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sayan1101/final_summarize | 2023-10-17T17:55:29.000Z | [
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# Dataset Card for "final_summarize"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 453 | [
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hf-internal-testing/fixtures-captioning | 2023-11-02T10:43:47.000Z | [
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kjappelbaum/chemnlp-opv | 2023-10-18T14:09:11.000Z | [
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Santp98/processed_licibert_dataset | 2023-10-20T00:27:07.000Z | [
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# Dataset Card for "processed_licibert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 610 | [
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NoahBSchwartz/RLHF_with_Open_Ended_and_Multiple_Choice | 2023-10-18T21:27:19.000Z | [
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phanvancongthanh/enamine_diversity_standardized | 2023-10-19T06:22:56.000Z | [
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# Dataset Card for "enamine_diversity_standardized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 489 | [
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jamestalentium/cnn_dailymail_10_finetune | 2023-10-21T02:10:01.000Z | [
"region:us"
] | jamestalentium | null | null | 0 | 9 | 2023-10-21T01:42:07 | ---
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# Dataset Card for "cnn_dailymail_10_finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 546 | [
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jamestalentium/cnn_dailymail_10_rm | 2023-10-21T02:10:03.000Z | [
"region:us"
] | jamestalentium | null | null | 0 | 9 | 2023-10-21T01:42:09 | ---
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# Dataset Card for "cnn_dailymail_10_rm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 540 | [
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jamestalentium/cnn_dailymail_10_test | 2023-10-21T02:10:03.000Z | [
"region:us"
] | jamestalentium | null | null | 0 | 9 | 2023-10-21T01:42:10 | ---
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# Dataset Card for "cnn_dailymail_10_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 537 | [
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jamestalentium/dialogsum_10_finetune | 2023-10-21T02:10:10.000Z | [
"region:us"
] | jamestalentium | null | null | 0 | 9 | 2023-10-21T01:42:20 | ---
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# Dataset Card for "dialogsum_10_finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 576 | [
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jamestalentium/dialogsum_10_rm | 2023-10-21T02:10:11.000Z | [
"region:us"
] | jamestalentium | null | null | 0 | 9 | 2023-10-21T01:42:22 | ---
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# Dataset Card for "dialogsum_10_rm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 570 | [
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jamestalentium/dialogsum_10_test | 2023-10-21T02:10:12.000Z | [
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] | jamestalentium | null | null | 0 | 9 | 2023-10-21T01:42:23 | ---
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# Dataset Card for "dialogsum_10_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 553 | [
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jamestalentium/xsum_10_finetune | 2023-10-21T02:10:19.000Z | [
"region:us"
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# Dataset Card for "xsum_10_finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 539 | [
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jamestalentium/xsum_10_rm | 2023-10-21T02:10:21.000Z | [
"region:us"
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# Dataset Card for "xsum_10_rm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 533 | [
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jamestalentium/xsum_10_test | 2023-10-21T02:10:21.000Z | [
"region:us"
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# Dataset Card for "xsum_10_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 530 | [
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adityarra07/test_GPT_1000 | 2023-10-22T13:35:16.000Z | [
"region:us"
] | adityarra07 | null | null | 0 | 9 | 2023-10-22T13:35:11 | ---
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# Dataset Card for "test_GPT_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 488 | [
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datkai/final_news_vnexpress | 2023-10-22T14:13:15.000Z | [
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AdapterOcean/physics_dataset_standardized_cluster_1 | 2023-10-23T01:52:03.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 9 | 2023-10-22T18:30:48 | ---
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# Dataset Card for "physics_dataset_standardized_cluster_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 582 | [
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AdapterOcean/biology_dataset_standardized_cluster_1 | 2023-10-23T14:45:01.000Z | [
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# Dataset Card for "biology_dataset_standardized_cluster_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 581 | [
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sminchoi/guanaco-llama2-test | 2023-10-23T07:26:25.000Z | [
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StivenLancheros/christmas-edits_dataset2 | 2023-10-23T10:35:18.000Z | [
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# Dataset Card for "christmas-edits_dataset2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 473 | [
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Alamerton/pangolin-synthetic-sycophancy | 2023-10-23T10:58:46.000Z | [
"region:us"
] | Alamerton | null | null | 0 | 9 | 2023-10-23T10:57:59 | Entry not found | 15 | [
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mstz/victorian_authorship | 2023-10-26T14:10:56.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"victorian",
"text-classification",
"region:us"
] | mstz | null | @phdthesis{gungor2018benchmarking,
title={Benchmarking authorship attribution techniques using over a thousand books by fifty victorian era novelists},
author={Gungor, Abdulmecit},
year={2018},
school={Purdue University}
} | 0 | 9 | 2023-10-23T12:27:28 | ---
language:
- en
tags:
- victorian
- text-classification
pretty_name: Victorian authorship
size_categories:
- 10K<n<100K
task_categories:
- text-classification
license: cc
---
# Victorian authorship
The [Victorian authorship dataset](https://scholarworks.iupui.edu/server/api/core/bitstreams/708a9870-915e-4d59-b54d-938af563c196/content).
Which Victorian author wrote the given text?
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| authorship | Classification | Which Victorian author wrote the given text?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/victorian_authorship", "authorship")["train"]
```
# Features
|**Feature** |**Type** |
|-------------------|---------------|
| text | `[string]` |
# Citation
Cite this dataset as
```
@phdthesis{gungor2018benchmarking,
title={Benchmarking authorship attribution techniques using over a thousand books by fifty victorian era novelists},
author={Gungor, Abdulmecit},
year={2018},
school={Purdue University}
}
``` | 1,273 | [
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am96149/first | 2023-11-01T10:21:47.000Z | [
"region:us"
] | am96149 | null | null | 0 | 9 | 2023-10-23T13:39:20 | This dataset is a subset of the Open Assistant dataset, which you can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
This dataset was used to train Guanaco with QLoRA.
For further information, please see the original dataset.
License: Apache 2.0 | 395 | [
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royzhong/cve-2023-llama2 | 2023-10-24T00:20:03.000Z | [
"region:us"
] | royzhong | null | null | 0 | 9 | 2023-10-24T00:19:40 | Entry not found | 15 | [
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lzy337/attack_data_hf | 2023-10-24T12:24:10.000Z | [
"region:us"
] | lzy337 | null | null | 0 | 9 | 2023-10-24T11:35:51 | ---
configs:
- config_name: default
data_files:
- split: train
path:
- toxicity/toxic.jsonl.gpt3.n=25.out1.split.annotated.jsonl.filtered_train.jsonl
- split: test
path:
- toxicity/toxic.jsonl.gpt3.n=25.out1.split.annotated.jsonl.filtered_test.jsonl
- split: dev
path:
- toxicity/toxic.jsonl.gpt3.n=25.out1.split.annotated.jsonl.filtered_dev.jsonl
---
Toxicity contail three types of data. 1. from realtoxicty prompt .2 response from gpt3.5 generation as prompt 3. same as 2 but it comes from gpt4
| 534 | [
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sunjun/pubmedqa_sj | 2023-10-24T12:51:58.000Z | [
"region:us"
] | sunjun | null | null | 0 | 9 | 2023-10-24T12:51:52 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: QUESTION
dtype: string
- name: CONTEXT
dtype: string
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dtype: string
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dtype: string
- name: context
dtype: string
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sequence: string
- name: answer_index
dtype: int64
splits:
- name: validation
num_bytes: 1454799
num_examples: 500
- name: test
num_bytes: 1477607
num_examples: 500
download_size: 1667194
dataset_size: 2932406
---
# Dataset Card for "pubmedqa_sj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 786 | [
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quyanh/dataset-helm | 2023-10-25T08:45:57.000Z | [
"region:us"
] | quyanh | null | null | 0 | 9 | 2023-10-24T15:31:31 | Entry not found | 15 | [
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Intuit-GenSRF/combined_toxicity_profanity_v2_eval_only | 2023-10-24T17:44:29.000Z | [
"region:us"
] | Intuit-GenSRF | null | null | 0 | 9 | 2023-10-24T17:44:15 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: string
- name: encoded_labels
sequence: int64
splits:
- name: train
num_bytes: 313551093
num_examples: 710497
download_size: 161511342
dataset_size: 313551093
---
# Dataset Card for "combined_toxicity_profanity_v2_eval_only"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 559 | [
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M-A-D/DarEn-space-test | 2023-10-25T11:15:19.000Z | [
"region:us"
] | M-A-D | null | null | 0 | 9 | 2023-10-25T10:30:55 | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: translation
dtype: string
- name: translated
dtype: bool
- name: corrected
dtype: bool
- name: correction
dtype: float64
splits:
- name: train
num_bytes: 725711
num_examples: 5583
download_size: 0
dataset_size: 725711
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "DarEn-space-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 593 | [
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absanjay/spam-test-data | 2023-10-25T16:59:11.000Z | [
"region:us"
] | absanjay | null | null | 0 | 9 | 2023-10-25T16:56:34 | Entry not found | 15 | [
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0.0379... |
GHOFRANEE/SOPHIAE | 2023-10-26T09:04:38.000Z | [
"region:us"
] | GHOFRANEE | null | null | 0 | 9 | 2023-10-26T07:13:58 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 6853714.0
num_examples: 94
- name: validation
num_bytes: 6853714.0
num_examples: 94
- name: test
num_bytes: 6853714.0
num_examples: 94
download_size: 5009808
dataset_size: 20561142.0
---
# Dataset Card for "SOPHIAE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 695 | [
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H4438/multi-choices-text | 2023-10-28T07:11:04.000Z | [
"region:us"
] | H4438 | null | null | 0 | 9 | 2023-10-27T06:35:45 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: question
dtype: string
- name: options
list:
- name: answer
dtype: string
- name: key
dtype: string
- name: answer
struct:
- name: answer
dtype: string
- name: key
dtype: string
- name: solution
dtype: string
- name: type
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
- name: grade
dtype: string
- name: subject
dtype: string
splits:
- name: train
num_bytes: 118528289
num_examples: 68953
download_size: 61685616
dataset_size: 118528289
---
# Dataset Card for "multi-choices-text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 875 | [
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toilaluan/t2i_reward | 2023-10-28T07:27:46.000Z | [
"region:us"
] | toilaluan | null | null | 0 | 9 | 2023-10-27T07:35:19 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: model_type
dtype: string
- name: request_id
dtype: int64
- name: topic
dtype: string
- name: reward
dtype: float64
- name: individual_rewards
struct:
- name: image_rewarder
dtype: float64
- name: hps_v2_rewarder
dtype: float64
splits:
- name: train
num_bytes: 154200
num_examples: 1800
download_size: 36440
dataset_size: 154200
---
# Dataset Card for "t2i_reward"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 686 | [
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M0hammed87/DictionaryTrain | 2023-10-31T01:19:58.000Z | [
"region:us"
] | M0hammed87 | null | null | 0 | 9 | 2023-10-28T10:33:23 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
---
# Guanaco-1k: Lazy Llama 2 Formatting
This is a subset (1000 samples) of the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing).
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.
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marcus2000/sentiment2to1 | 2023-10-29T20:52:55.000Z | [
"region:us"
] | marcus2000 | null | null | 0 | 9 | 2023-10-29T20:50:15 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: string
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dtype: int64
splits:
- name: train
num_bytes: 4281800
num_examples: 3350
- name: test
num_bytes: 441642
num_examples: 373
download_size: 2338740
dataset_size: 4723442
---
# Dataset Card for "sentiment2to1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 571 | [
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toilaluan/t2i_reward_v4 | 2023-10-30T09:19:31.000Z | [
"region:us"
] | toilaluan | null | null | 0 | 9 | 2023-10-30T03:45:50 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: model_type
dtype: string
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dtype: int64
- name: topic
dtype: string
- name: reward
dtype: float64
- name: individual_rewards
struct:
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dtype: float64
- name: pick_rewarder
dtype: float64
- name: image_rewarder
dtype: float64
- name: hps_v2_rewarder
dtype: float64
splits:
- name: train
num_bytes: 115800
num_examples: 1125
download_size: 43681
dataset_size: 115800
---
# Dataset Card for "t2i_reward_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 793 | [
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kejian/SciReviewGen | 2023-11-01T02:29:09.000Z | [
"region:us"
] | kejian | null | null | 0 | 9 | 2023-10-31T03:15:07 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 1017206768
num_examples: 84705
- name: validation
num_bytes: 52660512
num_examples: 4410
- name: test
num_bytes: 54202617
num_examples: 4457
download_size: 507188880
dataset_size: 1124069897
---
# Dataset Card for "SciReviewGen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 707 | [
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Neurogpt/autotrain-data-stroke-classifier | 2023-10-31T08:25:11.000Z | [
"task_categories:image-classification",
"region:us"
] | Neurogpt | null | null | 0 | 9 | 2023-10-31T07:55:36 | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: stroke-classifier
## Dataset Description
This dataset has been automatically processed by AutoTrain for project stroke-classifier.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<233x197 L PIL image>",
"target": 0
},
{
"image": "<233x197 L PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['notStroke', 'stroke'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1600 |
| valid | 945 |
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minoosh/shEMO_transcripts | 2023-10-31T20:20:42.000Z | [
"region:us"
] | minoosh | null | null | 0 | 9 | 2023-10-31T20:20:26 | ---
dataset_info:
features:
- name: transcription
dtype: string
- name: emotion
dtype:
class_label:
names:
'0': A
'1': H
'2': N
'3': S
'4': W
'5': F
splits:
- name: train
num_bytes: 255721.6
num_examples: 2400
- name: test
num_bytes: 31965.2
num_examples: 300
- name: valid
num_bytes: 31965.2
num_examples: 300
download_size: 173563
dataset_size: 319652.0
---
# Dataset Card for "shEMO_transcripts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 655 | [
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research-dump/mcqa_hoax_1h10r_def_bigbench | 2023-10-31T21:53:23.000Z | [
"region:us"
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derekiya/bigquery-v1 | 2023-11-01T07:12:47.000Z | [
"region:us"
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Arabic-Clip/mscoco_captions_en_ar_ViT_B_16_plus_240_1st_caption | 2023-11-01T14:11:40.000Z | [
"region:us"
] | Arabic-Clip | null | null | 0 | 9 | 2023-11-01T14:10:51 | Entry not found | 15 | [
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tyzhu/squad_train50000_eval1000_dec | 2023-11-02T10:04:58.000Z | [
"region:us"
] | tyzhu | null | null | 0 | 9 | 2023-11-02T10:04:45 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: text
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: validation
num_bytes: 3184837
num_examples: 1000
- name: train
num_bytes: 169722340
num_examples: 50000
download_size: 35308668
dataset_size: 172907177
---
# Dataset Card for "squad_train50000_eval1000_dec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 937 | [
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midas/nus | 2022-03-05T03:35:59.000Z | [
"region:us"
] | midas | \ | @InProceedings{10.1007/978-3-540-77094-7_41,
author="Nguyen, Thuy Dung
and Kan, Min-Yen",
editor="Goh, Dion Hoe-Lian
and Cao, Tru Hoang
and Solvberg, Ingeborg Torvik
and Rasmussen, Edie",
title="Keyphrase Extraction in Scientific Publications",
booktitle="Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers",
year="2007",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="317--326",
isbn="978-3-540-77094-7"
} | 0 | 8 | 2022-03-02T23:29:22 | ## Dataset Summary
A dataset for benchmarking keyphrase extraction and generation techniques from long document english scientific papers. For more details about the dataset please refer the original paper - [https://www.comp.nus.edu.sg/~kanmy/papers/icadl2007.pdf](https://www.comp.nus.edu.sg/~kanmy/papers/icadl2007.pdf)
Original source of the data - []()
## Dataset Structure
### Data Fields
- **id**: unique identifier of the document.
