
0.20300328731... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
codeparrot/codecomplex | codeparrot | 2022-10-25T09:30:16Z | 39 | 11 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"language:code",
"license:apache-2.0",
"region:us"
] | 2022-10-25T09:30:16Z | 2022-06-24T20:18:43.000Z | 2022-06-24T20:18:43 | ---
annotations_creators: []
language_creators:
- expert-generated
language:
- code
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: CodeComplex
---
# CodeComplex Dataset
## Dataset Description
[CodeComplex](https://github.com/yonsei-toc/CodeComple) consists of 4,200 Java codes submitted to programming competitions by human programmers and their complexity labels annotated by a group of algorithm experts.
### How to use it
You can load and iterate through the dataset with the following two lines of code:
```python
from datasets import load_dataset
ds = load_dataset("codeparrot/codecomplex", split="train")
print(next(iter(ds)))
```
## Data Structure
```
DatasetDict({
train: Dataset({
features: ['src', 'complexity', 'problem', 'from'],
num_rows: 4517
})
})
```
### Data Instances
```python
{'src': 'import java.io.*;\nimport java.math.BigInteger;\nimport java.util.InputMismatchException;...',
'complexity': 'quadratic',
'problem': '1179_B. Tolik and His Uncle',
'from': 'CODEFORCES'}
```
### Data Fields
* src: a string feature, representing the source code in Java.
* complexity: a string feature, giving program complexity.
* problem: a string of the feature, representing the problem name.
* from: a string feature, representing the source of the problem.
complexity filed has 7 classes, where each class has around 500 codes each. The seven classes are constant, linear, quadratic, cubic, log(n), nlog(n) and NP-hard.
### Data Splits
The dataset only contains a train split.
## Dataset Creation
The authors first collected problem and solution codes in Java from CodeForces and they were inspected by experienced human annotators to label each code by their time complexity. After the labelling, they used different programming experts to verify the class of each data that the human annotators assigned.
## Citation Information
```
@article{JeonBHHK22,
author = {Mingi Jeon and Seung-Yeop Baik and Joonghyuk Hahn and Yo-Sub Han and Sang-Ki Ko},
title = {{Deep Learning-based Code Complexity Prediction}},
year = {2022},
}
``` | [
-0.5097919702529907,
-0.3065548837184906,
0.17924386262893677,
0.25846609473228455,
0.07703936100006104,
0.27944818139076233,
-0.3755101263523102,
-0.3563574552536011,
-0.17258967459201813,
0.34529778361320496,
-0.338498055934906,
-0.5452361106872559,
-0.6716673970222473,
0.241822689771652... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
imvladikon/nemo_corpus | imvladikon | 2023-11-24T10:36:57Z | 39 | 0 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-reuters-corpus",
"language:he",
"region:us"
] | 2023-11-24T10:36:57Z | 2022-06-28T16:51:45.000Z | 2022-06-28T16:51:45 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- he
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-reuters-corpus
task_categories:
- token-classification
task_ids:
- named-entity-recognition
train-eval-index:
- config: nemo_corpus
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: validation
test_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
# NEMO-Corpus - The Hebrew Named Entities and Morphology Corpus
## Config and Usage
Config:
* flat_token - flatten tags
* nested_token - nested tags
* flat_morph - flatten tags with morphologically presegmentized tokens
* nested_morph - nested tags with morphologically presegmentized tokens
Note: It seems that a couple of samples for the flat_token and nested_token are mistakenly presegmented, and as a result, these samples have white space in the token.
```python
from datasets import load_dataset
# the main corpus
ds = load_dataset('imvladikon/nemo_corpus', "flat_token")
for sample in ds["train"]:
print(sample)
# the nested corpus
ds = load_dataset('imvladikon/nemo_corpus', "nested_morph")
```
Getting classes and encoding/decoding could be done through these functions:
```
idx2label = dataset["train"].features["ner_tags"].feature.int2str
label2idx = dataset["train"].features["ner_tags"].feature.str2int
```
or just use raw_tags field.
## Fields
available fields (flat):
* "id"
* "sentence"
* "tokens"
* "raw_tags"
* "ner_tags"
Example of the one record for `flat`:
```json
{'id': '0', 'tokens': ['"', 'תהיה', 'נקמה', 'ו', 'בגדול', '.'], 'sentence': '" תהיה נקמה ו בגדול .', 'raw_tags': ['O', 'O', 'O', 'O', 'O', 'O'], 'ner_tags': [24, 24, 24, 24, 24, 24]}
```
Example of the one record for `nested`:
```json
{'id': '0', 'tokens': ['"', 'תהיה', 'נקמה', 'ו', 'בגדול', '.'], 'ner_tags': [24, 24, 24, 24, 24, 24], 'ner_tags_2': [24, 24, 24, 24, 24, 24], 'ner_tags_3': [24, 24, 24, 24, 24, 24], 'ner_tags_4': [24, 24, 24, 24, 24, 24]}
```
## Dataset Description
it's README.md of the [original repository](https://github.com/OnlpLab/NEMO-Corpus)
Named Entity (NER) annotations of the Hebrew Treebank (Haaretz newspaper) corpus, including: morpheme and token level NER labels, nested mentions, and more.
We publish the NEMO corpus in the TACL paper [*"Neural Modeling for Named Entities and Morphology (NEMO<sup>2</sup>)"*](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00404/107206/Neural-Modeling-for-Named-Entities-and-Morphology) [1], where we use it in extensive experiments and analyses, showing the importance of morphological boundaries for neural modeling of NER in morphologically rich languages. Code for these models and experiments can be found in the [NEMO code repo](https://github.com/OnlpLab/NEMO).
## Main features:
1. Morpheme, token-single and token-multi sequence labels. Morpheme labels provide exact boundaries, token-multi provide partial sub-word morphological but no exact boundaries, token-single provides only token-level information.
1. All annotations are in `BIOSE` format (`B`=Begin, `I`=Inside, `O`=Outside, `S`=Singleton, `E`=End).
1. Widely-used OntoNotes entity category set: `GPE` (geo-political entity), `PER` (person), `LOC` (location), `ORG` (organization), `FAC` (facility), `EVE` (event), `WOA` (work-of-art), `ANG` (language), `DUC` (product).
1. NEMO includes NER annotations for the two major versions of the Hebrew Treebank, UD (Universal Dependency) and SPMRL. These can be aligned to the other morphosyntactic information layers of the treebank using [bclm](https://github.com/OnlpLab/bclm)
1. We provide nested mentions. Only the first, widest, layer is used in the NEMO<sup>2</sup> paper. We invite you to take on this challenge!
1. Guidelines used for annotation are provided [here](./guidelines/).
1. Corpus was annotated by two native Hebrew speakers of academic education, and curated by the project manager. We provide the original annotations made by the annotators as well to promote work on [learning with disagreements](https://sites.google.com/view/semeval2021-task12/home).
1. Annotation was performed using [WebAnno](https://webanno.github.io/webanno/) (version 3.4.5)
## Legend for Files and Folder Structure
1. The two main [data](./data/) folders are [ud](./data/ud/) and [spmrl](./data/spmrl/), corresponding to the relevant Hebrew Treebank corpus version.
1. Both contain a `gold` folder ([spmrl/gold](./data/spmrl/gold/), [ud/gold](./data/ud/gold/)) of gold curated annotations.
1. Each `gold` folder contains files of the three input-output variants (morph, token-multi, token-single), for each of the treebank splits (train,dev,test).
1. Each `gold` folder also contains a `nested` subfolder ([spmrl/nested](./data/spmrl/gold/nested/), [ud/nested](./data/ud/gold/nested/)), which contains all layers of nested mentions (the first layer is the layer used in the non-nested files, and in the NEMO<sup>2</sup> paper [1])
1. The `ud` folder also contains an [ab_annotators](./data/ud/ab_annotators/) folder. This folder contains the original annotations made by each annotator (named `a`, `b`), including first-layer and nested annotatations.
1. *\*UPDATE 2021-09-06\** `ud` folder now contains a [pilot_annotations](./data/ud/pilot_annotations/) folder. This folder contains the original annotations made by each annotator in our two phase pilot (phase I - sentences 1-200 of dev; phase II - sentences 201-400 of dev).
## Basic Corpus Statistics
| | train | dev | test |
|------------------------------| --:| --:| --:|
| Sentences | 4,937 | 500 | 706 |
| Tokens | 93,504 | 8,531 | 12,619 |
| Morphemes | 127,031 | 11,301 | 16,828 |
| All mentions | 6,282 | 499 | 932 |
| Type: Person (PER) | 2,128 | 193 | 267 |
| Type: Organization (ORG) | 2,043 | 119 | 408 |
| Type: Geo-Political (GPE) | 1,377 | 121 | 195 |
| Type: Location (LOC) | 331 | 28 | 41 |
| Type: Facility (FAC) | 163 | 12 | 11 |
| Type: Work-of-Art (WOA) | 114 | 9 | 6 |
| Type: Event (EVE) | 57 | 12 | 0 |
| Type: Product (DUC) | 36 | 2 | 3 |
| Type: Language (ANG) | 33 | 3 | 1 |
## Aligned Treenbank Versions
The NEMO corpus matches the treebank version of [bclm v.1.0.0](https://github.com/OnlpLab/bclm/releases/tag/v1.0.0-alpha).
This version is based on the [HTB UD v2.2](https://github.com/UniversalDependencies/UD_Hebrew-HTB/releases/tag/r2.2) and the [latest SPMRL HTB version](https://github.com/OnlpLab/HebrewResources/tree/102674bb030f5836e1ab827feb63954ad7a6f8fe/HebrewTreebank/hebtb).
The changes contain (but might not be limited to the following):
1. Flagged and dropped duplicate and leaking sentences (between train and test). In addition to the sentences already removed in the bclm v1.0.0 HTB version, the following duplicate sentences were dropped as well (SPMRL sentence IDs): 5438, 5444, 5445, 5446, 5448, 5449, 5450, 5451, 5453, 5459 (in the bclm dataframes, these are marked in the `duplicate_sent_id` column).
To read the treebank (UD/SPMRL) in a way that matches the NEMO corpus, you can use the following:
```python
import bclm
dropped = [5438, 5444, 5445, 5446, 5448, 5449, 5450, 5451, 5453, 5459]
spdf = bclm.read_dataframe('spmrl') # load SPMRL treebank dataframe
global_dropped = [spdf[spdf.sent_id==d].global_sent_id.iat[0] for d in dropped]
uddf = bclm.read_dataframe('ud') # load UD treebank dataframe
uddf = uddf[(~uddf.global_sent_id.isin(global_dropped))] # remove extra duplicates
spdf = spdf[(~spdf.sent_id.isin(dropped))] # remove extra duplicates
# The resulting dataframes contain gold morph NER labels in the `biose_layer0`, `biose_layer1`... columns.
```
2. The UD treebank contains many more duplicates. In this version: all sentences exist in both UD and SPMRL versions, and all sentences and tokens are aligned between UD and SPMRL.
2. Fixed numbers that were originally reversed.
2. Fixed mismatches between tokens and morphemes.
2. Added Binyan feature.
2. No individual morphemes or tokens were added or removed, only complete sentences.
## Evaluation
An evaluation script is provided in the [NEMO code repo](https://github.com/OnlpLab/NEMO#evaluation) along with evaluation instructions.
## Citations
##### [1]
If you use the NEMO corpus in your research, please cite the NEMO<sup>2</sup> paper:
```bibtex
@article{10.1162/tacl_a_00404,
author = {Bareket, Dan and Tsarfaty, Reut},
title = "{Neural Modeling for Named Entities and Morphology (NEMO2)}",
journal = {Transactions of the Association for Computational Linguistics},
volume = {9},
pages = {909-928},
year = {2021},
month = {09},
abstract = "{Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically rich languages (MRLs) pose a challenge to this basic formulation, as the boundaries of named entities do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings (i.e., where no gold morphology is available). We empirically investigate these questions on a novel NER benchmark, with parallel token- level and morpheme-level NER annotations, which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture, in which NER precedes and prunes morphological decomposition, greatly outperforms the standard pipeline, where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks.}",
issn = {2307-387X},
doi = {10.1162/tacl_a_00404},
url = {https://doi.org/10.1162/tacl\_a\_00404},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00404/1962472/tacl\_a\_00404.pdf},
}
```
##### [2]
Please cite the Hebrew Treebank as well, described the following paper:
```bibtex
@article{sima2001building,
title={Building a tree-bank of modern Hebrew text},
author={Sima’an, Khalil and Itai, Alon and Winter, Yoad and Altman, Alon and Nativ, Noa},
journal={Traitement Automatique des Langues},
volume={42},
number={2},
pages={247--380},
year={2001},
publisher={Citeseer}
}
```
##### [3]
The UD version of the Hebrew Treebank is described in:
```bibtex
@inproceedings{sade-etal-2018-hebrew,
title = "The {H}ebrew {U}niversal {D}ependency Treebank: Past Present and Future",
author = "Sade, Shoval and
Seker, Amit and
Tsarfaty, Reut",
booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6016",
doi = "10.18653/v1/W18-6016",
pages = "133--143",
abstract = "The Hebrew treebank (HTB), consisting of 6221 morpho-syntactically annotated newspaper sentences, has been the only resource for training and validating statistical parsers and taggers for Hebrew, for almost two decades now. During these decades, the HTB has gone through a trajectory of automatic and semi-automatic conversions, until arriving at its UDv2 form. In this work we manually validate the UDv2 version of the HTB, and, according to our findings, we apply scheme changes that bring the UD HTB to the same theoretical grounds as the rest of UD. Our experimental parsing results with UDv2New confirm that improving the coherence and internal consistency of the UD HTB indeed leads to improved parsing performance. At the same time, our analysis demonstrates that there is more to be done at the point of intersection of UD with other linguistic processing layers, in particular, at the points where UD interfaces external morphological and lexical resources.",
}
``` | [
-0.40148067474365234,
-0.6540820598602295,
0.029762238264083862,
0.11701666563749313,
-0.28764280676841736,
-0.1565522998571396,
-0.23660533130168915,
-0.5411955118179321,
0.4286149740219116,
0.4966883659362793,
-0.5310245752334595,
-0.7641593813896179,
-0.6775803565979004,
0.3211990296840... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ShapeNet/ShapeNetCore | ShapeNet | 2023-09-20T15:05:48Z | 39 | 19 | null | [
"language:en",
"license:other",
"3D shapes",
"region:us"
] | 2023-09-20T15:05:48Z | 2022-08-26T09:34:57.000Z | 2022-08-26T09:34:57 | ---
language:
- en
pretty_name: ShapeNetCore
tags:
- 3D shapes
license: other
extra_gated_heading: Acknowledge license to accept the repository
extra_gated_prompt: >-
To request access to this ShapeNet repo, you will need to provide your **full name** (please provide both your first and last name), the name of your **advisor or the principal investigator (PI)** of your lab (in the PI/Advisor) fields, and the **school or company** that you are affiliated with (the **Affiliation** field).
After requesting access to this ShapeNet repo, you will be considered for access approval.
After access approval, you (the "Researcher") receive permission to use the ShapeNet database (the "Database") at Princeton University and Stanford University. In exchange for being able to join the ShapeNet community and receive such permission, Researcher hereby agrees to the following terms and conditions:
Researcher shall use the Database only for non-commercial research and educational purposes.
Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted 3D models that he or she may create from the Database.
Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
The law of the State of New Jersey shall apply to all disputes under this agreement.
For access to the data, please fill in your **full name** (both first and last name), the name of your **advisor or principal investigator (PI)**, and the name of the **school or company** you are affliated with.
Please actually fill out the fields (DO NOT put the word "Advisor" for PI/Advisor and the word "School" for "Affiliation", please specify the name of your advisor and the name of your school).
extra_gated_fields:
Name: text
PI/Advisor: text
Affiliation: text
Purpose: text
Country: text
I agree to use this dataset for non-commercial use ONLY: checkbox
---
This repository contains ShapeNetCore (v2), a subset of [ShapeNet](https://shapenet.org).
ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore are linked to an appropriate synset in [WordNet 3.0](https://wordnet.princeton.edu/).
Please see [DATA.md](DATA.md) for details about the data.
If you use ShapeNet data, you agree to abide by the [ShapeNet terms of use](https://shapenet.org/terms). You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions.
If you use this data, please cite the main ShapeNet technical report.
```
@techreport{shapenet2015,
title = {{ShapeNet: An Information-Rich 3D Model Repository}},
author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
number = {arXiv:1512.03012 [cs.GR]},
institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago},
year = {2015}
}
```
For more information, please contact us at shapenetwebmaster@gmail.com and indicate ShapeNetCore v2 in the title of your email.
| [
-0.09440137445926666,
-0.21979136765003204,
0.4112263023853302,
0.07662332057952881,
-0.18683123588562012,
-0.4498729407787323,
0.1571209579706192,
-0.6546458005905151,
0.35690563917160034,
0.6133618354797363,
-0.48073774576187134,
-0.6689183115959167,
-0.46643468737602234,
-0.016502317041... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
truongpdd/Covid19-NER-Vietnamese | truongpdd | 2022-09-09T03:03:32Z | 39 | 0 | null | [
"region:us"
] | 2022-09-09T03:03:32Z | 2022-09-09T03:03:24.000Z | 2022-09-09T03:03:24 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TheGreatRambler/mm2_level | TheGreatRambler | 2022-11-11T08:07:34Z | 39 | 5 | null | [
"task_categories:other",
"task_categories:object-detection",
"task_categories:text-retrieval",
"task_categories:token-classification",
"task_categories:text-generation",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:multilingual",
"license:cc-b... | 2022-11-11T08:07:34Z | 2022-09-18T20:15:00.000Z | 2022-09-18T20:15:00 | ---
language:
- multilingual
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
- object-detection
- text-retrieval
- token-classification
- text-generation
task_ids: []
pretty_name: Mario Maker 2 levels
tags:
- text-mining
---
# Mario Maker 2 levels
Part of the [Mario Maker 2 Dataset Collection](https://tgrcode.com/posts/mario_maker_2_datasets)
## Dataset Description
The Mario Maker 2 levels dataset consists of 26.6 million levels from Nintendo's online service totaling around 100GB of data. The dataset was created using the self-hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api) over the course of 1 month in February 2022.
### How to use it
The Mario Maker 2 levels dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following code:
```python
from datasets import load_dataset
ds = load_dataset("TheGreatRambler/mm2_level", streaming=True, split="train")
print(next(iter(ds)))
#OUTPUT:
{
'data_id': 3000004,
'name': 'カベキック',
'description': 'カベキックをとにかくするコースです。',
'uploaded': 1561644329,
'created': 1561674240,
'gamestyle': 4,
'theme': 0,
'difficulty': 0,
'tag1': 7,
'tag2': 10,
'game_version': 1,
'world_record': 8049,
'upload_time': 193540,
'upload_attempts': 1,
'num_comments': 60,
'clear_condition': 0,
'clear_condition_magnitude': 0,
'timer': 300,
'autoscroll_speed': 0,
'clears': 1646,
'attempts': 3168,
'clear_rate': 51.957070707070706,
'plays': 1704,
'versus_matches': 80,
'coop_matches': 27,
'likes': 152,
'boos': 118,
'unique_players_and_versus': 1391,
'weekly_likes': 0,
'weekly_plays': 1,
'uploader_pid': '5218390885570355093',
'first_completer_pid': '16824392528839047213',
'record_holder_pid': '5411258160547085075',
'level_data': [some binary data],
'unk2': 0,
'unk3': [some binary data],
'unk9': 3,
'unk10': 4,
'unk11': 1,
'unk12': 1
}
```
Level data is a binary blob describing the actual level and is equivalent to the level format Nintendo uses in-game. It is gzip compressed and needs to be decompressed to be read. To read it you only need to use the provided `level.ksy` kaitai struct file and install the kaitai struct runtime to parse it into an object:
```python
from datasets import load_dataset
from kaitaistruct import KaitaiStream
from io import BytesIO
from level import Level
import zlib
ds = load_dataset("TheGreatRambler/mm2_level", streaming=True, split="train")
level_data = next(iter(ds))["level_data"]
level = Level(KaitaiStream(BytesIO(zlib.decompress(level_data))))
# NOTE level.overworld.objects is a fixed size (limitation of Kaitai struct)
# must iterate by object_count or null objects will be included
for i in range(level.overworld.object_count):
obj = level.overworld.objects[i]
print("X: %d Y: %d ID: %s" % (obj.x, obj.y, obj.id))
#OUTPUT:
X: 1200 Y: 400 ID: ObjId.block
X: 1360 Y: 400 ID: ObjId.block
X: 1360 Y: 240 ID: ObjId.block
X: 1520 Y: 240 ID: ObjId.block
X: 1680 Y: 240 ID: ObjId.block
X: 1680 Y: 400 ID: ObjId.block
X: 1840 Y: 400 ID: ObjId.block
X: 2000 Y: 400 ID: ObjId.block
X: 2160 Y: 400 ID: ObjId.block
X: 2320 Y: 400 ID: ObjId.block
X: 2480 Y: 560 ID: ObjId.block
X: 2480 Y: 720 ID: ObjId.block
X: 2480 Y: 880 ID: ObjId.block
X: 2160 Y: 880 ID: ObjId.block
```
Rendering the level data into an image can be done using [Toost](https://github.com/TheGreatRambler/toost) if desired.
