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54ddf91b0bbb3c820729e5b4a3c993edbe22a591 |
This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4333 | 0.2163 | 0.2163 | 0.2163 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.3780 | 0.1827 | 0.1827 | 0.1827 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.3928 | 0.1898 | 0.1898 | 0.1898 | | allenai/ms2_sparse_oracle | [
"task_categories:summarization",
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-08-26T20:42:33+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other-MS^2", "extended|other-Cochrane"], "task_categories": ["summarization", "text2text-generation"], "paperswithcode_id": "multi-document-summarization", "pretty_name": "MSLR Shared Task"} | 2022-11-24T16:34:37+00:00 |
6c3e377d049a087ca6c116e91de57e8a7673a367 |
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"mean"`, i.e. the number of documents retrieved, `k`, is set as the mean number of documents seen across examples in this dataset, in this case `k==3`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8793 | 0.7460 | 0.6403 | 0.7417 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8748 | 0.7453 | 0.6361 | 0.7442 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8775 | 0.7480 | 0.6370 | 0.7443 | | allenai/multinews_sparse_mean | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | 2022-08-26T20:42:59+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": ["news-articles-summarization"], "paperswithcode_id": "multi-news", "pretty_name": "Multi-News", "train-eval-index": [{"config": "default", "task": "summarization", "task_id": "summarization", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"document": "text", "summary": "target"}, "metrics": [{"type": "rouge", "name": "Rouge"}]}]} | 2022-11-24T21:37:31+00:00 |
21dbd148b6f8581ce774fbe1a84d225aa0dd5a06 | # 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: autoevaluate/zero-shot-classification
* Dataset: autoevaluate/zero-shot-classification-sample
* Config: autoevaluate--zero-shot-classification-sample
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-autoevaluate__zero-shot-classification-sample-18ef74e8-21 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-26T23:13:02+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["autoevaluate/zero-shot-classification-sample"], "eval_info": {"task": "text_zero_shot_classification", "model": "autoevaluate/zero-shot-classification", "metrics": [], "dataset_name": "autoevaluate/zero-shot-classification-sample", "dataset_config": "autoevaluate--zero-shot-classification-sample", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-08-26T23:14:03+00:00 |
a3692ff6d4f7958e6eea80025ac7ae9f4472cfe0 |
# Dataset Card for "EnglishNLPDataset"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/BihterDass/EnglishTextClassificationDataset]
- **Repository:** [https://github.com/BihterDass/EnglishTextClassificationDataset]
- **Size of downloaded dataset files:** 8.71 MB
- **Size of the generated dataset:** 8.71 MB
### Dataset Summary
The dataset was compiled from user comments from e-commerce sites. It consists of 10,000 validations, 10,000 tests and 80000 train data. Data were classified into 3 classes (positive(pos), negative(neg) and natural(nor). The data is available to you on github.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
#### english-dataset-v1
- **Size of downloaded dataset files:** 8.71 MB
- **Size of the generated dataset:** 8.71 MB
### Data Fields
The data fields are the same among all splits.
#### english-dataset-v-v1
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `positive` (2), `natural` (1), `negative` (0).
### Data Splits
| |train |validation|test |
|----|--------:|---------:|---------:|
|Data| 80000 | 10000 | 10000 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@PnrSvc](https://github.com/PnrSvc) for adding this dataset. | BDas/EnglishNLPDataset | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | 2022-08-27T09:58:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "multi-label-classification"], "pretty_name": "EnglishNLPDataset"} | 2022-08-27T10:13:01+00:00 |
2a76ba3097a5386ab779d20e6a9f86c14de143e0 | # 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/xlm-roberta-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 [@sakamoto](https://huggingface.co/sakamoto) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-squad-d38f255e-13865909 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-27T12:12:48+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/xlm-roberta-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"}}} | 2022-08-27T12:15:49+00:00 |
cee0bbe45cd41cfbf181459fa786cedc4f075542 | sazibc/flowers | [
"license:mit",
"region:us"
] | 2022-08-27T19:19:25+00:00 | {"license": "mit"} | 2022-08-27T19:19:25+00:00 |
|
04f6537e418eeb88863d617eb27817cc496522d7 | This dataset we prepared using the Scanned receipts OCR and information extraction(SROIE) dataset.
The SROIE dataset contains 973 scanned receipts in English language.
Cropping the bounding boxes from each of the receipts to generate this text-recognition dataset resulted in 33626 images for train set and 18704 images for the test set.
The text annotations for all the images inside a split are stored in a metadata.jsonl file.
usage:
from dataset import load_dataset
data = load_dataset("priyank-m/SROIE_2019_text_recognition")
source of raw SROIE dataset:
https://www.kaggle.com/datasets/urbikn/sroie-datasetv2 | priyank-m/SROIE_2019_text_recognition | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"text-recognition",
"recognition",
"region:us"
] | 2022-08-27T19:56:31+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["image-to-text"], "task_ids": ["image-captioning"], "pretty_name": "SROIE_2019_text_recognition", "tags": ["text-recognition", "recognition"]} | 2022-08-27T20:38:24+00:00 |
ae9e759dd31d60479354cc06e4f4291c0c27bbca |
# Unsplash Lite Dataset Photos
This dataset is linked to the Unsplash Lite dataset containing data on 25K images from Unsplash. The dataset here only includes data from a single file `photos.tsv000`. The dataset builder script streams this data directly from the Unsplash 25K dataset source.
For full details, please see the [Unsplash Dataset GitHub repo](https://github.com/unsplash/datasets), or read the preview (copied from the repo) below.
---
# The Unsplash Dataset
The Unsplash Dataset is made up of over 250,000+ contributing global photographers and data sourced from hundreds of millions of searches across a nearly unlimited number of uses and contexts. Due to the breadth of intent and semantics contained within the Unsplash dataset, it enables new opportunities for research and learning.
The Unsplash Dataset is offered in two datasets:
- the Lite dataset: available for commercial and noncommercial usage, containing 25k nature-themed Unsplash photos, 25k keywords, and 1M searches
- the Full dataset: available for noncommercial usage, containing 3M+ high-quality Unsplash photos, 5M keywords, and over 250M searches
As the Unsplash library continues to grow, we’ll release updates to the dataset with new fields and new images, with each subsequent release being [semantically versioned](https://semver.org/).
We welcome any feedback regarding the content of the datasets or their format. With your input, we hope to close the gap between the data we provide and the data that you would like to leverage. You can [open an issue](https://github.com/unsplash/datasets/issues/new/choose) to report a problem or to let us know what you would like to see in the next release of the datasets.
For more on the Unsplash Dataset, see [our announcement](https://unsplash.com/blog/the-unsplash-dataset/) and [site](https://unsplash.com/data).
## Download
### Lite Dataset
The Lite dataset contains all of the same fields as the Full dataset, but is limited to ~25,000 photos. It can be used for both commercial and non-commercial usage, provided you abide by [the terms](https://github.com/unsplash/datasets/blob/master/TERMS.md).
[⬇️ Download the Lite dataset](https://unsplash.com/data/lite/latest) [~650MB compressed, ~1.4GB raw]
### Full Dataset
The Full dataset is available for non-commercial usage and all uses must abide by [the terms](https://github.com/unsplash/datasets/blob/master/TERMS.md). To access, please go to [unsplash.com/data](https://unsplash.com/data) and request access. The dataset weighs ~20 GB compressed (~43GB raw)).
## Documentation
See the [documentation for a complete list of tables and fields](https://github.com/unsplash/datasets/blob/master/DOCS.md).
## Usage
You can follow these examples to load the dataset in these common formats:
- [Load the dataset in a PostgreSQL database](https://github.com/unsplash/datasets/tree/master/how-to/psql)
- [Load the dataset in a Python environment](https://github.com/unsplash/datasets/tree/master/how-to/python)
- [Submit an example doc](https://github.com/unsplash/datasets/blob/master/how-to/README.md#submit-an-example)
## Share your work
We're making this data open and available with the hopes of enabling researchers and developers to discover interesting and useful connections in the data.
