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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
04-07-22/wep-probes | 04-07-22 | 2022-07-12T16:26:46Z | 12 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-07-12T16:26:46Z | 2022-07-12T11:48:56.000Z | 2022-07-12T11:48:56 | ---
license: apache-2.0
---
| [
-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 | |
demelin/understanding_fables | demelin | 2022-07-17T15:04:16Z | 12 | 1 | null | [
"task_categories:multiple-choice",
"task_categories:text-generation",
"task_ids:multiple-choice-qa",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"languag... | 2022-07-17T15:04:16Z | 2022-07-14T18:52:15.000Z | 2022-07-14T18:52:15 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- expert-generated
license:
- mit
multilinguality:
- monolingual
pretty_name: Understanding Fables
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- multiple-choice
- text-generation
- text-understanding
- text-comprehension
- natural-language-understanding
- natural-language-generation
task_ids:
- multiple-choice-qa
- language-modeling
---
# Dataset Card for Understanding Fables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Understanding Fables BIG-Bench entry](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/understanding_fables)
- **Repository:** [Understanding Fables BIG-Bench entry](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/understanding_fables)
- **Paper:** [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Denis Emelin](demelin.github.io)
### Dataset Summary
Fables are short narratives that aim to communicate a specific lesson or wisdom, referred to as the moral. Morals can be idiomatic or provide a succinct summary of the fable. Importantly, they make explicit the communicative intent of the fable and, thus, are highly relevant to its content. A computational model capable of natural language understanding should, when presented with a fable and a set of potentially relevant morals, rank the moral that accurately captures the message communicated by the fable above the rest. Additionally, fables represent a highly unusual narrative domain, where animals and inanimate objects are anthropomorphized and referred to with gendered pronouns, i.e., a rabbit may be a she, rather than an it. Thus, to understand fables, models must abstract away from patterns commonly encountered in their training data by applying human-like characteristics to non-human actors. Overall, for a computational model to perform well on this task, it must be capable of (1) successfully identifying the core message of a short narrative, (2) identifying a moral that expresses this message among a set of distractor morals, and (3) doing so within a narrative domain that is unlike the majority of pre-training data. Thus, the evaluated large language models would need to demonstrate cross-domain generalization capability in addition to narrative comprehension.
The dataset evaluates models' ability to comprehend written narratives by asking them to select the most appropriate moral for each fable, from a set of five alternatives. In addition to the correct answer, this set contains four distractor morals, which were selected semi-automatically. To identify challenging distractor morals for each fable, sentence similarity was computed between the embeddings of each sentence in the fable and all morals found in the entire dataset, keeping ten morals that were found to be most similar to any of the fable's sentences. From this set, four distractors were selected manually, so that neither of the final distractors represents a valid moral of the fable. By construction, such distractor items are likely to pose a challenge for models that disproportionally rely on shallow heuristics as opposed to more sophisticated language-understanding strategies.
### Supported Tasks and Leaderboards
- multiple-choice: The dataset can be used to evaluate models on their ability to rank a moral that is relevant to a specified fable above distractor morals, e.g. by assigning the correct choice a lower model perplexity. A [RoBERTa-based model](https://huggingface.co/roberta-base) can be used for this purpose.
- text-generation: The dataset can also be used to train models to generate appropriate morals conditioned on the fable text. A [GPT-based model](https://huggingface.co/EleutherAI/gpt-neo-2.7B) can be used for this purpose.
### Languages
The text in the dataset is in contemporary American English.
## Dataset Structure
### Data Instances
A typical data point consists of a single fable, five morals - four distractors and one that correctly captures the lesson imparted by the fable, and an integer label denoting the ID of the correct answer option. An example dataset entry looks as follows:
{"story": "On a warm winter's day, the ants were busy drying corn that they had collected during summer. While they were engaged in their work, a starving grasshopper passed by and begged them for a single grain to stop his hunger. The ants, in turn, asked him why he had not collected food during the summer to prepare for the harsh winter, to which the grasshopper replied that he spent the warm days singing. Mockingly, the ants said to him in unison: "Since you spent your summer singing, then you must dance hungrily to bed in winter." What is the moral of this story?",
"answer0": "Fine clothes may disguise, but silly words will disclose a fool.",
"answer1": "Better starve free than be a fat slave.",
"answer2": "Evil wishes, like chickens, come home to roost.",
"answer3": "Grasp at the shadow and you will lose the substance.",
"answer4": "It is thrifty to prepare today for the wants of tomorrow.",
"label": 4}
### Data Fields
- "story': The fable for which the appropriate moral has to be identified
- "answerN": Moral candidates to be evaluated by the model
- "label": The ID of the moral belonging to the fable
### Data Splits
*Understanding Fables* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .
## Dataset Creation
### Curation Rationale
To comply with the requirements for inclusion in BIG-bench, each fable was manually paraphrased, to ensure that the task cannot be solved by memorising web data. Following sources were used for fable collection:
- [https://www.aesopfables.com/aesop1.html](https://www.aesopfables.com/aesop1.html)
- [https://www.aesopfables.com/aesop2.html](https://www.aesopfables.com/aesop2.html)
- [https://www.aesopfables.com/aesop3.html](https://www.aesopfables.com/aesop3.html)
- [https://www.aesopfables.com/aesop4.html](https://www.aesopfables.com/aesop4.html)
### Source Data
#### Initial Data Collection and Normalization
Paraphrasing was done by an English speaker with native-like language proficiency and an academic background in literature. The created paraphrases differ from the originals in the identity of their participants (lion was replaced with tiger, wolf with coyote etc.), their sentence and narrative structure, and their register (archaic terms such as thou have been replaced with their modern counterparts). The phrasing of the morals has also been updated in cases where the original language was noticeably archaic (e.g., o'er reach -> overreach), with changes kept to a minimum. The mean string similarity between original fables and their paraphrases is consequently low at 0.26, according to the word-level DamerauLevenshtein distance. At the same time, great care was taken not to alter the content of the fables and preserve the relevance of their respective morals. This is evidenced by the high semantic similarity between the originals and their paraphrases, with a mean of 0.78, computed as the cosine similarity between the embeddings of the originals and their paraphrases, obtained using the Sentence-Transformers library. Moreover, duplicate and near-duplicate fables were removed from the final collection, as were several thematically problematic stories, e.g., ones with sexist undertones. In total, the dataset includes 189 paraphrased, unique fables.
#### Who are the source language producers?
Original authors and transcribers of the fables (unknown), the [dataset author](demelin.github.io).
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
Several thematically problematic fables, e.g., ones with sexist undertones, were removed by the author during the dataset creation process. However, it can be that the author overlooked other problematic or harmful biases present within the dataset.
### Other Known Limitations
The dataset is very limited in size due to the small number of thematically distinct fables available online. Similarly, the focus on English fables alone is a limiting factor to be addressed in future dataset iterations.