- **document**: Whitespace separated list of words in the document.
- **doc_bio_tags**: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| #datapoints |
|--|--|
| Test | 211 |
- Percentage of keyphrases that are named entities: 67.95% (named entities detected using scispacy - en-core-sci-lg model)
- Percentage of keyphrases that are noun phrases: 82.16% (noun phrases detected using spacy en-core-web-lg after removing determiners)
## Usage
### Full Dataset
```python
from datasets import load_dataset
# get entire dataset
dataset = load_dataset("midas/nus", "raw")
# sample from the test split
print("Sample from test dataset split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
Sample from test data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['Learning', 'Spatially', 'Variant', 'Dissimilarity', '-LRB-', 'Svad', '-RRB-', 'Measures', 'Clustering', 'algorithms', 'typically', 'operate', 'on', 'a', 'feature', 'vector', 'representation', 'of', 'the', 'data', 'and', 'find', 'clusters', 'that', 'are', 'compact', 'with', 'respect', 'to', 'an', 'assumed', '-LRB-', 'dis', '-RRB-', 'similarity', 'measure', 'between', 'the', 'data', 'points', 'in', 'feature', 'space', '.', 'This', 'makes', 'the', 'type', 'of', 'clusters', 'identified', 'highly', 'dependent', 'on', 'the', 'assumed', 'similarity', 'measure', '.', 'Building', 'on', 'recent', 'work', 'in', 'this', 'area', ',', 'we', 'formally', 'define', 'a', 'class', 'of', 'spatially', 'varying', 'dissimilarity', 'measures', 'and', 'propose', 'algorithms', 'to', 'learn', 'the', 'dissimilarity', 'measure', 'automatically', 'from', 'the', 'data', '.', 'The', 'idea', 'is', 'to', 'identify', 'clusters', 'that', 'are', 'compact', 'with', 'respect', 'to', 'the', 'unknown', 'spatially', 'varying', 'dissimilarity', 'measure', '.', 'Our', 'experiments', 'show', 'that', 'the', 'proposed', 'algorithms', 'are', 'more', 'stable', 'and', 'achieve', 'better', 'accuracy', 'on', 'various', 'textual', 'data', 'sets', 'when', 'compared', 'with', 'similar', 'algorithms', 'proposed', 'in', 'the', 'literature', '.', 'H.', '2.8', '-LSB-', 'Database', 'Management', '-RSB-', ':', 'Database', 'Applications-Data', 'Mining', 'Algorithms', 'Clustering', 'plays', 'a', 'major', 'role', 'in', 'data', 'mining', 'as', 'a', 'tool', 'to', 'discover', 'structure', 'in', 'data', '.', 'Object', 'clustering', 'algorithms', 'operate', 'on', 'a', 'feature', 'vector', 'representation', 'of', 'the', 'data', 'and', 'find', 'clusters', 'that', 'are', 'compact', 'with', 'respect', 'to', 'an', 'assumed', '-LRB-', 'dis', '-RRB-', 'similarity', 'measure', 'between', 'the', 'data', 'points', 'in', 'feature', 'space', '.', 'As', 'a', 'consequence', ',', 'the', 'nature', 'of', 'clusters', 'identified', 'by', 'a', 'clustering', 'algorithm', 'is', 'highly', 'dependent', 'on', 'the', 'assumed', 'similarity', 'measure', '.', 'The', 'most', 'commonly', 'used', 'dissimilarity', 'measure', ',', 'namely', 'the', 'Euclidean', 'metric', ',', 'assumes', 'that', 'the', 'dissimilarity', 'measure', 'is', 'isotropic', 'and', 'spatially', 'invariant', ',', 'and', 'Permission', 'to', 'make', 'digital', 'or', 'hard', 'copies', 'of', 'all', 'or', 'part', 'of', 'this', 'work', 'for', 'personal', 'or', 'classroom', 'use', 'is', 'granted', 'without', 'fee', 'provided', 'that', 'copies', 'are', 'not', 'made', 'or', 'distributed', 'for', 'profit', 'or', 'commercial', 'advantage', 'and', 'that', 'copies', 'bear', 'this', 'notice', 'and', 'the', 'full', 'citation', 'on', 'the', 'first', 'page', '.', 'To', 'copy', 'otherwise', ',', 'to', 'republish', ',', 'to', 'post', 'on', 'servers', 'or', 'to', 'redistribute', 'to', 'lists', ',', 'requires', 'prior', 'specific', 'permission', 'and/or', 'a', 'fee', '.', 'KDD', "'", '04', ',', 'August', '22', '25', ',', '2004', ',', 'Seattle', ',', 'Washington', ',', 'USA', '.', 'Copyright', '2004', 'ACM', '1-58113-888-1', '/', '04/0008', '...', '$', '5.00', '.', 'it', 'is', 'effective', 'only', 'when', 'the', 'clusters', 'are', 'roughly', 'spherical', 'and', 'all', 'of', 'them', 'have', 'approximately', 'the', 'same', 'size', ',', 'which', 'is', 'rarely', 'the', 'case', 'in', 'practice', '-LSB-', '8', '-RSB-', '.', 'The', 'problem', 'of', 'finding', 'non-spherical', 'clusters', 'is', 'often', 'addressed', 'by', 'utilizing', 'a', 'feature', 'weighting', 'technique', '.', 'These', 'techniques', 'discover', 'a', 'single', 'set', 'of', 'weights', 'such', 'that', 'relevant', 'features', 'are', 'given', 'more', 'importance', 'than', 'irrelevant', 'features', '.', 'However', ',', 'in', 'practice', ',', 'each', 'cluster', 'may', 'have', 'a', 'different', 'set', 'of', 'relevant', 'features', '.', 'We', 'consider', 'Spatially', 'Varying', 'Dissimilarity', '-LRB-', 'SVaD', '-RRB-', 'measures', 'to', 'address', 'this', 'problem', '.', 'Diday', 'et', '.', 'al.', '-LSB-', '4', '-RSB-', 'proposed', 'the', 'adaptive', 'distance', 'dynamic', 'clusters', '-LRB-', 'ADDC', '-RRB-', 'algorithm', 'in', 'this', 'vain', '.', 'A', 'fuzzified', 'version', 'of', 'ADDC', ',', 'popularly', 'known', 'as', 'the', 'Gustafson-Kessel', '-LRB-', 'GK', '-RRB-', 'algorithm', '-LSB-', '7', '-RSB-', 'uses', 'a', 'dynamically', 'updated', 'covariance', 'matrix', 'so', 'that', 'each', 'cluster', 'can', 'have', 'its', 'own', 'norm', 'matrix', '.', 'These', 'algorithms', 'can', 'deal', 'with', 'hyperelliposoidal', 'clusters', 'of', 'various', 'sizes', 'and', 'orientations', '.', 'The', 'EM', 'algorithm', '-LSB-', '2', '-RSB-', 'with', 'Gaussian', 'probability', 'distributions', 'can', 'also', 'be', 'used', 'to', 'achieve', 'similar', 'results', '.', 'However', ',', 'the', 'above', 'algorithms', 'are', 'computationally', 'expensive', 'for', 'high-dimensional', 'data', 'since', 'they', 'invert', 'covariance', 'matrices', 'in', 'every', 'iteration', '.', 'Moreover', ',', 'matrix', 'inversion', 'can', 'be', 'unstable', 'when', 'the', 'data', 'is', 'sparse', 'in', 'relation', 'to', 'the', 'dimensionality', '.', 'One', 'possible', 'solution', 'to', 'the', 'problems', 'of', 'high', 'computation', 'and', 'instability', 'arising', 'out', 'of', 'using', 'covariance', 'matrices', 'is', 'to', 'force', 'the', 'matrices', 'to', 'be', 'diagonal', ',', 'which', 'amounts', 'to', 'weighting', 'each', 'feature', 'differently', 'in', 'different', 'clusters', '.', 'While', 'this', 'restricts', 'the', 'dissimilarity', 'measures', 'to', 'have', 'axis', 'parallel', 'isometry', ',', 'the', 'weights', 'also', 'provide', 'a', 'simple', 'interpretation', 'of', 'the', 'clusters', 'in', 'terms', 'of', 'relevant', 'features', ',', 'which', 'is', 'important', 'in', 'knowledge', 'discovery', '.', 'Examples', 'of', 'such', 'algorithms', 'are', 'SCAD', 'and', 'Fuzzy-SKWIC', '-LSB-', '5', ',', '6', '-RSB-', ',', 'which', 'perform', 'fuzzy', 'clustering', 'of', 'data', 'while', 'simultaneously', 'finding', 'feature', 'weights', 'in', 'individual', 'clusters', '.', 'In', 'this', 'paper', ',', 'we', 'generalize', 'the', 'idea', 'of', 'the', 'feature', 'weighting', 'approach', 'to', 'define', 'a', 'class', 'of', 'spatially', 'varying', 'dissimilarity', 'measures', 'and', 'propose', 'algorithms', 'that', 'learn', 'the', 'dissimilarity', 'measure', 'automatically', 'from', 'the', 'given', 'data', 'while', 'performing', 'the', 'clustering', '.', 'The', 'idea', 'is', 'to', 'identify', 'clusters', 'inherent', 'in', 'the', 'data', 'that', 'are', 'compact', 'with', 'respect', 'to', 'the', 'unknown', 'spatially', 'varying', 'dissimilarity', 'measure', '.', 'We', 'compare', 'the', 'proposed', 'algorithms', 'with', 'a', 'diagonal', 'version', 'of', 'GK', '-LRB-', 'DGK', '-RRB-', 'and', 'a', 'crisp', 'version', 'of', 'SCAD', '-LRB-', 'CSCAD', '-RRB-', 'on', 'a', 'variety', 'of', 'data', 'sets', '.', 'Our', 'algorithms', 'perform', 'better', 'than', 'DGK', 'and', 'CSCAD', ',', 'and', 'use', 'more', 'stable', 'update', 'equations', 'for', 'weights', 'than', 'CSCAD', '.', 'The', 'rest', 'of', 'the', 'paper', 'is', 'organized', 'as', 'follows', '.', 'In', 'the', 'next', 'section', ',', 'we', 'define', 'a', 'general', 'class', 'of', 'dissimilarity', 'measures', '611', 'Research', 'Track', 'Poster', 'and', 'formulate', 'two', 'objective', 'functions', 'based', 'on', 'them', '.', 'In', 'Section', '3', ',', 'we', 'derive', 'learning', 'algorithms', 'that', 'optimize', 'the', 'objective', 'functions', '.', 'We', 'present', 'an', 'experimental', 'study', 'of', 'the', 'proposed', 'algorithms', 'in', 'Section', '4', '.', 'We', 'compare', 'the', 'performance', 'of', 'the', 'proposed', 'algorithms', 'with', 'that', 'of', 'DGK', 'and', 'CSCAD', '.', 'These', 'two', 'algorithms', 'are', 'explained', 'in', 'Appendix', 'A.', 'Finally', ',', 'we', 'summarize', 'our', 'contributions', 'and', 'conclude', 'with', 'some', 'future', 'directions', 'in', 'Section', '5', '.', 'We', 'first', 'define', 'a', 'general', 'class', 'of', 'dissimilarity', 'measures', 'and', 'formulate', 'a', 'few', 'objective', 'functions', 'in', 'terms', 'of', 'the', 'given', 'data', 'set', '.', 'Optimization', 'of', 'the', 'objective', 'functions', 'would', 'result', 'in', 'learning', 'the', 'underlying', 'dissimilarity', 'measure', '.', '2.1', 'SVaD', 'Measures', 'In', 'the', 'following', 'definition', ',', 'we', 'generalize', 'the', 'concept', 'of', 'dissimilarity', 'measures', 'in', 'which', 'the', 'weights', 'associated', 'with', 'features', 'change', 'over', 'feature', 'space', '.', 'Definition', '2.1', 'We', 'define', 'the', 'measure', 'of', 'dissimilarity', 'of', 'x', 'from', 'y', '1', 'to', 'be', 'a', 'weighted', 'sum', 'of', 'M', 'dissimilarity', 'measures', 'between', 'x', 'and', 'y', 'where', 'the', 'values', 'of', 'the', 'weights', 'depend', 'on', 'the', 'region', 'from', 'which', 'the', 'dissimilarity', 'is', 'being', 'measured', '.', 'Let', 'P', '=', '-LCB-', 'R', '1', ',', '...', ',', 'R', 'K', '-RCB-', 'be', 'a', 'collection', 'of', 'K', 'regions', 'that', 'partition', 'the', 'feature', 'space', ',', 'and', 'w', '1', ',', 'w', '2', ',', '...', ',', 'and', 'w', 'K', 'be', 'the', 'weights', 'associated', 'with', 'R', '1', ',', 'R', '2', ',', '...', ',', 'and', 'R', 'K', ',', 'respectively', '.', 'Let', 'g', '1', ',', 'g', '2', ',', '...', ',', 'and', 'g', 'M', 'be', 'M', 'dissimilarity', 'measures', '.', 'Then', ',', 'each', 'w', 'j', ',', 'j', '=', '1', ',', '...', ',', 'K', ',', 'is', 'an', 'M', '-', 