We'd love to see what you create, whether that's a research paper, a machine learning model, a blog post, or just an interesting discovery in the data. Send us an email at [[email protected]](mailto:[email protected]).
If you're using the dataset in a research paper, you can attribute the dataset as `Unsplash Lite Dataset 1.2.0` or `Unsplash Full Dataset 1.2.0` and link to the permalink [`unsplash.com/data`](https://unsplash.com/data).
----
The Unsplash Dataset is made available for research purposes. [It cannot be used to redistribute the images contained within](https://github.com/unsplash/datasets/blob/master/TERMS.md). To use the Unsplash library in a product, see [the Unsplash API](https://unsplash.com/developers).
 | jamescalam/unsplash-25k-photos | [
"task_categories:image-to-image",
"task_categories:image-classification",
"task_categories:image-to-text",
"task_categories:text-to-image",
"task_categories:zero-shot-image-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"images",
"unsplash",
"photos",
"region:us"
] | 2022-08-27T21:01:09+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["image-to-image", "image-classification", "image-to-text", "text-to-image", "zero-shot-image-classification"], "task_ids": [], "pretty_name": "Unsplash Lite 25K Photos", "tags": ["images", "unsplash", "photos"]} | 2022-09-13T12:02:46+00:00 |
82e568dfe8ee3e016c18290dbbbddd010479eb87 | 30,000 256x256 mel spectrograms of 5 second samples that have been used in music, sourced from [WhoSampled](https://whosampled.com) and [YouTube](https://youtube.com). The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models.
```
x_res = 256
y_res = 256
sample_rate = 22050
n_fft = 2048
hop_length = 512
``` | teticio/audio-diffusion-breaks-256 | [
"task_categories:image-to-image",
"size_categories:10K<n<100K",
"audio",
"spectrograms",
"region:us"
] | 2022-08-27T21:11:40+00:00 | {"annotations_creators": [], "language_creators": [], "language": [], "license": [], "multilinguality": [], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["image-to-image"], "task_ids": [], "pretty_name": "Mel spectrograms of sampled music", "tags": ["audio", "spectrograms"]} | 2022-11-09T10:50:38+00:00 |
5cadc7b30860162ea82aa2729102c02485d152b3 | CC12M of flax-community/conceptual-captions-12 translated from English to Korean. | QuoQA-NLP/KoCC12M | [
"region:us"
] | 2022-08-28T05:30:31+00:00 | {} | 2022-08-28T05:44:47+00:00 |
71d5c298b9dc85f34b468eb393301fa436405bbb | # 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: nlpconnect/deberta-v3-xsmall-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 [@ankur310974](https://huggingface.co/ankur310974) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-squad_v2-c78baf7d-13885910 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-28T09:49:42+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "nlpconnect/deberta-v3-xsmall-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"}}} | 2022-08-28T09:52:35+00:00 |
7ad42c0cbd4e102579d6323231e05a87c739318b | # 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: nlpconnect/deberta-v3-xsmall-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 [@ankur310794](https://huggingface.co/ankur310794) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-squad-4690f1f9-13895911 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-28T09:49:47+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad"], "eval_info": {"task": "extractive_question_answering", "model": "nlpconnect/deberta-v3-xsmall-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"}}} | 2022-08-28T09:52:24+00:00 |
2afbf37414683a8ad881fe0dc8913b1f246b9aa7 | English Debate Motions gathered by University of Tokyo Debate Society
@misc{english-debate-motions-utds,
title={english-debate-motions-utds},
author={members of the University of Tokyo Debate Society},
year={2022},
}
| kokhayas/english-debate-motions-utds | [
"region:us"
] | 2022-08-28T11:54:21+00:00 | {} | 2022-08-30T02:18:43+00:00 |
72aa912bbf09c96c6cf38bb76bec24e8d8a82367 |
# Dataset Card for "UnpredicTable-full" - 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 | unpredictable/unpredictable_full | [
"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-classification",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:closed-book-qa",
"task_ids:open-book-qa",
"task_ids:language-modeling",
"task_ids:multi-class-classification",
"task_ids:natural-language-inference",
"task_ids:topic-classification",
"task_ids:multi-label-classification",
"task_ids:tabular-multi-class-classification",
"task_ids:tabular-multi-label-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-08-28T15:35:07+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "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"], "pretty_name": "UnpredicTable-full"} | 2022-08-28T17:42:31+00:00 |
ec38db9a85ca5dca7ef9211bbb73cc27e1a47208 |
# Dataset Card for "UnpredicTable-5k" - 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
| unpredictable/unpredictable_5k | [
"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-classification",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:closed-book-qa",
"task_ids:open-book-qa",
"task_ids:language-modeling",
"task_ids:multi-class-classification",
"task_ids:natural-language-inference",
"task_ids:topic-classification",
"task_ids:multi-label-classification",
"task_ids:tabular-multi-class-classification",
"task_ids:tabular-multi-label-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-08-28T16:37:14+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "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"], "pretty_name": "UnpredicTable-5k"} | 2022-08-28T17:13:41+00:00 |
76db35834d995d0bd5d14d1352277461fe3f225f |
# 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 | unpredictable/unpredictable_support-google-com | [
"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-classification",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:closed-book-qa",
"task_ids:open-book-qa",
"task_ids:language-modeling",
"task_ids:multi-class-classification",
"task_ids:natural-language-inference",
"task_ids:topic-classification",
"task_ids:multi-label-classification",
"task_ids:tabular-multi-class-classification",
"task_ids:tabular-multi-label-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-08-28T17:12:13+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "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"], "pretty_name": "UnpredicTable-support-google-com"} | 2022-08-28T17:25:26+00:00 |
7b0b1a6c2c61cc1f9304725ceb54c826be65816f |
# Dataset Card for "UnpredicTable-unique" - 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 | unpredictable/unpredictable_unique | [
"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-classification",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:closed-book-qa",
"task_ids:open-book-qa",
"task_ids:language-modeling",
"task_ids:multi-class-classification",
"task_ids:natural-language-inference",
"task_ids:topic-classification",
"task_ids:multi-label-classification",
"task_ids:tabular-multi-class-classification",
"task_ids:tabular-multi-label-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-08-28T17:12:33+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "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"], "pretty_name": "UnpredicTable-unique"} | 2022-08-28T17:26:18+00:00 |
103c2fe8cb50ef4f095da366e90254008bae0bb8 | williamlee/test2 | [
"license:apache-2.0",
"region:us"
] | 2022-08-29T01:00:50+00:00 | {"license": "apache-2.0"} | 2022-08-29T01:00:50+00:00 |
|
5f17b065b8739c725a84d3a6965ed7f040cdae04 |
The Wikipedia finetune data used to train visual features for the adaption of vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?".
The data has been created from the "20200501.en" revision of the [wikipedia dataset](https://huggingface.co/datasets/wikipedia) on Huggingface.
| Lo/adapt-pre-trained-VL-models-to-text-data-Wikipedia-finetune | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-08-29T07:17:43+00:00 | {"language": ["en"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"]} | 2022-08-29T07:27:33+00:00 |
d6fe56688ae0435f11bcc1860fe7de01e0d3ffe4 |
The LXMERT text train data used to train BERT-base baselines and adapt vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?".
The data has been created from the data made available by the [LXMERT repo](https://github.com/airsplay/lxmert).
| Lo/adapt-pre-trained-VL-models-to-text-data-LXMERT | [
"multilinguality:monolingual",
"language:en",
"license:mit",
"region:us"
] | 2022-08-29T07:19:10+00:00 | {"language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"]} | 2022-08-29T07:30:05+00:00 |
ea1623c9c1f7b042aff76cbcf1ca5c0a3ef8e114 |
The LXMERT text finetune data used to train visual features for the adaption of vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?".