## Additional Information
### Dataset Curators
[Denis Emelin](demelin.github.io)
### Licensing Information
MIT
### Citation Information
@article{Srivastava2022BeyondTI,
title={Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},
author={Aarohi Srivastava and Abhinav Rastogi and Abhishek B Rao and Abu Awal Md Shoeb and Abubakar Abid and Adam Fisch and Adam R. Brown and Adam Santoro and Aditya Gupta and Adri{\`a} Garriga-Alonso and Agnieszka Kluska and Aitor Lewkowycz and Akshat Agarwal and Alethea Power and Alex Ray and Alex Warstadt and Alexander W. Kocurek and Ali Safaya and Ali Tazarv and Alice Xiang and Alicia Parrish and Allen Nie and Aman Hussain and Amanda Askell and Amanda Dsouza and Ameet Annasaheb Rahane and Anantharaman S. Iyer and Anders Johan Andreassen and Andrea Santilli and Andreas Stuhlmuller and Andrew M. Dai and Andrew D. La and Andrew Kyle Lampinen and Andy Zou and Angela Jiang and Angelica Chen and Anh Vuong and Animesh Gupta and Anna Gottardi and Antonio Norelli and Anu Venkatesh and Arash Gholamidavoodi and Arfa Tabassum and Arul Menezes and Arun Kirubarajan and Asher Mullokandov and Ashish Sabharwal and Austin Herrick and Avia Efrat and Aykut Erdem and Ayla Karakacs and Bridget R. Roberts and Bao Sheng Loe and Barret Zoph and Bartlomiej Bojanowski and Batuhan Ozyurt and Behnam Hedayatnia and Behnam Neyshabur and Benjamin Inden and Benno Stein and Berk Ekmekci and Bill Yuchen Lin and Blake Stephen Howald and Cameron Diao and Cameron Dour and Catherine Stinson and Cedrick Argueta and C'esar Ferri Ram'irez and Chandan Singh and Charles Rathkopf and Chenlin Meng and Chitta Baral and Chiyu Wu and Chris Callison-Burch and Chris Waites and Christian Voigt and Christopher D. Manning and Christopher Potts and Cindy Tatiana Ramirez and Clara Rivera and Clemencia Siro and Colin Raffel and Courtney Ashcraft and Cristina Garbacea and Damien Sileo and Daniel H Garrette and Dan Hendrycks and Dan Kilman and Dan Roth and Daniel Freeman and Daniel Khashabi and Daniel Levy and Daniel Gonz'alez and Danny Hernandez and Danqi Chen and Daphne Ippolito and Dar Gilboa and David Dohan and D. Drakard and David Jurgens and Debajyoti Datta and Deep Ganguli and Denis Emelin and Denis Kleyko and Deniz Yuret and Derek Chen and Derek Tam and Dieuwke Hupkes and Diganta Misra and Dilyar Buzan and Dimitri Coelho Mollo and Diyi Yang and Dong-Ho Lee and Ekaterina Shutova and Ekin Dogus Cubuk and Elad Segal and Eleanor Hagerman and Elizabeth Barnes and Elizabeth P. Donoway and Ellie Pavlick and Emanuele Rodol{\`a} and Emma FC Lam and Eric Chu and Eric Tang and Erkut Erdem and Ernie Chang and Ethan A. Chi and Ethan Dyer and Ethan Jerzak and Ethan Kim and Eunice Engefu Manyasi and Evgenii Zheltonozhskii and Fan Xia and Fatemeh Siar and Fernando Mart'inez-Plumed and Francesca Happ'e and François Chollet and Frieda Rong and Gaurav Mishra and Genta Indra Winata and Gerard de Melo and Germ{\'a}n Kruszewski and Giambattista Parascandolo and Giorgio Mariani and Gloria Wang and Gonzalo Jaimovitch-L'opez and Gregor Betz and Guy Gur-Ari and Hana Galijasevic and Han Sol Kim and Hannah Rashkin and Hanna Hajishirzi and Harsh Mehta and Hayden Bogar and Henry Shevlin and Hinrich Sch{\"u}tze and Hiromu Yakura and Hongming Zhang and Hubert Wong and Ian Aik-Soon Ng and Isaac Noble and Jaap Jumelet and Jack Geissinger and John Kernion and Jacob Hilton and Jaehoon Lee and Jaime Fern{\'a}ndez Fisac and J. Brooker Simon and James Koppel and James Zheng and James Zou and Jan Koco'n and Jana Thompson and Jared Kaplan and Jarema Radom and Jascha Narain Sohl-Dickstein and Jason Phang and Jason Wei and Jason Yosinski and Jekaterina Novikova and Jelle Bosscher and Jenni Marsh and Jeremy Kim and Jeroen Taal and Jesse Engel and Jesujoba Oluwadara Alabi and Jiacheng Xu and Jiaming Song and Jillian Tang and Jane W Waweru and John Burden and John Miller and John U. Balis and Jonathan Berant and Jorg Frohberg and Jos Rozen and Jos{\'e} Hern{\'a}ndez-Orallo and Joseph Boudeman and Joseph Jones and Joshua B. Tenenbaum and Joshua S. Rule and Joyce Chua and Kamil Kanclerz and Karen Livescu and Karl Krauth and Karthik Gopalakrishnan and Katerina Ignatyeva and Katja Markert and Kaustubh D. Dhole and Kevin Gimpel and Kevin Ochieng’ Omondi and Kory Wallace Mathewson and Kristen Chiafullo and Ksenia Shkaruta and Kumar Shridhar and Kyle McDonell and Kyle Richardson and Laria Reynolds and Leo Gao and Li Zhang and Liam Dugan and Lianhui Qin and Lidia Contreras-Ochando and Louis-Philippe Morency and Luca Moschella and Luca Lam and Lucy Noble and Ludwig Schmidt and Luheng He and Luis Oliveros Col'on and Luke Metz and Lutfi Kerem cSenel and Maarten Bosma and Maarten Sap and Maartje ter Hoeve and Madotto Andrea and Maheen Saleem Farooqi and Manaal Faruqui and Mantas Mazeika and Marco Baturan and Marco Marelli and Marco Maru and M Quintana and Marie Tolkiehn and Mario Giulianelli and Martha Lewis and Martin Potthast and Matthew Leavitt and Matthias Hagen and M'aty'as Schubert and Medina Baitemirova and Melissa Arnaud and Melvin Andrew McElrath and Michael A. Yee and Michael Cohen and Mi Gu and Michael I. Ivanitskiy and Michael Starritt and Michael Strube and Michal Swkedrowski and Michele Bevilacqua and Michihiro Yasunaga and Mihir Kale and Mike Cain and Mimee Xu and Mirac Suzgun and Monica Tiwari and Mohit Bansal and Moin Aminnaseri and Mor Geva and Mozhdeh Gheini and T MukundVarma and Nanyun Peng and Nathan Chi and Nayeon Lee and Neta Gur-Ari Krakover and Nicholas Cameron and Nicholas S. Roberts and Nicholas Doiron and Nikita Nangia and Niklas Deckers and Niklas Muennighoff and Nitish Shirish Keskar and Niveditha Iyer and Noah Constant and Noah Fiedel and Nuan Wen and Oliver Zhang and Omar Agha and Omar Elbaghdadi and Omer Levy and Owain Evans and Pablo Antonio Moreno Casares and Parth Doshi and Pascale Fung and Paul Pu Liang and Paul Vicol and Pegah Alipoormolabashi and Peiyuan Liao and Percy Liang and Peter W. Chang and Peter Eckersley and Phu Mon Htut and Pi-Bei Hwang and P. Milkowski and Piyush S. Patil and Pouya Pezeshkpour and Priti Oli and Qiaozhu Mei and QING LYU and Qinlang Chen and Rabin Banjade and Rachel Etta Rudolph and Raefer Gabriel and Rahel Habacker and Ram'on Risco Delgado and Rapha{\"e}l Milli{\`e}re and Rhythm Garg and Richard Barnes and Rif A. Saurous and Riku Arakawa and Robbe Raymaekers and Robert Frank and Rohan Sikand and Roman Novak and Roman Sitelew and Ronan Le Bras and Rosanne Liu and Rowan Jacobs and Rui Zhang and Ruslan Salakhutdinov and Ryan Chi and Ryan Lee and Ryan Stovall and Ryan Teehan and Rylan Yang and Sahib J. Singh and Saif M. Mohammad and Sajant Anand and Sam Dillavou and Sam Shleifer and Sam Wiseman and Samuel Gruetter and Sam Bowman and Samuel S. Schoenholz and Sanghyun Han and Sanjeev Kwatra and Sarah A. Rous and Sarik Ghazarian and Sayan Ghosh and Sean Casey and Sebastian Bischoff and Sebastian Gehrmann and Sebastian Schuster and Sepideh Sadeghi and Shadi Sameh Hamdan and Sharon Zhou and Shashank Srivastava and Sherry Shi and Shikhar Singh and Shima Asaadi and Shixiang Shane Gu and Shubh Pachchigar and Shubham Toshniwal and Shyam Upadhyay and Shyamolima Debnath and Siamak Shakeri and Simon Thormeyer and Simone Melzi and Siva Reddy and Sneha Priscilla Makini and Soo-hwan Lee and Spencer Bradley Torene and Sriharsha Hatwar and Stanislas Dehaene and Stefan Divic and Stefano Ermon and Stella Rose Biderman and Stephanie C. Lin and Stephen Prasad and Steven T. Piantadosi and Stuart M. Shieber and Summer Misherghi and Svetlana Kiritchenko and Swaroop Mishra and Tal Linzen and Tal Schuster and Tao Li and Tao Yu and Tariq A. Ali and Tatsuo Hashimoto and Te-Lin Wu and Theo Desbordes and Theodore Rothschild and Thomas Phan and Tianle Wang and Tiberius Nkinyili and Timo Schick and T. N. Kornev and Timothy Telleen-Lawton and Titus Tunduny and Tobias Gerstenberg and Trenton Chang and Trishala Neeraj and Tushar Khot and Tyler O. Shultz and Uri Shaham and Vedant Misra and Vera Demberg and Victoria Nyamai and Vikas Raunak and Vinay V. Ramasesh and Vinay Uday Prabhu and Vishakh Padmakumar and Vivek Srikumar and William Fedus and William Saunders and William Zhang and W Vossen and Xiang Ren and Xiaoyu F Tong and Xinyi Wu and Xudong Shen and Yadollah Yaghoobzadeh and Yair Lakretz and Yang Song and Yasaman Bahri and Ye Ji Choi and Yichi Yang and Yiding Hao and Yifu Chen and Yonatan Belinkov and Yu Hou and Yu Hou and Yushi Bai and Zachary Seid and Zhao Xinran and Zhuoye Zhao and Zi Fu Wang and Zijie J. Wang and Zirui Wang and Ziyi Wu and Sahib Singh and Uri Shaham},
journal={ArXiv},
year={2022},
volume={abs/2206.04615}
} | [
-0.33586400747299194,
-0.5659460425376892,
0.39272642135620117,
0.39911913871765137,
-0.37748098373413086,
-0.03442159667611122,
0.06418181210756302,
-0.7125949263572693,
-0.031753312796354294,
0.47198182344436646,
-0.493146151304245,
-0.5423383712768555,
-0.5017472505569458,
0.24403271079... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-project-emotion-d66bcc95-10955472 | autoevaluate | 2022-07-15T12:46:34Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T12:46:34Z | 2022-07-15T12:45:49.000Z | 2022-07-15T12:45:49 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: Abdelrahman-Rezk/distilbert-base-uncased-finetuned-emotion
metrics: []
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: Abdelrahman-Rezk/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@postpandas](https://huggingface.co/postpandas) for evaluating this model. | [
-0.47010478377342224,
-0.2848700284957886,
0.2580743432044983,
0.25180310010910034,
-0.02538091130554676,
-0.08933372050523758,
-0.12276627868413925,
-0.4463098347187042,
0.06444885581731796,
0.23624402284622192,
-0.9460836052894592,
-0.305189311504364,
-0.7361724972724915,
0.0798416808247... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gorkemozkaya/blended_en_tr | gorkemozkaya | 2022-07-19T05:29:32Z | 12 | 0 | null | [
"license:other",
"region:us"
] | 2022-07-19T05:29:32Z | 2022-07-19T04:36:09.000Z | 2022-07-19T04:36:09 | ---
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 | |
voidful/DRCD | voidful | 2022-07-20T08:33:48Z | 12 | 0 | null | [
"license:cc-by-3.0",
"region:us"
] | 2022-07-20T08:33:48Z | 2022-07-20T08:16:09.000Z | 2022-07-20T08:16:09 | ---
license: cc-by-3.0
---
| [
-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 | |
biglam/hansard_speech | biglam | 2022-07-27T12:30:30Z | 12 | 2 | null | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_ids:multi-class-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categor... | 2022-07-27T12:30:30Z | 2022-07-22T21:57:59.000Z | 2022-07-22T21:57:59 | ---
annotations_creators:
- no-annotation
language:
- 'en'
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Hansard Speeches
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- speeches
- politics
- parliament
- British
task_categories:
- text-classification
- text-generation
task_ids:
- multi-class-classification
- language-modeling
- masked-language-modeling
---
# Dataset Card for Hansard speech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://evanodell.com/projects/datasets/hansard-data/
- **Repository:** https://github.com/evanodell/hansard-data3
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Evan Odell](https://github.com/evanodell)
### Dataset Summary
A dataset containing every speech in the House of Commons from May 1979-July 2020. Quoted from the dataset homepage
> Please contact me if you find any errors in the dataset. The integrity of the public Hansard record is questionable at times, and while I have improved it, the data is presented "as is".