'dimensional', 'vector', 'where', 'its', 'l-th', 'component', ',', 'w', 'jl', 'is', 'associated', 'with', 'g', 'l', '.', 'Let', 'W', 'denote', 'the', 'K-tuple', '-LRB-', 'w', '1', ',', '...', ',', 'w', 'K', '-RRB-', 'and', 'let', 'r', 'be', 'a', 'real', 'number', '.', 'Then', ',', 'the', 'dissimilarity', 'of', 'x', 'from', 'y', 'is', 'given', 'by', ':', 'f', 'W', '-LRB-', 'x', ',', 'y', '-RRB-', '=', 'M', 'l', '=', '1', 'w', 'r', 'jl', 'g', 'l', '-LRB-', 'x', ',', 'y', '-RRB-', ',', 'if', 'y', 'R', 'j', '.', '-LRB-', '1', '-RRB-', 'We', 'refer', 'to', 'f', 'W', 'as', 'a', 'Spatially', 'Variant', 'Dissimilarity', '-LRB-', 'SVaD', '-RRB-', 'measure', '.', 'Note', 'that', 'f', 'W', 'need', 'not', 'be', 'symmetric', 'even', 'if', 'g', 'i', 'are', 'symmetric', '.', 'Hence', ',', 'f', 'W', 'is', 'not', 'a', 'metric', '.', 'Moreover', ',', 'the', 'behavior', 'of', 'f', 'W', 'depends', 'on', 'the', 'behavior', 'of', 'g', 'i', '.', 'There', 'are', 'many', 'ways', 'to', 'define', 'g', 'i', '.', 'We', 'list', 'two', 'instances', 'of', 'f', 'W', '.', 'Example', '2.1', '-LRB-', 'Minkowski', '-RRB-', 'Let', 'd', 'be', 'the', 'feature', 'space', 'and', 'M', '=', 'd.', 'Let', 'a', 'point', 'x', 'd', 'be', 'represented', 'as', '-LRB-', 'x', '1', ',', '...', ',', 'x', 'd', '-RRB-', '.', 'Then', ',', 'when', 'g', 'i', '-LRB-', 'x', ',', 'y', '-RRB-', '=', '|', 'x', 'i', '-', 'y', 'i', '|', 'p', 'for', 'i', '=', '1', ',', '...', ',', 'd', ',', 'and', 'p', '1', ',', 'the', 'resulting', 'SVaD', 'measure', ',', 'f', 'M', 'W', 'is', 'called', 'Minkowski', 'SVaD', '-LRB-', 'MSVaD', '-RRB-', 'measure', '.', 'That', 'is', ',', 'f', 'M', 'W', '-LRB-', 'x', ',', 'y', '-RRB-', '=', 'd', 'l', '=', '1', 'w', 'r', 'jl', '|', 'x', 'l', '-', 'y', 'l', '|', 'p', ',', 'if', 'y', 'R', 'j', '.', '-LRB-', '2', '-RRB-', 'One', 'may', 'note', 'that', 'when', 'w', '1', '=', '=', 'w', 'K', 'and', 'p', '=', '2', ',', 'f', 'M', 'W', 'is', 'the', 'weighted', 'Euclidean', 'distance', '.', 'When', 'p', '=', '2', ',', 'we', 'call', 'f', 'M', 'W', 'a', 'Euclidean', 'SVaD', '-LRB-', 'ESVaD', '-RRB-', 'measure', 'and', 'denote', 'it', 'by', 'f', 'E', 'W', '.', '1', 'We', 'use', 'the', 'phrase', '``', 'dissimilarity', 'of', 'x', 'from', 'y', "''", 'rather', 'than', '``', 'dissimilarity', 'between', 'x', 'and', 'y', "''", 'because', 'we', 'consider', 'a', 'general', 'situation', 'where', 'the', 'dissimilarity', 'measure', 'depends', 'on', 'the', 'location', 'of', 'y', '.', 'As', 'an', 'example', 'of', 'this', 'situation', 'in', 'text', 'mining', ',', 'when', 'the', 'dissimilarity', 'is', 'measured', 'from', 'a', 'document', 'on', '`', 'terrorism', "'", 'to', 'a', 'document', 'x', ',', 'a', 'particular', 'set', 'of', 'keywords', 'may', 'be', 'weighted', 'heavily', 'whereas', 'when', 'the', 'dissimilarity', 'is', 'measured', 'from', 'a', 'document', 'on', '`', 'football', "'", 'to', 'x', ',', 'a', 'different', 'set', 'of', 'keywords', 'may', 'be', 'weighted', 'heavily', '.', 'Example', '2.2', '-LRB-', 'Cosine', '-RRB-', 'Let', 'the', 'feature', 'space', 'be', 'the', 'set', 'of', 'points', 'with', 'l', '2', 'norm', 'equal', 'to', 'one', '.', 'That', 'is', ',', 'x', '2', '=', '1', 'for', 'all', 'points', 'x', 'in', 'feature', 'space', '.', 'Then', ',', 'when', 'g', 'l', '-LRB-', 'x', ',', 'y', '-RRB-', '=', '-LRB-', '1/d', '-', 'x', 'l', 'y', 'l', '-RRB-', 'for', 'l', '=', '1', ',', '...', ',', 'd', ',', 'the', 'resulting', 'SVaD', 'measure', 'f', 'C', 'W', 'is', 'called', 'a', 'Cosine', 'SVaD', '-LRB-', 'CSVaD', '-RRB-', 'measure', ':', 'f', 'C', 'W', '-LRB-', 'x', ',', 'y', '-RRB-', '=', 'd', 'i', '=', '1', 'w', 'r', 'jl', '-LRB-', '1/d', '-', 'x', 'l', 'y', 'l', '-RRB-', ',', 'if', 'y', 'R', 'j', '.', '-LRB-', '3', '-RRB-', 'In', 'the', 'formulation', 'of', 'the', 'objective', 'function', 'below', ',', 'we', 'use', 'a', 'set', 'of', 'parameters', 'to', 'represent', 'the', 'regions', 'R', '1', ',', 'R', '2', ',', '...', ',', 'and', 'R', 'K', '.', 'Let', 'c', '1', ',', 'c', '2', ',', '...', ',', 'and', 'c', 'K', 'be', 'K', 'points', 'in', 'feature', 'space', '.', 'Then', 'y', 'R', 'j', 'iff', 'f', 'W', '-LRB-', 'y', ',', 'c', 'j', '-RRB-', '<', 'f', 'W', '-LRB-', 'y', ',', 'c', 'i', '-RRB-', 'for', 'i', '=', 'j.', '-LRB-', '4', '-RRB-', 'In', 'the', 'case', 'of', 'ties', ',', 'y', 'is', 'assigned', 'to', 'the', 'region', 'with', 'the', 'lowest', 'index', '.', 'Thus', ',', 'the', 'K-tuple', 'of', 'points', 'C', '=', '-LRB-', 'c', '1', ',', 'c', '2', ',', '...', ',', 'c', 'K', '-RRB-', 'defines', 'a', 'partition', 'in', 'feature', 'space', '.', 'The', 'partition', 'induced', 'by', 'the', 'points', 'in', 'C', 'is', 'similar', 'in', 'nature', 'to', 'a', 'Voronoi', 'tessellation', '.', 'We', 'use', 'the', 'notation', 'f', 'W', ',', 'C', 'whenever', 'we', 'use', 'the', 'set', 'C', 'to', 'parameterize', 'the', 'regions', 'used', 'in', 'the', 'dissimilarity', 'measure', '.', '2.2', 'Objective', 'Function', 'for', 'Clustering', 'The', 'goal', 'of', 'the', 'present', 'work', 'is', 'to', 'identify', 'the', 'spatially', 'varying', 'dissimilarity', 'measure', 'and', 'the', 'associated', 'compact', 'clusters', 'simultaneously', '.', 'It', 'is', 'worth', 'mentioning', 'here', 'that', ',', 'as', 'in', 'the', 'case', 'of', 'any', 'clustering', 'algorithm', ',', 'the', 'underlying', 'assumption', 'in', 'this', 'paper', 'is', 'the', 'existence', 'of', 'such', 'a', 'dissimilarity', 'measure', 'and', 'clusters', 'for', 'a', 'given', 'data', 'set', '.', 'Let', 'x', '1', ',', 'x', '2', ',', '...', ',', 'and', 'x', 'n', 'be', 'n', 'given', 'data', 'points', '.', 'Let', 'K', 'be', 'a', 'given', 'positive', 'integer', '.', 'Assuming', 'that', 'C', 'represents', 'the', 'cluster', 'centers', ',', 'let', 'us', 'assign', 'each', 'data', 'point', 'x', 'i', 'to', 'a', 'cluster', 'R', 'j', 'with', 'the', 'closest', 'c', 'j', 'as', 'the', 'cluster', 'center', '2', ',', 'i.e.', ',', 'j', '=', 'arg', 'min', 'l', 'f', 'W', ',', 'C', '-LRB-', 'x', 'i', ',', 'c', 'l', '-RRB-', '.', '-LRB-', '5', '-RRB-', 'Then', ',', 'the', 'within-cluster', 'dissimilarity', 'is', 'given', 'by', 'J', '-LRB-', 'W', ',', 'C', '-RRB-', '=', 'K', 'j', '=', '1', 'x', 'i', 'R', 'j', 'M', 'l', '=', '1', 'w', 'r', 'jl', 'g', 'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '.', '-LRB-', '6', '-RRB-', 'J', '-LRB-', 'W', ',', 'C', '-RRB-', 'represents', 'the', 'sum', 'of', 'the', 'dissimilarity', 'measures', 'of', 'all', 'the', 'data', 'points', 'from', 'their', 'closest', 'centroids', '.', 'The', 'objective', 'is', 'to', 'find', 'W', 'and', 'C', 'that', 'minimize', 'J', '-LRB-', 'W', ',', 'C', '-RRB-', '.', 'To', 'avoid', 'the', 'trivial', 'solution', 'to', 'J', '-LRB-', 'W', ',', 'C', '-RRB-', ',', 'we', 'consider', 'a', 'normalization', 'condition', 'on', 'w', 'j', ',', 'viz.', ',', 'M', 'l', '=', '1', 'w', 'jl', '=', '1', '.', '-LRB-', '7', '-RRB-', 'Note', 'that', 'even', 'with', 'this', 'condition', ',', 'J', '-LRB-', 'W', ',', 'C', '-RRB-', 'has', 'a', 'trivial', 'solution', ':', 'w', 'jp', '=', '1', 'where', 'p', '=', 'arg', 'min', 'l', 'x', 'i', 'R', 'j', 'g', 'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', ',', 'and', 'the', 'remaining', 'weights', 'are', 'zero', '.', 'One', 'way', 'to', 'avoid', 'convergence', 'of', 'w', 'j', 'to', 'unit', 'vectors', 'is', 'to', 'impose', 'a', 'regularization', 'condition', 'on', 'w', 'j', '.', 'We', 'consider', 'the', 'following', 'two', 'regularization', 'measures', 'in', 'this', 'paper', ':', '-LRB-', '1', '-RRB-', 'Entropy', 'measure', ':', 'M', 'l', '=', '1', 'w', 'jl', 'log', '-LRB-', 'w', 'jl', '-RRB-', 'and', '-LRB-', '2', '-RRB-', 'Gini', 'measure', ':', 'M', 'l', '=', '1', 'w', '2', 'jl', '.', '2', 'We', 'use', 'P', '=', '-LCB-', 'R', '1', ',', 'R', '2', ',', '...', ',', 'R', 'K', '-RCB-', 'to', 'represent', 'the', 'corresponding', 'partition', 'of', 'the', 'data', 'set', 'as', 'well', '.', 'The', 'intended', 'interpretation', '-LRB-', 'cluster', 'or', 'region', '-RRB-', 'would', 'be', 'evident', 'from', 'the', 'context', '.', '612', 'Research', 'Track', 'Poster', 'The', 'problem', 'of', 'determining', 'the', 'optimal', 'W', 'and', 'C', 'is', 'similar', 'to', 'the', 'traditional', 'clustering', 'problem', 'that', 'is', 'solved', 'by', 'the', 'K-Means', 'Algorithm', '-LRB-', 'KMA', '-RRB-', 'except', 'for', 'the', 'additional', 'W', 'matrix', '.', 'We', 'propose', 'a', 'class', 'of', 'iterative', 'algorithms', 'similar', 'to', 'KMA', '.', 'These', 'algorithms', 'start', 'with', 'a', 'random', 'partition', 'of', 'the', 'data', 'set', 'and', 'iteratively', 'update', 'C', ',', 'W', 'and', 'P', 'so', 'that', 'J', '-LRB-', 'W', ',', 'C', '-RRB-', 'is', 'minimized', '.', 'These', 'iterative', 'algorithms', 'are', 'instances', 'of', 'Alternating', 'Optimization', '-LRB-', 'AO', '-RRB-', 'algorithms', '.', 'In', '-LSB-', '1', '-RSB-', ',', 'it', 'is', 'shown', 'that', 'AO', 'algorithms', 'converge', 'to', 'a', 'local', 'optimum', 'under', 'some', 'conditions', '.', 'We', 'outline', 'the', 'algorithm', 'below', 'before', 'actually', 'describing', 'how', 'to', 'update', 'C', ',', 'W', 'and', 'P', 'in', 'every', 'iteration', '.', 'Randomly', 'assign', 'the', 'data', 'points', 'to', 'K', 'clusters', '.', 'REPEAT', 'Update', 'C', ':', 'Compute', 'the', 'centroid', 'of', 'each', 'cluster', 'c', 'j', '.', 'Update', 'W', ':', 'Compute', 'the', 'w', 'jl', 'j', ',', 'l.', 'Update', 'P', ':', 'Reassign', 'the', 'data', 'points', 'to', 'the', 'clusters', '.', 'UNTIL', '-LRB-', 'termination', 'condition', 'is', 'reached', '-RRB-', '.', 'In', 'the', 'above', 'algorithm', ',', 'the', 'update', 'of', 'C', 'depends', 'on', 'the', 'definition', 'of', 'g', 'i', ',', 'and', 'the', 'update', 'of', 'W', 'on', 'the', 'regularization', 'terms', '.', 'The', 'update', 'of', 'P', 'is', 'done', 'by', 'reassigning', 'the', 'data', 'points', 'according', 'to', '-LRB-', '5', '-RRB-', '.', 'Before', 'explaining', 'the', 'computation', 'of', 'C', 'in', 'every', 'iteration', 'for', 'various', 'g', 'i', ',', 'we', 'first', 'derive', 'update', 'equations', 'for', 'W', 'for', 'various', 'regularization', 'measures', '.', '3.1', 'Update', 'of', 'Weights', 'While', 'updating', 'weights', ',', 'we', 'need', 'to', 'find', 'the', 'values', 'of', 'weights', 'that', 'minimize', 'the', 'objective', 'function', 'for', 'a', 'given', 'C', 'and', 'P', '.', 'As', 'mentioned', 'above', ',', 