The data has been created from the data made available by the [LXMERT repo](https://github.com/airsplay/lxmert).
| Lo/adapt-pre-trained-VL-models-to-text-data-LXMERT-finetune | [
"multilinguality:monolingual",
"language:en",
"license:mit",
"region:us"
] | 2022-08-29T07:20:45+00:00 | {"language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"]} | 2022-08-29T07:31:45+00:00 |
3d2ddf11220d67832edb32043e9abdbfb8d035af | ashwinperti/yelpnew | [
"license:eupl-1.1",
"region:us"
] | 2022-08-29T07:38:42+00:00 | {"license": "eupl-1.1"} | 2022-08-29T07:38:42+00:00 |
|
683b752aaead07750f544d18639ee871f912a697 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: autoevaluate/binary-classification
* Dataset: glue
* Config: sst2
* 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. | autoevaluate/autoeval-staging-eval-project-glue-f7900ebf-13965913 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T08:37:05+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "binary_classification", "model": "autoevaluate/binary-classification", "metrics": [], "dataset_name": "glue", "dataset_config": "sst2", "dataset_split": "validation", "col_mapping": {"text": "sentence", "target": "label"}}} | 2022-08-29T08:37:29+00:00 |
513ed4cfbc29df4be9c167bef472b3a4aeae7dca | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: autoevaluate/glue-mrpc
* Dataset: glue
* Config: mrpc
* 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. | autoevaluate/autoeval-staging-eval-project-glue-e9a4b61a-13985914 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T09:05:27+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "autoevaluate/glue-mrpc", "metrics": [], "dataset_name": "glue", "dataset_config": "mrpc", "dataset_split": "validation", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-29T09:05:51+00:00 |
6b3840bc7bb94a480e42c79200caf31a3b598fd1 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: autoevaluate/glue-qqp
* Dataset: glue
* Config: qqp
* 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. | autoevaluate/autoeval-staging-eval-project-glue-4805e982-13995915 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T09:05:33+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "autoevaluate/glue-qqp", "metrics": [], "dataset_name": "glue", "dataset_config": "qqp", "dataset_split": "validation", "col_mapping": {"text1": "question1", "text2": "question2", "target": "label"}}} | 2022-08-29T09:07:21+00:00 |
9cee6f8497cb95ce974e7e7e511c347c5a572d8f | # 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: autoevaluate/extractive-question-answering
* Dataset: autoevaluate/squad-sample
* Config: autoevaluate--squad-sample
* Split: test
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. | autoevaluate/autoeval-staging-eval-project-autoevaluate__squad-sample-11b52eb1-14005916 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T09:24:43+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["autoevaluate/squad-sample"], "eval_info": {"task": "extractive_question_answering", "model": "autoevaluate/extractive-question-answering", "metrics": [], "dataset_name": "autoevaluate/squad-sample", "dataset_config": "autoevaluate--squad-sample", "dataset_split": "test", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}} | 2022-08-29T09:25:07+00:00 |
ec3c96f7624cc7b419297c51779b9800826a818c | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: mrm8488/deberta-v3-small-finetuned-mrpc
* Dataset: glue
* Config: mrpc
* 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. | autoevaluate/autoeval-staging-eval-project-glue-f16e6c43-14015917 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:06:48+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "mrm8488/deberta-v3-small-finetuned-mrpc", "metrics": [], "dataset_name": "glue", "dataset_config": "mrpc", "dataset_split": "validation", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-29T11:07:18+00:00 |
64dea239da2de88405fb3120dc26f511eaff7891 | # 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: anindabitm/sagemaker-distilbert-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-emotion-af6a16fe-14025918 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:06:53+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "anindabitm/sagemaker-distilbert-emotion", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-08-29T11:07:19+00:00 |
3160df47c1c1eef5087fa86fb551b61adfe2f552 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: ARTeLab/it5-summarization-fanpage
* Dataset: samsum
* Config: samsum
* Split: test
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. | autoevaluate/autoeval-staging-eval-project-samsum-afdf25d0-14035919 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:26:34+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "ARTeLab/it5-summarization-fanpage", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2022-08-29T11:27:41+00:00 |
a4895d7e5d6f96414fce19ef999a68f0adc509e9 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch
* Dataset: samsum
* Config: samsum
* Split: test
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. | autoevaluate/autoeval-staging-eval-project-samsum-afdf25d0-14035921 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:26:43+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2022-08-29T11:29:49+00:00 |
60630ce757b999088709d5d6816592c9b7fdbd89 | # 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: Adrian/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. | autoevaluate/autoeval-staging-eval-project-squad_v2-82949658-14045922 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:26:48+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "Adrian/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"}}} | 2022-08-29T11:29:21+00:00 |
f94df08f28998f2e61b9017f89692664e0530679 | # 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: Aiyshwariya/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. | autoevaluate/autoeval-staging-eval-project-squad_v2-82949658-14045923 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:27:22+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "Aiyshwariya/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"}}} | 2022-08-29T11:30:22+00:00 |
c82c3e92c8ce1011435ff34246d830634d4f3ab3 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: Lvxue/finetuned-mt5-small-10epoch
* Dataset: wmt16
* Config: de-en
* Split: test
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. | autoevaluate/autoeval-staging-eval-project-wmt16-a5e2262a-14055924 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:27:26+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["wmt16"], "eval_info": {"task": "translation", "model": "Lvxue/finetuned-mt5-small-10epoch", "metrics": [], "dataset_name": "wmt16", "dataset_config": "de-en", "dataset_split": "test", "col_mapping": {"source": "translation.en", "target": "translation.de"}}} | 2022-08-29T11:28:47+00:00 |
d04e8305a7b1fe40ced830c06b1b435aa0252f6a | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: mrm8488/deberta-v3-small-finetuned-cola
* Dataset: glue
* Config: cola
* 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. | autoevaluate/autoeval-staging-eval-project-glue-c88eb4d4-14065928 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:27:29+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "binary_classification", "model": "mrm8488/deberta-v3-small-finetuned-cola", "metrics": [], "dataset_name": "glue", "dataset_config": "cola", "dataset_split": "validation", "col_mapping": {"text": "sentence", "target": "label"}}} | 2022-08-29T11:27:58+00:00 |
a45bcb2ef853109b882d5f6c7cb99c3bd54bb223 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: mrm8488/deberta-v3-large-finetuned-mnli
* Dataset: glue
* Config: mnli
* Split: validation_matched
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. | autoevaluate/autoeval-staging-eval-project-glue-ca80bfc9-14105932 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:27:57+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "mrm8488/deberta-v3-large-finetuned-mnli", "metrics": [], "dataset_name": "glue", "dataset_config": "mnli", "dataset_split": "validation_matched", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}} | 2022-08-29T11:31:56+00:00 |
81d0f6caa3ab9c6300a0bab43cfb0fdc10d53b05 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: mrm8488/deberta-v3-small-finetuned-qnli
* Dataset: glue
* Config: qnli