### Supported Tasks and Leaderboards
- `text-classification`: This dataset can be used to classify various texts (transcribed from speeches) as different time periods or as different types
- `language-modeling`: This dataset can contribute to the training or the evaluation of language models for historical texts.
### Languages
`en:GB`
## Dataset Structure
### Data Instances
```
{
'id': 'uk.org.publicwhip/debate/1979-05-17a.390.0',
'speech': "Since the Minister for Consumer Affairs said earlier that the bread price rise would be allowed, in view of developing unemployment in the baking industry, and since the Mother's Pride bakery in my constituency is about to close, will the right hon. Gentleman give us a firm assurance that there will be an early debate on the future of the industry, so that the Government may announce that, thanks to the price rise, those workers will not now be put out of work?",
'display_as': 'Eric Heffer',
'party': 'Labour',
'constituency': 'Liverpool, Walton',
'mnis_id': '725',
'date': '1979-05-17',
'time': '',
'colnum': '390',
'speech_class': 'Speech',
'major_heading': 'BUSINESS OF THE HOUSE',
'minor_heading': '',
'oral_heading': '',
'year': '1979',
'hansard_membership_id': '5612',
'speakerid': 'uk.org.publicwhip/member/11615',
'person_id': '',
'speakername': 'Mr. Heffer',
'url': '',
'government_posts': [],
'opposition_posts': [],
'parliamentary_posts': ['Member, Labour Party National Executive Committee']
}
```
### Data Fields
|Variable|Description|
|---|---|
|id|The ID as assigned by mysociety|
|speech|The text of the speech|
|display_as| The standardised name of the MP.|
|party|The party an MP is member of at time of speech|
|constituency| Constituency represented by MP at time of speech|
|mnis_id| The MP's Members Name Information Service number|
|date|Date of speech|
|time|Time of speech|
|colnum |Column number in hansard record|
|speech_class |Type of speech|
|major_heading| Major debate heading|
|minor_heading| Minor debate heading|
|oral_heading| Oral debate heading|
|year |Year of speech|
|hansard_membership_id| ID used by mysociety|
|speakerid |ID used by mysociety|
|person_id |ID used by mysociety|
|speakername| MP name as appeared in Hansard record for speech|
|url| link to speech|
|government_posts| Government posts held by MP (list)|
|opposition_posts |Opposition posts held by MP (list)|
|parliamentary_posts| Parliamentary posts held by MP (list)|
### Data Splits
Train: 2694375
## Dataset Creation
### Curation Rationale
This dataset contains all the speeches made in the House of Commons and can be used for a number of deep learning tasks like detecting how language and societal views have changed over the >40 years. The dataset also provides language closer to the spoken language used in an elite British institution.
### Source Data
#### Initial Data Collection and Normalization
The dataset is created by getting the data from [data.parliament.uk](http://data.parliament.uk/membersdataplatform/memberquery.aspx). There is no normalization.
#### Who are the source language producers?
[N/A]
### Annotations
#### Annotation process
None
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
This is public information, so there should not be any personal and sensitive information
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to understand how language use and society's views have changed over time.
### Discussion of Biases
Because of the long time period this dataset spans, it might contain language and opinions that are unacceptable in modern society.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
This dataset was built on top of [parlparse](https://github.com/mysociety/parlparse) by [Evan Odell](https://github.com/evanodell)
### Licensing Information
Creative Commons Attribution 4.0 International License
### Citation Information
```
@misc{odell, evan_2021,
title={Hansard Speeches 1979-2021: Version 3.1.0},
DOI={10.5281/zenodo.4843485},
abstractNote={<p>Full details are available at <a href="https://evanodell.com/projects/datasets/hansard-data">https://evanodell.com/projects/datasets/hansard-data</a></p> <p><strong>Version 3.1.0 contains the following changes:</strong></p> <p>- Coverage up to the end of April 2021</p>},
note={This release is an update of previously released datasets. See full documentation for details.},
publisher={Zenodo},
author={Odell, Evan},
year={2021},
month={May} }
```
Thanks to [@shamikbose](https://github.com/shamikbose) for adding this dataset. | [
-0.33221861720085144,
-0.545589029788971,
0.24298906326293945,
-0.06440024077892303,
-0.2312624752521515,
-0.2086581587791443,
-0.4984075725078583,
-0.1411525160074234,
0.3628230690956116,
0.5063633322715759,
-0.42113709449768066,
-0.7617073059082031,
-0.6711734533309937,
-0.02380851842463... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
i-am-holmes/conll2003 | i-am-holmes | 2022-07-23T11:00:06Z | 12 | 0 | null | [
"region:us"
] | 2022-07-23T11:00:06Z | 2022-07-23T10:33:23.000Z | 2022-07-23T10:33:23 | 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 | |
autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835580 | autoevaluate | 2022-07-25T22:39:44Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-25T22:39:44Z | 2022-07-25T22:35:07.000Z | 2022-07-25T22:35:07 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: deepset/deberta-v3-base-squad2
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/deberta-v3-base-squad2
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo ](https://huggingface.co/mbartolo ) for evaluating this model. | [
-0.45867812633514404,
-0.5068468451499939,
0.3778272867202759,
0.1985725462436676,
-0.013406861573457718,
0.12028142809867859,
0.1789322942495346,
-0.41554486751556396,
0.01700361631810665,
0.4296422302722931,
-1.2811830043792725,
-0.14566776156425476,
-0.5509825944900513,
-0.0376878827810... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855584 | autoevaluate | 2022-07-26T08:20:20Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-26T08:20:20Z | 2022-07-26T08:17:41.000Z | 2022-07-26T08:17:41 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/tinybert-6l-768d-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/tinybert-6l-768d-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. | [
-0.44860386848449707,
-0.41499629616737366,
0.3913974165916443,
0.08063344657421112,
0.08035159111022949,
0.08733361959457397,
0.08573740720748901,
-0.37271496653556824,
-0.000653305382002145,
0.4019879102706909,
-1.3185148239135742,
-0.054295070469379425,
-0.49824759364128113,
0.003151027... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
moyix/debian_csrc | moyix | 2022-07-27T20:54:47Z | 12 | 1 | null | [
"license:mit",
"region:us"
] | 2022-07-27T20:54:47Z | 2022-07-27T16:42:52.000Z | 2022-07-27T16:42:52 | ---
license: mit
---
| [
-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 | |
jonathanli/legal-advice-reddit | jonathanli | 2023-02-23T16:39:28Z | 12 | 6 | null | [
"language:en",
"reddit",
"law",
"region:us"
] | 2023-02-23T16:39:28Z | 2022-07-27T20:19:25.000Z | 2022-07-27T20:19:25 | ---
language:
- en
tags:
- reddit
- law
pretty_name: Legal Advice Reddit
---
# Dataset Card for Legal Advice Reddit Dataset
## Dataset Description
- **Paper: [Parameter-Efficient Legal Domain Adaptation](https://aclanthology.org/2022.nllp-1.10/)**
- **Point of Contact: jxl@queensu.ca**
### Dataset Summary
New dataset introduced in [Parameter-Efficient Legal Domain Adaptation](https://aclanthology.org/2022.nllp-1.10) (Li et al., NLLP 2022) from the Legal Advice Reddit community (known as "/r/legaldvice"), sourcing the Reddit posts from the Pushshift
Reddit dataset. The dataset maps the text and title of each legal question posted into one of eleven classes, based on the original Reddit
post's "flair" (i.e., tag). Questions are typically informal and use non-legal-specific language. Per the Legal Advice Reddit rules, posts
must be about actual personal circumstances or situations. We limit the number of labels to the top eleven classes and remove the other
samples from the dataset.