'we', 'consider', 'the', 'two', 'regularization', 'measures', 'for', 'w', 'jl', 'and', 'derive', 'update', 'equations', '.', 'If', 'we', 'consider', 'the', 'entropy', 'regularization', 'with', 'r', '=', '1', ',', 'the', 'objective', 'function', 'becomes', ':', 'J', 'EN', 'T', '-LRB-', 'W', ',', 'C', '-RRB-', '=', 'K', 'j', '=', '1', 'x', 'i', 'R', 'j', 'M', 'l', '=', '1', 'w', 'jl', 'g', 'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '+', 'K', 'j', '=', '1', 'j', 'M', 'l', '=', '1', 'w', 'jl', 'log', '-LRB-', 'w', 'jl', '-RRB-', '+', 'K', 'j', '=', '1', 'j', 'M', 'l', '=', '1', 'w', 'jl', '-', '1', '.', '-LRB-', '8', '-RRB-', 'Note', 'that', 'j', 'are', 'the', 'Lagrange', 'multipliers', 'corresponding', 'to', 'the', 'normalization', 'constraints', 'in', '-LRB-', '7', '-RRB-', ',', 'and', 'j', 'represent', 'the', 'relative', 'importance', 'given', 'to', 'the', 'regularization', 'term', 'relative', 'to', 'the', 'within-cluster', 'dissimilarity', '.', 'Differentiating', 'J', 'EN', 'T', '-LRB-', 'W', ',', 'C', '-RRB-', 'with', 'respect', 'to', 'w', 'jl', 'and', 'equating', 'it', 'to', 'zero', ',', 'we', 'obtain', 'w', 'jl', '=', 'exp', '-', '-LRB-', 'j', '+', 'x', 'i', 'Rj', 'g', 'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '-RRB-', 'j', '-', '1', '.', 'Solving', 'for', 'j', 'by', 'substituting', 'the', 'above', 'value', 'of', 'w', 'jl', 'in', '-LRB-', '7', '-RRB-', 'and', 'substituting', 'the', 'value', 'of', 'j', 'back', 'in', 'the', 'above', 'equation', ',', 'we', 'obtain', 'w', 'jl', '=', 'exp', 'x', 'i', 'R', 'j', 'g', 'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '/', 'j', 'M', 'n', '=', '1', 'exp', 'x', 'i', 'R', 'j', 'g', 'n', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '/', 'j', '.', '-LRB-', '9', '-RRB-', 'If', 'we', 'consider', 'the', 'Gini', 'measure', 'for', 'regularization', 'with', 'r', '=', '2', ',', 'the', 'corresponding', 'w', 'jl', 'that', 'minimizes', 'the', 'objective', 'function', 'can', 'be', 'shown', 'to', 'be', 'w', 'jl', '=', '1', '/', '-LRB-', 'j', '+', 'x', 'i', 'R', 'j', 'g', 'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '-RRB-', 'M', 'n', '=', '1', '-LRB-', '1', '/', '-LRB-', 'j', '+', 'x', 'i', 'R', 'j', 'g', 'n', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '-RRB-', '-RRB-', '.', '-LRB-', '10', '-RRB-', 'In', 'both', 'cases', ',', 'the', 'updated', 'value', 'of', 'w', 'jl', 'is', 'inversely', 'related', 'Algorithm', 'Update', 'Equations', 'Acronyms', 'P', 'C', 'W', 'EEnt', '-LRB-', '5', '-RRB-', '-LRB-', '11', '-RRB-', '-LRB-', '9', '-RRB-', 'EsGini', '-LRB-', '5', '-RRB-', '-LRB-', '11', '-RRB-', '-LRB-', '10', '-RRB-', 'CEnt', '-LRB-', '5', '-RRB-', '-LRB-', '12', '-RRB-', '-LRB-', '9', '-RRB-', 'CsGini', '-LRB-', '5', '-RRB-', '-LRB-', '12', '-RRB-', '-LRB-', '10', '-RRB-', 'Table', '1', ':', 'Summary', 'of', 'algorithms', '.', 'to', 'x', 'i', 'R', 'j', 'g', 'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '.', 'This', 'has', 'various', 'interpretations', 'based', 'on', 'the', 'nature', 'of', 'g', 'l', '.', 'For', 'example', ',', 'when', 'we', 'consider', 'the', 'ESVaD', 'measure', ',', 'w', 'jl', 'is', 'inversely', 'related', 'to', 'the', 'variance', 'of', 'l-th', 'element', 'of', 'the', 'data', 'vectors', 'in', 'the', 'j-th', 'cluster', '.', 'In', 'other', 'words', ',', 'when', 'the', 'variance', 'along', 'a', 'particular', 'dimension', 'is', 'high', 'in', 'a', 'cluster', ',', 'then', 'the', 'dimension', 'is', 'less', 'important', 'to', 'the', 'cluster', '.', 'This', 'popular', 'heuristic', 'has', 'been', 'used', 'in', 'various', 'contexts', '-LRB-', 'such', 'as', 'relevance', 'feedback', '-RRB-', 'in', 'the', 'literature', '-LSB-', '9', '-RSB-', '.', 'Similarly', ',', 'when', 'we', 'consider', 'the', 'CSVaD', 'measure', ',', 'w', 'jl', 'is', 'directly', 'proportional', 'to', 'the', 'correlation', 'of', 'the', 'j-th', 'dimension', 'in', 'the', 'l-th', 'cluster', '.', '3.2', 'Update', 'of', 'Centroids', 'Learning', 'ESVaD', 'Measures', ':', 'Substituting', 'the', 'ESVaD', 'measure', 'in', 'the', 'objective', 'function', 'and', 'solving', 'the', 'first', 'order', 'necessary', 'conditions', ',', 'we', 'observe', 'that', 'c', 'jl', '=', '1', '|', 'R', 'j', '|', 'x', 'i', 'R', 'j', 'x', 'il', '-LRB-', '11', '-RRB-', 'minimizes', 'J', 'ESV', 'AD', '-LRB-', 'W', ',', 'C', '-RRB-', '.', 'Learning', 'CSVaD', 'Measures', ':', 'Let', 'x', 'il', '=', 'w', 'jl', 'x', 'il', ',', 'then', 'using', 'the', 'Cauchy-Swartz', 'inequality', ',', 'it', 'can', 'be', 'shown', 'that', 'c', 'jl', '=', '1', '|', 'R', 'j', '|', 'x', 'i', 'R', 'j', 'x', 'il', '-LRB-', '12', '-RRB-', 'maximizes', 'x', 'i', 'R', 'j', 'd', 'l', '=', '1', 'w', 'jl', 'x', 'il', 'c', 'jl', '.', 'Hence', ',', '-LRB-', '12', '-RRB-', 'also', 'minimizes', 'the', 'objective', 'function', 'when', 'CSVaD', 'is', 'used', 'as', 'the', 'dissimilarity', 'measure', '.', 'Table', '1', 'summarizes', 'the', 'update', 'equations', 'used', 'in', 'various', 'algorithms', '.', 'We', 'refer', 'to', 'this', 'set', 'of', 'algorithms', 'as', 'SVaD', 'learning', 'algorithms', '.', 'In', 'this', 'section', ',', 'we', 'present', 'an', 'experimental', 'study', 'of', 'the', 'algorithms', 'described', 'in', 'the', 'previous', 'sections', '.', 'We', 'applied', 'the', 'proposed', 'algorithms', 'on', 'various', 'text', 'data', 'sets', 'and', 'compared', 'the', 'performance', 'of', 'EEnt', 'and', 'EsGini', 'with', 'that', 'of', 'K-Means', ',', 'CSCAD', 'and', 'DGK', 'algorithms', '.', 'The', 'reason', 'for', 'choosing', 'the', 'K-Means', 'algorithm', '-LRB-', 'KMA', '-RRB-', 'apart', 'from', 'CSCAD', 'and', 'DGK', 'is', 'that', 'it', 'provides', 'a', 'baseline', 'for', 'assessing', 'the', 'advantages', 'of', 'feature', 'weighting', '.', 'KMA', 'is', 'also', 'a', 'popular', 'algorithm', 'for', 'text', 'clustering', '.', 'We', 'have', 'included', 'a', 'brief', 'description', 'of', 'CSCAD', 'and', 'DGK', 'algorithms', 'in', 'Appendix', 'A.', 'Text', 'data', 'sets', 'are', 'sparse', 'and', 'high', 'dimensional', '.', 'We', 'consider', 'standard', 'labeled', 'document', 'collections', 'and', 'test', 'the', 'proposed', 'algorithms', 'for', 'their', 'ability', 'to', 'discover', 'dissimilarity', 'measures', 'that', 'distinguish', 'one', 'class', 'from', 'another', 'without', 'actually', 'considering', 'the', 'class', 'labels', 'of', 'the', 'documents', '.', 'We', 'measure', 'the', 'success', 'of', 'the', 'algorithms', 'by', 'the', 'purity', 'of', 'the', 'regions', 'that', 'they', 'discover', '.', '613', 'Research', 'Track', 'Poster', '4.1', 'Data', 'Sets', 'We', 'performed', 'our', 'experiments', 'on', 'three', 'standard', 'data', 'sets', ':', '20', 'News', 'Group', ',', 'Yahoo', 'K1', ',', 'and', 'Classic', '3', '.', 'These', 'data', 'sets', 'are', 'described', 'below', '.', '20', 'News', 'Group', '3', ':', 'We', 'considered', 'different', 'subsets', 'of', '20', 'News', 'Group', 'data', 'that', 'are', 'known', 'to', 'contain', 'clusters', 'of', 'varying', 'degrees', 'of', 'separation', '-LSB-', '10', '-RSB-', '.', 'As', 'in', '-LSB-', '10', '-RSB-', ',', 'we', 'considered', 'three', 'random', 'samples', 'of', 'three', 'subsets', 'of', 'the', '20', 'News', 'Group', 'data', '.', 'The', 'subsets', 'denoted', 'by', 'Binary', 'has', '250', 'documents', 'each', 'from', 'talk.politics.mideast', 'and', 'talk.politics.misc', '.', 'Multi5', 'has', '100', 'documents', 'each', 'from', 'comp.graphics', ',', 'rec.motorcycles', ',', 'rec.sport.baseball', ',', 'sci.space', ',', 'and', 'talk.politics.mideast', '.', 'Finally', ',', 'Multi10', 'has', '50', 'documents', 'each', 'from', 'alt.atheism', ',', 'comp', '.', 'sys.mac.hardware', ',', 'misc.forsale', ',', 'rec.autos', ',', 'rec.sport.hockey', ',', 'sci.crypt', ',', 'sci.electronics', ',', 'sci.med', ',', 'sci.space', ',', 'and', 'talk.politics', '.', 'gun', '.', 'It', 'may', 'be', 'noted', 'that', 'Binary', 'data', 'sets', 'have', 'two', 'highly', 'overlapping', 'classes', '.', 'Each', 'of', 'Multi5', 'data', 'sets', 'has', 'samples', 'from', '5', 'distinct', 'classes', ',', 'whereas', 'Multi10', 'data', 'sets', 'have', 'only', 'a', 'few', 'samples', 'from', '10', 'different', 'classes', '.', 'The', 'size', 'of', 'the', 'vocabulary', 'used', 'to', 'represent', 'the', 'documents', 'in', 'Binary', 'data', 'set', 'is', 'about', '4000', ',', 'Multi5', 'about', '3200', 'and', 'Multi10', 'about', '2800', '.', 'We', 'observed', 'that', 'the', 'relative', 'performance', 'of', 'the', 'algorithms', 'on', 'various', 'samples', 'of', 'Binary', ',', 'Multi5', 'and', 'Multi10', 'data', 'sets', 'was', 'similar', '.', 'Hence', ',', 'we', 'report', 'results', 'on', 'only', 'one', 'of', 'them', '.', 'Yahoo', 'K1', '4', ':', 'This', 'data', 'set', 'contains', '2340', 'Reuters', 'news', 'articles', 'downloaded', 'from', 'Yahoo', 'in', '1997', '.', 'There', 'are', '494', 'from', 'Health', ',', '1389', 'from', 'Entertainment', ',', '141', 'from', 'Sports', ',', '114', 'from', 'Politics', ',', '60', 'from', 'Technology', 'and', '142', 'from', 'Business', '.', 'After', 'preprocessing', ',', 'the', 'documents', 'from', 'this', 'data', 'set', 'are', 'represented', 'using', '12015', 'words', '.', 'Note', 'that', 'this', 'data', 'set', 'has', 'samples', 'from', '6', 'different', 'classes', '.', 'Here', ',', 'the', 'distribution', 'of', 'data', 'points', 'across', 'the', 'class', 'is', 'uneven', ',', 'ranging', 'from', '60', 'to', '1389', '.', 'Classic', '3', '5', ':', 'Classic', '3', 'data', 'set', 'contains', '1400', 'aerospace', 'systems', 'abstracts', 'from', 'the', 'Cranfield', 'collection', ',', '1033', 'medical', 'abstracts', 'from', 'the', 'Medline', 'collection', 'and', '1460', 'information', 'retrieval', 'abstracts', 'from', 'the', 'Cisi', 'collection', ',', 'making', 'up', '3893', 'documents', 'in', 'all', '.', 'After', 'preprocessing', ',', 'this', 'data', 'set', 'has', '4301', 'words', '.', 'The', 'points', 'are', 'almost', 'equally', 'distributed', 'among', 'the', 'three', 'distinct', 'classes', '.', 'The', 'data', 'sets', 'were', 'preprocessed', 'using', 'two', 'major', 'steps', '.', 'First', ',', 'a', 'set', 'of', 'words', '-LRB-', 'vocabulary', '-RRB-', 'is', 'extracted', 'and', 'then', 'each', 'document', 'is', 'represented', 'with', 'respect', 'to', 'this', 'vocabulary', '.', 'Finding', 'the', 'vocabulary', 'includes', ':', '-LRB-', '1', '-RRB-', 'elimination', 