* 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. | autoevaluate/autoeval-staging-eval-project-glue-91d4fe29-14115933 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:28:08+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "mrm8488/deberta-v3-small-finetuned-qnli", "metrics": [], "dataset_name": "glue", "dataset_config": "qnli", "dataset_split": "validation", "col_mapping": {"text1": "question", "text2": "sentence", "target": "label"}}} | 2022-08-29T11:28:56+00:00 |
ca26aa07d44b0cf23ae600e6fcf1690a0c2992c5 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Intel/roberta-base-mrpc
* Dataset: glue
* Config: mrpc
* Split: train
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. | autoevaluate/autoeval-staging-eval-project-glue-f56b6c46-14085930 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:28:15+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "Intel/roberta-base-mrpc", "metrics": [], "dataset_name": "glue", "dataset_config": "mrpc", "dataset_split": "train", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-29T11:28:55+00:00 |
3034f92e343d8e9629ba792ece2bfbfb067a5181 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: JeremiahZ/roberta-base-qqp
* Dataset: glue
* Config: qqp
* 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. | autoevaluate/autoeval-staging-eval-project-glue-f1585abe-14095931 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:28:15+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "JeremiahZ/roberta-base-qqp", "metrics": [], "dataset_name": "glue", "dataset_config": "qqp", "dataset_split": "validation", "col_mapping": {"text1": "question1", "text2": "question2", "target": "label"}}} | 2022-08-29T11:31:25+00:00 |
a4c35f2ecd42cb2bfca9ea1cda04793fae25b6b9 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: mrm8488/deberta-v3-small-finetuned-sst2
* Dataset: glue
* Config: sst2
* 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. | autoevaluate/autoeval-staging-eval-project-glue-f6cacc01-14075929 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:28:20+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "binary_classification", "model": "mrm8488/deberta-v3-small-finetuned-sst2", "metrics": [], "dataset_name": "glue", "dataset_config": "sst2", "dataset_split": "validation", "col_mapping": {"text": "sentence", "target": "label"}}} | 2022-08-29T11:28:52+00:00 |
2536141082d13670fa08230b1c7f2cd4c8ad43f1 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Alireza1044/mobilebert_rte
* Dataset: glue
* Config: rte
* 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. | autoevaluate/autoeval-staging-eval-project-glue-67467c9c-14145936 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:29:17+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "Alireza1044/mobilebert_rte", "metrics": [], "dataset_name": "glue", "dataset_config": "rte", "dataset_split": "validation", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-29T11:29:38+00:00 |
b090c700a076dcf043522e5ddce467f6add05a67 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: JeremiahZ/roberta-base-rte
* Dataset: glue
* Config: rte
* 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. | autoevaluate/autoeval-staging-eval-project-glue-67467c9c-14145935 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:29:29+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "JeremiahZ/roberta-base-rte", "metrics": [], "dataset_name": "glue", "dataset_config": "rte", "dataset_split": "validation", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-29T11:30:00+00:00 |
269ed925eb51425013b692d0ac25ef66f51611d5 | # 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: mrm8488/bert-mini-finetuned-age_news-classification
* Dataset: ag_news
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-ag_news-default-684001-14155939 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T11:47:10+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["ag_news"], "eval_info": {"task": "multi_class_classification", "model": "mrm8488/bert-mini-finetuned-age_news-classification", "metrics": [], "dataset_name": "ag_news", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-08-29T11:47:36+00:00 |
01ca83ee3481af6129dca76258ee734f20013aa4 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: andi611/distilbert-base-uncased-qa-boolq
* Dataset: boolq
* Config: default
* 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. | autoevaluate/autoeval-staging-eval-boolq-default-049b58-14205948 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T13:36:04+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["boolq"], "eval_info": {"task": "natural_language_inference", "model": "andi611/distilbert-base-uncased-qa-boolq", "metrics": [], "dataset_name": "boolq", "dataset_config": "default", "dataset_split": "validation", "col_mapping": {"text1": "question", "text2": "passage", "target": "answer"}}} | 2022-08-29T13:36:34+00:00 |
b509d87f11b98dee9d10d6f037479b98824e9fbe | # 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: bergum/xtremedistil-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-emotion-default-63bd40-14245951 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T15:05:14+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "bergum/xtremedistil-emotion", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-08-29T15:05:35+00:00 |
1d6374d26b730848ac2e01cf6bbca222f6e973f1 | jonaskoenig/future_time_references | [
"license:mit",
"region:us"
] | 2022-08-29T17:08:07+00:00 | {"license": "mit"} | 2022-08-29T17:17:36+00:00 |
|
062592d41bbc04c0715c50f75184907f2adc70ca |
# Dataset Card for blogspot raw dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is a corpus of raw blogposts from [blogspot](https://blogger.com) mostly in the English language. It was obtained by scraping corpora of [webarchive](https://archive.org) and [commoncrawl](https://commoncrawl.org).
### Supported Tasks and Leaderboards
The dataset may be used for training language models or serve other research interests.
### Languages
Mostly English language, but some outliers may occur.
## Dataset Structure
[Distribution](https://huggingface.co/datasets/mschi/blogspot_raw/blob/main/blospot_comm_dist.png)
The distribution of the blog posts over time can be viewed at ./blogspot_dist_comm.png
### Data Instances
[More Information Needed]
### Data Fields
text: string
URL: string
date: string
comment: int
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
The dataset was constructed by utilizing the [WARC-dl pipeline](https://github.com/webis-de/web-archive-keras). It was executed on cluster architecture. The corpora of archive.org and commoncrawl.org contain WARC files that contain HTML which gets parsed by the pipeline. The pipeline extracts HTML from the WARC files and applies distributed filtering to efficiently filter for the desired content.
### Source Data
#### Initial Data Collection and Normalization
The corpora "corpus-commoncrawl-main-2022-05" and "corpus-iwo-internet-archive-wide00001" have been searched for the content present in this dataset.
Search terms have been inserted into the preciously mentioned pipeline to filter URLs for "blogspot.com" and characteristic timestamp information contained in the URL (e.g. "/01/2007"). The HTML documents were parsed for specific tags to obtain the timestamps. Further, the data was labeled with the "comment" label if there were some comment markers in the URL, indicating that the retrieved text is from the main text of a blog post or from the comments section. The texts are stored raw and no further processing has been done.
#### Who are the source language producers?
Since [blogspot](https://blogger.com) provides a high-level framework to allow people everywhere in the world to set up and maintain a blog, the producers of the texts may not be further specified.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
Texts are raw and unfiltered, thus personal and sensitive information, as well as explicit language, may be present in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
The retrieval of the timestamps from the HTML documents was not 100% accurate, so a small proportion of wrong or nonsense timestamps can be present in the data. Also we can not guarantee the correctness of the timestamps as well as the "comment" labels.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was constructed during the course "Big Data and Language Technologies" of the Text Mining and Retrieval Group, Department of Computer Science at the University of Leipzig.
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@jonaskonig](https://github.com/jonaskonig), [@maschirmer](https://github.com/maschirmer) and [@1BlattPapier](https://github.com/1BlattPapier) for contributing.