### Citation Information
```
@inproceedings{li-etal-2022-parameter,
title = "Parameter-Efficient Legal Domain Adaptation",
author = "Li, Jonathan and
Bhambhoria, Rohan and
Zhu, Xiaodan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nllp-1.10",
pages = "119--129",
}
``` | [
-0.2832292914390564,
-0.7711061239242554,
0.21433407068252563,
0.23890991508960724,
-0.725050151348114,
-0.2577284276485443,
-0.30266129970550537,
-0.26362887024879456,
0.2775785028934479,
0.5746586918830872,
-0.6787559390068054,
-0.8555245995521545,
-0.5271628499031067,
0.1026169285178184... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sepidmnorozy/Arabic_sentiment | sepidmnorozy | 2022-08-02T16:12:59Z | 12 | 0 | null | [
"region:us"
] | 2022-08-02T16:12:59Z | 2022-08-02T15:43:48.000Z | 2022-08-02T15:43:48 | 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 | |
cakiki/test | cakiki | 2022-08-19T13:22:35Z | 12 | 0 | null | [
"license:cc-by-sa-3.0",
"region:us"
] | 2022-08-19T13:22:35Z | 2022-08-12T14:32:23.000Z | 2022-08-12T14:32:23 | ---
license: cc-by-sa-3.0
---
| [
-0.12853367626667023,
-0.18616794049739838,
0.6529126763343811,
0.4943627417087555,
-0.19319313764572144,
0.23607443273067474,
0.36071979999542236,
0.05056338757276535,
0.5793654322624207,
0.7400138974189758,
-0.6508103013038635,
-0.23783987760543823,
-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sepidmnorozy/Finnish_sentiment | sepidmnorozy | 2022-08-16T09:00:07Z | 12 | 1 | null | [
"region:us"
] | 2022-08-16T09:00:07Z | 2022-08-16T08:58:58.000Z | 2022-08-16T08:58:58 | 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 | |
sepidmnorozy/Japanese_sentiment | sepidmnorozy | 2022-08-16T09:24:31Z | 12 | 1 | null | [
"region:us"
] | 2022-08-16T09:24:31Z | 2022-08-16T09:23:37.000Z | 2022-08-16T09:23:37 | 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 | |
SamuelYang/bookcorpus | SamuelYang | 2022-08-21T04:04:42Z | 12 | 0 | null | [
"region:us"
] | 2022-08-21T04:04:42Z | 2022-08-21T03:57:31.000Z | 2022-08-21T03:57:31 | 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 | |
unpredictable/unpredictable_support-google-com | unpredictable | 2022-08-28T18:25:26Z | 12 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-28T18:25:26Z | 2022-08-28T18:12:13.000Z | 2022-08-28T18:12:13 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-support-google-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-support-google-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/AnonCodeShare/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/unpredictable/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/unpredictable/unpredictable_support-google-com)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Licensing Information
Apache 2.0 | [
-0.37989509105682373,
-0.7803653478622437,
0.18810300529003143,
-0.014855869114398956,
-0.0810370221734047,
-0.07991638779640198,
-0.27683448791503906,
-0.4568255841732025,
0.04360422119498253,
0.32748517394065857,
-0.8472862839698792,
-0.7827833294868469,
-0.6948362588882446,
0.1824155151... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
carlosejimenez/mscoco_train_2014_openai_clip-vit-base-patch32 | carlosejimenez | 2022-09-01T19:25:52Z | 12 | 0 | null | [
"region:us"
] | 2022-09-01T19:25:52Z | 2022-09-01T01:41:55.000Z | 2022-09-01T01:41:55 | 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 | |
autoevaluate/autoeval-staging-eval-emotion-default-139135-14996090 | autoevaluate | 2022-09-01T15:39:48Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-01T15:39:48Z | 2022-09-01T15:39:13.000Z | 2022-09-01T15:39:13 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: bhadresh-savani/roberta-base-emotion
metrics: ['roc_auc', 'mae']
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: bhadresh-savani/roberta-base-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@gmoney](https://huggingface.co/gmoney) for evaluating this model. | [
-0.3647810220718384,
-0.24983838200569153,
0.2820088267326355,
0.2130187451839447,
0.07047092169523239,
-0.049506574869155884,
-0.16751883924007416,
-0.46930131316185,
-0.03135528787970543,
0.23582608997821808,
-0.9586385488510132,
-0.2349860519170761,
-0.7473217844963074,
0.03208322077989... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sagawa/ord-uniq-canonicalized | sagawa | 2022-09-04T02:41:10Z | 12 | 0 | null | [
"task_categories:text2text-generation",
"task_categories:translation",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"license:apache-2.0",
"ord",
"chemical",
"reaction",
"region:us"
] | 2022-09-04T02:41:10Z | 2022-09-03T04:28:23.000Z | 2022-09-03T04:28:23 | ---
annotations_creators: []
language_creators: []
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: canonicalized ORD
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- ord
- chemical
- reaction
task_categories:
- text2text-generation
- translation
task_ids: []
---
### dataset description
We downloaded open-reaction-database(ORD) dataset from [here](https://github.com/open-reaction-database/ord-data). As a preprocess, we removed overlapping data and canonicalized them using RDKit.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
```python:
from rdkit import Chem
def canonicalize(mol):
mol = Chem.MolToSmiles(Chem.MolFromSmiles(mol),True)
return mol
```
We randomly split the preprocessed data into train, validation and test. The ratio is 8:1:1. | [
-0.5020514726638794,
-0.4556243419647217,
0.13795043528079987,
0.11919853091239929,
-0.18829108774662018,
-0.05844494700431824,
-0.2711896300315857,
-0.028028320521116257,
0.3944993019104004,
0.298022985458374,
-0.775763213634491,
-0.6688541173934937,
-0.1299898326396942,
0.352842658758163... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
neuralworm/stable-diffusion-discord-prompts | neuralworm | 2022-09-15T03:52:04Z | 12 | 18 | null | [
"region:us"
] | 2022-09-15T03:52:04Z | 2022-09-09T03:32:22.000Z | 2022-09-09T03:32:22 | stable-diffusion-discord-prompts
All messages from dreambot from all dream-[1-50] channels in stable-diffusion discord
source:
https://github.com/bartman081523/stable-diffusion-discord-prompts | [
-0.4316316545009613,
-0.8477601408958435,
0.956156313419342,
0.45152348279953003,
-0.11595913022756577,
0.5168270468711853,
0.2539588510990143,
0.20674292743206024,
1.1420023441314697,
0.8169348835945129,
-1.0842788219451904,
-0.4308828115463257,
-0.41982951760292053,
-0.1311739981174469,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mrm8488/rotowire-sbnation | mrm8488 | 2022-09-15T20:40:46Z | 12 | 0 | null | [
"region:us"
] | 2022-09-15T20:40:46Z | 2022-09-15T20:40:00.000Z | 2022-09-15T20:40:00 | Entry not found | [
-0.3227647542953491,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965083122253,
0.7915717959403992,
0.07618629932403564,
0.7746022343635559,
0.2563222348690033,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
darcksky/teste | darcksky | 2022-09-16T02:25:50Z | 12 | 0 | null | [
"region:us"
] | 2022-09-16T02:25:50Z | 2022-09-16T02:23:07.000Z | 2022-09-16T02:23:07 | Entry not found | [
-0.3227647542953491,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965083122253,
0.7915717959403992,
0.07618629932403564,
0.7746022343635559,
0.2563222348690033,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
carlosejimenez/cc12m_openai_clip-vit-base-patch32_image_image_retrieval_pairs_2022-09-15_SHORT | carlosejimenez | 2022-09-16T03:36:01Z | 12 | 0 | null | [
"region:us"
] | 2022-09-16T03:36:01Z | 2022-09-16T03:34:24.000Z | 2022-09-16T03:34:24 | Entry not found | [
-0.3227647542953491,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965083122253,
0.7915717959403992,
0.07618629932403564,
0.7746022343635559,
0.2563222348690033,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mediabiasgroup/BABE | mediabiasgroup | 2023-08-23T05:24:17Z | 12 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-08-23T05:24:17Z | 2022-09-18T03:18:38.000Z | 2022-09-18T03:18:38 | ---
license: cc-by-nc-sa-4.0
---
# Please cite as
```
@InProceedings{Spinde2021f,