'of', 'the', 'standard', 'list', 'of', 'stop', 'words', 'from', 'the', 'documents', ',', '-LRB-', '2', '-RRB-', 'application', 'of', 'Porter', 'stemming', '6', 'for', 'term', 'normalization', ',', 'and', '-LRB-', '3', '-RRB-', 'keeping', 'only', 'the', 'words', 'which', 'appear', 'in', 'at', 'least', '3', 'documents', '.', 'We', 'represent', 'each', 'document', 'by', 'the', 'unitized', 'frequency', 'vector', '.', '4.2', 'Evaluation', 'of', 'Algorithms', 'We', 'use', 'the', 'accuracy', 'measure', 'to', 'compare', 'the', 'performance', 'of', 'various', 'algorithms', '.', 'Let', 'a', 'ij', 'represent', 'the', 'number', 'of', 'data', 'points', 'from', 'class', 'i', 'that', 'are', 'in', 'cluster', 'j', '.', 'Then', 'the', 'accuracy', 'of', 'the', 'partition', 'is', 'given', 'by', 'j', 'max', 'i', 'a', 'ij', '/', 'n', 'where', 'n', 'is', 'the', 'total', 'number', 'of', 'data', 'points', '.', 'It', 'is', 'to', 'be', 'noted', 'that', 'points', 'coming', 'from', 'a', 'single', 'class', 'need', 'not', 'form', 'a', 'single', 'cluster', '.', 'There', 'could', 'be', 'multiple', '3', 'http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20', '.', 'tar.gz', '4', 'ftp://ftp.cs.umn.edu/dept/users/boley/PDDPdata/doc-K', '5', 'ftp://ftp.cs.cornell.edu/pub/smart', '6', 'http://www.tartarus.org/~martin/PorterStemmer/', 'Iteration', '0', '1', '2', '3', '4', '5', 'J', '-LRB-', 'W', ',', 'C', '-RRB-', '334.7', '329.5', '328.3', '328.1', '327.8', 'Accuracy', '73.8', '80.2', '81.4', '81.6', '82', '82', 'Table', '2', ':', 'Evolution', 'of', 'J', '-LRB-', 'W', ',', 'C', '-RRB-', 'and', 'Accuracies', 'with', 'iterations', 'when', 'EEnt', 'applied', 'on', 'a', 'Multi5', 'data', '.', 'clusters', 'in', 'a', 'class', 'that', 'represent', 'sub-classes', '.', 'We', 'study', 'the', 'performance', 'of', 'SVaD', 'learning', 'algorithms', 'for', 'various', 'values', 'of', 'K', ',', 'i.e.', ',', 'the', 'number', 'of', 'clusters', '.', '4.3', 'Experimental', 'Setup', 'In', 'our', 'implementations', ',', 'we', 'have', 'observed', 'that', 'the', 'proposed', 'algorithms', ',', 'if', 'applied', 'on', 'randomly', 'initialized', 'centroids', ',', 'show', 'unstable', 'behavior', '.', 'One', 'reason', 'for', 'this', 'behavior', 'is', 'that', 'the', 'number', 'of', 'parameters', 'that', 'are', 'estimated', 'in', 'feature-weighting', 'clustering', 'algorithms', 'is', 'twice', 'as', 'large', 'as', 'that', 'estimated', 'by', 'the', 'traditional', 'KMA', '.', 'We', ',', 'therefore', ',', 'first', 'estimate', 'the', 'cluster', 'centers', 'giving', 'equal', 'weights', 'to', 'all', 'the', 'dimensions', 'using', 'KMA', 'and', 'then', 'fine-tune', 'the', 'cluster', 'centers', 'and', 'the', 'weights', 'using', 'the', 'feature-weighting', 'clustering', 'algorithms', '.', 'In', 'every', 'iteration', ',', 'the', 'new', 'sets', 'of', 'weights', 'are', 'updated', 'as', 'follows', '.', 'Let', 'w', 'n', '-LRB-', 't', '+1', '-RRB-', 'represent', 'the', 'weights', 'com-puted', 'using', 'one', 'of', '-LRB-', '9', '-RRB-', ',', '-LRB-', '10', '-RRB-', ',', '-LRB-', '14', '-RRB-', 'or', '-LRB-', '15', '-RRB-', 'in', 'iteration', '-LRB-', 't', '+', '1', '-RRB-', 'and', 'w', '-LRB-', 't', '-RRB-', 'the', 'weights', 'in', 'iteration', 't.', 'Then', ',', 'the', 'weights', 'in', 'iteration', '-LRB-', 't', '+', '1', '-RRB-', 'are', 'w', '-LRB-', 't', '+', '1', '-RRB-', '=', '-LRB-', '1', '-', '-LRB-', 't', '-RRB-', '-RRB-', 'w', '-LRB-', 't', '-RRB-', '+', '-LRB-', 't', '-RRB-', 'w', 'n', '-LRB-', 't', '+', '1', '-RRB-', ',', '-LRB-', '13', '-RRB-', 'where', '-LRB-', 't', '-RRB-', '-LSB-', '0', ',', '1', '-RSB-', 'decreases', 'with', 't', '.', 'That', 'is', ',', '-LRB-', 't', '-RRB-', '=', '-LRB-', 't', '1', '-RRB-', ',', 'for', 'a', 'given', 'constant', '-LSB-', '0', ',', '1', '-RSB-', '.', 'In', 'our', 'experiments', ',', 'we', 'observed', 'that', 'the', 'variance', 'of', 'purity', 'values', 'for', 'different', 'initial', 'values', 'of', '-LRB-', '0', '-RRB-', 'and', 'above', '0.5', 'is', 'very', 'small', '.', 'Hence', ',', 'we', 'report', 'the', 'results', 'for', '-LRB-', '0', '-RRB-', '=', '0.5', 'and', '=', '0.5', '.', 'We', 'set', 'the', 'value', 'of', 'j', '=', '1', '.', 'It', 'may', 'be', 'noted', 'that', 'when', 'the', 'documents', 'are', 'represented', 'as', 'unit', 'vectors', ',', 'KMA', 'with', 'the', 'cosine', 'dissimilarity', 'measure', 'and', 'Euclidean', 'distance', 'measure', 'would', 'yield', 'the', 'same', 'clusters', '.', 'This', 'is', 'essentially', 'the', 'same', 'as', 'Spherical', 'K-Means', 'algorithms', 'described', 'in', '-LSB-', '3', '-RSB-', '.', 'Therefore', ',', 'we', 'consider', 'only', 'the', 'weighted', 'Euclidean', 'measure', 'and', 'restrict', 'our', 'comparisons', 'to', 'EEnt', 'and', 'EsGini', 'in', 'the', 'experiments', '.', 'Since', 'the', 'clusters', 'obtained', 'by', 'KMA', 'are', 'used', 'to', 'initialize', 'all', 'other', 'algorithms', 'considered', 'here', ',', 'and', 'since', 'the', 'results', 'of', 'KMA', 'are', 'sensitive', 'to', 'initialization', ',', 'the', 'accuracy', 'numbers', 'reported', 'in', 'this', 'section', 'are', 'averages', 'over', '10', 'random', 'initializations', 'of', 'KMA', '.', '4.4', 'Results', 'and', 'Observations', '4.4.1', 'Effect', 'of', 'SVaD', 'Measures', 'on', 'Accuracies', 'In', 'Table', '2', ',', 'we', 'show', 'a', 'sample', 'run', 'of', 'EEnt', 'algorithm', 'on', 'one', 'of', 'the', 'Multi5', 'data', 'sets', '.', 'This', 'table', 'shows', 'the', 'evolution', 'of', 'J', '-LRB-', 'W', ',', 'C', '-RRB-', 'and', 'the', 'corresponding', 'accuracies', 'of', 'the', 'clusters', 'with', 'the', 'iterations', '.', 'The', 'accuracy', ',', 'shown', 'at', 'iteration', '0', ',', 'is', 'that', 'of', 'the', 'clusters', 'obtained', 'by', 'KMA', '.', 'The', 'purity', 'of', 'clusters', 'increases', 'with', 'decrease', 'in', 'the', 'value', 'of', 'the', 'objective', 'function', 'defined', 'using', 'SVaD', 'measures', '.', 'We', 'have', 'observed', 'a', 'similar', 'behavior', 'of', 'EEnt', 'and', 'EsGini', 'on', 'other', 'data', 'sets', 'also', '.', 'This', 'validates', 'our', 'hypothesis', 'that', 'SVaD', 'measures', 'capture', 'the', 'underlying', 'structure', 'in', 'the', 'data', 'sets', 'more', 'accurately', '.', '614', 'Research', 'Track', 'Poster', '4.4.2', 'Comparison', 'with', 'Other', 'Algorithms', 'Figure', '1', 'to', 'Figure', '5', 'show', 'average', 'accuracies', 'of', 'various', 'algorithms', 'on', 'the', '5', 'data', 'sets', 'for', 'various', 'number', 'of', 'clusters', '.', 'The', 'accuracies', 'of', 'KMA', 'and', 'DGK', 'are', 'very', 'close', 'to', 'each', 'other', 'and', 'hence', ',', 'in', 'the', 'figures', ',', 'the', 'lines', 'corresponding', 'to', 'these', 'algorithms', 'are', 'indistinguishable', '.', 'The', 'lines', 'corresponding', 'to', 'CSCAD', 'are', 'also', 'close', 'to', 'that', 'of', 'KMA', 'in', 'all', 'the', 'cases', 'except', 'Class', '3', '.', 'General', 'observations', ':', 'The', 'accuracies', 'of', 'SVaD', 'algorithms', 'follow', 'the', 'trend', 'of', 'the', 'accuracies', 'of', 'other', 'algorithms', '.', 'In', 'all', 'our', 'experiments', ',', 'both', 'SVaD', 'learning', 'algorithms', 'improve', 'the', 'accuracies', 'of', 'clusters', 'obtained', 'by', 'KMA', '.', 'It', 'is', 'observed', 'in', 'our', 'experiments', 'that', 'the', 'improvement', 'could', 'be', 'as', 'large', 'as', '8', '%', 'in', 'some', 'instances', '.', 'EEnt', 'and', 'EsGini', 'consis-tently', 'perform', 'better', 'than', 'DGK', 'on', 'all', 'data', 'sets', 'and', 'for', 'all', 'values', 'of', 'K.', 'EEnt', 'and', 'EsGini', 'perform', 'better', 'than', 'CSCAD', 'on', 'all', 'data', 'sets', 'excepts', 'in', 'the', 'case', 'of', 'Classic', '3', 'and', 'for', 'a', 'few', 'values', 'of', 'K.', 'Note', 'that', 'the', 'weight', 'update', 'equation', 'of', 'CSCAD', '-LRB-', '15', '-RRB-', 'may', 'result', 'in', 'negative', 'values', 'of', 'w', 'jl', '.', 'Our', 'experience', 'with', 'CSCAD', 'shows', 'that', 'it', 'is', 'quite', 'sensitive', 'to', 'initialization', 'and', 'it', 'may', 'have', 'convergence', 'problems', '.', 'In', 'contrast', ',', 'it', 'may', 'be', 'observed', 'that', 'w', 'jl', 'in', '-LRB-', '9', '-RRB-', 'and', '-LRB-', '10', '-RRB-', 'are', 'always', 'positive', '.', 'Moreover', ',', 'in', 'our', 'experience', ',', 'these', 'two', 'versions', 'are', 'much', 'less', 'sensitive', 'to', 'the', 'choice', 'of', 'j', '.', 'Data', 'specific', 'observations', ':', 'When', 'K', '=', '2', ',', 'EEnt', 'and', 'EsGini', 'could', 'not', 'further', 'improve', 'the', 'results', 'of', 'KMA', 'on', 'the', 'Binary', 'data', 'set', '.', 'The', 'reason', 'is', 'that', 'the', 'data', 'set', 'contains', 'two', 'highly', 'overlapping', 'classes', '.', 'However', ',', 'for', 'other', 'values', 'of', 'K', ',', 'they', 'marginally', 'improve', 'the', 'accuracies', '.', 'In', 'the', 'case', 'of', 'Multi5', ',', 'the', 'accuracies', 'of', 'the', 'algorithms', 'are', 'non-monotonic', 'with', 'K', '.', 'The', 'improvement', 'of', 'accuracies', 'is', 'large', 'for', 'intermediate', 'values', 'of', 'K', 'and', 'small', 'for', 'extreme', 'values', 'of', 'K', '.', 'When', 'K', '=', '5', ',', 'KMA', 'finds', 'relatively', 'stable', 'clusters', '.', 'Hence', ',', 'SVaD', 'algorithms', 'are', 'unable', 'to', 'improve', 'the', 'accuracies', 'as', 'much', 'as', 'they', 'did', 'for', 'intermediate', 'values', 'of', 'K.', 'For', 'larger', 'values', 'of', 'K', ',', 'the', 'clusters', 'are', 'closely', 'spaced', 'and', 'hence', 'there', 'is', 'little', 'scope', 'for', 'improvement', 'by', 'the', 'SVaD', 'algorithms', '.', 'Multi10', 'data', 'sets', 'are', 'the', 'toughest', 'to', 'cluster', 'because', 'of', 'the', 'large', 'number', 'of', 'classes', 'present', 'in', 'the', 'data', '.', 'In', 'this', 'case', ',', 'the', 'accuracies', 'of', 'the', 'algorithms', 'are', 'monotonically', 'increasing', 'with', 'the', 'number', 'of', 'clusters', '.', 'The', 'extent', 'of', 'improvement', 'of', 'accuracies', 'of', 'SVaD', 'algorithms', 'over', 'KMA', 'is', 'almost', 'constant', 'over', 'the', 'entire', 'range', 'of', 'K', '.', 'This', 'reflects', 'the', 'fact', 'that', 'the', 'documents', 'in', 'Multi10', 'data', 'set', 'are', 'uniformly', 'distributed', 'over', 'feature', 'space', '.', 'The', 'distribution', 'of', 'documents', 'in', 'Yahoo', 'K1', 'data', 'set', 'is', 'highly', 'skewed', '.', 'The', 'extent', 'of', 