You can also download the full dataset. Note that this will download ~100GB:
```python
ds = load_dataset("TheGreatRambler/mm2_level", split="train")
```
## Data Structure
### Data Instances
```python
{
'data_id': 3000004,
'name': 'カベキック',
'description': 'カベキックをとにかくするコースです。',
'uploaded': 1561644329,
'created': 1561674240,
'gamestyle': 4,
'theme': 0,
'difficulty': 0,
'tag1': 7,
'tag2': 10,
'game_version': 1,
'world_record': 8049,
'upload_time': 193540,
'upload_attempts': 1,
'num_comments': 60,
'clear_condition': 0,
'clear_condition_magnitude': 0,
'timer': 300,
'autoscroll_speed': 0,
'clears': 1646,
'attempts': 3168,
'clear_rate': 51.957070707070706,
'plays': 1704,
'versus_matches': 80,
'coop_matches': 27,
'likes': 152,
'boos': 118,
'unique_players_and_versus': 1391,
'weekly_likes': 0,
'weekly_plays': 1,
'uploader_pid': '5218390885570355093',
'first_completer_pid': '16824392528839047213',
'record_holder_pid': '5411258160547085075',
'level_data': [some binary data],
'unk2': 0,
'unk3': [some binary data],
'unk9': 3,
'unk10': 4,
'unk11': 1,
'unk12': 1
}
```
### Data Fields
|Field|Type|Description|
|---|---|---|
|data_id|int|Data IDs are unique identifiers, gaps in the table are due to levels deleted by users or Nintendo|
|name|string|Course name|
|description|string|Course description|
|uploaded|int|UTC timestamp for when the level was uploaded|
|created|int|Local timestamp for when the level was created|
|gamestyle|int|Gamestyle, enum below|
|theme|int|Theme, enum below|
|difficulty|int|Difficulty, enum below|
|tag1|int|The first tag, if it exists, enum below|
|tag2|int|The second tag, if it exists, enum below|
|game_version|int|The version of the game this level was made on|
|world_record|int|The world record in milliseconds|
|upload_time|int|The upload time in milliseconds|
|upload_attempts|int|The number of attempts it took the uploader to upload|
|num_comments|int|Number of comments, may not reflect the archived comments if there were more than 1000 comments|
|clear_condition|int|Clear condition, enum below|
|clear_condition_magnitude|int|If applicable, the magnitude of the clear condition|
|timer|int|The timer of the level|
|autoscroll_speed|int|A unit of how fast the configured autoscroll speed is for the level|
|clears|int|Course clears|
|attempts|int|Course attempts|
|clear_rate|float|Course clear rate as a float between 0 and 1|
|plays|int|Course plays, or "footprints"|
|versus_matches|int|Course versus matches|
|coop_matches|int|Course coop matches|
|likes|int|Course likes|
|boos|int|Course boos|
|unique_players_and_versus|int|All unique players that have ever played this level, including the number of versus matches|
|weekly_likes|int|The weekly likes on this course|
|weekly_plays|int|The weekly plays on this course|
|uploader_pid|string|The player ID of the uploader|
|first_completer_pid|string|The player ID of the user who first cleared this course|
|record_holder_pid|string|The player ID of the user who held the world record at time of archival |
|level_data|bytes|The GZIP compressed decrypted level data, kaitai struct file is provided for reading|
|unk2|int|Unknown|
|unk3|bytes|Unknown|
|unk9|int|Unknown|
|unk10|int|Unknown|
|unk11|int|Unknown|
|unk12|int|Unknown|
### Data Splits
The dataset only contains a train split.
## Enums
The dataset contains some enum integer fields. This can be used to convert back to their string equivalents:
```python
GameStyles = {
0: "SMB1",
1: "SMB3",
2: "SMW",
3: "NSMBU",
4: "SM3DW"
}
Difficulties = {
0: "Easy",
1: "Normal",
2: "Expert",
3: "Super expert"
}
CourseThemes = {
0: "Overworld",
1: "Underground",
2: "Castle",
3: "Airship",
4: "Underwater",
5: "Ghost house",
6: "Snow",
7: "Desert",
8: "Sky",
9: "Forest"
}
TagNames = {
0: "None",
1: "Standard",
2: "Puzzle solving",
3: "Speedrun",
4: "Autoscroll",
5: "Auto mario",
6: "Short and sweet",
7: "Multiplayer versus",
8: "Themed",
9: "Music",
10: "Art",
11: "Technical",
12: "Shooter",
13: "Boss battle",
14: "Single player",
15: "Link"
}
ClearConditions = {
137525990: "Reach the goal without landing after leaving the ground.",
199585683: "Reach the goal after defeating at least/all (n) Mechakoopa(s).",
272349836: "Reach the goal after defeating at least/all (n) Cheep Cheep(s).",
375673178: "Reach the goal without taking damage.",
426197923: "Reach the goal as Boomerang Mario.",
436833616: "Reach the goal while wearing a Shoe.",
713979835: "Reach the goal as Fire Mario.",
744927294: "Reach the goal as Frog Mario.",
751004331: "Reach the goal after defeating at least/all (n) Larry(s).",
900050759: "Reach the goal as Raccoon Mario.",
947659466: "Reach the goal after defeating at least/all (n) Blooper(s).",
976173462: "Reach the goal as Propeller Mario.",
994686866: "Reach the goal while wearing a Propeller Box.",
998904081: "Reach the goal after defeating at least/all (n) Spike(s).",
1008094897: "Reach the goal after defeating at least/all (n) Boom Boom(s).",
1051433633: "Reach the goal while holding a Koopa Shell.",
1061233896: "Reach the goal after defeating at least/all (n) Porcupuffer(s).",
1062253843: "Reach the goal after defeating at least/all (n) Charvaargh(s).",
1079889509: "Reach the goal after defeating at least/all (n) Bullet Bill(s).",
1080535886: "Reach the goal after defeating at least/all (n) Bully/Bullies.",
1151250770: "Reach the goal while wearing a Goomba Mask.",
1182464856: "Reach the goal after defeating at least/all (n) Hop-Chops.",
1219761531: "Reach the goal while holding a Red POW Block. OR Reach the goal after activating at least/all (n) Red POW Block(s).",
1221661152: "Reach the goal after defeating at least/all (n) Bob-omb(s).",
1259427138: "Reach the goal after defeating at least/all (n) Spiny/Spinies.",
1268255615: "Reach the goal after defeating at least/all (n) Bowser(s)/Meowser(s).",
1279580818: "Reach the goal after defeating at least/all (n) Ant Trooper(s).",
1283945123: "Reach the goal on a Lakitu's Cloud.",
1344044032: "Reach the goal after defeating at least/all (n) Boo(s).",
1425973877: "Reach the goal after defeating at least/all (n) Roy(s).",
1429902736: "Reach the goal while holding a Trampoline.",
1431944825: "Reach the goal after defeating at least/all (n) Morton(s).",
1446467058: "Reach the goal after defeating at least/all (n) Fish Bone(s).",
1510495760: "Reach the goal after defeating at least/all (n) Monty Mole(s).",
1656179347: "Reach the goal after picking up at least/all (n) 1-Up Mushroom(s).",
1665820273: "Reach the goal after defeating at least/all (n) Hammer Bro(s.).",
1676924210: "Reach the goal after hitting at least/all (n) P Switch(es). OR Reach the goal while holding a P Switch.",
1715960804: "Reach the goal after activating at least/all (n) POW Block(s). OR Reach the goal while holding a POW Block.",
1724036958: "Reach the goal after defeating at least/all (n) Angry Sun(s).",
1730095541: "Reach the goal after defeating at least/all (n) Pokey(s).",
1780278293: "Reach the goal as Superball Mario.",
1839897151: "Reach the goal after defeating at least/all (n) Pom Pom(s).",
1969299694: "Reach the goal after defeating at least/all (n) Peepa(s).",
2035052211: "Reach the goal after defeating at least/all (n) Lakitu(s).",
2038503215: "Reach the goal after defeating at least/all (n) Lemmy(s).",
2048033177: "Reach the goal after defeating at least/all (n) Lava Bubble(s).",
2076496776: "Reach the goal while wearing a Bullet Bill Mask.",
2089161429: "Reach the goal as Big Mario.",
2111528319: "Reach the goal as Cat Mario.",
2131209407: "Reach the goal after defeating at least/all (n) Goomba(s)/Galoomba(s).",
2139645066: "Reach the goal after defeating at least/all (n) Thwomp(s).",
2259346429: "Reach the goal after defeating at least/all (n) Iggy(s).",
2549654281: "Reach the goal while wearing a Dry Bones Shell.",
2694559007: "Reach the goal after defeating at least/all (n) Sledge Bro(s.).",
2746139466: "Reach the goal after defeating at least/all (n) Rocky Wrench(es).",
2749601092: "Reach the goal after grabbing at least/all (n) 50-Coin(s).",
2855236681: "Reach the goal as Flying Squirrel Mario.",
3036298571: "Reach the goal as Buzzy Mario.",
3074433106: "Reach the goal as Builder Mario.",
3146932243: "Reach the goal as Cape Mario.",
3174413484: "Reach the goal after defeating at least/all (n) Wendy(s).",
3206222275: "Reach the goal while wearing a Cannon Box.",
3314955857: "Reach the goal as Link.",
3342591980: "Reach the goal while you have Super Star invincibility.",
3346433512: "Reach the goal after defeating at least/all (n) Goombrat(s)/Goombud(s).",
3348058176: "Reach the goal after grabbing at least/all (n) 10-Coin(s).",
3353006607: "Reach the goal after defeating at least/all (n) Buzzy Beetle(s).",
3392229961: "Reach the goal after defeating at least/all (n) Bowser Jr.(s).",
3437308486: "Reach the goal after defeating at least/all (n) Koopa Troopa(s).",
3459144213: "Reach the goal after defeating at least/all (n) Chain Chomp(s).",
3466227835: "Reach the goal after defeating at least/all (n) Muncher(s).",
3481362698: "Reach the goal after defeating at least/all (n) Wiggler(s).",
3513732174: "Reach the goal as SMB2 Mario.",
3649647177: "Reach the goal in a Koopa Clown Car/Junior Clown Car.",
3725246406: "Reach the goal as Spiny Mario.",
3730243509: "Reach the goal in a Koopa Troopa Car.",
3748075486: "Reach the goal after defeating at least/all (n) Piranha Plant(s)/Jumping Piranha Plant(s).",
3797704544: "Reach the goal after defeating at least/all (n) Dry Bones.",
3824561269: "Reach the goal after defeating at least/all (n) Stingby/Stingbies.",
3833342952: "Reach the goal after defeating at least/all (n) Piranha Creeper(s).",
3842179831: "Reach the goal after defeating at least/all (n) Fire Piranha Plant(s).",
3874680510: "Reach the goal after breaking at least/all (n) Crates(s).",
3974581191: "Reach the goal after defeating at least/all (n) Ludwig(s).",
3977257962: "Reach the goal as Super Mario.",
4042480826: "Reach the goal after defeating at least/all (n) Skipsqueak(s).",
4116396131: "Reach the goal after grabbing at least/all (n) Coin(s).",
4117878280: "Reach the goal after defeating at least/all (n) Magikoopa(s).",
4122555074: "Reach the goal after grabbing at least/all (n) 30-Coin(s).",
4153835197: "Reach the goal as Balloon Mario.",
4172105156: "Reach the goal while wearing a Red POW Box.",
4209535561: "Reach the Goal while riding Yoshi.",
4269094462: "Reach the goal after defeating at least/all (n) Spike Top(s).",
4293354249: "Reach the goal after defeating at least/all (n) Banzai Bill(s)."
}
```
<!-- TODO create detailed statistics -->
## Dataset Creation
The dataset was created over a little more than a month in Febuary 2022 using the self hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api). As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.
## Considerations for Using the Data
The dataset consists of levels from many different Mario Maker 2 players globally and as such their titles and descriptions could contain harmful language. Harmful depictions could also be present in the level data, should you choose to render it.
| [
-0.5189028978347778,
-0.5137307047843933,
0.24674804508686066,
0.1613769382238388,
-0.02466094307601452,
0.1609397977590561,
-0.048928435891866684,
-0.5459641218185425,
0.44795769453048706,
0.3772777020931244,
-0.729365885257721,
-0.7693312168121338,
-0.6606497168540955,
0.1669801771640777... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dgrnd4/animals-10 | dgrnd4 | 2022-10-04T16:45:42Z | 39 | 3 | null | [
"license:other",
"region:us"
] | 2022-10-04T16:45:42Z | 2022-10-04T16:39:10.000Z | 2022-10-04T16:39:10 | ---
license: other
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tomekkorbak/detoxify-pile-chunk3-4200000-4250000 | tomekkorbak | 2022-10-06T04:32:21Z | 39 | 0 | null | [
"region:us"
] | 2022-10-06T04:32:21Z | 2022-10-06T04:32:13.000Z | 2022-10-06T04:32:13 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
maderix/flickr_bw_rgb | maderix | 2022-10-12T15:34:25Z | 39 | 5 | null | [
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:N/A",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-10-12T15:34:25Z | 2022-10-12T15:09:17.000Z | 2022-10-12T15:09:17 | ---
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
pretty_name: 'flickr_bw_rgb'
size_categories:
- n<1K
source_datasets:
- N/A
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for Flickr_bw_rgb
_Dataset A image-caption dataset which stores group of black and white and color images with corresponding
captions mentioning the content of the image with a 'colorized photograph of' or 'Black and white photograph of' suffix.
This dataset can then be used for fine-tuning image to text models.. Only a train split is provided.
## Examples
"train/<filename>.jpg" : containing the images in JPEG format
"train/metadata.jsonl" : Contains the metadata and the fields.
Dataset columns:
"file_name"
"caption"
## Citation
If you use this dataset, please cite it as:
```
@misc{maderix2022flickrbwrgb,
author = {maderix: maderix@gmail.com},
title = {flickr_bw_rgb},
year={2022},
howpublished= {\url{https://huggingface.co/datasets/maderix/flickr_bw_rgb/}}
}
``` | [
-0.5219917297363281,
-0.14638066291809082,
-0.09657303243875504,
0.39638638496398926,
-0.7074393033981323,
0.0282095056027174,
0.17267389595508575,
-0.08784617483615875,
0.23621518909931183,
0.4198598563671112,
-0.7453266382217407,
-0.37532925605773926,
-0.48996180295944214,
0.014057286083... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jamescalam/channel-metadata | jamescalam | 2022-10-26T01:05:55Z | 39 | 1 | null | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:afl-3.0",
"youtube",
"video",
"video metadata",
"tech",
"science and tech",
"region:us"... | 2022-10-26T01:05:55Z | 2022-10-14T05:29:45.000Z | 2022-10-14T05:29:45 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Tech Channels Metadata
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- video
- video metadata
- tech
- science and tech
task_categories:
- other
task_ids: []
---
Dataset containing video metadata from a few tech channels, i.e.
* [James Briggs](https://youtube.com/c/JamesBriggs)
* [Yannic Kilcher](https://www.youtube.com/c/YannicKilcher)
* [sentdex](https://www.youtube.com/c/sentdex)
* [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ)
* [AI Coffee Break with Letitia](https://www.youtube.com/c/AICoffeeBreak)
* [Alex Ziskind](https://youtube.com/channel/UCajiMK_CY9icRhLepS8_3ug) | [
-0.6173519492149353,
-0.4831438958644867,
0.2907837927341461,
0.07249698042869568,
-0.023873712867498398,
0.09017015248537064,
-0.11383123695850372,
0.5779020190238953,
0.6869503855705261,
0.6531174182891846,
-1.237919569015503,
-0.8261027336120605,
-0.9402349591255188,
0.02633868716657161... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
helena-balabin/pereira_fMRI_passages | helena-balabin | 2023-07-27T13:34:57Z | 39 | 0 | null | [
"region:us"
] | 2023-07-27T13:34:57Z | 2022-10-19T14:45:37.000Z | 2022-10-19T14:45:37 | ---
dataset_info:
features:
- name: language_lh
sequence:
sequence: float64
- name: language_rh
sequence:
sequence: float64
- name: vision_body
sequence:
sequence: float64
- name: vision_face
sequence:
sequence: float64
- name: vision_object
sequence:
sequence: float64
- name: vision_scene
sequence:
sequence: float64
- name: vision
sequence:
sequence: float64
- name: dmn
sequence:
sequence: float64
- name: task
sequence:
sequence: float64
- name: all
sequence:
sequence: float64
- name: paragraphs
sequence: string
- name: topic_indices
sequence: uint8
- name: permuted_paragraphs
sequence: string
splits:
- name: train
num_bytes: 1649652912
num_examples: 8
download_size: 1658872446
dataset_size: 1649652912
---
# Dataset Card for "pereira_fMRI_passages"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.4318171441555023,
-0.2581547796726227,
0.5305353999137878,
0.4338859021663666,
-0.2196269929409027,
-0.24627931416034698,
0.18162603676319122,
-0.295053094625473,
0.7362645864486694,
0.3161599636077881,
-0.8109650611877441,
-0.6059537529945374,
-0.7699500918388367,
-0.2177482545375824,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Rosenberg/genia | Rosenberg | 2022-10-23T12:08:03Z | 39 | 2 | null | [
"license:mit",
"region:us"
] | 2022-10-23T12:08:03Z | 2022-10-23T12:07:06.000Z | 2022-10-23T12:07:06 | ---
license: mit
---
| [
-0.1285339742898941,
-0.18616800010204315,
0.6529127359390259,
0.4943626821041107,
-0.1931934952735901,
0.2360742688179016,
0.360720157623291,
0.05056300014257431,
0.5793654322624207,
0.7400140166282654,
-0.6508105993270874,
-0.23783984780311584,
-0.7102248668670654,
-0.047826044261455536,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bond005/sberdevices_golos_100h_farfield | bond005 | 2022-10-27T04:23:04Z | 39 | 0 | golos | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"source_datasets:extended",
"language:... | 2022-10-27T04:23:04Z | 2022-10-26T05:04:50.000Z | 2022-10-26T05:04:50 | ---
pretty_name: Golos
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
license:
- other
multilinguality:
- monolingual
paperswithcode_id: golos
size_categories:
- 10K<n<100k
source_datasets:
- extended
task_categories:
- automatic-speech-recognition
- audio-classification
---
# Dataset Card for sberdevices_golos_100h_farfield
## 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:** [Golos ASR corpus](https://www.openslr.org/114)
- **Repository:** [Golos dataset](https://github.com/sberdevices/golos)
- **Paper:** [Golos: Russian Dataset for Speech Research](https://arxiv.org/pdf/2106.10161.pdf)
- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
- **Point of Contact:** [Nikolay Karpov](mailto:karpnv@gmail.com)
### Dataset Summary
Sberdevices Golos is a corpus of approximately 1200 hours of 16kHz Russian speech from crowd (reading speech) and farfield (communication with smart devices) domains, prepared by SberDevices Team (Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov). The data is derived from the crowd-sourcing platform, and has been manually annotated.