title = "Neural Media Bias Detection Using Distant Supervision With {BABE} - Bias Annotations By Experts",
author = "Spinde, Timo and
Plank, Manuel and
Krieger, Jan-David and
Ruas, Terry and
Gipp, Bela and
Aizawa, Akiko",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.101",
doi = "10.18653/v1/2021.findings-emnlp.101",
pages = "1166--1177",
}
``` | [
-0.5842202305793762,
-0.7604844570159912,
0.22846682369709015,
0.4261338412761688,
0.060627326369285583,
-0.12412068247795105,
-0.5497016906738281,
-0.43095624446868896,
0.7278941869735718,
0.23771904408931732,
-0.8745121955871582,
-0.5095365643501282,
-0.6582810878753662,
0.10479775816202... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
WillHeld/coqa | WillHeld | 2022-11-02T05:22:30Z | 12 | 0 | null | [
"region:us"
] | 2022-11-02T05:22:30Z | 2022-09-19T16:55:32.000Z | 2022-09-19T16:55:32 | 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 | |
spacemanidol/identify-noisy-queries | spacemanidol | 2022-09-19T20:57:27Z | 12 | 0 | null | [
"region:us"
] | 2022-09-19T20:57:27Z | 2022-09-19T20:37:18.000Z | 2022-09-19T20:37:18 | 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 | |
yuntian-deng/im2html-100k | yuntian-deng | 2022-09-19T20:43:52Z | 12 | 0 | null | [
"region:us"
] | 2022-09-19T20:43:52Z | 2022-09-19T20:42:43.000Z | 2022-09-19T20:42:43 | 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 | |
autoevaluate/autoeval-eval-Tristan__zero_shot_classification_test-Tristan__zero_sh-c10c5c-1520355008 | autoevaluate | 2022-09-21T03:16:17Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-21T03:16:17Z | 2022-09-21T02:23:04.000Z | 2022-09-21T02:23:04 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Tristan/zero_shot_classification_test
eval_info:
task: text_zero_shot_classification
model: Tristan/opt-66b-copy
metrics: []
dataset_name: Tristan/zero_shot_classification_test
dataset_config: Tristan--zero_shot_classification_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: Tristan/opt-66b-copy
* Dataset: Tristan/zero_shot_classification_test
* Config: Tristan--zero_shot_classification_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Tristan](https://huggingface.co/Tristan) for evaluating this model. | [
-0.2048356682062149,
-0.3857775330543518,
0.3625076711177826,
-0.13019581139087677,
-0.10174384713172913,
-0.03470470756292343,
0.09568430483341217,
-0.4716493487358093,
0.17497578263282776,
0.49251458048820496,
-0.9219593405723572,
-0.3607577085494995,
-0.6115976572036743,
-0.035089675337... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-squad_v2-squad_v2-e4ddf6-1520555010 | autoevaluate | 2022-09-21T04:32:36Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-21T04:32:36Z | 2022-09-21T04:30:06.000Z | 2022-09-21T04:30:06 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: SebastianS/distilbert-base-uncased-finetuned-squad-d5716d28
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: SebastianS/distilbert-base-uncased-finetuned-squad-d5716d28
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ColdYoungGuy](https://huggingface.co/ColdYoungGuy) for evaluating this model. | [
-0.4908549189567566,
-0.4433295726776123,
0.2906351685523987,
0.22785574197769165,
0.02990536577999592,
0.12648054957389832,
0.139850914478302,
-0.3768388032913208,
0.06921932101249695,
0.37874549627304077,
-1.3204060792922974,
-0.06404007226228714,
-0.4968043565750122,
0.08204944431781769... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
schnell/sample_text | schnell | 2022-09-21T06:13:08Z | 12 | 0 | null | [
"region:us"
] | 2022-09-21T06:13:08Z | 2022-09-21T06:12:51.000Z | 2022-09-21T06:12:51 | 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 | |
ysharma/short_jokes | ysharma | 2022-09-26T17:11:06Z | 12 | 1 | null | [
"license:mit",
"region:us"
] | 2022-09-26T17:11:06Z | 2022-09-26T16:57:00.000Z | 2022-09-26T16:57:00 | ---
license: mit
---
**Context**
Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generate new ones. Such problems, however, are difficult to solve due to a number of reasons, one of which is the lack of a database that gives an elaborate list of jokes. Thus, a large corpus of over 0.2 million jokes has been collected by scraping several websites containing funny and short jokes.
You can visit the [Github repository](https://github.com/amoudgl/short-jokes-dataset) from [amoudgl](https://github.com/amoudgl) for more information regarding collection of data and the scripts used.
**Content**
This dataset is in the form of a csv file containing 231,657 jokes. Length of jokes ranges from 10 to 200 characters. Each line in the file contains a unique ID and joke.
**Disclaimer**
It has been attempted to keep the jokes as clean as possible. Since the data has been collected by scraping websites, it is possible that there may be a few jokes that are inappropriate or offensive to some people.
**Note**
This dataset is taken from Kaggle dataset that can be found [here](https://www.kaggle.com/datasets/abhinavmoudgil95/short-jokes). | [
-0.39035308361053467,
-1.0291080474853516,
0.4756307899951935,
0.4874139130115509,
-0.721041202545166,
-0.35027554631233215,
-0.3448367714881897,
-0.41995111107826233,
0.3551759719848633,
0.4443950951099396,
-0.675657331943512,
-0.5822311639785767,
-0.3440748155117035,
0.43126145005226135,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ZhiweiXie/DreamboothTEST1 | ZhiweiXie | 2022-09-30T04:01:03Z | 12 | 0 | null | [
"region:us"
] | 2022-09-30T04:01:03Z | 2022-09-29T11:34:28.000Z | 2022-09-29T11:34:28 | 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 | |
mathemakitten/winobias_antistereotype_test | mathemakitten | 2022-09-29T15:10:54Z | 12 | 1 | null | [
"region:us"
] | 2022-09-29T15:10:54Z | 2022-09-29T15:10:31.000Z | 2022-09-29T15:10:31 | 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 | |
nielsr/markuplm-toy-dataset | nielsr | 2022-09-30T09:09:43Z | 12 | 0 | null | [
"region:us"
] | 2022-09-30T09:09:43Z | 2022-09-30T09:02:36.000Z | 2022-09-30T09:02:36 | 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 | |
Pitagorak/Stable | Pitagorak | 2022-10-01T04:30:01Z | 12 | 0 | null | [
"region:us"
] | 2022-10-01T04:30:01Z | 2022-10-01T04:22:24.000Z | 2022-10-01T04:22:24 | 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 | |
KevinSpaghetti/smm4h20 | KevinSpaghetti | 2022-10-06T13:09:25Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T13:09:25Z | 2022-10-01T11:21:29.000Z | 2022-10-01T11:21:29 | 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 | |
R0000/000 | R0000 | 2023-05-01T01:01:53Z | 12 | 0 | null | [
"region:us"
] | 2023-05-01T01:01:53Z | 2022-10-02T16:57:49.000Z | 2022-10-02T16:57:49 | 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 | |
illorg/martiii | illorg | 2022-10-02T17:14:14Z | 12 | 0 | null | [
"region:us"
] | 2022-10-02T17:14:14Z | 2022-10-02T17:10:30.000Z | 2022-10-02T17:10:30 | 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 | |
laion/laion2b-en-inappropriate | laion | 2022-10-19T10:34:51Z | 12 | 0 | null | [
"region:us"
] | 2022-10-19T10:34:51Z | 2022-10-02T18:18:16.000Z | 2022-10-02T18:18:16 | Found. Redirecting to https://cdn-lfs.huggingface.co/repos/a4/bd/a4bd682f95e6567fb7d8a857b2e0e4f3a08ec68c6495726a6c3e173ab207419e/835f3f7d88a86e05a882c6a6b6333da6ab874776385f85473798769d767c2fca?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1701480546&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwMTQ4MDU0Nn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9hNC9iZC9hNGJkNjgyZjk1ZTY1NjdmYjdkOGE4NTdiMmUwZTRmM2EwOGVjNjhjNjQ5NTcyNmE2YzNlMTczYWIyMDc0MTllLzgzNWYzZjdkODhhODZlMDVhODgyYzZhNmI2MzMzZGE2YWI4NzQ3NzYzODVmODU0NzM3OTg3NjlkNzY3YzJmY2E%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=Fv-TBuYMSRhh66YevM3UtsuxBW%7EGGOEwJodv0ehRAn6Vx6vhWRfK3NOC66OBZzkVgIZQDWTfGgFOrUELyqMXvRqtwl5wxJPyHbG-GKT5yZQsqmbYIOiBodVKsEXKjEZRN2gMqFvyEANsBHHEDcNHLl4hMtBsFXH6soe0gWLLBxpO7A2HlEvRJj1rUI3ZIE35PgJW9mOo29EGyYsGy7oGsrw0hEVmD%7Ei8SxUcgNBQWLEdnDwNydqjgRFVnD5c1gKFJGLeRpceTpw0XJLhffGprFJ-9-%7ES5ahuznrBFtTer1cH65VwuUx9CIxBHPncDzEdDa9th36vEzP3NDjAK9koQg__&Key-Pair-Id=KVTP0A1DKRTAX | [