'improvements', 'that', 'the', 'SVaD', 'algorithms', 'could', 'achieve', 'decrease', 'with', 'K.', 'For', 'higher', 'values', 'of', 'K', ',', 'KMA', 'is', 'able', 'to', 'find', 'almost', 'pure', 'sub-clusters', ',', 'resulting', 'in', 'accuracies', 'of', 'about', '90', '%', '.', 'This', 'leaves', 'little', 'scope', 'for', 'improvement', '.', 'The', 'performance', 'of', 'CSCAD', 'differs', 'noticeably', 'in', 'the', 'case', 'of', 'Classic', '3', '.', 'It', 'performs', 'better', 'than', 'the', 'SVaD', 'algorithms', 'for', 'K', '=', '3', 'and', 'better', 'than', 'EEnt', 'for', 'K', '=', '9', '.', 'However', ',', 'for', 'larger', 'values', 'of', 'K', ',', 'the', 'SVaD', 'algorithms', 'perform', 'better', 'than', 'the', 'rest', '.', 'As', 'in', 'the', 'case', 'of', 'Multi5', ',', 'the', 'improvements', 'of', 'SVaD', 'algorithms', 'over', 'others', 'are', 'significant', 'and', 'consistent', '.', 'One', 'may', 'recall', 'that', 'Multi5', 'and', 'Classic', '3', 'consist', 'of', 'documents', 'from', 'distinct', 'classes', '.', 'Therefore', ',', 'this', 'observation', 'implies', 'that', 'when', 'there', 'are', 'distinct', 'clusters', 'in', 'the', 'data', 'set', ',', 'KMA', 'yields', 'confusing', 'clusters', 'when', 'the', 'number', 'of', 'clusters', 'is', 'over-Figure', '1', ':', 'Accuracy', 'results', 'on', 'Binary', 'data', '.', 'Figure', '2', ':', 'Accuracy', 'results', 'on', 'Multi5', 'data', '.', 'specified', '.', 'In', 'this', 'scenario', ',', 'EEnt', 'and', 'EsGini', 'can', 'fine-tune', 'the', 'clusters', 'to', 'improve', 'their', 'purity', '.', 'We', 'have', 'defined', 'a', 'general', 'class', 'of', 'spatially', 'variant', 'dissimilarity', 'measures', 'and', 'proposed', 'algorithms', 'to', 'learn', 'the', 'measure', 'underlying', 'a', 'given', 'data', 'set', 'in', 'an', 'unsupervised', 'learning', 'framework', '.', 'Through', 'our', 'experiments', 'on', 'various', 'textual', 'data', 'sets', ',', 'we', 'have', 'shown', 'that', 'such', 'a', 'formulation', 'of', 'dissimilarity', 'measure', 'can', 'more', 'accurately', 'capture', 'the', 'hidden', 'structure', 'in', 'the', 'data', 'than', 'a', 'standard', 'Euclidean', 'measure', 'that', 'does', 'not', 'vary', 'over', 'feature', 'space', '.', 'We', 'have', 'also', 'shown', 'that', 'the', 'proposed', 'learning', 'algorithms', 'perform', 'better', 'than', 'other', 'similar', 'algorithms', 'in', 'the', 'literature', ',', 'and', 'have', 'better', 'stability', 'properties', '.', 'Even', 'though', 'we', 'have', 'applied', 'these', 'algorithms', 'only', 'to', 'text', 'data', 'sets', ',', 'the', 'algorithms', 'derived', 'here', 'do', 'not', 'assume', 'any', 'specific', 'characteristics', 'of', 'textual', 'data', 'sets', '.', 'Hence', ',', 'they', 'Figure', '3', ':', 'Accuracy', 'results', 'on', 'Multi10', 'data', '.', '615', 'Research', 'Track', 'Poster', 'Figure', '4', ':', 'Accuracy', 'results', 'on', 'Yahoo', 'K1', 'data', '.', 'Figure', '5', ':', 'Accuracy', 'results', 'on', 'Classic', '3', 'data', '.', 'are', 'applicable', 'to', 'general', 'data', 'sets', '.', 'Since', 'the', 'algorithms', 'perform', 'better', 'for', 'larger', 'K', ',', 'it', 'would', 'be', 'interesting', 'to', 'investigate', 'whether', 'they', 'can', 'be', 'used', 'to', 'find', 'subtopics', 'of', 'a', 'topic', '.', 'Finally', ',', 'it', 'will', 'be', 'interesting', 'to', 'learn', 'SVaD', 'measures', 'for', 'labeled', 'data', 'sets', '.', '-LSB-', '1', '-RSB-', 'J.', 'C.', 'Bezdek', 'and', 'R.', 'J.', 'Hathaway', '.', 'Some', 'notes', 'on', 'alternating', 'optimization', '.', 'In', 'Proceedings', 'of', 'the', '2002', 'AFSS', 'International', 'Conference', 'on', 'Fuzzy', 'Systems', '.', 'Calcutta', ',', 'pages', '288', '300', '.', 'Springer-Verlag', ',', '2002', '.', '-LSB-', '2', '-RSB-', 'A.', 'P.', 'Dempster', ',', 'N.', 'M.', 'Laird', ',', 'and', 'Rubin', '.', 'Maximum', 'likelihood', 'from', 'incomplete', 'data', 'via', 'the', 'EM', 'algorithm', '.', 'Journal', 'Royal', 'Statistical', 'Society', 'B', ',', '39', '-LRB-', '2', '-RRB-', ':', '1', '38', ',', '1977', '.', '-LSB-', '3', '-RSB-', 'I.', 'S.', 'Dhillon', 'and', 'D.', 'S.', 'Modha', '.', 'Concept', 'decompositions', 'for', 'large', 'sparse', 'text', 'data', 'using', 'clustering', '.', 'Machine', 'Learning', ',', '42', '-LRB-', '1', '-RRB-', ':', '143', '175', ',', 'January', '2001', '.', '-LSB-', '4', '-RSB-', 'E.', 'Diday', 'and', 'J.', 'C.', 'Simon', '.', 'Cluster', 'analysis', '.', 'In', 'K.', 'S.', 'Fu', ',', 'editor', ',', 'Pattern', 'Recognition', ',', 'pages', '47', '94', '.', 'Springer-Verlag', ',', '1976', '.', '-LSB-', '5', '-RSB-', 'H.', 'Frigui', 'and', 'O.', 'Nasraoui', '.', 'Simultaneous', 'clustering', 'and', 'attribute', 'discrimination', '.', 'In', 'Proceedings', 'of', 'FUZZIEEE', ',', 'pages', '158', '163', ',', 'San', 'Antonio', ',', '2000', '.', '-LSB-', '6', '-RSB-', 'H.', 'Frigui', 'and', 'O.', 'Nasraoui', '.', 'Simultaneous', 'categorization', 'of', 'text', 'documents', 'and', 'identification', 'of', 'cluster-dependent', 'keywords', '.', 'In', 'Proceedings', 'of', 'FUZZIEEE', ',', 'pages', '158', '163', ',', 'Honolulu', ',', 'Hawaii', ',', '2001', '.', '-LSB-', '7', '-RSB-', 'D.', 'E.', 'Gustafson', 'and', 'W.', 'C.', 'Kessel', '.', 'Fuzzy', 'clustering', 'with', 'the', 'fuzzy', 'covariance', 'matrix', '.', 'In', 'Proccedings', 'of', 'IEEE', 'CDC', ',', 'pages', '761', '766', ',', 'San', 'Diego', ',', 'California', ',', '1979', '.', '-LSB-', '8', '-RSB-', 'R.', 'Krishnapuram', 'and', 'J.', 'Kim', '.', 'A', 'note', 'on', 'fuzzy', 'clustering', 'algorithms', 'for', 'Gaussian', 'clusters', '.', 'IEEE', 'Transactions', 'on', 'Fuzzy', 'Systems', ',', '7', '-LRB-', '4', '-RRB-', ':', '453', '461', ',', 'Aug', '1999', '.', '-LSB-', '9', '-RSB-', 'Y.', 'Rui', ',', 'T.', 'S.', 'Huang', ',', 'and', 'S.', 'Mehrotra', '.', 'Relevance', 'feedback', 'techniques', 'in', 'interactive', 'content-based', 'image', 'retrieval', '.', 'In', 'Storage', 'and', 'Retrieval', 'for', 'Image', 'and', 'Video', 'Databases', '-LRB-', 'SPIE', '-RRB-', ',', 'pages', '25', '36', ',', '1998', '.', '-LSB-', '10', '-RSB-', 'N.', 'Slonim', 'and', 'N.', 'Tishby', '.', 'Document', 'clustering', 'using', 'word', 'clusters', 'via', 'the', 'information', 'bottleneck', 'method', '.', 'In', 'Proceedings', 'of', 'SIGIR', ',', 'pages', '208', '215', ',', '2000', '.', 'APPENDIX', 'A', '.', 'OTHER', 'FEATURE', 'WEIGHTING', 'CLUSTERING', 'TECHNIQUES', 'A.', '1', 'Diagonal', 'Gustafson-Kessel', '-LRB-', 'DGK', '-RRB-', 'Gustafson', 'and', 'Kessel', '-LSB-', '7', '-RSB-', 'associate', 'each', 'cluster', 'with', 'a', 'different', 'norm', 'matrix', '.', 'Let', 'A', '=', '-LRB-', 'A', '1', ',', '...', ',', 'A', 'k', '-RRB-', 'be', 'the', 'set', 'of', 'k', 'norm', 'matrices', 'associated', 'with', 'k', 'clusters', '.', 'Let', 'u', 'ji', 'is', 'the', 'fuzzy', 'membership', 'of', 'x', 'i', 'in', 'cluster', 'j', 'and', 'U', '=', '-LSB-', 'u', 'ji', '-RSB-', '.', 'By', 'restricting', 'A', 'j', 's', 'to', 'be', 'diagonal', 'and', 'u', 'ji', '-LCB-', '0', ',', '1', '-RCB-', ',', 'we', 'can', 'reformulate', 'the', 'original', 'optimization', 'problem', 'in', 'terms', 'of', 'SVaD', 'measures', 'as', 'follows', ':', 'min', 'C', ',', 'W', 'J', 'DGK', '-LRB-', 'C', ',', 'W', '-RRB-', '=', 'k', 'j', '=', '1', 'x', 'i', 'R', 'j', 'M', 'l', '=', '1', 'w', 'jl', 'g', 'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', ',', 'subject', 'to', 'l', 'w', 'jl', '=', 'j', '.', 'Note', 'that', 'this', 'problem', 'can', 'be', 'solved', 'using', 'the', 'same', 'AO', 'algorithms', 'described', 'in', 'Section', '3', '.', 'Here', ',', 'the', 'update', 'for', 'C', 'and', 'P', 'would', 'remain', 'the', 'same', 'as', 'that', 'discussed', 'in', 'Section', '3', '.', 'It', 'can', 'be', 'easily', 'shown', 'that', 'when', 'j', '=', '1', ',', 'j', ',', 'w', 'jl', '=', 'M', 'm', '=', '1', 'x', 'i', 'R', 'j', 'g', 'm', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '1/M', 'x', 'i', 'R', 'j', 'g', 'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '-LRB-', '14', '-RRB-', 'minimize', 'J', 'DGK', 'for', 'a', 'given', 'C.', 'A.', '2', 'Crisp', 'Simultaneous', 'Clustering', 'and', 'Attribute', 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'l', '-LRB-', 'x', 'i', ',', 'c', 'j', '-RRB-', '.', '-LRB-', '15', '-RRB-', 'Since', 'SCAD', 'uses', 'the', 'weighted', 'Euclidean', 'measure', ',', 'the', 'update', 'equations', 'of', 'centroids', 'in', 'CSCAD', 'remain', 'the', 'same', 'as', 'in', '-LRB-', '11', '-RRB-', '.', 'The', 'update', 'equation', 'for', 'w', 'jl', 'in', 'SCAD', 'is', 'quite', 'similar', 'to', '-LRB-', '15', '-RRB-', '.', 'One', 'may', 'note', 'that', ',', 'in', '-LRB-', '15', '-RRB-', ',', 'the', 'value', 'of', 'w', 'jl', 'can', 'become', 'negative', '.', 'In', '-LSB-', '5', '-RSB-', ',', 'a', 'heuristic', 'is', 'used', 'to', 'estimate', 'the', 'value', 'j', 'in', 'every', 'iteration', 'and', 'set', 'the', 'negative', 'values', 'of', 'w', 'jl', 'to', 'zero', 'before', 'normalizing', 'the', 'weights', '.', '616', 'Research', 'Track', 'Poster']
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'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases: ['dissimilarity measure', 'clustering', 'feature weighting']
Abstractive/absent Keyphrases: ['spatially varying dissimilarity (svad)', 'learning dissimilarity measures']
-----------
```
### Keyphrase Extraction
```python
from datasets import load_dataset
# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/nus", "extraction")
print("Samples for Keyphrase Extraction")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")
```
### Keyphrase Generation
```python
# get the dataset only for keyphrase generation
dataset = load_dataset("midas/nus", "generation")
print("Samples for Keyphrase Generation")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
## Citation Information
```
@InProceedings{10.1007/978-3-540-77094-7_41,
author="Nguyen, Thuy Dung
and Kan, Min-Yen",
editor="Goh, Dion Hoe-Lian
and Cao, Tru Hoang
and Solvberg, Ingeborg Torvik
and Rasmussen, Edie",
title="Keyphrase Extraction in Scientific Publications",
booktitle="Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers",
year="2007",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="317--326",
isbn="978-3-540-77094-7"
}
```
## Contributions
Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset
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0.02... |