| mschi/blogspot_raw | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_categories:time-series-forecasting",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:mit",
"blogspot",
"blogger",
"texts",
"region:us"
] | 2022-08-29T17:19:04+00:00 | {"annotations_creators": [], "language_creators": ["other"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-classification", "text-retrieval", "text-generation", "time-series-forecasting"], "task_ids": [], "pretty_name": "Blogspot_raw_texts", "tags": ["blogspot", "blogger", "texts"]} | 2022-09-13T07:48:23+00:00 |
c8b72f8c242a0d8e052de3041c50c5a5e8f2a38e | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: test_r3
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. | autoevaluate/autoeval-staging-eval-anli-plain_text-c507f2-14355972 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T19:24:33+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["anli"], "eval_info": {"task": "natural_language_inference", "model": "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli", "metrics": [], "dataset_name": "anli", "dataset_config": "plain_text", "dataset_split": "test_r3", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}} | 2022-08-29T19:25:09+00:00 |
0aa1d1e2793c68feafc3ea0267ffbdbb6e145bd2 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: test_r2
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. | autoevaluate/autoeval-staging-eval-anli-plain_text-1f482c-14395973 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T19:37:08+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["anli"], "eval_info": {"task": "natural_language_inference", "model": "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli", "metrics": [], "dataset_name": "anli", "dataset_config": "plain_text", "dataset_split": "test_r2", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}} | 2022-08-29T19:37:44+00:00 |
53ffa6b0c5abc115794bc3ac6d4524487cf12499 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: test_r1
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. | autoevaluate/autoeval-staging-eval-anli-plain_text-dfb10f-14405974 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T19:37:13+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["anli"], "eval_info": {"task": "natural_language_inference", "model": "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli", "metrics": [], "dataset_name": "anli", "dataset_config": "plain_text", "dataset_split": "test_r1", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}} | 2022-08-29T19:37:45+00:00 |
2b52953aaf495435ed9e0a4beeaf3190b7149f09 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: multi_nli
* Config: default
* Split: validation_matched
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. | autoevaluate/autoeval-staging-eval-multi_nli-default-68c6a6-14415975 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T19:49:38+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["multi_nli"], "eval_info": {"task": "natural_language_inference", "model": "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli", "metrics": [], "dataset_name": "multi_nli", "dataset_config": "default", "dataset_split": "validation_matched", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}} | 2022-08-29T19:51:17+00:00 |
b6aeba317590bd7a8fb11ba1d41bbcb1788dd388 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: multi_nli
* Config: default
* Split: validation_mismatched
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. | autoevaluate/autoeval-staging-eval-multi_nli-default-4a02ee-14425976 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-29T19:49:40+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["multi_nli"], "eval_info": {"task": "natural_language_inference", "model": "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli", "metrics": [], "dataset_name": "multi_nli", "dataset_config": "default", "dataset_split": "validation_mismatched", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}} | 2022-08-29T19:51:17+00:00 |
4d80aed9505bdcfd4f7bfa577c66467fb71db4c2 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Intel/roberta-base-mrpc
* Dataset: glue
* Config: mrpc
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xinhe](https://huggingface.co/xinhe) for evaluating this model. | autoevaluate/autoeval-staging-eval-glue-mrpc-4a87ed-14445977 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-30T01:39:34+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "Intel/roberta-base-mrpc", "metrics": [], "dataset_name": "glue", "dataset_config": "mrpc", "dataset_split": "validation", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-30T01:40:01+00:00 |
19ab9a4e0a4ad3dce1adbc4f0e6595d7c9ebc0d9 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Intel/bert-base-uncased-mrpc
* Dataset: glue
* Config: mrpc
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xinhe](https://huggingface.co/xinhe) for evaluating this model. | autoevaluate/autoeval-staging-eval-glue-mrpc-71a11b-14455978 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-30T01:39:36+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "Intel/bert-base-uncased-mrpc", "metrics": [], "dataset_name": "glue", "dataset_config": "mrpc", "dataset_split": "validation", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-30T01:40:01+00:00 |
64a374c848cde26885e77f50fa7de87d58697d5d | # national_library_of_korea_book_info
## Dataset Description
- **Homepage** [문화 빅데이터 플랫폼](https://www.culture.go.kr/bigdata/user/data_market/detail.do?id=63513d7b-9b87-4ec1-a398-0a18ecc45411)
- **Download Size** 759 MB
- **Generated Size** 2.33 GB
- **Total Size** 3.09 GB
국립중앙도서관에서 배포한, 국립중앙도서관에서 보관중인 도서 정보에 관한 데이터.
### License
other ([KOGL](https://www.kogl.or.kr/info/license.do#05-tab) (Korea Open Government License) Type-1)
- According to above KOGL, user can use public works freely and without fee regardless of its commercial use, and can change or modify to create secondary works when user complies with the terms provided as follows:
<details>
<summary>KOGL Type 1</summary>
1. Source Indication Liability
- Users who use public works shall indicate source or copyright as follows:
- EX : “000(public institution's name)'s public work is used according to KOGL”
- The link shall be provided when online hyperlink for the source website is available.
- Marking shall not be used to misguide the third party that the user is sponsored by public institution or user has a special relationship with public institutions.
2. Use Prohibited Information
- Personal information that is protected by Personal Information Protection Act, Promotion for Information Network Use and Information Protection Act, etc.
- Credit information protected by the Use and Protection of Credit Information Act, etc.
- Military secrets protected by Military Secret Protection Act, etc.
- Information that is the object of other rights such as trademark right, design right, design right or patent right, etc., or that is owned by third party's copyright.
- Other information that is use prohibited information according to other laws.
3. Public Institution's Liability Exemption
- Public institution does not guarantee the accuracy or continued service of public works.
- Public institution and its employees do not have any liability for any kind of damage or disadvantage that may arise by using public works.
4. Effect of Use Term Violation
- The use permission is automatically terminated when user violates any of the KOGL's Use Terms, and the user shall immediately stop using public works.
</details>
## Data Structure
### Data Instance
```python
>>> from datasets import load_dataset
>>>
>>> ds = load_dataset("Bingsu/national_library_of_korea_book_info", split="train")
>>> ds
Dataset({
features: ['isbn13', 'vol', 'title', 'author', 'publisher', 'price', 'img_url', 'description'],
num_rows: 7919278
})
```
```python
>>> ds.features
{'isbn13': Value(dtype='string', id=None),
'vol': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None),
'author': Value(dtype='string', id=None),
'publisher': Value(dtype='string', id=None),
'price': Value(dtype='string', id=None),
'img_url': Value(dtype='string', id=None),
'description': Value(dtype='string', id=None)}
```
or
```python
>>> import pandas as pd
>>>
>>> url = "https://huggingface.co/datasets/Bingsu/national_library_of_korea_book_info/resolve/main/train.csv.gz"
>>> df = pd.read_csv(url, low_memory=False)
```
```python
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7919278 entries, 0 to 7919277
Data columns (total 8 columns):
# Column Dtype
--- ------ -----
0 isbn13 object
1 vol object
2 title object
3 author object
4 publisher object
5 price object
6 img_url object
7 description object
dtypes: object(8)
memory usage: 483.4+ MB
```
### Null data
```python
>>> df.isnull().sum()
isbn13 3277
vol 5933882
title 19662
author 122998
publisher 1007553
price 3096535
img_url 3182882
description 4496194
dtype: int64
```
### Note
```python
>>> df[df["description"].str.contains("[해외주문원서]", regex=False) == True].head()["description"]
10773 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
95542 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
95543 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
96606 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
96678 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
Name: description, dtype: object
```
| Bingsu/national_library_of_korea_book_info | [
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:ko",
"license:other",
"region:us"
] | 2022-08-30T04:48:26+00:00 | {"language": ["ko"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "pretty_name": "national_library_of_korea_book_info"} | 2022-08-30T07:32:14+00:00 |
0bfc5269714a8861f29c1253bf89e6465eae8ab9 |
# Dataset Card for ScandiQA
## Dataset Description
- **Repository:** <https://github.com/alexandrainst/scandi-qa>
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:[email protected])
- **Size of downloaded dataset files:** 69 MB
- **Size of the generated dataset:** 67 MB
- **Total amount of disk used:** 136 MB
### Dataset Summary
ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish
languages. All samples come from the Natural Questions (NQ) dataset, which is a large
question answering dataset from Google searches. The Scandinavian questions and answers
come from the MKQA dataset, where 10,000 NQ samples were manually translated into,
among others, Danish, Norwegian, and Swedish. However, this did not include a
translated context, hindering the training of extractive question answering models.
We merged the NQ dataset with the MKQA dataset, and extracted contexts as either "long
answers" from the NQ dataset, being the paragraph in which the answer was found, or
otherwise we extract the context by locating the paragraphs which have the largest
cosine similarity to the question, and which contains the desired answer.
Further, many answers in the MKQA dataset were "language normalised": for instance, all
date answers were converted to the format "YYYY-MM-DD", meaning that in most cases
these answers are not appearing in any paragraphs. We solve this by extending the MKQA
answers with plausible "answer candidates", being slight perturbations or translations
of the answer.