-0.5831758975982666,
-0.8160493969917297,
0.6624410152435303,
0.35530829429626465,
-0.6256179213523865,
0.12295128405094147,
0.2036873996257782,
-0.21449442207813263,
0.9490237236022949,
0.8324860334396362,
-1.1638208627700806,
-0.8776707649230957,
-0.5936660170555115,
0.6173149943351746,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TCL98/images-maribel | TCL98 | 2022-10-03T21:07:09Z | 12 | 0 | null | [
"region:us"
] | 2022-10-03T21:07:09Z | 2022-10-02T23:05:04.000Z | 2022-10-02T23:05:04 | 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 | |
Fedeya/me | Fedeya | 2022-10-02T23:16:51Z | 12 | 0 | null | [
"license:unknown",
"region:us"
] | 2022-10-02T23:16:51Z | 2022-10-02T23:15:22.000Z | 2022-10-02T23:15:22 | ---
license: unknown
---
| [
-0.12853367626667023,
-0.18616794049739838,
0.6529126763343811,
0.4943627417087555,
-0.19319313764572144,
0.23607443273067474,
0.36071979999542236,
0.05056338757276535,
0.5793654322624207,
0.7400138974189758,
-0.6508103013038635,
-0.23783987760543823,
-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Sebasloco/aldairpv | Sebasloco | 2022-10-03T04:34:50Z | 12 | 0 | null | [
"region:us"
] | 2022-10-03T04:34:50Z | 2022-10-03T04:15:11.000Z | 2022-10-03T04:15:11 | 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 | |
youngdicey/sample | youngdicey | 2022-10-05T05:26:39Z | 12 | 0 | null | [
"license:openrail",
"region:us"
] | 2022-10-05T05:26:39Z | 2022-10-04T08:38:25.000Z | 2022-10-04T08:38:25 | ---
license: openrail
---
| [
-0.12853367626667023,
-0.18616794049739838,
0.6529126763343811,
0.4943627417087555,
-0.19319313764572144,
0.23607443273067474,
0.36071979999542236,
0.05056338757276535,
0.5793654322624207,
0.7400138974189758,
-0.6508103013038635,
-0.23783987760543823,
-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758608 | autoevaluate | 2022-10-04T09:42:23Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-04T09:42:23Z | 2022-10-04T09:25:28.000Z | 2022-10-04T09:25:28 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: ChuVN/longformer-base-4096-finetuned-squad2-length-1024-128window
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: ChuVN/longformer-base-4096-finetuned-squad2-length-1024-128window
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
-0.44136738777160645,
-0.5185573101043701,
0.29649436473846436,
0.25281816720962524,
-0.06905433535575867,
0.047538526356220245,
0.03586781024932861,
-0.4904467761516571,
0.11408524960279465,
0.48120033740997314,
-1.206241488456726,
0.03630789741873741,
-0.45480814576148987,
0.027689170092... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758610 | autoevaluate | 2022-10-04T09:28:54Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-04T09:28:54Z | 2022-10-04T09:25:41.000Z | 2022-10-04T09:25:41 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Palak/xlm-roberta-base_squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Palak/xlm-roberta-base_squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
-0.48545870184898376,
-0.42904725670814514,
0.28279024362564087,
0.1347770243883133,
-0.0098338034003973,
0.1546507179737091,
0.12254030257463455,
-0.36934152245521545,
0.060293618589639664,
0.5189074277877808,
-1.3114510774612427,
-0.08484673500061035,
-0.47873422503471375,
0.070878759026... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758613 | autoevaluate | 2022-10-04T09:29:08Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-04T09:29:08Z | 2022-10-04T09:26:01.000Z | 2022-10-04T09:26:01 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Paul-Vinh/bert-base-multilingual-cased-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Paul-Vinh/bert-base-multilingual-cased-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
-0.5160428881645203,
-0.46757039427757263,
0.28736284375190735,
0.2854594588279724,
0.03367435932159424,
0.0868946984410286,
-0.003788819070905447,
-0.39848607778549194,
0.10496170818805695,
0.4280647039413452,
-1.2375441789627075,
-0.1456747204065323,
-0.44589176774024963,
0.0771497040987... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758614 | autoevaluate | 2022-10-04T09:30:00Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-04T09:30:00Z | 2022-10-04T09:26:09.000Z | 2022-10-04T09:26:09 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Palak/microsoft_deberta-base_squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Palak/microsoft_deberta-base_squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
-0.462895005941391,
-0.48814964294433594,
0.24632063508033752,
0.2330564707517624,
-0.025770289823412895,
0.19673274457454681,
0.23279644548892975,
-0.3654152452945709,
0.09456905722618103,
0.42506834864616394,
-1.3017233610153198,
-0.06637672334909439,
-0.48930105566978455,
0.019994862377... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758616 | autoevaluate | 2022-10-04T09:28:48Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-04T09:28:48Z | 2022-10-04T09:26:20.000Z | 2022-10-04T09:26:20 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Plimpton/distilbert-base-uncased-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Plimpton/distilbert-base-uncased-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
-0.5033476948738098,
-0.43194523453712463,
0.2888680100440979,
0.16439859569072723,
0.01955459825694561,
0.12855690717697144,
0.13524454832077026,
-0.2912513315677643,
-0.00497222226113081,
0.37611979246139526,
-1.2568187713623047,
-0.09427446126937866,
-0.534698486328125,
0.09118073433637... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758617 | autoevaluate | 2022-10-04T09:29:27Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-04T09:29:27Z | 2022-10-04T09:26:28.000Z | 2022-10-04T09:26:28 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Neulvo/bert-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Neulvo/bert-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
-0.5827416777610779,
-0.5059337019920349,
0.19966617226600647,
0.19334658980369568,
0.0732976496219635,
0.01475478895008564,
0.12620244920253754,
-0.43770700693130493,
0.11673877388238907,
0.43134805560112,
-1.344785451889038,
-0.045398227870464325,
-0.4548748731613159,
0.0433424673974514,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Gr3en/Goebbels_Liberte_daction | Gr3en | 2022-10-04T09:44:35Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T09:44:35Z | 2022-10-04T09:31:47.000Z | 2022-10-04T09:31:47 | annotations_creators:
- no-annotation
language:
- en
language_creators:
- other
license:
- artistic-2.0
multilinguality:
- monolingual
pretty_name: "Libert\xE8 d'action by Heiner Goebbels"
size_categories:
- n<1K
source_datasets:
- original
tags: []
task_categories:
- text-to-image
task_ids: []
| [
-0.20491096377372742,
-0.34062209725379944,
0.28699421882629395,
0.4234634339809418,
-0.6728163361549377,
-0.13357122242450714,
-0.228204146027565,
-0.6466366052627563,
0.42205750942230225,
0.759552001953125,
-0.8174128532409668,
-0.7220167517662048,
-0.8761951923370361,
0.1653252840042114... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
abrahammg/abrahammg | abrahammg | 2022-10-04T09:57:45Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T09:57:45Z | 2022-10-04T09:54:47.000Z | 2022-10-04T09:54:47 | 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 | |
Besedo/artificial_weapon | Besedo | 2022-10-04T12:24:34Z | 12 | 1 | null | [
"task_categories:image-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"size_categories:1K<n<10K",
"weapon",
"image",
"region:us"
] | 2022-10-04T12:24:34Z | 2022-10-04T10:02:28.000Z | 2022-10-04T10:02:28 | ---
annotations_creators:
- machine-generated
language: []
language_creators:
- machine-generated
license: []
multilinguality: []
pretty_name: artificial_weapon
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- weapon
- image
task_categories:
- image-classification
task_ids: []
---
# 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:**
- **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 [@github-username](https://github.com/<github-username>) for adding this dataset.