philschmid/test_german_squad | 2021-10-25T13:55:14.000Z | [
"region:us"
] | philschmid | null | null | 2 | 8 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
piEsposito/br_quad_20 | 2021-02-05T16:05:55.000Z | [
"region:us"
] | piEsposito | Translates SQuAD 2.0 from english to portuguese using Google Cloud API | @article{2020braquad,
author = {{Esposito}, Wladimir and {Esposito}, Piero and {Tamais},
Ana Laura and {Gatti}, Daniel},
title = "{BrQuAD - Brazilian
Question-Answering Dataset: Dataset para benchmark de modelos de
Machine Learning para question-answering em
Portugu^es brasileiro traduzindo o SQuAD com Google Cloud API}",
year = 2020,
} | 0 | 8 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
poperson1205/mrtydi-v1.1-korean-fixed | 2022-01-02T08:18:25.000Z | [
"region:us"
] | poperson1205 | null | null | 1 | 8 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
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0.052520751953125,
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0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
yuvalkirstain/contract_nli_t5 | 2022-01-09T06:16:30.000Z | [
"region:us"
] | yuvalkirstain | null | null | 0 | 8 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
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0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
blo05/cleaned_wiki_en | 2022-03-30T10:12:38.000Z | [
"region:us"
] | blo05 | null | null | 0 | 8 | 2022-03-21T15:55:39 | Cleaned wikipedia dataset | 25 | [
[
-0.038543701171875,
-0.0208892822265625,
-0.0107574462890625,
-0.047088623046875,
-0.0248870849609375,
-0.0269622802734375,
-0.042327880859375,
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0.0361328125,
0.06427001953125,
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... |
huggan/few-shot-cat | 2022-04-12T14:06:50.000Z | [
"arxiv:2101.04775",
"region:us"
] | huggan | null | null | 1 | 8 | 2022-04-01T11:40:37 | # Citation
```
@article{DBLP:journals/corr/abs-2101-04775,
author = {Bingchen Liu and
Yizhe Zhu and
Kunpeng Song and
Ahmed Elgammal},
title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot
Image Synthesis},
journal = {CoRR},
volume = {abs/2101.04775},
year = {2021},
url = {https://arxiv.org/abs/2101.04775},
eprinttype = {arXiv},
eprint = {2101.04775},
timestamp = {Fri, 22 Jan 2021 15:16:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-04775.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | 676 | [
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0.00550079345703125,
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-0.02734375,... |
enoriega/biocreative_gene_mention | 2022-04-20T00:15:20.000Z | [
"region:us"
] | enoriega | Training and validation datasets for the BioCreative II gene mention task.
The data has been tokenized with [processors](https://github.com/clulab/processors)
## Features:
- __tokens__: Input token sequence
- __folded_tokens__: Same as tokens, but case-folded
- __tags__: POS tags of the input sequence tokens
- __labels__: BIO sequence tags | @article{cite-key,
Abstract = {Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.},
Author = {Smith, Larry and Tanabe, Lorraine K. and Ando, Rie Johnson nee and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph M. and Ganchev, Kuzman and Torii, Manabu and Liu, Hongfang and Haddow, Barry and Struble, Craig A. and Povinelli, Richard J. and Vlachos, Andreas and Baumgartner, William A. and Hunter, Lawrence and Carpenter, Bob and Tsai, Richard Tzong-Han and Dai, Hong-Jie and Liu, Feng and Chen, Yifei and Sun, Chengjie and Katrenko, Sophia and Adriaans, Pieter and Blaschke, Christian and Torres, Rafael and Neves, Mariana and Nakov, Preslav and Divoli, Anna and Ma{\~n}a-L{\'o}pez, Manuel and Mata, Jacinto and Wilbur, W. John},
Da = {2008/09/01},
Date-Added = {2022-04-15 17:35:45 -0700},
Date-Modified = {2022-04-15 17:35:45 -0700},
Doi = {10.1186/gb-2008-9-s2-s2},
Id = {Smith2008},
Isbn = {1474-760X},
Journal = {Genome Biology},
Number = {2},
Pages = {S2},
Title = {Overview of BioCreative II gene mention recognition},
Ty = {JOUR},
Url = {https://doi.org/10.1186/gb-2008-9-s2-s2},
Volume = {9},
Year = {2008},
Bdsk-Url-1 = {https://doi.org/10.1186/gb-2008-9-s2-s2}} | 0 | 8 | 2022-04-20T00:08:28 | Entry not found | 15 | [
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0.052520751953125,
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0.0513916015625,
0.016998291015625,
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-0.014984130859375,
-0.060394287109375,
0.0379... |
Goud/Goud-sum | 2022-07-04T16:02:36.000Z | [
"task_categories:summarization",
"task_ids:news-articles-headline-generation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"size_categories:100K<n<1M",
"source_datasets:original",
"region:us"
] | Goud | null | null | 2 | 8 | 2022-04-21T15:25:00 | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language: []
license: []
multilinguality: []
pretty_name: Goud-sum
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-headline-generation
---
# Dataset Card for Goud summarization 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:**[Needs More Information]
- **Repository:**[Needs More Information]
- **Paper:**[Goud.ma: a News Article Dataset for Summarization in Moroccan Darija](https://openreview.net/forum?id=BMVq5MELb9)
- **Leaderboard:**[Needs More Information]
- **Point of Contact:**[Needs More Information]
### Dataset Summary
Goud-sum contains 158k articles and their headlines extracted from [Goud.ma](https://www.goud.ma/) news website. The articles are written in the Arabic script. All headlines are in Moroccan Darija, while articles may be in Moroccan Darija, in Modern Standard Arabic, or a mix of both (code-switched Moroccan Darija).
### Supported Tasks and Leaderboards
Text Summarization
### Languages
* Moroccan Arabic (Darija)
* Modern Standard Arabic
## Dataset Structure
### Data Instances
The dataset consists of article-headline pairs in string format.
### Data Fields
* article: a string containing the body of the news article
* headline: a string containing the article's headline
* categories: a list of string of article categories
### Data Splits
Goud-sum dataset has 3 splits: _train_, _validation_, and _test_. Below are the number of instances in each split.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 139,288 |
| Validation | 9,497 |
| Test | 9,497 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The text was written by journalists at [Goud](https://www.goud.ma/).
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{issam2022goudma,
title={Goud.ma: a News Article Dataset for Summarization in Moroccan Darija},
author={Abderrahmane Issam and Khalil Mrini},
booktitle={3rd Workshop on African Natural Language Processing},
year={2022},
url={https://openreview.net/forum?id=BMVq5MELb9}
}
```
### Contributions
Thanks to [@issam9](https://github.com/issam9) and [@KhalilMrini](https://github.com/KhalilMrini) for adding this dataset.
| 4,254 | [
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0.03955078125,
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-0.06866455078125,
-0.0562744140625,
0.0075... |
h4iku/coconut_python2010_preprocessed | 2022-04-21T20:41:12.000Z | [
"region:us"
] | h4iku | null | null | 0 | 8 | 2022-04-21T20:34:45 | Entry not found | 15 | [
[
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-0.01495361328125,
0.05718994140625,
0.0288238525390625,
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0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
pietrolesci/fracas | 2022-04-25T08:40:07.000Z | [
"region:us"
] | pietrolesci | null | null | 0 | 8 | 2022-04-22T08:35:48 | ## Overview
Original dataset [here](https://github.com/felipessalvatore/NLI_datasets).
Below the original description reported for convenience.
```latex
@MISC{Fracas96,
author = {{The Fracas Consortium} and Robin Cooper and Dick Crouch and Jan Van Eijck and Chris Fox and Josef Van Genabith and Jan Jaspars and Hans Kamp and David Milward and Manfred Pinkal and Massimo Poesio and Steve Pulman and Ted Briscoe and Holger Maier and Karsten Konrad},
title = {Using the Framework},
year = {1996}
}
```
Adapted from [https://nlp.stanford.edu/~wcmac/downloads/fracas.xml](https://nlp.stanford.edu/~wcmac/downloads/fracas.xml). We took `P1, ..., Pn` as premise and H as hypothesis. Labels have been mapped as follows `{'yes': "entailment", 'no': 'contradiction', 'undef': "neutral", 'unknown': "neutral"}`. And we randomly split 80/20 for train/dev.
## Dataset curation
One hypothesis in the dev set and three hypotheses in the train set are empty and have been
filled in with the empty string `""`. Labels are encoded with custom NLI mapping, that is
```
{"entailment": 0, "neutral": 1, "contradiction": 2}
```
## Code to create the dataset
```python
import pandas as pd
from datasets import Features, Value, ClassLabel, Dataset, DatasetDict, load_dataset
from pathlib import Path
# load datasets
path = Path("<path to folder>/nli_datasets")
datasets = {}
for dataset_path in path.iterdir():
datasets[dataset_path.name] = {}
for name in dataset_path.iterdir():
df = pd.read_csv(name)
datasets[dataset_path.name][name.name.split(".")[0]] = df
ds = {}
for name, df_ in datasets["fracas"].items():
df = df_.copy()
assert df["label"].isna().sum() == 0
# fill-in empty hypothesis
df = df.fillna("")
# encode labels
df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
# cast to dataset
features = Features({
"premise": Value(dtype="string", id=None),
"hypothesis": Value(dtype="string", id=None),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
})
ds[name] = Dataset.from_pandas(df, features=features)
dataset = DatasetDict(ds)
dataset.push_to_hub("fracas", token="<token>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(ds.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
ds[i].to_pandas(),
ds[j].to_pandas(),
on=["label", "premise", "hypothesis"],
how="inner",
).shape[0],
)
#> train - dev: 0
``` | 2,601 | [
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0.0213470458984375,
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0.... |
pietrolesci/add_one_rte | 2022-04-25T08:48:42.000Z | [
"region:us"
] | pietrolesci | null | null | 0 | 8 | 2022-04-22T13:56:41 | ## Overview
Original data available [here](http://www.seas.upenn.edu/~nlp/resources/AN-composition.tgz).