Authors divide all dataset into train and test subsets. The training subset includes approximately 1000 hours. For experiments with a limited number of records, authors identified training subsets of shorter length: 100 hours, 10 hours, 1 hour, 10 minutes.
This dataset is a simpler version of the above mentioned Golos:
- it includes the farfield domain only (without any sound from the crowd domain);
- validation split is built on the 10-hour training subset;
- training split corresponds to the 100-hour training subset without sounds from the 10-hour training subset;
- test split is a full original test split.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER.
### Languages
The audio is in Russian.
## Dataset Structure
### Data Instances
A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided.
```
{'audio': {'path': None,
'array': array([ 1.22070312e-04, 1.22070312e-04, 9.15527344e-05, ...,
6.10351562e-05, 6.10351562e-05, 3.05175781e-05]), dtype=float64),
'sampling_rate': 16000},
'transcription': 'джой источники истории турции'}
```
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- transcription: the transcription of the audio file.
### Data Splits
This dataset is a simpler version of the original Golos:
- it includes the farfield domain only (without any sound from the crowd domain);
- validation split is built on the 10-hour training subset;
- training split corresponds to the 100-hour training subset without sounds from the 10-hour training subset;
- test split is a full original test split.
| | Train | Validation | Test |
| ----- | ------ | ---------- | ----- |
| examples | 9570 | 933 | 1916 |
| hours | 10.3h | 1.0h | 1.4h |
## 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
All recorded audio files were manually annotated on the crowd-sourcing platform.
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov.
### Licensing Information
[Public license with attribution and conditions reserved](https://github.com/sberdevices/golos/blob/master/license/en_us.pdf)
### Citation Information
```
@misc{karpov2021golos,
author = {Karpov, Nikolay and Denisenko, Alexander and Minkin, Fedor},
title = {Golos: Russian Dataset for Speech Research},
publisher = {arXiv},
year = {2021},
url = {https://arxiv.org/abs/2106.10161}
}
```
### Contributions
Thanks to [@bond005](https://github.com/bond005) for adding this dataset.
| [
-0.27049878239631653,
-0.40137481689453125,
-0.06635230779647827,
0.2545919418334961,
-0.3084430694580078,
-0.06672288477420807,
-0.43783310055732727,
-0.39769086241722107,
0.4271133244037628,
0.3422606289386749,
-0.7570516467094421,
-0.6925211548805237,
-0.5663592219352722,
0.066475190222... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SALT-NLP/spider_VALUE | SALT-NLP | 2022-10-27T21:40:03Z | 39 | 0 | null | [
"region:us"
] | 2022-10-27T21:40:03Z | 2022-10-27T21:21:27.000Z | 2022-10-27T21:21:27 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
allenai/objaverse | allenai | 2023-03-31T11:05:57Z | 39 | 251 | null | [
"language:en",
"license:odc-by",
"arxiv:2212.08051",
"region:us"
] | 2023-03-31T11:05:57Z | 2022-12-12T19:06:33.000Z | 2022-12-12T19:06:33 | ---
license: odc-by
language:
- en
viewer: false
---
# Objaverse
Objaverse is a Massive Dataset with 800K+ Annotated 3D Objects.
More documentation is coming soon. In the meantime, please see our [paper](https://arxiv.org/abs/2212.08051) and [website](https://objaverse.allenai.org/) for additional details.
# License
The use of the dataset as a whole is licensed under the [ODC-By v1.0](https://opendatacommons.org/licenses/by/1-0/) license. Individual objects in Objaverse are all licensed as creative commons distributable objects, and may be under the following licenses:
- [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) - 721K objects
- [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) - 25K objects
- [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) - 52K objects
- [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) - 16K objects
- [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) - 3.5K objects
The metadata will provide the license for each object.
# Citation
To cite Objaverse, please use the following BibTeX entry:
```bibtex
@article{objaverse,
title={Objaverse: A Universe of Annotated 3D Objects},
author={Matt Deitke and Dustin Schwenk and Jordi Salvador and Luca Weihs and
Oscar Michel and Eli VanderBilt and Ludwig Schmidt and
Kiana Ehsani and Aniruddha Kembhavi and Ali Farhadi},
journal={arXiv preprint arXiv:2212.08051},
year={2022}
}
``` | [
-0.8047175407409668,
-0.9002016186714172,
0.6280531287193298,
0.04201669618487358,
-0.11375489085912704,
-0.3924335837364197,
0.12576957046985626,
-0.8114016056060791,
0.1468675136566162,
1.0898005962371826,
-0.4304323196411133,
-0.5058952569961548,
-0.5298776030540466,
0.40819844603538513... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DFKI-SLT/fabner | DFKI-SLT | 2023-04-05T23:20:21Z | 39 | 0 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"manufacturing",
"2000-2020",
"region:us"
] | 2023-04-05T23:20:21Z | 2023-01-13T13:01:38.000Z | 2023-01-13T13:01:38 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: FabNER is a manufacturing text dataset for Named Entity Recognition.
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- manufacturing
- 2000-2020
task_categories:
- token-classification
task_ids:
- named-entity-recognition
dataset_info:
- config_name: fabner
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-MATE
'2': I-MATE
'3': O-MATE
'4': E-MATE
'5': S-MATE
'6': B-MANP
'7': I-MANP
'8': O-MANP
'9': E-MANP
'10': S-MANP
'11': B-MACEQ
'12': I-MACEQ
'13': O-MACEQ
'14': E-MACEQ
'15': S-MACEQ
'16': B-APPL
'17': I-APPL
'18': O-APPL
'19': E-APPL
'20': S-APPL
'21': B-FEAT
'22': I-FEAT
'23': O-FEAT
'24': E-FEAT
'25': S-FEAT
'26': B-PRO
'27': I-PRO
'28': O-PRO
'29': E-PRO
'30': S-PRO
'31': B-CHAR
'32': I-CHAR
'33': O-CHAR
'34': E-CHAR
'35': S-CHAR
'36': B-PARA
'37': I-PARA
'38': O-PARA
'39': E-PARA
'40': S-PARA
'41': B-ENAT
'42': I-ENAT
'43': O-ENAT
'44': E-ENAT
'45': S-ENAT
'46': B-CONPRI
'47': I-CONPRI
'48': O-CONPRI
'49': E-CONPRI
'50': S-CONPRI
'51': B-MANS
'52': I-MANS
'53': O-MANS
'54': E-MANS
'55': S-MANS
'56': B-BIOP
'57': I-BIOP
'58': O-BIOP
'59': E-BIOP
'60': S-BIOP
splits:
- name: train
num_bytes: 4394010
num_examples: 9435
- name: validation
num_bytes: 934347
num_examples: 2183
- name: test
num_bytes: 940136
num_examples: 2064
download_size: 3793613
dataset_size: 6268493
- config_name: fabner_bio
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-MATE
'2': I-MATE
'3': B-MANP
'4': I-MANP
'5': B-MACEQ
'6': I-MACEQ
'7': B-APPL
'8': I-APPL
'9': B-FEAT
'10': I-FEAT
'11': B-PRO
'12': I-PRO
'13': B-CHAR
'14': I-CHAR
'15': B-PARA
'16': I-PARA
'17': B-ENAT
'18': I-ENAT
'19': B-CONPRI
'20': I-CONPRI
'21': B-MANS
'22': I-MANS
'23': B-BIOP
'24': I-BIOP
splits:
- name: train
num_bytes: 4394010
num_examples: 9435
- name: validation
num_bytes: 934347
num_examples: 2183
- name: test
num_bytes: 940136
num_examples: 2064
download_size: 3793613
dataset_size: 6268493
- config_name: fabner_simple
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': MATE
'2': MANP
'3': MACEQ
'4': APPL
'5': FEAT
'6': PRO
'7': CHAR
'8': PARA
'9': ENAT
'10': CONPRI
'11': MANS
'12': BIOP
splits:
- name: train
num_bytes: 4394010
num_examples: 9435
- name: validation
num_bytes: 934347
num_examples: 2183
- name: test
num_bytes: 940136
num_examples: 2064
download_size: 3793613
dataset_size: 6268493
- config_name: text2tech
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': Technological System
'2': Method
'3': Material
'4': Technical Field
splits:
- name: train
num_bytes: 4394010
num_examples: 9435
- name: validation
num_bytes: 934347
num_examples: 2183
- name: test
num_bytes: 940136
num_examples: 2064
download_size: 3793613
dataset_size: 6268493
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407](https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407)
- **Paper:** ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
- **Size of downloaded dataset files:** 3.79 MB
- **Size of the generated dataset:** 6.27 MB
### Dataset Summary
FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition.
It is a collection of abstracts obtained from Web of Science through known journals available in manufacturing process
science research.
For every word, there were categories/entity labels defined namely Material (MATE), Manufacturing Process (MANP),
Machine/Equipment (MACEQ), Application (APPL), Features (FEAT), Mechanical Properties (PRO), Characterization (CHAR),
Parameters (PARA), Enabling Technology (ENAT), Concept/Principles (CONPRI), Manufacturing Standards (MANS) and
BioMedical (BIOP). Annotation was performed in all categories along with the output tag in 'BIOES' format:
B=Beginning, I-Intermediate, O=Outside, E=End, S=Single.
For details about the dataset, please refer to the paper: ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 3.79 MB
- **Size of the generated dataset:** 6.27 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["Revealed", "the", "location-specific", "flow", "patterns", "and", "quantified", "the", "speeds", "of", "various", "types", "of", "flow", "."],
"ner_tags": [0, 0, 0, 46, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
### Data Fields
#### fabner
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "B-MATE": 1, "I-MATE": 2, "O-MATE": 3, "E-MATE": 4, "S-MATE": 5, "B-MANP": 6, "I-MANP": 7, "O-MANP": 8, "E-MANP": 9, "S-MANP": 10, "B-MACEQ": 11, "I-MACEQ": 12, "O-MACEQ": 13, "E-MACEQ": 14, "S-MACEQ": 15, "B-APPL": 16, "I-APPL": 17, "O-APPL": 18, "E-APPL": 19, "S-APPL": 20, "B-FEAT": 21, "I-FEAT": 22, "O-FEAT": 23, "E-FEAT": 24, "S-FEAT": 25, "B-PRO": 26, "I-PRO": 27, "O-PRO": 28, "E-PRO": 29, "S-PRO": 30, "B-CHAR": 31, "I-CHAR": 32, "O-CHAR": 33, "E-CHAR": 34, "S-CHAR": 35, "B-PARA": 36, "I-PARA": 37, "O-PARA": 38, "E-PARA": 39, "S-PARA": 40, "B-ENAT": 41, "I-ENAT": 42, "O-ENAT": 43, "E-ENAT": 44, "S-ENAT": 45, "B-CONPRI": 46, "I-CONPRI": 47, "O-CONPRI": 48, "E-CONPRI": 49, "S-CONPRI": 50, "B-MANS": 51, "I-MANS": 52, "O-MANS": 53, "E-MANS": 54, "S-MANS": 55, "B-BIOP": 56, "I-BIOP": 57, "O-BIOP": 58, "E-BIOP": 59, "S-BIOP": 60}
```
#### fabner_bio
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "B-MATE": 1, "I-MATE": 2, "B-MANP": 3, "I-MANP": 4, "B-MACEQ": 5, "I-MACEQ": 6, "B-APPL": 7, "I-APPL": 8, "B-FEAT": 9, "I-FEAT": 10, "B-PRO": 11, "I-PRO": 12, "B-CHAR": 13, "I-CHAR": 14, "B-PARA": 15, "I-PARA": 16, "B-ENAT": 17, "I-ENAT": 18, "B-CONPRI": 19, "I-CONPRI": 20, "B-MANS": 21, "I-MANS": 22, "B-BIOP": 23, "I-BIOP": 24}
```
#### fabner_simple
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "MATE": 1, "MANP": 2, "MACEQ": 3, "APPL": 4, "FEAT": 5, "PRO": 6, "CHAR": 7, "PARA": 8, "ENAT": 9, "CONPRI": 10, "MANS": 11, "BIOP": 12}
```
#### text2tech
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "Technological System": 1, "Method": 2, "Material": 3, "Technical Field": 4}
```
### Data Splits
| | Train | Dev | Test |
|--------|-------|------|------|
| fabner | 9435 | 2183 | 2064 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{DBLP:journals/jim/KumarS22,
author = {Aman Kumar and
Binil Starly},
title = {"FabNER": information extraction from manufacturing process science
domain literature using named entity recognition},
journal = {J. Intell. Manuf.},
volume = {33},
number = {8},
pages = {2393--2407},
year = {2022},
url = {https://doi.org/10.1007/s10845-021-01807-x},
doi = {10.1007/s10845-021-01807-x},
timestamp = {Sun, 13 Nov 2022 17:52:57 +0100},
biburl = {https://dblp.org/rec/journals/jim/KumarS22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset. | [
-0.5756768584251404,
-0.8826786875724792,
0.2648547291755676,
0.01075439341366291,
-0.15777701139450073,
0.08048886805772781,
-0.23495151102542877,
-0.27424541115760803,
0.610088586807251,
0.4400820732116699,
-0.7336682677268982,
-0.9710372090339661,
-0.6461793184280396,
0.2602635025978088... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Aniemore/resd_annotated | Aniemore | 2023-07-14T07:59:51Z | 39 | 3 | null | [
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:ru",
"license:mit",
"voice",
"emotions",
"annotated",
"classification",
"doi:10.57967/hf/1272",
"region:us"
] | 2023-07-14T07:59:51Z | 2023-02-15T20:00:40.000Z | 2023-02-15T20:00:40 | ---
language: ru
dataset_info:
features:
- name: name
dtype: string
- name: path
dtype: string
- name: speech
dtype: audio
- name: text
dtype: string
- name: emotion
dtype: string
splits:
- name: train
num_bytes: 398878916.336
num_examples: 1116
- name: test
num_bytes: 96643276
num_examples: 280
download_size: 485513605
dataset_size: 495522192.336
license: mit
task_categories:
- audio-classification
tags:
- voice
- emotions
- annotated
- classification
pretty_name: RESD
size_categories:
- 1K<n<10K
---
# Dataset Card for "resd_annotated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7211395502090454,
-0.29460155963897705,
0.09668513387441635,
0.07449304312467575,
-0.3462425768375397,
-0.07418710738420486,
0.14536386728286743,
-0.2936820685863495,
0.9887826442718506,
0.6588473916053772,
-0.8836620450019836,
-0.6517181992530823,
-0.4705813527107239,
0.023981187492609... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mstz/student_performance | mstz | 2023-04-07T14:54:45Z | 39 | 0 | null | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"student performance",
"tabular_classification",
"binary_classification",
"region:us"
] | 2023-04-07T14:54:45Z | 2023-03-24T13:53:31.000Z | 2023-03-24T13:53:31 | ---
language:
- en
tags:
- student performance
- tabular_classification
- binary_classification
pretty_name: Student Performance
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- encoding
- math
- writing
- reading
license: cc
---
# Student performance
The [Student performance dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/student_performances) from Kaggle.
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| math | Binary classification | Has the student passed the math exam? |
| writing | Binary classification | Has the student passed the writing exam? |
| reading | Binary classification | Has the student passed the reading exam? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/student_performance", "math")["train"]
```
# Features
|**Feature** |**Type** |
|-----------------------------------|-----------|
|`is_male` |`bool` |
|`ethnicity` |`string` |
|`parental_level_of_education` |`int8` |
|`has_standard_lunch` |`bool` |
|`has_completed_preparation_test` |`bool` |
|`reading_score` |`int64` |
|`writing_score` |`int64` |
|`math_score` |`int64` | | [
-0.21350066363811493,
-0.6017742156982422,
0.12320129573345184,
0.24792751669883728,
0.23635320365428925,
0.1676507145166397,
-0.23396801948547363,
0.10366969555616379,
0.21187058091163635,
0.39957311749458313,
-0.5634909272193909,
-0.7222284078598022,
-0.617937445640564,
0.052162747830152... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sandhyasachidanandan/workshop-path-dataset | sandhyasachidanandan | 2023-04-26T19:29:25Z | 39 | 0 | null | [
"region:us"
] | 2023-04-26T19:29:25Z | 2023-04-26T19:29:21.000Z | 2023-04-26T19:29:21 | ---
dataset_info:
features:
- name: product_name
dtype: string
- name: product_description
dtype: string
- name: category_path
dtype: string
splits:
- name: train
num_bytes: 26820
num_examples: 100
download_size: 6735
dataset_size: 26820
---
# Dataset Card for "workshop-path-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.44064465165138245,
-0.2534318268299103,
0.4245816171169281,
0.32829755544662476,
-0.20217686891555786,
-0.028285805135965347,
0.33760908246040344,
-0.2488863170146942,
0.7732841968536377,
0.46486157178878784,
-1.277385950088501,
-0.8457223773002625,
-0.5358489751815796,
-0.4812611043453... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
leemeng/jcommonsenseqa-v1.1 | leemeng | 2023-04-28T08:13:50Z | 39 | 2 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-04-28T08:13:50Z | 2023-04-28T07:50:46.000Z | 2023-04-28T07:50:46 | ---
license: cc-by-4.0
dataset_info:
features:
- name: q_id
dtype: int64
- name: question
dtype: string
- name: choice0
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: choice3
dtype: string
- name: choice4
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1183829
num_examples: 8939
- name: validation
num_bytes: 148293
num_examples: 1119
download_size: 887894
dataset_size: 1332122
---
| [
-0.1285339742898941,
-0.18616800010204315,
0.6529127359390259,
0.4943626821041107,
-0.1931934952735901,
0.2360742688179016,
0.360720157623291,
0.05056300014257431,
0.5793654322624207,
0.7400140166282654,
-0.6508105993270874,
-0.23783984780311584,
-0.7102248668670654,
-0.047826044261455536,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
EleutherAI/fever | EleutherAI | 2023-04-30T00:09:28Z | 39 | 1 | fever | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0",
"knowledge-verification",
"region:us"... | 2023-04-30T00:09:28Z | 2023-04-30T00:07:16.000Z | 2023-04-30T00:07:16 | ---
language:
- en
paperswithcode_id: fever
annotations_creators:
- crowdsourced
language_creators:
- found
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
pretty_name: FEVER
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- text-classification
task_ids: []
tags:
- knowledge-verification
dataset_info:
- config_name: v1.0
features:
- name: id
dtype: int32
- name: label
dtype: string
- name: claim
dtype: string
- name: evidence_annotation_id
dtype: int32
- name: evidence_id
dtype: int32
- name: evidence_wiki_url
dtype: string
- name: evidence_sentence_id
dtype: int32
splits:
- name: train
num_bytes: 24147163
num_examples: 263822
- name: dev
num_bytes: 2696375
num_examples: 28625
- name: paper_dev
num_bytes: 1348943
num_examples: 14475
- name: paper_test
num_bytes: 1347432
num_examples: 14150
download_size: 44853972
dataset_size: 40043693
- config_name: v2.0
features:
- name: id
dtype: int32
- name: label
dtype: string
- name: claim
dtype: string
- name: evidence_annotation_id
dtype: int32
- name: evidence_id
dtype: int32
- name: evidence_wiki_url
dtype: string
- name: evidence_sentence_id
dtype: int32
splits:
- name: validation
num_bytes: 306243
num_examples: 2384
download_size: 392466
dataset_size: 306243
- config_name: wiki_pages
features:
- name: id
dtype: string
- name: text
dtype: string
- name: lines
dtype: string
splits:
- name: wikipedia_pages
num_bytes: 7254115038
num_examples: 5416537
download_size: 1713485474
dataset_size: 7254115038
---
# Dataset Card for "fever"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://fever.ai/](https://fever.ai/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
With billions of individual pages on the web providing information on almost every conceivable topic, we should have
the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this
information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to
transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot
of recent research and media coverage: false information coming from unreliable sources.
The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.
- FEVER Dataset: FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences
extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims
are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the
sentence(s) forming the necessary evidence for their judgment.
- FEVER 2.0 Adversarial Attacks Dataset: The FEVER 2.0 Dataset consists of 1174 claims created by the submissions of
participants in the Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating
adversarial examples that induce classification errors for the existing systems. Breakers submitted a dataset of up to
1000 instances with equal number of instances for each of the three classes (Supported, Refuted NotEnoughInfo). Only
novel claims (i.e. not contained in the original FEVER dataset) were considered as valid entries to the shared task.