| [
-0.47841677069664,
-0.5084842443466187,
0.14602938294410706,
0.278889000415802,
-0.21702472865581512,
0.24832050502300262,
-0.3366999328136444,
-0.3758932054042816,
0.6720380783081055,
0.6457639932632446,
-0.9167346358299255,
-1.2200127840042114,
-0.7551794052124023,
0.07273735105991364,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
esc-benchmark/submissions | esc-benchmark | 2022-10-14T14:33:27Z | 12 | 0 | null | [
"region:us"
] | 2022-10-14T14:33:27Z | 2022-10-04T12:28:58.000Z | 2022-10-04T12:28:58 | 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 | |
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c793f9-1654758678 | autoevaluate | 2022-10-04T12:40:31Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-04T12:40:31Z | 2022-10-04T12:33:51.000Z | 2022-10-04T12:33:51 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-3b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test
dataset_config: mathemakitten--winobias_antistereotype_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: bigscience/bloom-3b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@opfaffel@gmail.com](https://huggingface.co/opfaffel@gmail.com) for evaluating this model. | [
-0.2756839096546173,
-0.34579819440841675,
0.3695236146450043,
0.09532805532217026,
-0.008898352272808552,
-0.13475270569324493,
0.0644591748714447,
-0.5332282185554504,
0.1438256800174713,
0.2684982419013977,
-0.8631603121757507,
-0.3169906735420227,
-0.6487082839012146,
-0.09657275676727... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nickmuchi/corise-celeb-identities | nickmuchi | 2022-10-04T12:57:25Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T12:57:25Z | 2022-10-04T12:57:12.000Z | 2022-10-04T12:57:12 | 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 | |
Kamag/e | Kamag | 2022-10-04T13:25:39Z | 12 | 0 | null | [
"license:unknown",
"region:us"
] | 2022-10-04T13:25:39Z | 2022-10-04T13:24:03.000Z | 2022-10-04T13:24:03 | ---
license: unknown
---
| [
-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 | |
etiennefd/codex_borgia | etiennefd | 2022-10-04T16:10:47Z | 12 | 0 | null | [
"license:wtfpl",
"region:us"
] | 2022-10-04T16:10:47Z | 2022-10-04T14:16:41.000Z | 2022-10-04T14:16:41 | ---
license: wtfpl
---
| [
-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 | |
autoevaluate/autoeval-eval-lewtun__dog_food-lewtun__dog_food-7ca01a-1656458705 | autoevaluate | 2022-10-04T14:46:03Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-04T14:46:03Z | 2022-10-04T14:45:25.000Z | 2022-10-04T14:45:25 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- lewtun/dog_food
eval_info:
task: image_multi_class_classification
model: NimaBoscarino/dog_food
metrics: []
dataset_name: lewtun/dog_food
dataset_config: lewtun--dog_food
dataset_split: test
col_mapping:
image: image
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: NimaBoscarino/dog_food
* Dataset: lewtun/dog_food
* Config: lewtun--dog_food
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@NimaBoscarino](https://huggingface.co/NimaBoscarino) for evaluating this model. | [
-0.513152003288269,
-0.190521240234375,
0.05483261123299599,
0.1168399229645729,
0.010658946819603443,
-0.12455867230892181,
0.10770067572593689,
-0.5626900792121887,
0.14458319544792175,
0.291140615940094,
-0.7827684283256531,
-0.2155364751815796,
-0.7383240461349487,
-0.04449482262134552... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
H2KP/cdip-annotations-formnet-v1 | H2KP | 2022-10-04T16:56:42Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T16:56:42Z | 2022-10-04T16:47:21.000Z | 2022-10-04T16:47:21 | 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 | |
olm/olm-wikipedia-20220920 | olm | 2022-10-18T19:18:25Z | 12 | 0 | null | [
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"pretraining",
"language modelling",
"wikipedia",
"web",
"region:us"
] | 2022-10-18T19:18:25Z | 2022-10-04T17:05:41.000Z | 2022-10-04T17:05:41 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: OLM September 2022 Wikipedia
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- pretraining
- language modelling
- wikipedia
- web
task_categories: []
task_ids: []
---
# Dataset Card for OLM September 2022 Wikipedia
Pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from a September 2022 Wikipedia snapshot. | [
-0.570074200630188,
-0.0863209143280983,
0.2189919352531433,
-0.09065168350934982,
-0.3774357736110687,
-0.3933463990688324,
0.2603844702243805,
-0.34876102209091187,
0.5981398820877075,
0.7677512764930725,
-1.0156728029251099,
-0.6199056506156921,
-0.16821099817752838,
-0.2733769118785858... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
irving777/prueba2022 | irving777 | 2022-10-04T23:52:17Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T23:52:17Z | 2022-10-04T17:36:15.000Z | 2022-10-04T17:36:15 | prueba | [
-0.32102274894714355,
-0.4474636912345886,
0.37481680512428284,
0.8886045217514038,
-0.45619097352027893,
0.07012468576431274,
-0.30511826276779175,
-0.3370986580848694,
0.9511964917182922,
0.6891880631446838,
-0.0402761772274971,
-0.16564995050430298,
-0.7090906500816345,
0.02161753736436... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
smallpinktinyturtle/taemo | smallpinktinyturtle | 2022-10-04T17:44:22Z | 12 | 0 | null | [
"license:unknown",
"region:us"
] | 2022-10-04T17:44:22Z | 2022-10-04T17:39:27.000Z | 2022-10-04T17:39:27 | ---
license: unknown
---
| [
-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 | |
Sebasloco/SugarLenna | Sebasloco | 2022-10-04T18:31:35Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T18:31:35Z | 2022-10-04T18:21:57.000Z | 2022-10-04T18:21:57 | Entry not found | [
-0.3227647542953491,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965083122253,
0.7915717959403992,
0.07618629932403564,
0.7746022343635559,
0.2563222348690033,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Sebasloco/delt4 | Sebasloco | 2022-10-04T19:03:06Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T19:03:06Z | 2022-10-04T18:36:28.000Z | 2022-10-04T18:36:28 | 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 | |
Sebasloco/mrsbonie | Sebasloco | 2022-10-04T18:49:00Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T18:49:00Z | 2022-10-04T18:45:02.000Z | 2022-10-04T18:45:02 | 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 | |
Jakeblack/JakeFotos | Jakeblack | 2022-10-04T20:18:22Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T20:18:22Z | 2022-10-04T20:13:51.000Z | 2022-10-04T20:13:51 | 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 | |
suresh-subramanian/celeb-identities | suresh-subramanian | 2022-10-06T16:40:02Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T16:40:02Z | 2022-10-04T20:59:10.000Z | 2022-10-04T20:59:10 | 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 | |
lize00/aikomari | lize00 | 2022-10-04T21:58:18Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T21:58:18Z | 2022-10-04T21:49:45.000Z | 2022-10-04T21:49:45 | 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 | |
nuphantom/l1 | nuphantom | 2022-10-04T22:14:18Z | 12 | 0 | null | [
"license:other",
"region:us"
] | 2022-10-04T22:14:18Z | 2022-10-04T22:13:51.000Z | 2022-10-04T22:13:51 | ---
license: other
---
| [
-0.12853367626667023,
-0.18616794049739838,
0.6529126763343811,
0.4943627417087555,
-0.19319313764572144,
0.23607443273067474,
0.36071979999542236,
0.05056338757276535,
0.5793654322624207,
0.7400138974189758,
-0.6508103013038635,
-0.23783987760543823,
-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tomekkorbak/detoxify-pile-chunk3-2400000-2450000 | tomekkorbak | 2022-10-04T22:45:14Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T22:45:14Z | 2022-10-04T22:45:06.000Z | 2022-10-04T22:45:06 | 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 | |
tomekkorbak/detoxify-pile-chunk3-2500000-2550000 | tomekkorbak | 2022-10-04T22:58:00Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T22:58:00Z | 2022-10-04T22:57:52.000Z | 2022-10-04T22:57:52 | 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 | |
choosistant/labelled_review_data | choosistant | 2022-10-05T00:28:59Z | 12 | 0 | null | [
"region:us"
] | 2022-10-05T00:28:59Z | 2022-10-04T22:59:32.000Z | 2022-10-04T22:59:32 | 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 | |
tomekkorbak/detoxify-pile-chunk3-1950000-2000000 | tomekkorbak | 2022-10-04T23:16:02Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T23:16:02Z | 2022-10-04T23:15:55.000Z | 2022-10-04T23:15:55 | 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 | |
josephSC15/imagenes | josephSC15 | 2022-10-04T23:40:06Z | 12 | 0 | null | [
"region:us"
] | 2022-10-04T23:40:06Z | 2022-10-04T23:38:38.000Z | 2022-10-04T23:38:38 | 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 | |
Harsit/xnli2.0_chinese | Harsit | 2022-10-05T05:30:29Z | 12 | 0 | null | [
"region:us"
] | 2022-10-05T05:30:29Z | 2022-10-05T05:30:13.000Z | 2022-10-05T05:30:13 | 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 | |
perrynelson/waxal-wolof2 | perrynelson | 2022-10-05T14:44:04Z | 12 | 0 | null | [
"region:us"
] | 2022-10-05T14:44:04Z | 2022-10-05T14:43:57.000Z | 2022-10-05T14:43:57 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: duration
dtype: float64
- name: transcription
dtype: string
splits:
- name: test
num_bytes: 179976390.6
num_examples: 1075
download_size: 178716765
dataset_size: 179976390.6
---
# Dataset Card for "waxal-wolof2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5111514329910278,
-0.36963897943496704,
0.29443085193634033,
0.2530532479286194,
-0.20885193347930908,
0.10610363632440567,
0.19585534930229187,
-0.4153163433074951,
0.6804103851318359,
0.6079888939857483,
-0.8474122285842896,
-0.9002283215522766,
-0.7080129384994507,
-0.397319853305816... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arbml/ElecMorocco | arbml | 2022-11-03T13:57:53Z | 12 | 0 | null | [
"region:us"
] | 2022-11-03T13:57:53Z | 2022-10-05T20:52:57.000Z | 2022-10-05T20:52:57 | 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 | |
arbml/NETransliteration | arbml | 2022-11-03T14:01:07Z | 12 | 0 | null | [
"region:us"
] | 2022-11-03T14:01:07Z | 2022-10-05T21:45:07.000Z | 2022-10-05T21:45:07 | 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 | |
arbml/arData | arbml | 2022-11-03T14:16:32Z | 12 | 1 | null | [
"region:us"
] | 2022-11-03T14:16:32Z | 2022-10-05T22:16:04.000Z | 2022-10-05T22:16:04 | 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 | |
arbml/shakkelha | arbml | 2022-11-03T14:22:56Z | 12 | 0 | null | [
"region:us"
] | 2022-11-03T14:22:56Z | 2022-10-05T22:29:48.000Z | 2022-10-05T22:29:48 | 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 | |
nateraw/stable-diffusion-gallery | nateraw | 2022-10-12T18:15:49Z | 12 | 0 | null | [
"region:us"
] | 2022-10-12T18:15:49Z | 2022-10-05T22:32:14.000Z | 2022-10-05T22:32:14 | 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 | |
devourthemoon/laion-publicdomain | devourthemoon | 2022-10-14T21:49:45Z | 12 | 0 | null | [
"region:us"
] | 2022-10-14T21:49:45Z | 2022-10-05T22:39:16.000Z | 2022-10-05T22:39:16 | annotations_creators:
- machine-generated
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: laion-publicdomain
size_categories:
- 100K<n<1M
source_datasets:
-laion/laion2B-en
tags:
- laion
task_categories:
- text-to-image
# Dataset Card for laion-publicdomain
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [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)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/devourthemoon/laion-publicdomain
- **Repository:** https://huggingface.co/datasets/devourthemoon/laion-publicdomain
- **Paper:** do i look like a scientist to you
- **Leaderboard:**
- **Point of Contact:** @devourthemoon on twitter
### Dataset Summary
This dataset contains metadata about images from the [LAION2B-eb dataset](https://huggingface.co/laion/laion2B-en) curated to a reasonable best guess of 'ethically sourced' images.