With the contexts extracted, we translated these to Danish, Swedish and Norwegian using
the [DeepL translation service](https://www.deepl.com/pro-api?cta=header-pro-api) for
Danish and Swedish, and the [Google Translation
service](https://cloud.google.com/translate/docs/reference/rest/) for Norwegian. After
translation we ensured that the Scandinavian answers do indeed occur in the translated
contexts.
As we are filtering the MKQA samples at both the "merging stage" and the "translation
stage", we are not able to fully convert the 10,000 samples to the Scandinavian
languages, and instead get roughly 8,000 samples per language. These have further been
split into a training, validation and test split, with the latter two containing
roughly 750 samples. The splits have been created in such a way that the proportion of
samples without an answer is roughly the same in each split.
### Supported Tasks and Leaderboards
Training machine learning models for extractive question answering is the intended task
for this dataset. No leaderboard is active at this point.
### Languages
The dataset is available in Danish (`da`), Swedish (`sv`) and Norwegian (`no`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 69 MB
- **Size of the generated dataset:** 67 MB
- **Total amount of disk used:** 136 MB
An example from the `train` split of the `da` subset looks as follows.
```
{
'example_id': 123,
'question': 'Er dette en test?',
'answer': 'Dette er en test',
'answer_start': 0,
'context': 'Dette er en testkontekst.',
'answer_en': 'This is a test',
'answer_start_en': 0,
'context_en': "This is a test context.",
'title_en': 'Train test'
}
```
### Data Fields
The data fields are the same among all splits.
- `example_id`: an `int64` feature.
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `answer_start`: an `int64` feature.
- `context`: a `string` feature.
- `answer_en`: a `string` feature.
- `answer_start_en`: an `int64` feature.
- `context_en`: a `string` feature.
- `title_en`: a `string` feature.
### Data Splits
| name | train | validation | test |
|----------|------:|-----------:|-----:|
| da | 6311 | 749 | 750 |
| sv | 6299 | 750 | 749 |
| no | 6314 | 749 | 750 |
## Dataset Creation
### Curation Rationale
The Scandinavian languages does not have any gold standard question answering dataset.
This is not quite gold standard, but the fact both the questions and answers are all
manually translated, it is a solid silver standard dataset.
### Source Data
The original data was collected from the [MKQA](https://github.com/apple/ml-mkqa/) and
[Natural Questions](https://ai.google.com/research/NaturalQuestions) datasets from
Apple and Google, respectively.
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) curated this dataset.
### Licensing Information
The dataset is licensed under the [CC BY-SA 4.0
license](https://creativecommons.org/licenses/by-sa/4.0/).
| alexandrainst/scandi-qa | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:mkqa",
"source_datasets:natural_questions",
"language:da",
"language:sv",
"language:no",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-08-30T08:46:59+00:00 | {"language": ["da", "sv", false], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["mkqa", "natural_questions"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "ScandiQA"} | 2023-01-16T13:51:25+00:00 |
7b18a94b22ed20a4651164cb34365c94f35441d0 | pokameswaran/ami-6h | [
"license:cc-by-4.0",
"region:us"
] | 2022-08-30T08:57:28+00:00 | {"license": "cc-by-4.0"} | 2022-08-31T08:17:59+00:00 |
|
d322d49c6234ed7c3fd867ef57a2aed1539a5b20 | roskoN/stereoset_german | [
"license:cc-by-sa-4.0",
"region:us"
] | 2022-08-30T12:49:14+00:00 | {"license": "cc-by-sa-4.0"} | 2022-08-30T13:53:55+00:00 |
|
2c1e9e1a4deba071907e637095df2467c0c29472 | # Dataset Card for Auditor_Review
This file is a copy, the original version is hosted at [data.world](https://data.world/rshah/diabetes) | demo-org/diabetes | [
"task_categories:text-classification",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"region:us"
] | 2022-08-30T20:06:15+00:00 | {"language": ["en"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "pretty_name": "Diabetes"} | 2022-08-30T20:08:59+00:00 |
03a3c90f11ff6485cd4955a23f0a6e07b5158936 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2
* Dataset: billsum
* 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 [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-billsum-default-dd3eba-14585981 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-30T23:24:17+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["billsum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2", "metrics": [], "dataset_name": "billsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "summary"}}} | 2022-08-31T06:44:21+00:00 |
c2248d5acd8782d3046775ac52db8eb3dad50305 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11
* Dataset: billsum
* 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 [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-billsum-default-3fec5f-14625986 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-30T23:51:49+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["billsum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11", "metrics": [], "dataset_name": "billsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "summary"}}} | 2022-09-01T09:02:44+00:00 |
f4f32ebb0db7da41e075f69405e7e396dd93d2d0 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
* Dataset: billsum
* 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 [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-billsum-default-3fec5f-14625985 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-30T23:51:49+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["billsum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP", "metrics": [], "dataset_name": "billsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "summary"}}} | 2022-09-01T03:09:46+00:00 |
7977d7e4d2c8bd3f9da965a99d6057387f58875a | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
* Dataset: xsum
* 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 [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-xsum-default-6f5db0-14615984 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-30T23:51:53+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}} | 2022-09-01T12:24:17+00:00 |
0cd9acdb0ea6acb0442697499b54a323105dc95d | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
* Dataset: billsum
* 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 [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-billsum-default-3fec5f-14625987 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-30T23:52:00+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["billsum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13", "metrics": [], "dataset_name": "billsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "summary"}}} | 2022-09-01T07:04:11+00:00 |
d51d497dbd52f384789619ba69627cd55541ecd9 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-samsum-samsum-f593d1-14645991 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-30T23:52:23+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2022-08-31T00:18:28+00:00 |
22b0f359dc343c3842ae0b3b25410185a06dc368 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-samsum-samsum-f593d1-14645992 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-30T23:52:23+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2022-08-31T00:33:07+00:00 |
626afad55214c9e1949031f8a19c13834f5b817f |
# Dataset Card for pixta-ai/Plane-images-in-multiple-scenes
## Dataset Description
- **Homepage:** https://www.pixta.ai/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
4,000 Plane images in multiple scenes, including multiple types of planes disproportionately, the passenger plan are the majorities.
Each image contains from 1 to 10 visible planes
For more details, please refer to the link: https://www.pixta.ai/
Or send your inquiries to [email protected]
### Supported Tasks and Leaderboards
object-detection, computer-vision: The dataset can be used to train or enhance model for object detection.