The submissions were then manually evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER
annotation guidelines requirements).
### Supported Tasks and Leaderboards
The task is verification of textual claims against textual sources.
When compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the
passage to verify each claim is given, and in recent years it typically consists a single sentence, while in
verification systems it is retrieved from a large set of documents in order to form the evidence.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
#### v1.0
- **Size of downloaded dataset files:** 44.86 MB
- **Size of the generated dataset:** 40.05 MB
- **Total amount of disk used:** 84.89 MB
An example of 'train' looks as follows.
```
'claim': 'Nikolaj Coster-Waldau worked with the Fox Broadcasting Company.',
'evidence_wiki_url': 'Nikolaj_Coster-Waldau',
'label': 'SUPPORTS',
'id': 75397,
'evidence_id': 104971,
'evidence_sentence_id': 7,
'evidence_annotation_id': 92206}
```
#### v2.0
- **Size of downloaded dataset files:** 0.39 MB
- **Size of the generated dataset:** 0.30 MB
- **Total amount of disk used:** 0.70 MB
#### wiki_pages
- **Size of downloaded dataset files:** 1.71 GB
- **Size of the generated dataset:** 7.25 GB
- **Total amount of disk used:** 8.97 GB
An example of 'wikipedia_pages' looks as follows.
```
{'text': 'The following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world . ',
'lines': '0\tThe following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world .\n1\t',
'id': '1928_in_association_football'}
```
### Data Fields
The data fields are the same among all splits.
#### v1.0
- `id`: a `int32` feature.
- `label`: a `string` feature.
- `claim`: a `string` feature.
- `evidence_annotation_id`: a `int32` feature.
- `evidence_id`: a `int32` feature.
- `evidence_wiki_url`: a `string` feature.
- `evidence_sentence_id`: a `int32` feature.
#### v2.0
- `id`: a `int32` feature.
- `label`: a `string` feature.
- `claim`: a `string` feature.
- `evidence_annotation_id`: a `int32` feature.
- `evidence_id`: a `int32` feature.
- `evidence_wiki_url`: a `string` feature.
- `evidence_sentence_id`: a `int32` feature.
#### wiki_pages
- `id`: a `string` feature.
- `text`: a `string` feature.
- `lines`: a `string` feature.
### Data Splits
#### v1.0
| | train | dev | paper_dev | paper_test |
|------|-------:|------:|----------:|-----------:|
| v1.0 | 311431 | 37566 | 18999 | 18567 |
#### v2.0
| | validation |
|------|-----------:|
| v2.0 | 2384 |
#### wiki_pages
| | wikipedia_pages |
|------------|----------------:|
| wiki_pages | 5416537 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
FEVER license:
```
These data annotations incorporate material from Wikipedia, which is licensed pursuant to the Wikipedia Copyright Policy. These annotations are made available under the license terms described on the applicable Wikipedia article pages, or, where Wikipedia license terms are unavailable, under the Creative Commons Attribution-ShareAlike License (version 3.0), available at http://creativecommons.org/licenses/by-sa/3.0/ (collectively, the “License Termsâ€). You may not use these files except in compliance with the applicable License Terms.
```
### Citation Information
If you use "FEVER Dataset", please cite:
```bibtex
@inproceedings{Thorne18Fever,
author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},
title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}},
booktitle = {NAACL-HLT},
year = {2018}
}
```
If you use "FEVER 2.0 Adversarial Attacks Dataset", please cite:
```bibtex
@inproceedings{Thorne19FEVER2,
author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit},
title = {The {FEVER2.0} Shared Task},
booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}},
year = {2018}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq),
[@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun),
[@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | [
-0.5471979975700378,
-0.6866545081138611,
0.15270830690860748,
0.14032886922359467,
-0.2059536874294281,
-0.09807195514440536,
-0.2811519503593445,
-0.5318810343742371,
0.6165002584457397,
0.4107823073863983,
-0.5871056914329529,
-0.8343188762664795,
-0.5745019912719727,
0.2787407338619232... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yuchenlin/G-PlanET | yuchenlin | 2023-07-15T07:33:33Z | 39 | 3 | null | [
"task_categories:text-generation",
"task_categories:table-to-text",
"task_categories:table-question-answering",
"language:en",
"license:apache-2.0",
"arxiv:2209.00465",
"region:us"
] | 2023-07-15T07:33:33Z | 2023-05-11T00:54:50.000Z | 2023-05-11T00:54:50 | ---
task_categories:
- text-generation
- table-to-text
- table-question-answering
language:
- en
license: apache-2.0
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:** https://arxiv.org/abs/2209.00465
- **Leaderboard:**
- **Point of Contact:** yuchenlin1995@gmail.com
### Dataset Summary
This **G-PlanET** dataset is built on AI2 [ALFRED](https://leaderboard.allenai.org/alfred/submissions/get-started).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
-0.46052879095077515,
-0.40509793162345886,
0.20610801875591278,
0.03483505919575691,
-0.34303203225135803,
0.13329318165779114,
-0.2464527040719986,
-0.41952842473983765,
0.42752352356910706,
0.6362455487251282,
-0.8097149133682251,
-0.9344141483306885,
-0.8518665432929993,
-0.02077401801... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lighteval/pile_helm | lighteval | 2023-05-16T13:02:31Z | 39 | 0 | null | [
"region:us"
] | 2023-05-16T13:02:31Z | 2023-05-12T10:03:48.000Z | 2023-05-12T10:03:48 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
a6kme/minds14-mirror | a6kme | 2023-05-13T11:42:15Z | 39 | 0 | null | [
"task_categories:automatic-speech-recognition",
"task_ids:keyword-spotting",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
... | 2023-05-13T11:42:15Z | 2023-05-13T07:56:01.000Z | 2023-05-13T07:56:01 | ---
annotations_creators:
- expert-generated
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
- fr
- it
- es
- pt
- de
- nl
- ru
- pl
- cs
- ko
- zh
language_bcp47:
- en
- en-GB
- en-US
- en-AU
- fr
- it
- es
- pt
- de
- nl
- ru
- pl
- cs
- ko
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: 'MInDS-14'
size_categories:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
- speech-processing
task_ids:
- speech-recognition
- keyword-spotting
---
# MInDS-14
## Dataset Description
- **Fine-Tuning script:** [pytorch/audio-classification](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification)
- **Paper:** [Multilingual and Cross-Lingual Intent Detection from Spoken Data](https://arxiv.org/abs/2104.08524)
- **Total amount of disk used:** ca. 500 MB
MINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14
intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties.
## Example
MInDS-14 can be downloaded and used as follows:
```py
from datasets import load_dataset
minds_14 = load_dataset("PolyAI/minds14", "fr-FR") # for French
# to download all data for multi-lingual fine-tuning uncomment following line
# minds_14 = load_dataset("PolyAI/all", "all")
# see structure
print(minds_14)
# load audio sample on the fly
audio_input = minds_14["train"][0]["audio"] # first decoded audio sample
intent_class = minds_14["train"][0]["intent_class"] # first transcription
intent = minds_14["train"].features["intent_class"].names[intent_class]
# use audio_input and language_class to fine-tune your model for audio classification
```
## Dataset Structure
We show detailed information the example configurations `fr-FR` of the dataset.
All other configurations have the same structure.
### Data Instances
**fr-FR**
- Size of downloaded dataset files: 471 MB
- Size of the generated dataset: 300 KB
- Total amount of disk used: 471 MB
An example of a datainstance of the config `fr-FR` looks as follows:
```
{
"path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav",
"audio": {
"path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav",
"array": array(
[0.0, 0.0, 0.0, ..., 0.0, 0.00048828, -0.00024414], dtype=float32
),
"sampling_rate": 8000,
},
"transcription": "je souhaite changer mon adresse",
"english_transcription": "I want to change my address",
"intent_class": 1,
"lang_id": 6,
}
```
### Data Fields
The data fields are the same among all splits.
- **path** (str): Path to the audio file
- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
- **transcription** (str): Transcription of the audio file
- **english_transcription** (str): English transcription of the audio file
- **intent_class** (int): Class id of intent
- **lang_id** (int): Id of language
### Data Splits
Every config only has the `"train"` split containing of *ca.* 600 examples.
## Dataset Creation
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
### Citation Information
```
@article{DBLP:journals/corr/abs-2104-08524,
author = {Daniela Gerz and
Pei{-}Hao Su and
Razvan Kusztos and
Avishek Mondal and
Michal Lis and
Eshan Singhal and
Nikola Mrksic and
Tsung{-}Hsien Wen and
Ivan Vulic},
title = {Multilingual and Cross-Lingual Intent Detection from Spoken Data},
journal = {CoRR},
volume = {abs/2104.08524},
year = {2021},
url = {https://arxiv.org/abs/2104.08524},
eprinttype = {arXiv},
eprint = {2104.08524},
timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-08524.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset
| [
-0.46738189458847046,
-0.5980509519577026,
0.36973637342453003,
0.2669966518878937,
-0.12211816012859344,
-0.34158211946487427,
-0.5432057976722717,
-0.3907387852668762,
0.32248517870903015,
0.4533486068248749,
-0.7834721803665161,
-0.9560457468032837,
-0.5506324172019958,
0.09728632122278... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
FanChen0116/syn_few7_7100_chat_all_data_pvi | FanChen0116 | 2023-06-01T02:38:40Z | 39 | 0 | null | [
"region:us"
] | 2023-06-01T02:38:40Z | 2023-05-31T08:34:59.000Z | 2023-05-31T08:34:59 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: labels
sequence:
class_label:
names:
'0': O
'1': I-time
'2': B-date
'3': B-last_name
'4': B-people
'5': I-date
'6': I-people
'7': I-last_name
'8': I-first_name
'9': B-first_name
'10': B-time
- name: request_slot
sequence: string
splits:
- name: train
num_bytes: 558759
num_examples: 3335
- name: validation
num_bytes: 646729
num_examples: 3731
- name: test
num_bytes: 646729
num_examples: 3731
download_size: 92716
dataset_size: 1852217
---
# Dataset Card for "syn_few7_7100_chat_all_data_pvi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.31743016839027405,
-0.2044840157032013,
0.18833808600902557,
0.26547691226005554,
-0.2494935393333435,
-0.08976920694112778,
0.22371573746204376,
-0.16362687945365906,
1.0481188297271729,
0.3653820753097534,
-0.8517699837684631,
-0.6325012445449829,
-0.35506224632263184,
-0.175031125545... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
CIRAL/ciral-corpus | CIRAL | 2023-06-27T19:01:03Z | 39 | 0 | null | [
"language:ha",
"language:so",
"language:sw",
"language:yo",
"license:apache-2.0",
"region:us"
] | 2023-06-27T19:01:03Z | 2023-06-01T20:05:01.000Z | 2023-06-01T20:05:01 | ---
language:
- ha
- so
- sw
- yo
mutilinguality:
- multilingual
task-categories:
- text-retrieval
license: apache-2.0
viewer: true
---
# Dataset Summary
CIRAL is a collection for cross-lingual information retrieval research across four (4) African languages. The collection comprises English queries and query-passage relevance judgements manually annotated by native speakers.
This dataset stores passages which have been culled from news websites for CIRAL.
## Dataset Structure
This dataset is configured by language. An example of a passage data entry is
```json
{
'docid': 'DOCID#0#0',
'title': 'This is the title of a sample passage',
'text': 'This is the content of a sample passage',
'url': 'https:/\/\this-is-a-sample-url.com'
}
```
## Load Dataset
An example to load the dataset
```python
language = "hausa"
dataset = load_dataset("ciral/ciral-corpus", language)
```
## Citation
...
| [
-0.4407525956630707,
-0.4228622317314148,
0.19248168170452118,
0.28470179438591003,
-0.2174438238143921,
0.08636093884706497,
-0.35011234879493713,
-0.1746327430009842,
0.576088011264801,
0.7195374369621277,
-0.4049180746078491,
-0.9527519941329956,
-0.37629103660583496,
0.3297294378280639... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dmayhem93/agieval-sat-en-without-passage | dmayhem93 | 2023-06-18T17:31:43Z | 39 | 0 | null | [
"license:mit",
"arxiv:2304.06364",
"region:us"
] | 2023-06-18T17:31:43Z | 2023-06-18T12:51:12.000Z | 2023-06-18T12:51:12 | ---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 154762
num_examples: 206
download_size: 85136
dataset_size: 154762
license: mit
---
# Dataset Card for "agieval-sat-en-without-passage"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo.
MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | [
-0.2787812352180481,
-0.5386655330657959,
0.3505707085132599,
0.36788153648376465,
-0.38620051741600037,
-0.21414190530776978,
0.11536765843629837,
-0.4942888021469116,
-0.0017134598456323147,
0.6574452519416809,
-0.7383328080177307,
-0.5668697953224182,
-0.4717787504196167,
-0.06120911613... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dmayhem93/agieval-sat-math | dmayhem93 | 2023-06-18T17:32:05Z | 39 | 6 | null | [
"license:mit",
"arxiv:2304.06364",
"region:us"
] | 2023-06-18T17:32:05Z | 2023-06-18T12:51:24.000Z | 2023-06-18T12:51:24 | ---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 110388
num_examples: 220
download_size: 57002
dataset_size: 110388
license: mit
---
# Dataset Card for "agieval-sat-math"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo.
MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | [
-0.30836835503578186,
-0.6597541570663452,
0.08315117657184601,
0.35629308223724365,
-0.22811061143875122,
-0.06573422253131866,
0.03990735858678818,
-0.24673113226890564,
-0.051092468202114105,
0.5163847804069519,
-0.7176838517189026,
-0.484518826007843,
-0.43332910537719727,
-0.272025018... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Patt/MultiRC_TH_drop | Patt | 2023-07-20T15:26:22Z | 39 | 0 | null | [
"task_categories:text-classification",
"language:en",
"language:th",
"arxiv:1907.04307",
"region:us"
] | 2023-07-20T15:26:22Z | 2023-06-22T13:20:37.000Z | 2023-06-22T13:20:37 | ---
task_categories:
- text-classification
language:
- en
- th
dataset_info:
features:
- name: paragraph
dtype: string
- name: paragraph_TH
dtype: string
- name: question
dtype: string
- name: question_TH
dtype: string
- name: answer
dtype: string
- name: answer_TH
dtype: string
- name: idx
struct:
- name: answer
dtype: int64
- name: paragraph
dtype: int64
- name: question
dtype: int64
- name: label
dtype: int64
- name: score_paragraph
dtype: float64
- name: score_question
dtype: float64
- name: score_answer
dtype: float64
splits:
- name: train
num_bytes: 133061823
num_examples: 23520
- name: validation
num_bytes: 22534453
num_examples: 4212
- name: test
num_bytes: 42757726
num_examples: 8272
download_size: 5756232
dataset_size: 198354002
---
# Dataset Card for MultiRC_TH_drop
### Dataset Description
This dataset is Thai translated version of [multirc](https://huggingface.co/datasets/super_glue/viewer/multirc) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
The score was penalized by the length of original text compare to translated text. The row that any score < 0.66 was dropped. | [
-0.5792210102081299,
-0.5747436881065369,
0.017340945079922676,
0.21569469571113586,
-0.5390503406524658,
0.13692818582057953,
-0.4918600618839264,
-0.16126692295074463,
0.4688900113105774,
0.47556254267692566,
-0.9017436504364014,
-0.7712934017181396,
-0.5849436521530151,
0.31541979312896... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yulongmannlp/dev_orig | yulongmannlp | 2023-06-26T00:14:57Z | 39 | 0 | null | [
"region:us"
] | 2023-06-26T00:14:57Z | 2023-06-26T00:04:11.000Z | 2023-06-26T00:04:11 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622264862060547,
0.43461528420448303,
-0.52829909324646,
0.7012971639633179,
0.7915720343589783,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104477167129517,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yulongmannlp/adv_para | yulongmannlp | 2023-06-26T00:37:52Z | 39 | 0 | null | [
"region:us"
] | 2023-06-26T00:37:52Z | 2023-06-26T00:36:04.000Z | 2023-06-26T00:36:04 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622264862060547,
0.43461528420448303,
-0.52829909324646,
0.7012971639633179,
0.7915720343589783,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104477167129517,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DataHammer/scimrc | DataHammer | 2023-06-28T12:00:41Z | 39 | 6 | null | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-06-28T12:00:41Z | 2023-06-28T06:15:50.000Z | 2023-06-28T06:15:50 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
# Scientific Emotional Dialogue
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a dataset for question answering on scientific research papers. It consists of 21.297 questions-answer-evidence pairs.
### Supported Tasks and Leaderboards
- question-answering: The dataset can be used to train a model for Scientific Question Answering. Success on this task is typically measured by achieving a high F1 score.
### Languages
English
## Dataset Structure
### Data Instances
A typical instance in the dataset:
```
{
"question": "What aim do the authors have by improving Wiki(GOLD) results?",
"answer": "The aim is not to tune their model specifically on this class hierarchy. They instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset.",
"evidence": "The results for each class type are shown in Table TABREF19 , with some specific examples shown in Figure FIGREF18 . For the Wiki(gold) we quote the micro-averaged F-1 scores for the entire top level entity category. The total F-1 score on the OntoNotes dataset is 88%, and the total F-1 cross-validation score on the 112 class Wiki(gold) dataset is 53%. It is worth noting that one could improve Wiki(gold) results by training directly using this dataset. However, the aim is not to tune our model specifically on this class hierarchy. We instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset. The results in Table TABREF19 (OntoNotes) only show the main 7 categories in OntoNotes which map to Wiki(gold) for clarity. The other categories (date, time, norp, language, ordinal, cardinal, quantity, percent, money, law) have F-1 scores between 80-90%, with the exception of time (65%)\nIt is worth noting that one could improve Wiki(GOLD) results by training directly using this dataset. However, the aim is not to tune our model specifically on this class hierarchy. We instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset.",
"yes_no": false
}
```
| [
-0.4366612136363983,
-0.6903831362724304,
0.06848426163196564,
0.07479256391525269,
-0.041460469365119934,
0.042942702770233154,
-0.16534225642681122,
-0.48465242981910706,
0.0808868333697319,
0.2540450394153595,
-0.7345383763313293,
-0.7663187384605408,
-0.4187333881855011,
0.383084475994... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wu981526092/MGSD | wu981526092 | 2023-08-26T06:22:56Z | 39 | 1 | null | [
"task_categories:text-classification",
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | 2023-08-26T06:22:56Z | 2023-06-29T18:05:33.000Z | 2023-06-29T18:05:33 | ---
license: mit
task_categories:
- text-classification
- token-classification
language:
- en
size_categories:
- 10K<n<100K
---
# MULTI-GRAIN STEREOTYPE DATASET (MGSD)
The MULTI-GRAIN STEREOTYPE DATASET (MGSD) is a comprehensive dataset designed for the research and analysis of stereotypes in natural language processing. It provides granular annotations at both the sentence and token levels, enabling various studies and applications in stereotype detection.
## Dataset Structure
The dataset contains the following columns:
- **text_with_marker**: Contains the original text with markers (`===`) highlighting potential stereotype tokens.
- **text_no_marker**: The text without any markers, suitable for models that operate at the sentence level.
- **label**: Indicates if the sentence is a stereotype, anti-stereotype, or unrelated.
- **stereotype_type**: Describes the type of stereotype e.g., race, gender, profession.
- **binary_class**: A binary classification of the stereotype e.g., stereotype_race, unrelated.
- **multi_class**: A multi-class classification label e.g., stereotype_race, stereotype_gender.
- **original_dataset**: Source of the data.
## Usage
This dataset can be used to train models for various tasks:
1. **Sentence-level Stereotype Detection**: Using the `text_no_marker` column as input and `binary_label` or `multi_label` as target.