## Dataset Structure
### Data Fields
See the [laion2B](https://laion.ai/blog/laion-400-open-dataset/) release notes.
## Dataset Creation
### Curation Rationale
This dataset contains images whose URLs are either from archive.org or whose license is Creative Commons of some sort.
This is a useful first pass at "public use" images, as the Creative Commons licenses are primarily voluntary and intended for public use,
and archive.org is a website that archives public domain images.
### Source Data
The source dataset is at laion/laion2B-en and is not affiliated with this project.
### Annotations
#### Annotation process
Laion2B-en is assembled from Common Crawl data.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
#### Is this dataset as ethical as possible?
*No.* This dataset exists as a proof of concept. Further research could improve the sourcing of the dataset in a number of ways, particularly improving the attribution of files to their original authors.
#### Can I willingly submit my own images to be included in the dataset?
This is a long term goal of this project with the ideal being the generation of 'personalized' AI models for artists. Contact @devourthemoon on Twitter if this interests you.
#### Is this dataset as robust as e.g. LAION2B?
Absolutely not. About 0.17% of the images in the LAION2B dataset matched the filters, leading to just over 600k images in this dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Licensing Information
When using images from this dataset, please acknowledge the combination of Creative Commons licenses.
This dataset itself follows CC-BY-4.0
| [
-0.35712283849716187,
-0.29115670919418335,
0.426728218793869,
0.17289558053016663,
-0.3397931158542633,
-0.16662782430648804,
-0.17739340662956238,
-0.7222571969032288,
0.42895859479904175,
0.5784163475036621,
-0.629683256149292,
-0.8218160271644592,
-0.5305103063583374,
0.196277588605880... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Xangal/Xangal | Xangal | 2022-10-06T01:08:37Z | 12 | 0 | null | [
"license:openrail",
"region:us"
] | 2022-10-06T01:08:37Z | 2022-10-06T00:57:32.000Z | 2022-10-06T00:57:32 | ---
license: openrail
---
| [
-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 | |
ywchoi/pmc_2 | ywchoi | 2022-10-06T01:23:06Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T01:23:06Z | 2022-10-06T01:09:45.000Z | 2022-10-06T01:09:45 | 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 | |
tomekkorbak/detoxify-pile-chunk3-3700000-3750000 | tomekkorbak | 2022-10-06T02:06:27Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T02:06:27Z | 2022-10-06T02:06:20.000Z | 2022-10-06T02:06:20 | 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 | |
tomekkorbak/detoxify-pile-chunk3-3500000-3550000 | tomekkorbak | 2022-10-06T02:53:22Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T02:53:22Z | 2022-10-06T02:53:14.000Z | 2022-10-06T02:53:14 | 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 | |
tomekkorbak/detoxify-pile-chunk3-3550000-3600000 | tomekkorbak | 2022-10-06T03:10:19Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T03:10:19Z | 2022-10-06T03:10:11.000Z | 2022-10-06T03:10:11 | 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 | |
tomekkorbak/detoxify-pile-chunk3-3900000-3950000 | tomekkorbak | 2022-10-06T04:06:55Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T04:06:55Z | 2022-10-06T04:06:46.000Z | 2022-10-06T04:06:46 | 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 | |
ywchoi/pmc_6 | ywchoi | 2022-10-06T05:15:09Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T05:15:09Z | 2022-10-06T04:07:31.000Z | 2022-10-06T04:07:31 | 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 | |
tomekkorbak/detoxify-pile-chunk3-3950000-4000000 | tomekkorbak | 2022-10-06T04:12:17Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T04:12:17Z | 2022-10-06T04:12:08.000Z | 2022-10-06T04:12:08 | 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 | |
tomekkorbak/detoxify-pile-chunk3-4050000-4100000 | tomekkorbak | 2022-10-06T04:19:25Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T04:19:25Z | 2022-10-06T04:19:17.000Z | 2022-10-06T04:19:17 | 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 | |
autoevaluate/autoeval-eval-conll2003-conll2003-df31a4-1679759345 | autoevaluate | 2022-10-06T13:23:18Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-06T13:23:18Z | 2022-10-06T13:22:06.000Z | 2022-10-06T13:22:06 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: bhadresh-savani/electra-base-discriminator-finetuned-conll03-english
metrics: []
dataset_name: conll2003
dataset_config: conll2003
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: bhadresh-savani/electra-base-discriminator-finetuned-conll03-english
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@opfaffel@gmail.com](https://huggingface.co/opfaffel@gmail.com) for evaluating this model. | [
-0.3752204477787018,
-0.3002122640609741,
0.016690270975232124,
0.08274509757757187,
-0.09006121009588242,
-0.008527150377631187,
0.004327577538788319,
-0.5367346405982971,
0.06792882829904556,
0.3540993630886078,
-0.7794892191886902,
-0.23132340610027313,
-0.6391915678977966,
0.0483453795... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arbml/quran_uthmani | arbml | 2022-11-03T15:11:24Z | 12 | 0 | null | [
"region:us"
] | 2022-11-03T15:11:24Z | 2022-10-06T14:13:52.000Z | 2022-10-06T14:13:52 | 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 | |
arbml/ArSarcasm_v2 | arbml | 2022-11-03T15:13:40Z | 12 | 0 | null | [
"region:us"
] | 2022-11-03T15:13:40Z | 2022-10-06T14:16:59.000Z | 2022-10-06T14:16:59 | 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 | |
Peter12475/ICONN | Peter12475 | 2022-10-06T16:24:21Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T16:24:21Z | 2022-10-06T16:18:24.000Z | 2022-10-06T16:18:24 | 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 | |
RezaPriyagung/zapri | RezaPriyagung | 2022-10-06T16:44:30Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T16:44:30Z | 2022-10-06T16:33:21.000Z | 2022-10-06T16:33:21 | 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 | |
ywchoi/pmc_8 | ywchoi | 2022-10-06T18:20:57Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T18:20:57Z | 2022-10-06T16:49:49.000Z | 2022-10-06T16:49:49 | 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 | |
tomekkorbak/detoxify-pile-chunk3-4250000-4300000 | tomekkorbak | 2022-10-06T16:51:59Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T16:51:59Z | 2022-10-06T16:51:51.000Z | 2022-10-06T16:51:51 | 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 | |
tomekkorbak/detoxify-pile-chunk3-4300000-4350000 | tomekkorbak | 2022-10-06T17:07:21Z | 12 | 0 | null | [
"region:us"
] | 2022-10-06T17:07:21Z | 2022-10-06T17:07:14.000Z | 2022-10-06T17:07:14 | 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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.