### Languages
English
### License
Academic & commercial usage | pixta-ai/Plane-images-in-multiple-scenes | [
"region:us"
] | 2022-08-31T01:43:12+00:00 | {"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]} | 2022-09-05T03:23:05+00:00 |
44488c9a08a774143dca37c60c28116c766e48fd | # 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: mrp/bert-finetuned-squad
* 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 [@saminaminaeheh](https://huggingface.co/saminaminaeheh) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad-plain_text-d52fee-14655993 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T05:42:20+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad"], "eval_info": {"task": "extractive_question_answering", "model": "mrp/bert-finetuned-squad", "metrics": ["bleu", "rouge"], "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"}}} | 2022-08-31T05:45:10+00:00 |
a8b2fb9790419752e26300ce37c9eabc36411bd4 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: sgugger/glue-mrpc
* Dataset: glue
* Config: mrpc
* 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. | autoevaluate/autoeval-staging-eval-glue-mrpc-e15d1b-14665994 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T06:30:52+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "sgugger/glue-mrpc", "metrics": [], "dataset_name": "glue", "dataset_config": "mrpc", "dataset_split": "validation", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-31T06:31:19+00:00 |
1a3a5ca04db7486f9737e64f16c54c1d2b48fba4 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Intel/camembert-base-mrpc
* Dataset: glue
* Config: mrpc
* 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. | autoevaluate/autoeval-staging-eval-glue-mrpc-e15d1b-14665997 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T06:33:16+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "Intel/camembert-base-mrpc", "metrics": [], "dataset_name": "glue", "dataset_config": "mrpc", "dataset_split": "validation", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-31T06:33:42+00:00 |
55730ed50204cd1be2d9f3d0f828b34a762f6ae9 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: sgugger/bert-finetuned-mrpc
* Dataset: glue
* Config: mrpc
* 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. | autoevaluate/autoeval-staging-eval-glue-mrpc-e15d1b-14666001 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T06:36:03+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "sgugger/bert-finetuned-mrpc", "metrics": [], "dataset_name": "glue", "dataset_config": "mrpc", "dataset_split": "validation", "col_mapping": {"text1": "sentence1", "text2": "sentence2", "target": "label"}}} | 2022-08-31T06:36:29+00:00 |
09a1805befbcdb794978a12558e99ea3d8dd2cb1 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Alireza1044/mobilebert_qqp
* Dataset: glue
* Config: qqp
* 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. | autoevaluate/autoeval-staging-eval-glue-qqp-c973af-14676003 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T06:36:15+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "Alireza1044/mobilebert_qqp", "metrics": [], "dataset_name": "glue", "dataset_config": "qqp", "dataset_split": "validation", "col_mapping": {"text1": "question1", "text2": "question2", "target": "label"}}} | 2022-08-31T06:38:38+00:00 |
0434b76db92af9825be658211a80b3ce2fcb41ba | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: gchhablani/bert-base-cased-finetuned-qqp
* Dataset: glue
* Config: qqp
* 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. | autoevaluate/autoeval-staging-eval-glue-qqp-c973af-14676011 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T06:40:25+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "gchhablani/bert-base-cased-finetuned-qqp", "metrics": [], "dataset_name": "glue", "dataset_config": "qqp", "dataset_split": "validation", "col_mapping": {"text1": "question1", "text2": "question2", "target": "label"}}} | 2022-08-31T06:43:33+00:00 |
0b092afb93ac87046ff0da854e0f025408b23915 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Alireza1044/mobilebert_mnli
* Dataset: glue
* Config: mnli
* Split: validation_matched
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. | autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686015 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T06:44:05+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "Alireza1044/mobilebert_mnli", "metrics": [], "dataset_name": "glue", "dataset_config": "mnli", "dataset_split": "validation_matched", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}} | 2022-08-31T06:44:58+00:00 |
8d30d6afd086cb75a9a24e114001dcbadd64c5b4 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Jiva/xlm-roberta-large-it-mnli
* Dataset: glue
* Config: mnli
* Split: validation_matched
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. | autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686017 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T06:45:37+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "Jiva/xlm-roberta-large-it-mnli", "metrics": [], "dataset_name": "glue", "dataset_config": "mnli", "dataset_split": "validation_matched", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}} | 2022-08-31T06:48:14+00:00 |
b55cb6fad539ade72ccb0bf50f7cc661dc764116 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: nbhimte/tiny-bert-mnli-distilled
* Dataset: glue
* Config: mnli
* Split: validation_matched
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. | autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686020 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T06:50:59+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "nbhimte/tiny-bert-mnli-distilled", "metrics": [], "dataset_name": "glue", "dataset_config": "mnli", "dataset_split": "validation_matched", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}} | 2022-08-31T06:51:25+00:00 |
2207c72eb1dfd42516e8bb8e8e428a1f15fc0f9e | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: JeremiahZ/roberta-base-qnli
* Dataset: glue
* Config: qnli
* 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. | autoevaluate/autoeval-staging-eval-glue-qnli-1747ab-14696022 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T06:53:03+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "JeremiahZ/roberta-base-qnli", "metrics": [], "dataset_name": "glue", "dataset_config": "qnli", "dataset_split": "validation", "col_mapping": {"text1": "question", "text2": "sentence", "target": "label"}}} | 2022-08-31T06:53:56+00:00 |
3efd19530c8c048329718b508bd997f08a1066ff |
This repository contains ShapeNetCore (v2) in [GLTF](https://en.wikipedia.org/wiki/GlTF) format, a subset of [ShapeNet](https://shapenet.org).
ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore are linked to an appropriate synset in [WordNet 3.0](https://wordnet.princeton.edu/).
If you use ShapeNet data, you agree to abide by the [ShapeNet terms of use](https://shapenet.org/terms). You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions.
If you use this data, please cite the main ShapeNet technical report.
```
@techreport{shapenet2015,
title = {{ShapeNet: An Information-Rich 3D Model Repository}},
author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
number = {arXiv:1512.03012 [cs.GR]},
institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago},
year = {2015}
}
```
For more information, please contact us at [email protected] and indicate ShapeNetCore v2 in the title of your email.
| ShapeNet/shapenetcore-gltf | [
"language:en",
"license:other",
"3D shapes",
"region:us"
] | 2022-08-31T07:04:32+00:00 | {"language": ["en"], "license": "other", "pretty_name": "ShapeNetCore", "tags": ["3D shapes"], "extra_gated_heading": "Acknowledge license to accept the repository", "extra_gated_prompt": "To request access to this ShapeNet repo, you will need to provide your **full name** (please provide both your first and last name), the name of your **advisor or the principal investigator (PI)** of your lab (in the PI/Advisor) fields, and the **school or company** that you are affiliated with (the **Affiliation** field). After requesting access to this ShapeNet repo, you will be considered for access approval. \n\nAfter access approval, you (the \"Researcher\") receive permission to use the ShapeNet database (the \"Database\") at Princeton University and Stanford University. In exchange for being able to join the ShapeNet community and receive such permission, Researcher hereby agrees to the following terms and conditions: Researcher shall use the Database only for non-commercial research and educational purposes. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted 3D models that he or she may create from the Database. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. The law of the State of New Jersey shall apply to all disputes under this agreement.\n\nFor access to the data, please fill in your **full name** (both first and last name), the name of your **advisor or principal investigator (PI)**, and the name of the **school or company** you are affliated with. Please actually fill out the fields (DO NOT put the word \"Advisor\" for PI/Advisor and the word \"School\" for \"Affiliation\", please specify the name of your advisor and the name of your school).", "extra_gated_fields": {"Name": "text", "PI/Advisor": "text", "Affiliation": "text", "Purpose": "text", "Country": "text", "I agree to use this dataset for non-commercial use ONLY": "checkbox"}} | 2023-09-20T14:03:13+00:00 |
75b1f32f2ebf11639ee2e1f0df219a0b9bcd1ef6 |
This repository contains ShapeNetCore (v2) in [GLB](https://en.wikipedia.org/wiki/GlTF#GLB) format, a subset of [ShapeNet](https://shapenet.org).
ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore are linked to an appropriate synset in [WordNet 3.0](https://wordnet.princeton.edu/).
If you use ShapeNet data, you agree to abide by the [ShapeNet terms of use](https://shapenet.org/terms). You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions.
If you use this data, please cite the main ShapeNet technical report.