2. **Token-level Stereotype Detection**: Using the `text_with_marker` to identify the position of the token in the sentence. | [
-0.7164341807365417,
-0.588527500629425,
0.4777514636516571,
0.32386961579322815,
-0.2815364599227905,
0.03608746826648712,
0.10990317165851593,
-0.26180145144462585,
0.4935207664966583,
0.34791335463523865,
-0.6475872993469238,
-1.1421780586242676,
-0.7329581379890442,
0.2063559740781784,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
beyond/rlhf-reward-single-round-trans_chinese | beyond | 2023-07-05T13:03:15Z | 39 | 29 | null | [
"region:us"
] | 2023-07-05T13:03:15Z | 2023-07-05T13:02:55.000Z | 2023-07-05T13:02:55 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 12139022
num_examples: 19862
- name: test
num_bytes: 3117841
num_examples: 4996
download_size: 10699367
dataset_size: 15256863
---
# Dataset Card for "rlhf-reward-single-round-trans_chinese"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.25583842396736145,
-0.3446066677570343,
-0.012887611985206604,
0.37746837735176086,
-0.4797392785549164,
-0.06881333887577057,
0.15251226723194122,
-0.24965617060661316,
0.8944696187973022,
0.5960627794265747,
-1.1349353790283203,
-0.8066648840904236,
-0.47788572311401367,
-0.1700821220... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pytorch-survival/gbsg_pycox | pytorch-survival | 2023-07-12T01:53:49Z | 39 | 0 | null | [
"region:us"
] | 2023-07-12T01:53:49Z | 2023-07-12T00:32:24.000Z | 2023-07-12T00:32:24 | ---
dataset_info:
features:
- name: x0
dtype: float32
- name: x1
dtype: float32
- name: x2
dtype: float32
- name: x3
dtype: float32
- name: x4
dtype: float32
- name: x5
dtype: float32
- name: x6
dtype: float32
- name: event_time
dtype: float32
- name: event_indicator
dtype: int32
splits:
- name: train
num_bytes: 80352
num_examples: 2232
download_size: 34711
dataset_size: 80352
---
# Dataset Card for "gbsg_pycox"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5810496807098389,
-0.22967544198036194,
0.21131274104118347,
0.2978135645389557,
-0.12982800602912903,
-0.11002959311008453,
0.23675566911697388,
-0.10191719979047775,
0.7569771409034729,
0.40560218691825867,
-0.6488245725631714,
-0.7448176145553589,
-0.5963921546936035,
-0.304426372051... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
fhirfly/medicalquestions | fhirfly | 2023-10-28T17:54:21Z | 39 | 4 | null | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"medical",
"region:us"
] | 2023-10-28T17:54:21Z | 2023-07-13T16:46:49.000Z | 2023-07-13T16:46:49 | ---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- medical
pretty_name: FhirFly Medical Questions
size_categories:
- 10K<n<100K
---
# 🤗 Dataset Card: fhirfly/medicalquestions
## Dataset Overview
- Dataset name: fhirfly/medicalquestions
- Dataset size: 25,102 questions
- Labels: 1 (medical), 0 (non-medical)
- Distribution: Evenly distributed between medical and non-medical questions
## Dataset Description
The fhirfly/medicalquestions dataset is a collection of 25,102 questions labeled as either medical or non-medical. The dataset aims to provide a diverse range of questions covering various medical and non-medical domains.
The questions in the dataset have been manually labeled by domain experts based on the context and content of each question. Each question is assigned a label of 1 if it is determined to be a medical question and a label of 0 if it is classified as a non-medical question.
## Dataset Structure
The dataset consists of a single file containing the following columns:
- **Text**: The text of the question.
- **Label**: The label assigned to each question, either 1 (medical) or 0 (non-medical).
The questions are evenly distributed between medical and non-medical categories, ensuring a balanced dataset for training and evaluation.
## Potential Biases
Efforts have been made to ensure that the dataset is representative of various medical and non-medical topics. However, it is important to acknowledge that biases may exist in the dataset due to the subjective nature of labeling questions. Biases could be present in terms of the types of questions included, the representation of certain medical conditions or non-medical topics, or the labeling process itself.
It is recommended to perform thorough evaluation and analysis of the dataset to identify and mitigate potential biases during model training and deployment. Care should be taken to address any biases to ensure fair and unbiased predictions.
## Dataset Quality
The fhirfly/medicalquestions dataset has undergone manual labeling by domain experts, which helps maintain a high level of quality and accuracy. However, human labeling is not entirely immune to errors or subjectivity.
To ensure the quality of the dataset, a thorough review process has been conducted to minimize errors and maintain consistency in labeling. Nonetheless, it is advisable to validate and verify the data as part of your specific use case to ensure it meets your requirements.
## Data License
The fhirfly/medicalquestions dataset is released under the MIT license. Please refer to the license file accompanying the dataset for more information on its usage and any restrictions that may apply.
## Dataset Citation
If you use the fhirfly/medicalquestions dataset in your work, please cite it as:
```
@dataset{fhirfly/medicalquestions,
title = {fhirfly/medicalquestions},
author = {fhirfly},
year = {2023},
publisher = {Hugging Face},
version = {1.0.0},
url = {https://huggingface.co/datasets/fhirfly/medicalquestions}
}
``` | [
-0.2944018542766571,
-0.6048973798751831,
0.10124421864748001,
0.24898818135261536,
-0.12482338398694992,
-0.09073282033205032,
0.1320602297782898,
-0.6793378591537476,
0.5940116047859192,
0.48330405354499817,
-0.7879933714866638,
-0.663722038269043,
-0.6272023320198059,
0.1957480162382125... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nlpkevinl/whatsthatbook | nlpkevinl | 2023-08-15T07:29:24Z | 39 | 0 | null | [
"task_categories:text-retrieval",
"language:en",
"license:odc-by",
"arxiv:2305.15053",
"region:us"
] | 2023-08-15T07:29:24Z | 2023-07-26T15:29:14.000Z | 2023-07-26T15:29:14 | ---
license: odc-by
task_categories:
- text-retrieval
language:
- en
pretty_name: whatsthatbook
extra_gated_prompt: "To access this dataset, you agree to the terms and conditions from the GoodReads website stated here: https://www.goodreads.com/about/terms"
extra_gated_fields:
I agree to use to the terms and conditions: checkbox
---
# Dataset Card for WhatsThatBook
## Dataset Description
- **Paper: https://arxiv.org/abs/2305.15053**
- **Point of Contact: k-lin@berkeley.edu**
### Dataset Summary
A collection of tip-of-the-tongue queries for book searches. The dataset was curated from GoodReads community forum user queries. It seves as a training and evaluation
resource for tip-of-the-tongue book queries. The user queries contain the interactions on the community forum and the documents are books with associated metadata.
### Supported Tasks and Leaderboards
WhatsThatBook is intended for information retrieval tasks including but not limited to standard retrieval, using just the original query posted by the user
and interactive settings, where the system asks clarification queries to narrow down the user's information needs.
### Languages
The dataset is primary in English, some book descriptions may contain other languages.
## Dataset Structure
### Data Fields
Data fields for WhatsThatBook queries:
- `question`: Inital query posted to the community forum
- `question_posted_date`: The date that the query was posted in YYYY-MM-DD format
- `book_id`: ID of the gold book used for evaluation
- `answers`: List of the gold book descriptions
The fields for the books:
- `title`: The title of the book
- `author`: The author of the book
- `author_url`: Link to the author page
- `description` The blurb of the book that contains description of the plot or
- `isbn_13`: The ISBN 13 number
- `date`: String representation of the date from the book webpage
- `parsed_dates`:A list of the publication date parsed out in YYYY-MM-DD format
- `image_link`: original link to image
- `ratings`: Total number of ratings
- `reviews`: Total number of reviews
- `genres`: Dictionary of genre tags to number of times tagged with that genre
- `id`: ID of the book, corresponding to the query file
### Data Splits
The dataset is comprised of two parts, WTB (WhatsThatBook), as well as TOMT (tip-of-my-tongue). WhatsThatBook contains standard train, dev, and test splits, and TOMT serves as an evaluation set.
## Dataset Creation
### Source Data
## Additional Information
### Dataset Curators
1. Kevin Lin, UC Berkeley, k-lin@berkeley.edu
2. Kyle Lo, Allen Institue For Artificial Intelligence, kylel@allenai.org
### Citation Information
```
@article{lin2023decomposing,
title={Decomposing Complex Queries for Tip-of-the-tongue Retrieval},
author={Lin, Kevin and Lo, Kyle and Gonzalez, Joseph E and Klein, Dan},
journal={arXiv preprint arXiv:2305.15053},
year={2023}
}
```
| [
-0.4200352132320404,
-0.5765782594680786,
0.14817215502262115,
-0.04216628521680832,
-0.261917382478714,
-0.23704031109809875,
-0.0584222637116909,
-0.4352584481239319,
0.4287104904651642,
0.6613031029701233,
-0.8145191073417664,
-0.8793155550956726,
-0.38024088740348816,
0.077982209622859... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
morpheuslord/cve-llm-training | morpheuslord | 2023-08-30T06:26:48Z | 39 | 7 | null | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] | 2023-08-30T06:26:48Z | 2023-08-15T06:15:00.000Z | 2023-08-15T06:15:00 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: CVE-LLm-Dataset
data_source: Custom data collected from the CVE database
data_formats: JSONL
---
# CVE-llm_dataset
This dataset is intended to train an LLM model for an utterly CVE-focused input and output.
## Data extraction:
For the data extraction, I first downloaded the CVE database from NVD lists and then loaded them using the `cve_dataset_2.py` and `cve_dataset.py` both have produce different datasets one is for llama and the other is for openai GPT.
The CVE json files are mapped in this format:
```
cves:
|
├─1999
| ├─0xxx
| | ├─CVE-1999-0001.json
| | ├─....
| | └─CVE-1999-0999.json
| └─1xxx
| ├─CVE-1999-1000.json
| ├─....
| └─CVE-1999-1598.json
└─2023
```
The programs traverse through these folders extract the data in the files and arrange them into usable formats for the fine-tuning process.
## llama2 Model dataset:
The llama2 fine-tuned dataset follows this format:
```
{
"instruction": "Explain CVE-1999-0001",
"input": "Explain the vulnerability: CVE-1999-0001",
"output": "ip_input.c in BSD-derived TCP/IP implementations allows remote attackers to cause a denial of service (crash or hang) via crafted packets.\nAffected Products: n/a\nReferences: [{'tags': ['x_refsource_CONFIRM'], 'url': 'http://www.openbsd.org/errata23.html#tcpfix'}, {'name': '5707', 'tags': ['vdb-entry', 'x_refsource_OSVDB'], 'url': 'http://www.osvdb.org/5707'}]\nCVE State: PUBLISHED"
}
```
The instruction is what we instruct the AI to do with the data provided For example we can command the AI `To take in user input analyze it and then based on what he asks returns an answer` This is also where we can add a `role` or a `personal` to the AI.
The input is the user Input of the main query or data that must be processed by the AI. This is a crucial piece of information that the AI will process in order to provide an output.
The output is the format that we define and tell the AI to generate answers in that format or provide that answer to the question asked. | [
-0.32709071040153503,
-0.7903976440429688,
0.2792648673057556,
0.050472162663936615,
-0.3236471712589264,
-0.21515271067619324,
0.12558431923389435,
-0.18171393871307373,
0.16840407252311707,
0.9651035666465759,
-0.7433644533157349,
-0.6795248985290527,
-0.49047425389289856,
-0.09481573104... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Sofoklis/vrna_dataset | Sofoklis | 2023-10-10T15:58:27Z | 39 | 0 | null | [
"region:us"
] | 2023-10-10T15:58:27Z | 2023-09-06T13:22:48.000Z | 2023-09-06T13:22:48 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: name
dtype: string
- name: caption
dtype: string
- name: split_caption
dtype: string
- name: matrix
dtype: image
splits:
- name: train
num_bytes: 328952.0
num_examples: 80
- name: test
num_bytes: 82238.0
num_examples: 20
- name: valid
num_bytes: 263161.6
num_examples: 64
download_size: 549331
dataset_size: 674351.6
---
# Dataset Card for "vrna_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5897886753082275,
-0.2661450207233429,
0.13073422014713287,
0.22046460211277008,
-0.35173746943473816,
0.03430962935090065,
0.4675815999507904,
-0.04801258444786072,
1.0472861528396606,
0.4685933589935303,
-0.6928264498710632,
-0.6814365386962891,
-0.4413866698741913,
-0.307684928178787... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ahmed000000000/cybersec | ahmed000000000 | 2023-09-17T21:16:25Z | 39 | 0 | null | [
"region:us"
] | 2023-09-17T21:16:25Z | 2023-09-17T21:15:07.000Z | 2023-09-17T21:15:07 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622264862060547,
0.43461528420448303,
-0.52829909324646,
0.7012971639633179,
0.7915720343589783,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104477167129517,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
shossain/govreport-summarization-tokenized | shossain | 2023-09-20T07:04:40Z | 39 | 0 | null | [
"region:us"
] | 2023-09-20T07:04:40Z | 2023-09-20T06:19:21.000Z | 2023-09-20T06:19:21 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 69604
num_examples: 973
download_size: 22673
dataset_size: 69604
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "govreport-summarization-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.4529753625392914,
-0.22076362371444702,
0.08056457340717316,
0.20722338557243347,
-0.40371373295783997,
0.058917880058288574,
0.11116214096546173,
-0.022441258653998375,
1.12617826461792,
0.554128885269165,
-0.5307256579399109,
-0.8273975849151611,
-0.7575028538703918,
-0.22969198226928... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
FreedomIntelligence/Arabic-preference-data-RLHF | FreedomIntelligence | 2023-09-21T09:13:49Z | 39 | 1 | null | [
"region:us"
] | 2023-09-21T09:13:49Z | 2023-09-21T09:11:51.000Z | 2023-09-21T09:11:51 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Aples/FineTune_Dataset_Aples_1K | Aples | 2023-09-27T19:26:31Z | 39 | 0 | null | [
"license:mit",
"region:us"
] | 2023-09-27T19:26:31Z | 2023-09-27T19:23:20.000Z | 2023-09-27T19:23:20 | ---
license: mit
---
| [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yashnbx/l27b-E02-large-b10-1314-3 | yashnbx | 2023-09-30T16:29:18Z | 39 | 0 | null | [
"region:us"
] | 2023-09-30T16:29:18Z | 2023-09-30T16:28:57.000Z | 2023-09-30T16:28:57 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: test
num_bytes: 1013014
num_examples: 146
- name: train
num_bytes: 9077266
num_examples: 1314
download_size: 1662927
dataset_size: 10090280
---
# Dataset Card for "l27b-E02-large-b10-1314-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.6715719699859619,
-0.20642130076885223,
0.3868418037891388,
0.40806037187576294,
-0.2040187120437622,
-0.03809454292058945,
0.11966609954833984,
-0.19930621981620789,
1.109570026397705,
0.4201990067958832,
-0.8161139488220215,
-0.6142500042915344,
-0.46714338660240173,
-0.08092524856328... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
renumics/emodb | renumics | 2023-11-09T09:18:16Z | 39 | 0 | null | [
"region:us"
] | 2023-11-09T09:18:16Z | 2023-10-04T04:49:02.000Z | 2023-10-04T04:49:02 | ---
dataset_info:
features:
- name: age
dtype: float32
- name: gender
dtype:
class_label:
names:
'0': female
'1': male
- name: emotion
dtype:
class_label:
names:
'0': anger
'1': boredom
'2': disgust
'3': fear
'4': happiness
'5': neutral
'6': sadness
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 47623397.0
num_examples: 535
download_size: 46870260
dataset_size: 47623397.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "emodb"
This is a mirror for the emodb dataset. You can find the original version here:
http://emodb.bilderbar.info/docu/
## Explore this dataset
You can interactively explore this dataset with Spotlight:
```python
import datasets
from renumics import spotlight
ds = datasets.load_dataset('renumics/emodb', split='all')
spotlight.show(ds)
```
| [
-0.5913172364234924,
-0.7897250056266785,
0.23155692219734192,
0.040708672255277634,
-0.5085639953613281,
-0.02530750073492527,
0.05150545388460159,
-0.0004105107218492776,
1.0236210823059082,
0.6258023381233215,
-0.8178410530090332,
-0.6434116959571838,
-0.3034009039402008,
0.264182329177... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Trelis/big_patent_sample | Trelis | 2023-10-09T13:32:05Z | 39 | 1 | bigpatent | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1k",
"source_datasets:big_patent",
"language:en",
"license:cc-by-4.0",
"patent-summarization",
"arxiv:1906.03741",
"region:us"
] | 2023-10-09T13:32:05Z | 2023-10-06T12:07:45.000Z | 2023-10-06T12:07:45 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1k
source_datasets:
- big_patent
task_categories:
- summarization
task_ids: []
paperswithcode_id: bigpatent
pretty_name: Big Patent Sample
tags:
- patent-summarization
---
# Sampled big_patent Dataset
This is a sampled big_patent dataset - sampled down for shorter fine-tunings.
The data is sampled with the aim of providing an even distribution across data lengths. The distribution is quite flat up until 1 million characters in length, making the dataset good for training on lengths up to 250,000 tokens.
# Dataset Card for Big Patent
## 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:** [Big Patent](https://evasharma.github.io/bigpatent/)
- **Repository:**
- **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741)
- **Leaderboard:**
- **Point of Contact:** [Lu Wang](mailto:wangluxy@umich.edu)
### Dataset Summary
BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.
Each US patent application is filed under a Cooperative Patent Classification (CPC) code.
There are nine such classification categories:
- a: Human Necessities
- b: Performing Operations; Transporting
- c: Chemistry; Metallurgy
- d: Textiles; Paper
- e: Fixed Constructions
- f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting
- g: Physics
- h: Electricity
- y: General tagging of new or cross-sectional technology
Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:
```python
from datasets import load_dataset
ds = load_dataset("big_patent") # default is 'all' CPC codes
ds = load_dataset("big_patent", "all") # the same as above
ds = load_dataset("big_patent", "a") # only 'a' CPC codes
ds = load_dataset("big_patent", codes=["a", "b"])
```
To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`:
```python
ds = load_dataset("big_patent", codes="all", version="1.0.0")
ds = load_dataset("big_patent", codes="a", version="1.0.0")
ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0")
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section.
```
{
'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...',
'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...'
}
```
### Data Fields
- `description`: detailed description of patent.
- `abstract`: Patent abastract.
### Data Splits
| | train | validation | test |
|:----|------------------:|-------------:|-------:|
| all | 1207222 | 67068 | 67072 |
| a | 174134 | 9674 | 9675 |
| b | 161520 | 8973 | 8974 |
| c | 101042 | 5613 | 5614 |
| d | 10164 | 565 | 565 |
| e | 34443 | 1914 | 1914 |
| f | 85568 | 4754 | 4754 |
| g | 258935 | 14385 | 14386 |
| h | 257019 | 14279 | 14279 |
| y | 124397 | 6911 | 6911 |
## 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
```bibtex
@article{DBLP:journals/corr/abs-1906-03741,
author = {Eva Sharma and
Chen Li and
Lu Wang},
title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
Summarization},
journal = {CoRR},
volume = {abs/1906.03741},
year = {2019},
url = {http://arxiv.org/abs/1906.03741},
eprinttype = {arXiv},
eprint = {1906.03741},
timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. | [
-0.3162391185760498,
-0.37655138969421387,
0.1370326578617096,
0.7797120809555054,
-0.22105616331100464,
-0.009585268795490265,
-0.19997930526733398,
-0.38152649998664856,
0.5463310480117798,
0.3582291901111603,
-0.11289192736148834,
-0.7792425155639648,
-0.5546914339065552,
0.055502187460... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dhkim123/jy_finetune_sd | dhkim123 | 2023-10-11T21:56:16Z | 39 | 0 | null | [
"region:us"
] | 2023-10-11T21:56:16Z | 2023-10-11T05:38:44.000Z | 2023-10-11T05:38:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 37668449.2
num_examples: 1300
download_size: 35715363
dataset_size: 37668449.2
---
# Dataset Card for "jy_finetune_sd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.777073085308075,
-0.3366057872772217,
0.26705724000930786,
0.13095347583293915,
-0.20835894346237183,
-0.23654191195964813,
0.06268839538097382,
0.01697261445224285,
0.9778012633323669,
0.5303617119789124,
-0.8772494196891785,
-0.6979880928993225,
-0.5478960275650024,
-0.287829399108886... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
satyanshu404/trec-cast-2019 | satyanshu404 | 2023-11-02T14:16:22Z | 39 | 1 | null | [
"arxiv:2003.13624",
"region:us"
] | 2023-11-02T14:16:22Z | 2023-10-12T10:07:14.000Z | 2023-10-12T10:07:14 | # TREC Conversational Assistance Track (CAsT)
There are currently few datasets appropriate for training and evaluating models for Conversational Information Seeking (CIS). The main aim of TREC CAsT is to advance research on conversational search systems. The goal of the track is to create a reusable benchmark for open-domain information centric conversational dialogues.
# Year 1 (TREC 2019)
* Read the [TREC 2019 Overview](https://arxiv.org/abs/2003.13624) paper.
## 2019 Data
### Topics
* [Training topics] - 30 example training topics
* [Training judgments] - The judgments are graded on a three point scale (2 very relevant, 1 relevant, and 0 not relevant).
* [Evaluation topics]- 50 evaluation topics
### Sample of Dataset
* Title: US Judicial history
* Description: Judicial history in the US including key court cases and what they established.
* Prompts:
1. What are the most important US Supreme Court cases?
2. What did plessy v. ferguson establish?
3. How about marbury vs madison?
4. Was it unanimous?
5. What was the implication of roe vs wade?
6. What were the main arguments?
7. What was the point of the brown v board of education?
8. What were the main arguments?
9. Why is it important today?
### Collection
* The corpus is a combination of three standard TREC collections: MARCO Ranking passages, Wikipedia (TREC CAR), and News (Washington Post)
* The [MS MARCO Passage Ranking collection](https://msmarco.blob.core.windows.net/msmarcoranking/collection.tar.gz) - This file only includes the passage id and passage text. For convenience, we also provide a passage id -> URL mapping file in TSV format [pid to URL file](http://boston.lti.cs.cmu.edu/vaibhav2/cast/marco_pas_url.tsv).
* The [TREC CAR paragraph collection v2.0](http://trec-car.cs.unh.edu/datareleases/v2.0/paragraphCorpus.v2.0.tar.xz)
* The [TREC Washington Post Corpus version 2](https://ir.nist.gov/wapo/WashingtonPost.v2.tar.gz): Note this is behind a password and requires an organizational agreement, to obtain it see: https://ir.nist.gov/wapo/
### Document ID format
* The document id format is `[collection_id_paragraph_id]` with collection id and paragraph id separated by an underscore.
* The collection ids are in the set: `{MARCO, CAR, WAPO}`.
* The paragraph ids are: standard provided by MARCO and CAR. For WAPO the paragraph ID is `[article_id-paragraph_index]` where the paragraph_index is the *starting from 1-based* index of the paragraph using the provided paragraph markup separated by a single dash.
* Example WaPo combined document id: `[WAPO_903cc1eab726b829294d1abdd755d5ab-1]`, or CAR: `[CAR_6869dee46ab12f0f7060874f7fc7b1c57d53144a]`
## Code and tools
* [TREC-CAsT Tools](https://github.com/gla-ial/trec-cast-tools) repository with code and scripts for processing data.
* The tools contain scripts for parsing the collection into standard indexing formats. It also provides APIs for working with the topics (in text, json, and protocol buffer formats).
| [
-0.46340087056159973,
-0.7443260550498962,
0.529461681842804,
0.07836559414863586,
-0.3774261176586151,
0.25244298577308655,
-0.18427389860153198,
-0.1561838835477829,
0.23337818682193756,
0.43045487999916077,
-0.47247424721717834,
-0.7557024359703064,
-0.4349628686904907,
-0.0713202953338... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
erico25/aminer_title_abstract_v10 | erico25 | 2023-10-23T20:43:49Z | 39 | 0 | null | [
"size_categories:1M<n<10M",
"language:en",
"region:us"
] | 2023-10-23T20:43:49Z | 2023-10-23T19:07:34.000Z | 2023-10-23T19:07:34 | ---
dataset_info:
features:
- name: title
dtype: string
- name: abstract
dtype: string
splits:
- name: train
num_bytes: 2628760201
num_examples: 2548532
download_size: 0
dataset_size: 2628760201
language:
- en
size_categories:
- 1M<n<10M
---
# Dataset Card for "aminer_title_abstract_v10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.6322628259658813,
0.04629809781908989,
0.4828195571899414,
0.17103658616542816,
-0.15485644340515137,
-0.12102637439966202,
0.3893585801124573,
-0.224775493144989,
0.7910004258155823,
0.3142724335193634,
-0.6722745299339294,
-0.789857804775238,
-0.7411952018737793,
0.10314728319644928,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
krasaee/nietzsche | krasaee | 2023-10-26T07:47:27Z | 39 | 0 | null | [
"region:us"
] | 2023-10-26T07:47:27Z | 2023-10-24T16:54:51.000Z | 2023-10-24T16:54:51 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9929433
num_examples: 60480
download_size: 6288420
dataset_size: 9929433
---
# Dataset Card for "nietzsche"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.79643315076828,
-0.14036957919597626,
0.4908256530761719,
0.16667641699314117,
-0.18795287609100342,
-0.14197322726249695,
0.2122792750597,
-0.19943545758724213,
0.9703095555305481,
0.3507809042930603,
-0.9706792235374451,
-0.7587977647781372,
-0.7481629848480225,
-0.38234788179397583,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
baohuynhbk14/vietnamese-guanaco-llama2-1k | baohuynhbk14 | 2023-10-27T08:01:25Z | 39 | 0 | null | [
"region:us"
] | 2023-10-27T08:01:25Z | 2023-10-27T07:39:39.000Z | 2023-10-27T07:39:39 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
anforsm/movie_posters-100k-torchvision | anforsm | 2023-10-30T15:06:04Z | 39 | 1 | null | [
"region:us"
] | 2023-10-30T15:06:04Z | 2023-10-30T06:44:24.000Z | 2023-10-30T06:44:24 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: int64
- name: image
sequence:
sequence:
sequence: float32
- name: title
dtype: string
- name: genres
list:
- name: id
dtype: int64
- name: name
dtype: string
- name: overview
dtype: string
- name: popularity
dtype: float64
- name: release_date
dtype: string
- name: budget
dtype: int64
- name: revenue
dtype: int64
- name: tagline
dtype: string
- name: original_language
dtype: string
- name: runtime
dtype: int64
splits:
- name: train
num_bytes: 25531277848.2
num_examples: 85770
- name: test
num_bytes: 2836808649.8
num_examples: 9530
download_size: 20999210873
dataset_size: 28368086498.0
---
# Dataset Card for "movie_posters-100k-torchvision"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5597794055938721,
0.008476204238831997,
0.17086321115493774,
0.2648391127586365,
-0.36732152104377747,
0.12690255045890808,
0.2852441072463989,
0.1357639729976654,
0.8266597986221313,
0.7473640441894531,
-0.7474534511566162,
-0.771815836429596,
-0.776897668838501,
-0.2516210377216339,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
singh-aditya/MACCROBAT_biomedical_ner | singh-aditya | 2023-11-05T02:19:17Z | 39 | 2 | null | [
"task_categories:token-classification",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"biology",
"medical",
"region:us"
] | 2023-11-05T02:19:17Z | 2023-11-04T19:57:50.000Z | 2023-11-04T19:57:50 | ---
license: mit
task_categories:
- token-classification
language:
- en
tags:
- biology
- medical
size_categories:
- 1M<n<10M
field:
- data
---
# MACCROBAT-biomedical-ner
This data is the same data from [here](https://figshare.com/articles/dataset/MACCROBAT2018/9764942), the only difference is that it has been converted into the Huggingface dataset format. So it can be easily loaded and can be used wherever need.
To convert from the orginal format to huggingface dataset format, followed the following steps (**To know in more detail look at the `create_dataset.py` file**):
* Read corresponding `*.txt` and `*.ann` file.
* Used `pandas` to convert the `*.ann` file into dataframe.
* After converting into dataframe, did some processing and converted NER label information into:
```JSON
{
"text": "ner-text",
"label": "ner-label",
"start": 10,
"end": 20
}
```
* Standard labels are converted into `B-Tag` and `I-tag`, where `B`- stands for begning of the tag and `I` - stands for inside the tag.
* Finally the JSON is created and uploaded here.
## Source Data
This ZIP-compressed file contains 200 source documents (in plain text, on sentence per line) and 200 annotation documents (in brat standoff format). Documents are named using PubMed document IDs, e.g. "15939911.txt" contains text from the document "A young man with palpitations and Ebstein's anomaly of the tricuspid valve" by Marcu and Donohue. Text is from PubMed Central full-text documents but has been edited to include only clinical case report details. All annotations were created manually.
"MACCROBAT2020" is the second release of this dataset, following "MACCROBAT2018". The consistency and format of annotations has been improved in the newest version.
## Uses
Use below snippet to load the data properly and it can be used to finetune medical based NER model with some additional processing.
```Python
from datasets import load_dataset
# load the data
medical_ner_data = load_dataset("singh-aditya/MACCROBAT_biomedical_ner")
print(medical_ner_data)
```
```
DatasetDict({
train: Dataset({
features: ['ner_labels', 'tokens', 'full_text', 'ner_info'],
num_rows: 200
})
})
```
<!-- Address questions around how the dataset is intended to be used. -->
## Dataset Structure
```
{
'full_text': "CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations.\nThe symptoms occurred during rest, 2–3 times per week, lasted up to 30 minutes at a time and were associated with dyspnea.\nExcept for a grade 2/6 holosystolic tricuspid regurgitation murmur (best heard at the left sternal border with inspiratory accentuation), physical examination yielded unremarkable findings.\nAn electrocardiogram (ECG) revealed normal sinus rhythm and a Wolff– Parkinson– White pre-excitation pattern (Fig.1: Top), produced by a right-sided accessory pathway.\nTransthoracic echocardiography demonstrated the presence of Ebstein's anomaly of the tricuspid valve, with apical displacement of the valve and formation of an “atrialized” right ventricle (a functional unit between the right atrium and the inlet [inflow] portion of the right ventricle) (Fig.2).\nThe anterior tricuspid valve leaflet was elongated (Fig.2C, arrow), whereas the septal leaflet was rudimentary (Fig.2C, arrowhead).\nContrast echocardiography using saline revealed a patent foramen ovale with right-to-left shunting and bubbles in the left atrium (Fig.2D).\nThe patient underwent an electrophysiologic study with mapping of the accessory pathway, followed by radiofrequency ablation (interruption of the pathway using the heat generated by electromagnetic waves at the tip of an ablation catheter).\nHis post-ablation ECG showed a prolonged PR interval and an odd “second” QRS complex in leads III, aVF and V2–V4 (Fig.1Bottom), a consequence of abnormal impulse conduction in the “atrialized” right ventricle.\nThe patient reported no recurrence of palpitations at follow-up 6 months after the ablation.\n",
'ner_info': [
{
'text': '28-year-old',
'label': 'AGE',
'start': 8,
'end': 19
},
{'text': 'previously healthy', 'label': 'HISTORY', 'start': 20, 'end': 38}, {'text': 'man', 'label': 'SEX', 'start': 39, 'end': 42}, {'text': 'presented', 'label': 'CLINICAL_EVENT', 'start': 43, 'end': 52}, {'text': '6-week', 'label': 'DURATION', 'start': 60, 'end': 66}, {'text': 'palpitations', 'label': 'SIGN_SYMPTOM', 'start': 78, 'end': 90}, {'text': 'symptoms', 'label': 'COREFERENCE', 'start': 96, 'end': 104}, {'text': 'rest', 'label': 'CLINICAL_EVENT', 'start': 121, 'end': 125}, {'text': '2–3 times per week', 'label': 'FREQUENCY', 'start': 127, 'end': 145}, {'text': 'up to 30 minutes at a time', 'label': 'DETAILED_DESCRIPTION', 'start': 154, 'end': 180}, {'text': 'dyspnea', 'label': 'SIGN_SYMPTOM', 'start': 206, 'end': 213}, {'text': 'grade 2/6', 'label': 'LAB_VALUE', 'start': 228, 'end': 237}, {'text': 'holosystolic', 'label': 'DETAILED_DESCRIPTION', 'start': 238, 'end': 250}, {'text': 'tricuspid', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 251, 'end': 260}, {'text': 'regurgitation murmur', 'label': 'SIGN_SYMPTOM', 'start': 261, 'end': 281}, {'text': 'left sternal border', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 301, 'end': 320}, {'text': 'inspiratory accentuation', 'label': 'DETAILED_DESCRIPTION', 'start': 326, 'end': 350}, {'text': 'physical examination', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 353, 'end': 373}, {'text': 'unremarkable', 'label': 'LAB_VALUE', 'start': 382, 'end': 394}, {'text': 'electrocardiogram', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 408, 'end': 425}, {'text': 'ECG', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 427, 'end': 430}, {'text': 'normal', 'label': 'LAB_VALUE', 'start': 441, 'end': 447}, {'text': 'sinus rhythm', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 448, 'end': 460}, {'text': 'Wolff– Parkinson– White pre-excitation pattern', 'label': 'SIGN_SYMPTOM', 'start': 467, 'end': 513}, {'text': 'right-sided', 'label': 'DETAILED_DESCRIPTION', 'start': 542, 'end': 553}, {'text': 'accessory pathway', 'label': 'DISEASE_DISORDER', 'start': 554, 'end': 571}, {'text': 'Transthoracic', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 573, 'end': 586}, {'text': 'echocardiography', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 587, 'end': 603}, {'text': "Ebstein's anomaly", 'label': 'DISEASE_DISORDER', 'start': 633, 'end': 650}, {'text': 'tricuspid valve', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 658, 'end': 673}, {'text': 'apical displacement', 'label': 'SIGN_SYMPTOM', 'start': 680, 'end': 699}, {'text': 'valve', 'label': 'COREFERENCE', 'start': 707, 'end': 712}, {'text': 'atrialized', 'label': 'DISEASE_DISORDER', 'start': 734, 'end': 744}, {'text': 'right ventricle', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 746, 'end': 761}, {'text': 'right atrium', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 793, 'end': 805}, {'text': 'inlet', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 814, 'end': 819}, {'text': 'right ventricle', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 844, 'end': 859}, {'text': 'anterior tricuspid valve leaflet', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 874, 'end': 906}, {'text': 'elongated', 'label': 'SIGN_SYMPTOM', 'start': 911, 'end': 920}, {'text': 'septal leaflet', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 950, 'end': 964}, {'text': 'rudimentary', 'label': 'SIGN_SYMPTOM', 'start': 969, 'end': 980}, {'text': 'Contrast', 'label': 'DETAILED_DESCRIPTION', 'start': 1002, 'end': 1010}, {'text': 'echocardiography', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1011, 'end': 1027}, {'text': 'using saline', 'label': 'DETAILED_DESCRIPTION', 'start': 1028, 'end': 1040}, {'text': 'patent foramen ovale', 'label': 'DISEASE_DISORDER', 'start': 1052, 'end': 1072}, {'text': 'right-to-left shunting', 'label': 'SIGN_SYMPTOM', 'start': 1078, 'end': 1100}, {'text': 'bubbles', 'label': 'SIGN_SYMPTOM', 'start': 1105, 'end': 1112}, {'text': 'left atrium', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 1120, 'end': 1131}, {'text': 'electrophysiologic study', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1167, 'end': 1191}, {'text': 'mapping', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1197, 'end': 1204}, {'text': 'accessory pathway', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 1212, 'end': 1229}, {'text': 'radiofrequency', 'label': 'DETAILED_DESCRIPTION', 'start': 1243, 'end': 1257}, {'text': 'ablation', 'label': 'THERAPEUTIC_PROCEDURE', 'start': 1258, 'end': 1266}, {'text': 'ablation catheter', 'label': 'THERAPEUTIC_PROCEDURE', 'start': 1363, 'end': 1380}, {'text': 'ECG', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1401, 'end': 1404}, {'text': 'prolonged', 'label': 'LAB_VALUE', 'start': 1414, 'end': 1423}, {'text': 'PR interval', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1424, 'end': 1435}, {'text': 'odd', 'label': 'LAB_VALUE', 'start': 1443, 'end': 1446}, {'text': '“second”', 'label': 'LAB_VALUE', 'start': 1447, 'end': 1455}, {'text': 'QRS complex', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1456, 'end': 1467}, {'text': 'leads III, aVF and V2–V4', 'label': 'DETAILED_DESCRIPTION', 'start': 1471, 'end': 1495}, {'text': 'abnormal impulse conduction', 'label': 'DISEASE_DISORDER', 'start': 1528, 'end': 1555}, {'text': 'atrialized', 'label': 'DISEASE_DISORDER', 'start': 1564, 'end': 1574}, {'text': 'right ventricle', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 1576, 'end': 1591}, {'text': 'palpitations', 'label': 'SIGN_SYMPTOM', 'start': 1631, 'end': 1643}, {'text': 'follow-up', 'label': 'CLINICAL_EVENT', 'start': 1647, 'end': 1656}, {'text': '6 months after', 'label': 'DATE', 'start': 1657, 'end': 1671}],
'tokens': ['CASE: A ', '28-year-old', ' ', 'previously healthy', ' ', 'man', ' ', 'presented', ' with a ', '6-week', ' history of ', 'palpitations', '.\nThe ', 'symptoms', ' occurred during ', 'rest', ', ', '2–3 times per week', ', lasted ', 'up to 30 minutes at a time', ' and were associated with ', 'dyspnea', '.\nExcept for a ', 'grade 2/6', ' ', 'holosystolic', ' ', 'tricuspid', ' ', 'regurgitation murmur', ' (best heard at the ', 'left sternal border', ' with ', 'inspiratory accentuation', '), ', 'physical examination', ' yielded ', 'unremarkable', ' findings.\nAn ', 'electrocardiogram', ' (', 'ECG', ') revealed ', 'normal', ' ', 'sinus rhythm', ' and a ', 'Wolff– Parkinson– White pre-excitation pattern', ' (Fig.1: Top), produced by a ', 'right-sided', ' ', 'accessory pathway', '.\n', 'Transthoracic', ' ', 'echocardiography', ' demonstrated the presence of ', "Ebstein's anomaly", ' of the ', 'tricuspid valve', ', with ', 'apical displacement', ' of the ', 'valve', ' and formation of an “', 'atrialized', '” ', 'right ventricle', ' (a functional unit between the ', 'right atrium', ' and the ', 'inlet', ' [inflow] portion of the ', 'right ventricle', ') (Fig.2).\nThe ', 'anterior tricuspid valve leaflet', ' was ', 'elongated', ' (Fig.2C, arrow), whereas the ', 'septal leaflet', ' was ', 'rudimentary', ' (Fig.2C, arrowhead).\n', 'Contrast', ' ', 'echocardiography', ' ', 'using saline', ' revealed a ', 'patent foramen ovale', ' with ', 'right-to-left shunting', ' and ', 'bubbles', ' in the ', 'left atrium', ' (Fig.2D).\nThe patient underwent an ', 'electrophysiologic study', ' with ', 'mapping', ' of the ', 'accessory pathway', ', followed by ', 'radiofrequency', ' ', 'ablation', ' (interruption of the pathway using the heat generated by electromagnetic waves at the tip of an ', 'ablation catheter', ').\nHis post-ablation ', 'ECG', ' showed a ', 'prolonged', ' ', 'PR interval', ' and an ', 'odd', ' ', '“second”', ' ', 'QRS complex', ' in ', 'leads III, aVF and V2–V4', ' (Fig.1Bottom), a consequence of ', 'abnormal impulse conduction', ' in the “', 'atrialized', '” ', 'right ventricle', '.\nThe patient reported no recurrence of ', 'palpitations', ' at ', 'follow-up', ' ', '6 months after', ' the ablation.\n'],