```
@techreport{shapenet2015,
title = {{ShapeNet: An Information-Rich 3D Model Repository}},
author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
number = {arXiv:1512.03012 [cs.GR]},
institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago},
year = {2015}
}
```
For more information, please contact us at [email protected] and indicate ShapeNetCore v2 in the title of your email.
| ShapeNet/shapenetcore-glb | [
"language:en",
"license:other",
"3D shapes",
"region:us"
] | 2022-08-31T07:04:51+00:00 | {"language": ["en"], "license": "other", "pretty_name": "ShapeNetCore", "tags": ["3D shapes"], "extra_gated_heading": "Acknowledge license to accept the repository", "extra_gated_prompt": "To request access to this ShapeNet repo, you will need to provide your **full name** (please provide both your first and last name), the name of your **advisor or the principal investigator (PI)** of your lab (in the PI/Advisor) fields, and the **school or company** that you are affiliated with (the **Affiliation** field). After requesting access to this ShapeNet repo, you will be considered for access approval. \n\nAfter access approval, you (the \"Researcher\") receive permission to use the ShapeNet database (the \"Database\") at Princeton University and Stanford University. In exchange for being able to join the ShapeNet community and receive such permission, Researcher hereby agrees to the following terms and conditions: Researcher shall use the Database only for non-commercial research and educational purposes. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted 3D models that he or she may create from the Database. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. The law of the State of New Jersey shall apply to all disputes under this agreement.\n\nFor access to the data, please fill in your **full name** (both first and last name), the name of your **advisor or principal investigator (PI)**, and the name of the **school or company** you are affliated with. Please actually fill out the fields (DO NOT put the word \"Advisor\" for PI/Advisor and the word \"School\" for \"Affiliation\", please specify the name of your advisor and the name of your school).", "extra_gated_fields": {"Name": "text", "PI/Advisor": "text", "Affiliation": "text", "Purpose": "text", "Country": "text", "I agree to use this dataset for non-commercial use ONLY": "checkbox"}} | 2023-09-20T14:04:40+00:00 |
7bcd8d67060c921ea89a52433ce80e7dc753784c | # 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: autoevaluate/multi-class-classification
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-a741994f-efcd-40c8-8652-be4f42ba26cd-31 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T07:09:13+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "autoevaluate/multi-class-classification", "metrics": ["matthews_correlation"], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-08-31T07:10:00+00:00 |
9ad5d61faaa69bf55d889259015496b6d39ea90a | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: gchhablani/bert-base-cased-finetuned-qnli
* Dataset: glue
* Config: qnli
* 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. | autoevaluate/autoeval-staging-eval-glue-qnli-1747ab-14696030 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T07:09:23+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "natural_language_inference", "model": "gchhablani/bert-base-cased-finetuned-qnli", "metrics": [], "dataset_name": "glue", "dataset_config": "qnli", "dataset_split": "validation", "col_mapping": {"text1": "question", "text2": "sentence", "target": "label"}}} | 2022-08-31T07:10:09+00:00 |
adc9a8b5f8384baca023be9e41de453cdecb5c01 | lucifertrj/AnimeQuotes | [
"license:apache-2.0",
"region:us"
] | 2022-08-31T10:03:26+00:00 | {"license": "apache-2.0"} | 2022-08-31T10:03:26+00:00 |
|
17fac3405c9f2fd59b18ef5cbb6f73fede1f3c40 | cakiki/ORCAS | [
"license:cc-by-4.0",
"region:us"
] | 2022-08-31T10:34:29+00:00 | {"license": "cc-by-4.0"} | 2022-08-31T10:44:09+00:00 |
|
cc04efc6edd44fc890b7625b82e36e023a353c59 |
# Dataset Card for SANAD
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:https://data.mendeley.com/datasets/57zpx667y9/2**
### Dataset Summary
SANAD Dataset is a large collection of Arabic news articles that can be used in different Arabic NLP tasks such as Text Classification and Word Embedding. The articles were collected using Python scripts written specifically for three popular news websites: AlKhaleej, AlArabiya and Akhbarona. All datasets have seven categories [Culture, Finance, Medical, Politics, Religion, Sports and Tech], except AlArabiya which doesn’t have [Religion]. SANAD contains a total number of 190k+ articles.
### 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
license: cc-by-4.0
### Citation Information
```
@article{einea2019sanad,
title={Sanad: Single-label arabic news articles dataset for automatic text categorization},
author={Einea, Omar and Elnagar, Ashraf and Al Debsi, Ridhwan},
journal={Data in brief},
volume={25},
pages={104076},
year={2019},
publisher={Elsevier}
}
```
### Contributions
| khalidalt/SANAD | [
"license:cc-by-4.0",
"region:us"
] | 2022-08-31T12:34:53+00:00 | {"license": "cc-by-4.0"} | 2022-09-03T18:36:00+00:00 |
0ba220dc33e8abebcec92addaef1504c35cefbfd | SharedBailii/NER-BAILII-UK-CCA | [
"license:apache-2.0",
"region:us"
] | 2022-08-31T18:53:44+00:00 | {"license": "apache-2.0"} | 2022-08-31T19:01:12+00:00 |
|
a9ce33acf817e1d68c82a1fd3ab615c3515f0852 | Ramamurthi/yelp_reviews_encoded_hidden_outputs | [
"license:mit",
"region:us"
] | 2022-08-31T20:31:04+00:00 | {"license": "mit"} | 2022-08-31T20:31:04+00:00 |
|
78d2052bec6926a380c29fafca8557bced46ad43 | # 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/tinyroberta-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906065 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:49:09+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/tinyroberta-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T20:51:46+00:00 |
ce4204c2bd9b8eb2d0872b9b0ea63f0200030771 | # 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/roberta-base-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906066 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:49:15+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/roberta-base-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T20:52:06+00:00 |
101220450c4e9337566488a595372390246937c9 | # 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/roberta-large-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906067 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:49:20+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/roberta-large-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T20:53:49+00:00 |
72b9520267fa0633669d76cdf4968d6c25521b96 | # 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/xlm-roberta-base-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906068 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:52:17+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/xlm-roberta-base-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T20:55:28+00:00 |
cca945ceb6b114937af9e69853666dc3d12ef1c0 | # 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/xlm-roberta-large-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906069 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:52:38+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/xlm-roberta-large-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T20:57:53+00:00 |
b66f3c90f539de1eb33ae4b3b6e84c86e67d644a | # 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/roberta-base-squad2-covid
* 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906070 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:54:22+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/roberta-base-squad2-covid", "metrics": ["bertscore"], "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"}}} | 2022-08-31T20:57:24+00:00 |
ec55bc782a252819ffe12f8097640286e5130157 | # 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/roberta-base-squad2-distilled
* 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906071 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:55:58+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/roberta-base-squad2-distilled", "metrics": ["bertscore"], "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"}}} | 2022-08-31T20:58:49+00:00 |
85b74f86f553a969c7d22d22ee177c07739ede2f | # 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/xlm-roberta-base-squad2-distilled
* 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906072 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:57:56+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/xlm-roberta-base-squad2-distilled", "metrics": ["bertscore"], "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"}}} | 2022-08-31T21:01:20+00:00 |
6441cc0b487b62b88a44999da1d1a6df5051db1d | # 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/bert-base-cased-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916074 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:58:30+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/bert-base-cased-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T21:01:33+00:00 |
9e7039c7a58178ec63a3938b449bbd35ebf912df | # 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: deepakvk/roberta-base-squad2-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906073 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T20:59:21+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepakvk/roberta-base-squad2-finetuned-squad", "metrics": ["bertscore"], "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"}}} | 2022-08-31T21:02:14+00:00 |
30825b4b2d8e9b5671ec15a8218bdda56f470b0b | # 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/bert-medium-squad2-distilled
* 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916077 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T21:01:55+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/bert-medium-squad2-distilled", "metrics": ["bertscore"], "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"}}} | 2022-08-31T21:04:31+00:00 |
ac5b4b0694f05ab94ed402208b645204dbc7f685 | # 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/bert-base-uncased-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916076 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T21:03:09+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/bert-base-uncased-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T21:06:09+00:00 |
70eb6800ed3b65b6ef9c1b424928669979a9e322 | # 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/bert-large-uncased-whole-word-masking-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916078 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T21:05:09+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/bert-large-uncased-whole-word-masking-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T21:10:07+00:00 |
4ae1a5e50013521e0d49bacbc0e4759230b2e0c7 | # 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-large-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916080 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T21:06:43+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/deberta-v3-large-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T21:13:11+00:00 |
2455dc91a08af79fa79ed41e9a60ceec159629c0 | # 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916075 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T21:09:08+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "deepset/tinybert-6l-768d-squad2", "metrics": ["bertscore"], "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"}}} | 2022-08-31T21:11:47+00:00 |
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