'ner_labels': [0, 5, 0, 39, 0, 65, 0, 13, 0, 32, 0, 69, 0, 18, 0, 13, 0, 35, 0, 22, 0, 69, 0, 42, 0, 22, 0, 12, 0, 69, 0, 12, 0, 22, 0, 24, 0, 42, 0, 24, 0, 24, 0, 42, 0, 24, 0, 69, 0, 22, 0, 26, 0, 12, 0, 24, 0, 26, 0, 12, 0, 69, 0, 18, 0, 26, 0, 12, 0, 12, 0, 12, 0, 12, 0, 12, 0, 69, 0, 12, 0, 69, 0, 22, 0, 24, 0, 22, 0, 26, 0, 69, 0, 69, 0, 12, 0, 24, 0, 24, 0, 12, 0, 22, 0, 75, 0, 75, 0, 24, 0, 42, 0, 24, 0, 42, 0, 42, 0, 24, 0, 22, 0, 26, 0, 26, 0, 12, 0, 69, 0, 13, 0, 19, 0]}
```
## NER-Lables
```Python
NER_lables = [
"O",
"B-ACTIVITY",
"I-ACTIVITY",
"I-ADMINISTRATION",
"B-ADMINISTRATION",
"B-AGE",
"I-AGE",
"I-AREA",
"B-AREA",
"B-BIOLOGICAL_ATTRIBUTE",
"I-BIOLOGICAL_ATTRIBUTE",
"I-BIOLOGICAL_STRUCTURE",
"B-BIOLOGICAL_STRUCTURE",
"B-CLINICAL_EVENT",
"I-CLINICAL_EVENT",
"B-COLOR",
"I-COLOR",
"I-COREFERENCE",
"B-COREFERENCE",
"B-DATE",
"I-DATE",
"I-DETAILED_DESCRIPTION",
"B-DETAILED_DESCRIPTION",
"I-DIAGNOSTIC_PROCEDURE",
"B-DIAGNOSTIC_PROCEDURE",
"I-DISEASE_DISORDER",
"B-DISEASE_DISORDER",
"B-DISTANCE",
"I-DISTANCE",
"B-DOSAGE",
"I-DOSAGE",
"I-DURATION",
"B-DURATION",
"I-FAMILY_HISTORY",
"B-FAMILY_HISTORY",
"B-FREQUENCY",
"I-FREQUENCY",
"I-HEIGHT",
"B-HEIGHT",
"B-HISTORY",
"I-HISTORY",
"I-LAB_VALUE",
"B-LAB_VALUE",
"I-MASS",
"B-MASS",
"I-MEDICATION",
"B-MEDICATION",
"I-NONBIOLOGICAL_LOCATION",
"B-NONBIOLOGICAL_LOCATION",
"I-OCCUPATION",
"B-OCCUPATION",
"B-OTHER_ENTITY",
"I-OTHER_ENTITY",
"B-OTHER_EVENT",
"I-OTHER_EVENT",
"I-OUTCOME",
"B-OUTCOME",
"I-PERSONAL_BACKGROUND",
"B-PERSONAL_BACKGROUND",
"B-QUALITATIVE_CONCEPT",
"I-QUALITATIVE_CONCEPT",
"I-QUANTITATIVE_CONCEPT",
"B-QUANTITATIVE_CONCEPT",
"B-SEVERITY",
"I-SEVERITY",
"B-SEX",
"I-SEX",
"B-SHAPE",
"I-SHAPE",
"B-SIGN_SYMPTOM",
"I-SIGN_SYMPTOM",
"B-SUBJECT",
"I-SUBJECT",
"B-TEXTURE",
"I-TEXTURE",
"B-THERAPEUTIC_PROCEDURE",
"I-THERAPEUTIC_PROCEDURE",
"I-TIME",
"B-TIME",
"B-VOLUME",
"I-VOLUME",
"I-WEIGHT",
"B-WEIGHT",
]
```
**BibTeX:**
```JSON
{
article= Caufield2020,
author = "J. Harry Caufield",
title = "{MACCROBAT}",
year = "2020",
month = "1",
url = "https://figshare.com/articles/dataset/MACCROBAT2018/9764942",
doi = "10.6084/m9.figshare.9764942.v2"
}
``` | [
-0.7240431308746338,
-0.34267812967300415,
0.24641256034374237,
0.24626493453979492,
-0.5149473547935486,
-0.07266843318939209,
0.18726283311843872,
-0.08830682188272476,
0.7281467914581299,
0.49851158261299133,
-0.6854816675186157,
-1.0104631185531616,
-0.32193148136138916,
0.308141082525... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ktrinh38/eva-pix2pix | ktrinh38 | 2023-11-04T20:33:49Z | 39 | 0 | null | [
"region:us"
] | 2023-11-04T20:33:49Z | 2023-11-04T20:33:10.000Z | 2023-11-04T20:33:10 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 712910099.55
num_examples: 4291
download_size: 337563830
dataset_size: 712910099.55
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eva-pix2pix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.603121280670166,
-0.25258252024650574,
0.34406232833862305,
0.1643005907535553,
-0.10537345707416534,
0.10179026424884796,
0.25309184193611145,
-0.14049184322357178,
0.8131775856018066,
0.6203403472900391,
-0.743351936340332,
-0.5905857086181641,
-0.578197181224823,
-0.36665433645248413... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ShrinivasSK/hi_te_1 | ShrinivasSK | 2023-11-06T19:22:07Z | 39 | 0 | null | [
"region:us"
] | 2023-11-06T19:22:07Z | 2023-11-06T19:21:57.000Z | 2023-11-06T19:21:57 | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 5287422.6
num_examples: 18000
- name: test
num_bytes: 587491.4
num_examples: 2000
download_size: 2682481
dataset_size: 5874914.0
---
# Dataset Card for "hi_te_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.6980561017990112,
-0.4967464208602905,
0.07140454649925232,
0.21622030436992645,
-0.18666799366474152,
-0.2524581551551819,
0.33228743076324463,
-0.18986739218235016,
1.09060800075531,
0.5998806953430176,
-0.7905035614967346,
-0.8799585103988647,
-0.6054392457008362,
-0.2538867294788360... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
epptt/erukaLabels | epptt | 2023-11-16T05:51:08Z | 39 | 0 | null | [
"region:us"
] | 2023-11-16T05:51:08Z | 2023-11-06T23:07:46.000Z | 2023-11-06T23:07:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: "train.json"
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | [
-0.5322356224060059,
-0.5534716844558716,
0.1290130317211151,
0.23470574617385864,
-0.39626216888427734,
-0.11762470752000809,
-0.03545304760336876,
-0.6389272212982178,
0.5699821710586548,
0.7838326096534729,
-0.7834625840187073,
-0.917327344417572,
-0.55633145570755,
0.13078095018863678,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
stas/openwebtext-synthetic-testing | stas | 2023-11-14T07:31:20Z | 39 | 3 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-14T07:31:20Z | 2023-11-14T07:30:30.000Z | 2023-11-14T07:30:30 | ---
license: apache-2.0
---
Using 10 records from [openwebtext-10k](https://huggingface.co/datasets/stas/openwebtext-10k) this dataset is written for very fast testing and can produce a repeat of these 10 records in a form of 1, 2, 3, 4, 5, 10, 100, 300 or 1k records splits, e.g.:
```
$ python -c 'from datasets import load_dataset; \
ds=load_dataset("stas/openwebtext-synthetic-testing", split="10.repeat"); print(len(ds))'
10
$ python -c 'from datasets import load_dataset; \
ds=load_dataset("stas/openwebtext-synthetic-testing", split="1k.repeat"); print(len(ds))'
1000
```
Each record is just a single `text` record of several paragraphs long - web articles.
As this is used for very fast functional testing on CI there is no `train` or `validation` splits, you can just repeat the same records.
| [
-0.6987168788909912,
-0.5949839949607849,
0.05590592697262764,
0.6328563690185547,
-0.251590371131897,
-0.2100653052330017,
-0.26669326424598694,
-0.2642074525356293,
0.28601399064064026,
0.21166633069515228,
-0.5344089269638062,
-0.31489768624305725,
0.04629320278763771,
0.475589871406555... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
marvy/book-covers | marvy | 2023-11-19T16:24:11Z | 39 | 0 | null | [
"region:us"
] | 2023-11-19T16:24:11Z | 2023-11-19T16:22:43.000Z | 2023-11-19T16:22:43 | ---
dataset_info:
features:
- name: image
dtype: image
- name: title
dtype: string
splits:
- name: train
num_bytes: 286874817.68
num_examples: 32581
download_size: 283302050
dataset_size: 286874817.68
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
doubledsbv/german-prefs_v4_prepared | doubledsbv | 2023-11-20T09:23:48Z | 39 | 0 | null | [
"region:us"
] | 2023-11-20T09:23:48Z | 2023-11-20T09:23:39.000Z | 2023-11-20T09:23:39 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 207351857
num_examples: 57380
- name: test
num_bytes: 6452941
num_examples: 1775
download_size: 120828622
dataset_size: 213804798
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mrbmaryam/zephyr_train_2500 | mrbmaryam | 2023-11-20T21:26:26Z | 39 | 0 | null | [
"region:us"
] | 2023-11-20T21:26:26Z | 2023-11-20T21:26:09.000Z | 2023-11-20T21:26:09 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622264862060547,
0.43461528420448303,
-0.52829909324646,
0.7012971639633179,
0.7915720343589783,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104477167129517,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
barbaroo/Sprotin_parallel | barbaroo | 2023-11-21T13:30:57Z | 39 | 0 | null | [
"region:us"
] | 2023-11-21T13:30:57Z | 2023-11-21T13:30:24.000Z | 2023-11-21T13:30:24 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/b50562e5 | result-kand2-sdxl-wuerst-karlo | 2023-11-23T03:38:23Z | 39 | 0 | null | [
"region:us"
] | 2023-11-23T03:38:23Z | 2023-11-23T03:38:22.000Z | 2023-11-23T03:38:22 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 170
num_examples: 10
download_size: 1334
dataset_size: 170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b50562e5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.650661289691925,
-0.0033841398544609547,
0.1924002468585968,
0.3684273958206177,
-0.08945410698652267,
-0.16792859137058258,
0.34630462527275085,
-0.33025801181793213,
0.7411776185035706,
0.429939329624176,
-0.8680625557899475,
-0.6793990135192871,
-0.5009608268737793,
-0.07270675152540... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
euisuh15/python-piss-my-name | euisuh15 | 2023-11-26T19:11:01Z | 39 | 0 | null | [
"region:us"
] | 2023-11-26T19:11:01Z | 2023-11-26T19:10:16.000Z | 2023-11-26T19:10:16 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rbawden/DiaBLa | rbawden | 2022-10-25T14:21:10Z | 38 | 1 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:fr",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-25T14:21:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- fr
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: DiaBLa
language_bcp47:
- en-UK
- fr-FR
---
# Dataset Card for DiaBLa: Bilingual dialogue parallel evaluation set
## 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:** [almanach.inria.fr/software_and_resources/custom/DiaBLa-en.html](http://almanach.inria.fr/software_and_resources/custom/DiaBLa-en.html)
- **Repository:** [github.com/rbawden/DiaBLa-dataset](https://github.com/rbawden/DiaBLa-dataset)
- **Paper:** [Bawden et al. (2021). DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation. Language Resources and Evaluation(55). Pages 635–660. Springer Verlag. 10.1007/s10579-020-09514-4.](https://hal.inria.fr/hal-03021633)
- **Point of contact:** rachel.bawden[at]inria.fr
### Dataset Summary
The dataset is an English-French dataset for the evaluation of Machine Translation (MT) for informal, written bilingual dialogue.
The dataset contains 144 spontaneous dialogues (5,700+ sentences) between native English and French speakers, mediated by one of two neural MT systems in a range of role-play settings. See below for some basic statistics. The dialogues are accompanied by fine-grained sentence-level judgments of MT quality, produced by the dialogue participants themselves, as well as by manually normalised versions and reference translations produced a posteriori. See here for information about evaluation.
The motivation for the corpus is two-fold: to provide:
- a unique resource for evaluating MT models for dialogue (i.e. in context)
- a corpus for the analysis of MT-mediated communication
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (mainly UK) and French
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 37 MB
- **Number of parallel utterances:** 5748
Each example is highly annotated and is associated with dialogue context. An example from the test set looks as follows (only the first and last utterances of the dialogue history are shown for readability purposes):
```
{
"id": "dialogue-2018-04-25T16-20-36.087170_french_english_1_2_25",
"mt": "Tu m'en veux pour \u00e7a ?",
"norm": "",
"orig": "Are you blaming me for this?",
"ref": "C'est moi que vous critiquez pour \u00e7a\u00a0?",
"utterance_meta": {
"eval_judgment": "medium",
"eval_verbatim": "",
"eval_problems": [
"coherence"
],
"lang": "english"
},
"dialogue_meta": {
"start_time": "2018-04-25T16:20:36.087170",
"end_time": "",
"translation_model": "baseline",
"final_evaluation_user1": {
"style": "average",
"coherence": "average",
"grammaticality": "good",
"meaning": "average",
"word_choice": "average"
},
"final_evaluation_user2": {
"style": "",
"coherence": "",
"grammaticality": "",
"meaning": "",
"word_choice": ""
},
"scenario": [
[
"You are both stuck in a lift at work.",
"Vous \u00eates tous les deux bloqu\u00e9(e)s dans un ascenseur au travail."
],
[
"You are an employee and you are with your boss.",
"Vous \u00eates un(e) employ\u00e9(e) et vous \u00eates avez votre patron(ne)"
],
[
"You are the boss and are with an employee.",
"Vous \u00eates le ou la patron(ne) et vous \u00eates avec un(e) employ\u00e9(e)"
]
],
"user1": {
"role_num": 1,
"role": [
"You are an employee and you are with your boss.",
"Vous \u00eates un(e) employ\u00e9(e) et vous \u00eates avez votre patron(ne)"
],
"initiated_dialogue": true,
"turn_number": 2,
"lang": "french"
},
"user2": {
"role_num": 2,
"role": [
"You are the boss and are with an employee.",
"Vous \u00eates le ou la patron(ne) et vous \u00eates avec un(e) employ\u00e9(e)"
],
"initiated_dialogue": false,
"turn_number": 1,
"lang": "english"
}
},
"dialogue_history": [
{
"id": "dialogue-2018-04-25T16-20-36.087170_french_english_1_2_0",
"orig": "We appear to have stopped moving.",
"norm": "",
"mt": "On semble avoir arr\u00eat\u00e9 de bouger.",
"ref": "J'ai l'impression qu'on s'est arr\u00eat\u00e9s.",
"utterance_meta": {
"eval_judgment": "medium",
"eval_verbatim": "",
"eval_problems": [
"style"
],
"lang": "english"
}
},
[...]
{
"id": "dialogue-2018-04-25T16-20-36.087170_french_english_1_2_24",
"orig": "La sonnerie s'est arr\u00eat\u00e9, je pense que personne ne va nous r\u00e9pondre.",
"norm": "",
"mt": "The ringing stopped, and I don't think anyone's gonna answer us.",
"ref": "It stopped ringing. I don't think anybody's going to reply.",
"utterance_meta": {
"eval_judgment": "perfect",
"eval_verbatim": "",
"eval_problems": [],
"lang": "french"
}
}
]
}
```
### Data Fields
#### plain_text
- `id`: a `string` feature.
- `orig`: a `string` feature.
- `norm`: a `string` feature.
- `mt`: a `string` feature.
- `ref`: a `string` feature.
- `utterance_meta`: a dictionary feature containing:
- `eval_judgment`: a `string` feature.
- `eval_verbatim`: a `string` feature.
- `eval_problems`: a list feature containing:
- up to 5 `string` features.
- `lang`: a `string` feature.
- `dialogue_meta`: a dictionary feature containing:
- `start_time` : a `string` feature.
- `end_time`: a `string` feature.
- `translation_model`: a `string` feature.
- `final_evaluation_user1`: a dictionary feature containing:
- `style`: a `string` feature.
- `coherence`: a `string` feature.
- `grammaticality`: a `string` feature.
- `meaning`: a `string` feature.
- `word_choice`: a `string` feature.
- `final_evaluation_user2`: a dictionary feature containing:
- `style`: a `string` feature.
- `coherence`: a `string` feature.
- `grammaticality`: a `string` feature.
- `meaning`: a `string` feature.
- `word_choice`: a `string` feature.
- `scenario`: a list feature containing
- 3 lists each containing 2 `string` features.
- `user1`: a dictionary feature containing:
- `role_num`: an `int` feature.
- `role`: a list feature containing:
- 2 `string` features.
- `initiated_dialogue`: a `bool` feature.
- `turn_number`: an `int` value.
- `lang`: a `string` value.
- `user2`: a dictionary feature containing:
- `role_num`: an `int` feature.
- `role`: a list feature containing:
- 2 `string` features.
- `initiated_dialogue`: a `bool` feature.
- `turn_number`: an `int` value.
- `lang`: a `string` value.
- `dialogue_history`: a list feature containing:
- dictionary features containing:
- `id`: a `string` feature.
- `orig`: a `string` feature.
- `norm`: a `string` feature.
- `mt`: a `string` feature.
- `ref`: a `string` feature.
- `utterance_meta`: a dictionary feature containing:
- `eval_judgment`: a `string` feature.
- `eval_verbatim`: a `string` feature.
- `eval_problems`: a list feature containing:
- up to 5 `string` features.
- `lang`: a `string` feature.
### Data Splits
DiaBLa is a test set only.
| name |test |
|----------|------:|
|plain_text| 5748|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Original data was collected through a [dedicated online chat platform](https://github.com/rbawden/diabla-chat-interface) and involved native speakers of English and of French. As well as producing the original text, participants also annotated the quality of the machine-translated outputs of their partners' utterances (which they saw instead of their partners' original text) based on their monolingual intuitions and the dialogue context.
Each dialogue is assigned one of 12 role-play scenarios and where appropriate each participant is assigned a role to play in the dialogue.
#### Who are the source language producers?
The source text producers were native French and native English volunteers (mainly British English). See the paper for very basic information concerning their backgrounds (age categories and experience in NLP).
### Annotations
#### Annotation process
On top of the original dialogue text (a mixture of utterances in English and in French), the following "annotations" are provided:
- machine translated version of the original text (produced in real time and presented during the dialogue), produced by one of two MT systems, both trained using [Marian](https://github.com/marian-nmt/marian).
- judgments of MT quality by participants (overall quality, particular problems, verbatim comments)
- manually produced normalised version of the original text (for spelling mistakes, grammatical errors, missing punctuation, etc.)
- manually produced reference translations
#### Who are the annotators?
The judgments of MT quality were produced by the dialogue participants themselves in real time. The normalised version of the text and the reference translations were manually produced by the authors of the paper. Translations were always done into the translator's native language and all translations were verified and post-edited by a bilingual English-French speaker.
### Personal and Sensitive Information
A priori the dataset does not contain personal and sensitive information. Participants were instructed not to give any personal information and to assume the roles assigned in the role play scenario. Usernames were anonymised prior to distribution and any mention of either usernames or real names in the dialogues were replaced by generic names of the same gender as the participant. Only basic user information was collected to get an idea of the distribution of participants and to potentially see how multilingual ability influences quality judgments (rough age categories, experience in NLP or research, native languages, familiarity with the other language (either English or French), other languages spoken and gender). Gender was included because it is an important factor in translation (particularly for the direction English-to-French), and this was explained in advance to the participants in the FAQs.
## 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
The dataset was collected by Rachel Bawden, Eric Bilinski, Thomas Lavergne and Sophie Rosset (see citation below).
### Licensing Information
The dataset is available under a CC BY-SA 4.0 licence.
### Citation Information
If you use or are inspired by this dataset, please cite:
```
@article{bawden_DiaBLa:-A-Corpus-of_2021,
author = {Bawden, Rachel and Bilinski, Eric and Lavergne, Thomas and Rosset, Sophie},
doi = {10.1007/s10579-020-09514-4},
title = {DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation},
year = {2021},
journal = {Language Resources and Evaluation},
publisher = {Springer Verlag},
volume = {55},
pages = {635--660},
url = {https://hal.inria.fr/hal-03021633},
pdf = {https://hal.inria.fr/hal-03021633/file/diabla-lre-personal-formatting.pdf},
}
```
### Contributions
This dataset was added by Rachel Bawden [@rbawden](https://github.com/rbawden). | [
-0.5425506234169006,
-0.7322291731834412,
0.3697434365749359,
0.5639804601669312,
-0.1805303692817688,
0.09924111515283585,
-0.12267187982797623,
-0.12711305916309357,
0.41842740774154663,
0.652106523513794,
-0.8804033994674683,
-0.8662673234939575,
-0.49101147055625916,
0.5491994619369507... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jquiros/suicide | jquiros | 2022-03-08T11:23:20Z | 38 | 4 | null | [
"region:us"
] | 2022-03-08T11:23:20Z | 2022-03-08T11:20:09.000Z | 2022-03-08T11:20:09 | Entry not found | [
-0.3227649927139282,
-0.225684255361557,
0.862226128578186,
0.43461498618125916,
-0.5282987952232361,
0.7012963891029358,
0.7915717363357544,
0.07618629932403564,
0.7746025919914246,
0.2563219666481018,
-0.7852816581726074,
-0.2257382869720459,
-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
crystina-z/no-nonself-mrtydi | crystina-z | 2022-04-10T02:02:35Z | 38 | 0 | null | [
"region:us"
] | 2022-04-10T02:02:35Z | 2022-03-08T20:04:07.000Z | 2022-03-08T20:04:07 | Entry not found | [
-0.3227649927139282,
-0.225684255361557,
0.862226128578186,
0.43461498618125916,
-0.5282987952232361,
0.7012963891029358,
0.7915717363357544,
0.07618629932403564,
0.7746025919914246,
0.2563219666481018,
-0.7852816581726074,
-0.2257382869720459,
-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
stjokerli/TextToText_wic_seqio | stjokerli | 2022-03-18T04:56:49Z | 38 | 0 | null | [
"region:us"
] | 2022-03-18T04:56:49Z | 2022-03-13T09:30:58.000Z | 2022-03-13T09:30:58 | Entry not found | [
-0.3227649927139282,
-0.225684255361557,
0.862226128578186,
0.43461498618125916,
-0.5282987952232361,
0.7012963891029358,
0.7915717363357544,
0.07618629932403564,
0.7746025919914246,
0.2563219666481018,
-0.7852816581726074,
-0.2257382869720459,
-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Chris1/GTA5 | Chris1 | 2022-04-06T14:44:22Z | 38 | 0 | null | [
"region:us"
] | 2022-04-06T14:44:22Z | 2022-04-06T13:02:07.000Z | 2022-04-06T13:02:07 | Entry not found | [
-0.3227649927139282,
-0.225684255361557,
0.862226128578186,
0.43461498618125916,
-0.5282987952232361,
0.7012963891029358,
0.7915717363357544,
0.07618629932403564,
0.7746025919914246,
0.2563219666481018,
-0.7852816581726074,
-0.2257382869720459,
-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
scikit-learn/credit-card-clients | scikit-learn | 2022-06-20T15:42:14Z | 38 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2022-06-20T15:42:14Z | 2022-06-20T14:57:10.000Z | 2022-06-20T14:57:10 | ---
license: cc0-1.0
---
## Default of Credit Card Clients Dataset
The following was retrieved from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients).
**Dataset Information**
This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.
**Content**
There are 25 variables:
- ID: ID of each client
- LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit
- SEX: Gender (1=male, 2=female)
- EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)
- MARRIAGE: Marital status (1=married, 2=single, 3=others)
- AGE: Age in years
- PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above)
- PAY_2: Repayment status in August, 2005 (scale same as above)
- PAY_3: Repayment status in July, 2005 (scale same as above)
- PAY_4: Repayment status in June, 2005 (scale same as above)
- PAY_5: Repayment status in May, 2005 (scale same as above)
- PAY_6: Repayment status in April, 2005 (scale same as above)
- BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)
- BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)
- BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)
- BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)
- BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)
- BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)
- PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)
- PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)
- PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)
- PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)
- PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)
- PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)
- default.payment.next.month: Default payment (1=yes, 0=no)
**Inspiration**
Some ideas for exploration:
How does the probability of default payment vary by categories of different demographic variables?
Which variables are the strongest predictors of default payment?
**Acknowledgements**
Any publications based on this dataset should acknowledge the following:
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
| [
-0.20623581111431122,
-0.4662413001060486,
0.21763896942138672,
-0.06409598141908646,
-0.13395977020263672,
-0.2815612852573395,
0.223565936088562,
-0.1311955451965332,
0.07311364263296127,
0.9917935132980347,
-0.382113516330719,
-0.7576791644096375,
-0.36529484391212463,
-0.09609708935022... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jakartaresearch/indoqa | jakartaresearch | 2022-12-17T06:07:27Z | 38 | 1 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:id",
"license:cc-by-nd-4.0",
"indoqa",
"qa",
"question-answering"... | 2022-12-17T06:07:27Z | 2022-08-13T10:54:08.000Z | 2022-08-13T10:54:08 | ---
annotations_creators:
- expert-generated
language:
- id
language_creators:
- found
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
pretty_name: Indonesian Question Answering Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- indoqa
- qa
- question-answering
- indonesian
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for Indonesian Question Answering Dataset
## 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
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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 [@fhrzn](https://github.com/fhrzn)[@Kalzaik](https://github.com/Kalzaik) [@ibamibrahim](https://github.com/ibamibrahim) [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. | [
-0.5596947073936462,
-0.779777467250824,
0.0233513955026865,
0.31879952549934387,
-0.326363205909729,
-0.018054915592074394,
-0.16507212817668915,
-0.32631006836891174,
0.5443535447120667,
0.8801376819610596,
-0.7869605422019958,
-0.8764442801475525,
-0.6577150821685791,
0.4416554272174835... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
teticio/audio-diffusion-256 | teticio | 2022-11-09T10:49:48Z | 38 | 3 | null | [
"task_categories:image-to-image",
"size_categories:10K<n<100K",
"audio",
"spectrograms",
"region:us"
] | 2022-11-09T10:49:48Z | 2022-08-25T17:32:42.000Z | 2022-08-25T17:32:42 | ---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: Mel spectrograms of music
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- audio
- spectrograms
task_categories:
- image-to-image
task_ids: []
---
Over 20,000 256x256 mel spectrograms of 5 second samples of music from my Spotify liked playlist. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models.
```
x_res = 256
y_res = 256
sample_rate = 22050
n_fft = 2048
hop_length = 512
``` | [
-0.7777500152587891,
-0.2157420665025711,
0.7321594953536987,
0.4212033450603485,
-0.07960247993469238,
-0.18307003378868103,
-0.28970441222190857,
-0.3536110818386078,
0.5059683918952942,
0.34585556387901306,
-0.42328378558158875,
-0.9210429787635803,
-0.313353568315506,
-0.26719149947166... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
din0s/asqa | din0s | 2022-09-20T16:14:54Z | 38 | 0 | null | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|ambig_qa",
"language:en",
"license:apache-2.0",
"factoid questions",
"l... | 2022-09-20T16:14:54Z | 2022-09-19T22:25:51.000Z | 2022-09-19T22:25:51 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: ASQA
size_categories:
- 1K<n<10K
source_datasets:
- extended|ambig_qa
tags:
- factoid questions
- long-form answers
task_categories:
- question-answering
task_ids:
- open-domain-qa
---
# Dataset Card for ASQA
## 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)
- [Additional Information](#additional-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/google-research/language/tree/master/language/asqa
- **Paper:** https://arxiv.org/abs/2204.06092
- **Leaderboard:** https://ambigqa.github.io/asqa_leaderboard.html
### Dataset Summary
ASQA is the first long-form question answering dataset that focuses on ambiguous factoid questions. Different from previous long-form answers datasets, each question is annotated with both long-form answers and extractive question-answer pairs, which should be answerable by the generated passage. A generated long-form answer will be evaluated using both ROUGE and QA accuracy. In the paper, we show that these evaluation metrics are well-correlated with human judgments.
### Supported Tasks and Leaderboards
Long-form Question Answering. [Leaderboard](https://ambigqa.github.io/asqa_leaderboard.html)
### Languages
- English
## Dataset Structure
### Data Instances
```py
{
"ambiguous_question": "Where does the civil liberties act place the blame for the internment of u.s. citizens?",
"qa_pairs": [
{
"context": "No context provided",
"question": "Where does the civil liberties act place the blame for the internment of u.s. citizens by apologizing on behalf of them?",
"short_answers": [
"the people of the United States"
],
"wikipage": None
},
{
"context": "No context provided",
"question": "Where does the civil liberties act place the blame for the internment of u.s. citizens by making them pay reparations?",
"short_answers": [
"United States government"
],
"wikipage": None
}
],
"wikipages": [
{
"title": "Civil Liberties Act of 1988",
"url": "https://en.wikipedia.org/wiki/Civil%20Liberties%20Act%20of%201988"
}
],
"annotations": [
{
"knowledge": [
{
"content": "The Civil Liberties Act of 1988 (Pub.L. 100–383, title I, August 10, 1988, 102 Stat. 904, 50a U.S.C. § 1989b et seq.) is a United States federal law that granted reparations to Japanese Americans who had been interned by the United States government during World War II.",
"wikipage": "Civil Liberties Act of 1988"
}
],
"long_answer": "The Civil Liberties Act of 1988 is a United States federal law that granted reparations to Japanese Americans who had been interned by the United States government during World War II. In the act, the blame for the internment of U.S. citizens was placed on the people of the United States, by apologizing on behalf of them. Furthermore, the blame for the internment was placed on the United States government, by making them pay reparations."
}
],
"sample_id": -4557617869928758000
}
```
### Data Fields
- `ambiguous_question`: ambiguous question from AmbigQA.
- `annotations`: long-form answers to the ambiguous question constructed by ASQA annotators.
- `annotations/knowledge`: list of additional knowledge pieces.
- `annotations/knowledge/content`: a passage from Wikipedia.
- `annotations/knowledge/wikipage`: title of the Wikipedia page the passage was taken from.
- `annotations/long_answer`: annotation.
- `qa_pairs`: Q&A pairs from AmbigQA which are used for disambiguation.
- `qa_pairs/context`: additional context provided.
- `qa_pairs/question`: disambiguated question from AmbigQA.
- `qa_pairs/short_answers`: list of short answers from AmbigQA.
- `qa_pairs/wikipage`: title of the Wikipedia page the additional context was taken from.
- `sample_id`: the unique id of the sample
- `wikipages`: list of Wikipedia pages visited by AmbigQA annotators.
- `wikipages/title`: title of the Wikipedia page.
- `wikipages/url`: link to the Wikipedia page.
### Data Splits
| **Split** | **Instances** |
|-----------|---------------|
| Train | 4353 |
| Dev | 948 |
## Additional Information
### Contributions
Thanks to [@din0s](https://github.com/din0s) for adding this dataset. | [
-0.5463582873344421,
-0.7262665629386902,
0.49687543511390686,
-0.09552009403705597,
-0.14116840064525604,
-0.037118230015039444,
-0.08747050911188126,
-0.2835697531700134,
0.4076807498931885,
0.4947029948234558,
-0.596950113773346,
-0.5265423655509949,
-0.30089280009269714,
0.179322287440... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
IIC/SQUAC | IIC | 2022-10-11T11:52:45Z | 38 | 1 | null | [
"region:us"
] | 2022-10-11T11:52:45Z | 2022-10-11T11:52:34.000Z | 2022-10-11T11:52:34 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Dizex/InstaFoodSet | Dizex | 2022-12-11T20:07:40Z | 38 | 0 | null | [
"region:us"
] | 2022-12-11T20:07:40Z | 2022-11-06T19:39:47.000Z | 2022-11-06T19:39:47 | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: iob_tags
sequence: string
- name: iob_tags_ids
sequence: int64
splits:
- name: train
num_bytes: 346804
num_examples: 320
- name: val
num_bytes: 37219
num_examples: 40
- name: test
num_bytes: 39352
num_examples: 40
download_size: 84698
dataset_size: 423375
---
# Dataset Card for "InstaFoodSet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.41006234288215637,
-0.16151085495948792,
0.060299165546894073,
0.16038522124290466,
-0.12415237724781036,
0.22218400239944458,
0.24877263605594635,
-0.328553169965744,
1.0188878774642944,
0.525925874710083,
-0.7715709805488586,
-0.7286593317985535,
-0.49534305930137634,
-0.0631333142518... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
101arrowz/vox_celeb | 101arrowz | 2023-08-20T03:04:07Z | 38 | 1 | null | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_categories:image-classification",
"task_ids:speaker-identification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"size_... | 2023-08-20T03:04:07Z | 2022-11-13T01:43:46.000Z | 2022-11-13T01:43:46 | ---
annotations_creators:
- crowdsourced
language: []
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: VoxCeleb
size_categories:
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
- audio-classification
- image-classification
task_ids:
- speaker-identification
---
# Dataset Card for VoxCeleb
## 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
### Dataset Summary
VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube.
NOTE: Although this dataset can be automatically downloaded, you must manually request credentials to access it from the creators' website.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each datapoint has a path to the audio/video clip along with metadata about the speaker.
```
{
'file': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav',
'file_format': 'wav',
'dataset_id': 'vox1',
'speaker_id': 'id10271',
'speaker_gender': 'm',
'speaker_name': 'Ed_Westwick',
'speaker_nationality': 'UK',
'video_id': '_YimahVgI1A',
'clip_id': '00003',
'audio': {
'path': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav',
'array': array([...], dtype=float32),
'sampling_rate': 16000
}
}
```
### Data Fields
Each row includes the following fields:
- `file`: The path to the audio/video clip
- `file_format`: The file format in which the clip is stored (e.g. `wav`, `aac`, `mp4`)
- `dataset_id`: The ID of the dataset this clip is from (`vox1`, `vox2`)
- `speaker_id`: The ID of the speaker in this clip
- `speaker_gender`: The gender of the speaker (`m`/`f`)
- `speaker_name` (VoxCeleb1 only): The full name of the speaker in the clip
- `speaker_nationality` (VoxCeleb1 only): The speaker's country of origin
- `video_id`: The ID of the video from which this clip was taken
- `clip_index`: The index of the clip for this specific video
- `audio` (Audio dataset only): The audio signal data
### Data Splits
The dataset has a predefined dev set and test set. The dev set has been renamed to a "train" split.
## 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
The dataset includes recordings of clips (mostly of celebrities and public figures) from public YouTube videos. The names of speakers in VoxCeleb1 are provided.
## 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
The VoxCeleb authors request that anyone who uses VoxCeleb1 or VoxCeleb2 includes the following three citations:
```
@Article{Nagrani19,
author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman",
title = "Voxceleb: Large-scale speaker verification in the wild",
journal = "Computer Science and Language",
year = "2019",
publisher = "Elsevier",
}
@InProceedings{Chung18b,
author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.",
title = "VoxCeleb2: Deep Speaker Recognition",
booktitle = "INTERSPEECH",
year = "2018",
}
@InProceedings{Nagrani17,
author = "Nagrani, A. and Chung, J.~S. and Zisserman, A.",
title = "VoxCeleb: a large-scale speaker identification dataset",
booktitle = "INTERSPEECH",
year = "2017",
}
```
### Contributions
Thanks to [@101arrowz](https://github.com/101arrowz) for adding this dataset.
| [
-0.7007195949554443,
-0.7795397639274597,
0.12930825352668762,
0.24614810943603516,
-0.19460181891918182,
0.06788404285907745,
-0.5131685137748718,
-0.03965647146105766,
0.37010809779167175,
0.3776523470878601,
-0.77531498670578,
-1.0008189678192139,
-0.5688537359237671,
-0.034280583262443... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
shjwudp/chinese-c4 | shjwudp | 2023-06-20T11:40:06Z | 38 | 12 | null | [
"language:zh",
"license:cc-by-4.0",
"region:us"
] | 2023-06-20T11:40:06Z | 2022-11-15T01:27:26.000Z | 2022-11-15T01:27:26 | ---
license: cc-by-4.0
language:
- zh
---
## Introduction
Chinese-C4 is a clean Chinese internet dataset based on Common Crawl. The dataset is 46.29GB and has undergone multiple cleaning strategies, including Chinese filtering, heuristic cleaning based on punctuation, line-based hashing for deduplication, and repetition removal.
The dataset is open source and free for commercial use, and you are welcome to use the data and the cleaning strategies provided and contribute your cleaning strategies.
You can find the cleaning script for the dataset on GitHub [c4-dataset-script](https://github.com/shjwudp/c4-dataset-script).
| [
-0.3483254611492157,
-0.33034029603004456,
0.2448779046535492,
0.18547800183296204,
-0.4120955169200897,
-0.0557364858686924,
-0.20413517951965332,
-0.508743941783905,
0.07251019030809402,
0.6238188743591309,
-0.5017893314361572,
-0.8337077498435974,
0.10429196804761887,
0.2934835851192474... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
HuggingFaceM4/TextCaps | HuggingFaceM4 | 2022-12-09T01:38:32Z | 38 | 2 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-12-09T01:38:32Z | 2022-12-06T20:56:12.000Z | 2022-12-06T20:56:12 | ---
license: cc-by-4.0
---
| [
-0.1285339742898941,
-0.18616800010204315,
0.6529127359390259,
0.4943626821041107,
-0.1931934952735901,
0.2360742688179016,
0.360720157623291,
0.05056300014257431,
0.5793654322624207,
0.7400140166282654,
-0.6508105993270874,
-0.23783984780311584,
-0.7102248668670654,
-0.047826044261455536,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jonathan-roberts1/Optimal-31 | jonathan-roberts1 | 2023-03-31T17:06:29Z | 38 | 0 | null | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] | 2023-03-31T17:06:29Z | 2023-02-17T15:53:58.000Z | 2023-02-17T15:53:58 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': airport
'2': baseball diamond
'3': basketball court
'4': beach
'5': bridge
'6': chaparral
'7': church
'8': circular farmland
'9': commercial area
'10': dense residential
'11': desert
'12': forest
'13': freeway
'14': golf course
'15': ground track field
'16': harbor
'17': industrial area
'18': intersection
'19': island
'20': lake
'21': meadow
'22': medium residential
'23': mobile home park
'24': mountain
'25': overpass
'26': parking lot
'27': railway
'28': rectangular farmland
'29': roundabout
'30': runway
splits:
- name: train
num_bytes: 25100636.72
num_examples: 1860
download_size: 25105452
dataset_size: 25100636.72
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "Optimal-31"
## Dataset Description
- **Paper** [Scene classification with recurrent attention of VHR remote sensing images](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
### Licensing Information
[No license for now, cite the paper below.]
## Citation Information
[Scene classification with recurrent attention of VHR remote sensing images](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
```
@article{wang2018scene,
title = {Scene classification with recurrent attention of VHR remote sensing images},
author = {Wang, Qi and Liu, Shaoteng and Chanussot, Jocelyn and Li, Xuelong},
year = 2018,
journal = {IEEE Transactions on Geoscience and Remote Sensing},
publisher = {IEEE},
volume = 57,
number = 2,
pages = {1155--1167}
}
``` | [
-0.6191813945770264,
-0.16142036020755768,
0.26190951466560364,
0.1113378256559372,
-0.5422631502151489,
-0.24539227783679962,
0.10389702767133713,
-0.4494291841983795,
-0.38256919384002686,
0.30216631293296814,
-0.6494527459144592,
-0.6190582513809204,
-0.24737858772277832,
0.130532786250... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sid6i7/patient-doctor | sid6i7 | 2023-03-30T20:02:27Z | 38 | 4 | null | [
"region:us"
] | 2023-03-30T20:02:27Z | 2023-03-30T20:01:09.000Z | 2023-03-30T20:01:09 | Entry not found | [
-0.32276487350463867,
-0.22568444907665253,
0.8622263073921204,
0.43461570143699646,
-0.5282988548278809,
0.7012969255447388,
0.7915717363357544,
0.07618642598390579,
0.7746027112007141,
0.25632190704345703,
-0.7852815389633179,
-0.22573848068714142,
-0.910447895526886,
0.5715675354003906,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SkyHuReal/DrugBank-Alpaca | SkyHuReal | 2023-04-03T17:37:30Z | 38 | 0 | null | [
"license:afl-3.0",
"region:us"
] | 2023-04-03T17:37:30Z | 2023-04-03T15:39:50.000Z | 2023-04-03T15:39:50 | ---
license: afl-3.0
---
| [
-0.1285339742898941,
-0.18616800010204315,
0.6529127359390259,
0.4943626821041107,
-0.1931934952735901,
0.2360742688179016,
0.360720157623291,
0.05056300014257431,
0.5793654322624207,
0.7400140166282654,
-0.6508105993270874,
-0.23783984780311584,
-0.7102248668670654,
-0.047826044261455536,... | null | null | null | null | null | null | null | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.