## Dataset curation
`premise` and `hypothesis` columns have been cleaned following common practices ([1](https://github.com/rabeehk/robust-nli/blob/c32ff958d4df68ac2fad9bf990f70d30eab9f297/data/scripts/add_one_rte.py#L51-L52), [2](https://github.com/azpoliak/hypothesis-only-NLI/blob/b045230437b5ba74b9928ca2bac5e21ae57876b9/data/convert_add_1_rte.py#L31-L32)), that is
- remove HTML tags `<b>`, `<u>`, `</b>`, `</u>`
- normalize repeated white spaces
- strip
`mean_human_score` has been transformed into class labels following common practices ([1](https://github.com/rabeehk/robust-nli/blob/c32ff958d4df68ac2fad9bf990f70d30eab9f297/data/scripts/add_one_rte.py#L20-L35), [2](https://github.com/azpoliak/hypothesis-only-NLI/blob/b045230437b5ba74b9928ca2bac5e21ae57876b9/data/convert_add_1_rte.py#L6-L17)), that is
- for test set: `mean_human_score <= 3 -> "not-entailed"` and `mean_human_score >= 4 -> "entailed"` (anything between 3 and 4 has been removed)
- for all other splits: `mean_human_score < 3.5 -> "not-entailed"` else `"entailed"`
more details below.
## Code to generate the dataset
```python
import pandas as pd
from datasets import Features, Value, ClassLabel, Dataset, DatasetDict
def convert_label(score, is_test):
if is_test:
if score <= 3:
return "not-entailed"
elif score >= 4:
return "entailed"
return "REMOVE"
if score < 3.5:
return "not-entailed"
return "entailed"
ds = {}
for split in ("dev", "test", "train"):
# read data
df = pd.read_csv(f"<path to folder>/AN-composition/addone-entailment/splits/data.{split}", sep="\t", header=None)
df.columns = ["mean_human_score", "binary_label", "sentence_id", "adjective", "noun", "premise", "hypothesis"]
# clean text from html tags and useless spaces
for col in ("premise", "hypothesis"):
df[col] = (
df[col]
.str.replace("(<b>)|(<u>)|(</b>)|(</u>)", " ", regex=True)
.str.replace(" {2,}", " ", regex=True)
.str.strip()
)
# encode labels
if split == "test":
df["label"] = df["mean_human_score"].map(lambda x: convert_label(x, True))
df = df.loc[df["label"] != "REMOVE"]
else:
df["label"] = df["mean_human_score"].map(lambda x: convert_label(x, False))
assert df["label"].isna().sum() == 0
df["label"] = df["label"].map({"not-entailed": 0, "entailed": 1})
# cast to dataset
features = Features({
"mean_human_score": Value(dtype="float32"),
"binary_label": Value(dtype="string"),
"sentence_id": Value(dtype="string"),
"adjective": Value(dtype="string"),
"noun": Value(dtype="string"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["not-entailed", "entailed"]),
})
ds[split] = Dataset.from_pandas(df, features=features)
ds = DatasetDict(ds)
ds.push_to_hub("add_one_rte", token="<token>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(ds.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
ds[i].to_pandas(),
ds[j].to_pandas(),
on=["premise", "hypothesis", "label"],
how="inner",
).shape[0],
)
#> dev - test: 0
#> dev - train: 0
#> test - train: 0
``` | 3,487 | [
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0.023... |
pietrolesci/robust_nli_li_ts | 2022-04-25T11:49:51.000Z | [
"region:us"
] | pietrolesci | null | null | 0 | 8 | 2022-04-25T11:48:57 | This is part of `robust_NLI`but since there seems to be a bug when loading and downloading
`DatasetDict` containing datasets with different configurations, I loaded the datasets with
the differing configs as standalone datasets.
Issue here: [https://github.com/huggingface/datasets/issues/4211](https://github.com/huggingface/datasets/issues/4211) | 348 | [
[
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0.039... |
pietrolesci/robust_nli_is_sd | 2022-04-25T13:07:25.000Z | [
"region:us"
] | pietrolesci | null | null | 0 | 8 | 2022-04-25T11:49:03 | This is part of `robust_NLI`but since there seems to be a bug when loading and downloading
`DatasetDict` containing datasets with different configurations, I loaded the datasets with
the differing configs as standalone datasets.
Issue here: [https://github.com/huggingface/datasets/issues/4211](https://github.com/huggingface/datasets/issues/4211) | 348 | [
[
-0.0478515625,
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0.024169921875,
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-0.042022705078125,
0.03970336... |
pietrolesci/breaking_nli | 2022-04-25T13:37:23.000Z | [
"region:us"
] | pietrolesci | null | null | 0 | 8 | 2022-04-25T13:36:48 | ## Overview
Proposed by
```latex
@InProceedings{glockner_acl18,
author = {Glockner, Max and Shwartz, Vered and Goldberg, Yoav},
title = {Breaking NLI Systems with Sentences that Require Simple Lexical Inferences},
booktitle = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL)},
month = {July},
year = {2018},
address = {Melbourne, Australia}
}
```
Original dataset available [here](https://github.com/BIU-NLP/Breaking_NLI).
## Dataset curation
Labels encoded with the following mapping `{"entailment": 0, "neutral": 1, "contradiction": 2}`
and made available in the `label` column.
## Code to create the dataset
```python
import pandas as pd
from datasets import Features, Value, ClassLabel, Dataset, Sequence
# load data
with open("<path to folder>/dataset.jsonl", "r") as fl:
data = fl.read().split("\n")
df = pd.DataFrame([eval(i) for i in data if len(i) > 0])
# encode labels
df["label"] = df["gold_label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
# cast to dataset
features = Features({
"sentence1": Value(dtype="string", id=None),
"category": Value(dtype="string", id=None),
"gold_label": Value(dtype="string", id=None),
"annotator_labels": Sequence(feature=Value(dtype="string", id=None), length=3),
"pairID": Value(dtype="int32", id=None),
"sentence2": Value(dtype="string", id=None),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
})
ds = Dataset.from_pandas(df, features=features)
ds.push_to_hub("breaking_nli", token="<token>", split="all")
``` | 1,612 | [
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0.023681640625,
0.016815185546875,
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0.02215576171875,
0.042510986328125,
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-0.0433349609375,
-0.046966552734375,
0.03662109... |
BigScienceBiasEval/bias-shades | 2022-10-03T13:49:04.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | BigScienceBiasEval | This is a preliminary version of the bias SHADES dataset for evaluating LMs for social biases. | """
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = | 1 | 8 | 2022-04-28T16:46:11 | ---
license: cc-by-sa-4.0
---
Possibly a placeholder dataset for the original here: https://huggingface.co/datasets/bigscience-catalogue-data/bias-shades
# Data Statement for SHADES
> **How to use this document:**
> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.
> For full details, the best source is the original Data Statements paper, here: https://www.aclweb.org/anthology/Q18-1041/ .
> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as "DATASTATEMENT.md". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.
> Only blockquoted content should be deleted; the final about statement should be left intact.
Data set name: Bias-Shades
Citation (if available): TODO.
Data set developer(s): This dataset was compiled by dozens of research scientists through the BigScience open science collaboration. Collaborators, representing numerous cultures and languages, joined the project of their own volition.
Data statement author(s): Shayne Longpre, Aurélie Névéol, Shanya Sharma[Add name here if you add/edit the data statement :)].
Others who contributed to this document: N/A
License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).
## A. CURATION RATIONALE
> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.
This dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted. An initial set of sentences was inferred from stereotypes expressed in the crowS-pairs data set(Nangia et al.). Native speakers first crafted templates for sentences expressing a stereotype. These templates are marked for gender and plurality of the target nouns, so the template can be reused by substituting different targets. Next, the template-target noun pair combinations were annotated for the veracity/reliability of the expressed stereotype. The resulting sentences express common and less common stereotypes in a variety of cultures and languages.
## B. LANGUAGE VARIETY/VARIETIES
> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., "English as spoken in Palo Alto, California", or "Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin").
* BCP-47 language tags: en-US, fr-FR, hi-IN, es-DO, ar-LY, ru-RU, de-DE, nl-NL, ta-IN.
* Language variety description: English spoken by native speakers of the United States, native French people from metropolitan France, native Hindi and Tamil speakers from India, Spanish speakers from the Dominican Republic, Arabic speakers from Libya, Russian speakers from Russia, German speakers from Germany, and Dutch speakers from the Netherlands.
## C. CONTRIBUTOR DEMOGRAPHIC
> ## C. SPEAKER DEMOGRAPHIC
> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include:
Participants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Listed below.
Speakers:
* [ADD YOURSELF!]
* Shayne Longpre: English-speaking, male, 28 years old, culturally Canadian.
* Aurélie Névéol: French (native), English and Spanish speaking, female, 44 years old, culturally French (also familiar with American culture)
* Shanya Sharma: Hindi(native), English speaking, female, 24 years old, culturally Indian
* Margaret Mitchell: English, female, mid-30s, U.S.A.
* Maraim Masoud: Arabic, English Speaking female.
## D. ANNOTATOR DEMOGRAPHIC
> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:
Participants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Speaker and annotator contributors listed in section C.
## E. SPEECH SITUATION
N/A
## F. TEXT CHARACTERISTICS
> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.
Collected data is a collection of offensive stereotyped statements in numerous languages and cultures. They might be upsetting and/or offensive.
Along with these stereotyped statements are annotation judgements of how prevalent/real the expressed stereotypes are in the real world. Some statements were created from templates with substituted target nouns, and therefore may express an uncommon or unlikely stereotype.
## G. RECORDING QUALITY
N/A
## H. OTHER
> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.
## I. PROVENANCE APPENDIX
This initiative is part of the BigScience Workshop: https://bigscience.huggingface.co/.
## About this document
A data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.
Data Statements are from the University of Washington. Contact: [datastatements@uw.edu](mailto:datastatements@uw.edu). This document template is licensed as [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
This version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the [2020 LREC workshop on Data Statements](https://sites.google.com/uw.edu/data-statements-for-nlp/), by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski. | 7,432 | [
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0.04400634765625,
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-0.0574951171875,
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0.0... |
taln-ls2n/kptimes | 2022-09-23T07:38:28.000Z | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"region:us"
] | taln-ls2n | KPTimes benchmark dataset for keyphrase extraction an generation. | @inproceedings{gallina-etal-2019-kptimes,
title = "{KPT}imes: A Large-Scale Dataset for Keyphrase Generation on News Documents",
author = "Gallina, Ygor and
Boudin, Florian and
Daille, Beatrice",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "{--}" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-8617",
doi = "10.18653/v1/W19-8617",
pages = "130--135",
abstract = "Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https:// github.com/ygorg/KPTimes.",
} | 1 | 8 | 2022-05-06T09:34:40 | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- 100K<n<1M
pretty_name: KPTimes
---
# KPTimes Benchmark Dataset for Keyphrase Generation
## About
KPTimes is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 290K news articles in English collected from the [New York Times](https://www.nytimes.com/) and the [Japan
Times](https://www.japantimes.co.jp/).
Keyphrases were annotated by editors in a semi-automated manner (that is, editors revise a set of keyphrases proposed by an algorithm and provide additional keyphrases).
Details about the dataset can be found in the original paper [(Gallina et al., 2019)][gallina-2019].
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021].
Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in `prmu.py`. <u>P</u>resent keyphrases are ordered according to their first occurrence position in the text.
## Content and statistics
The dataset contains the following test split:
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: |
| Train | 259,923 | 921 | 5.03 | 45.61 | 15.57 | 29.63 | 9.19 |
| Validation | 10,000 | 921 | 5.02 | 45.22 | 15.78 | 29.60 | 9.41 |
| Test | 20,000 | 648 | 5.03 | 60.64 | 8.90 | 18.95 | 11.51 |
The following data fields are available :
- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
- **date**: publishing date (YYYY/MM/DD)
- **categories**: categories of the article (1 or 2 categories)
## References
- (Gallina et al., 2019) Ygor Gallina, Florian Boudin, and Beatrice Daille. 2019.
[KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents][gallina-2019].
In Proceedings of the 12th International Conference on Natural Language Generation, pages 130–135, Tokyo, Japan. Association for Computational Linguistics.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021].
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
[gallina-2019]: https://aclanthology.org/W19-8617/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/ | 3,407 | [
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strombergnlp/nlpcc-stance | 2022-10-25T21:47:26.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:cc-by-4.0",
"stance-detection",
"region:us"
] | strombergnlp | This is a stance prediction dataset in Chinese.
The data is that from a shared task, stance detection in Chinese microblogs, in NLPCC-ICCPOL 2016. It covers Task A, a mandatory supervised task which detects stance towards five targets of interest with given labeled data. | @incollection{xu2016overview,
title={Overview of nlpcc shared task 4: Stance detection in chinese microblogs},
author={Xu, Ruifeng and Zhou, Yu and Wu, Dongyin and Gui, Lin and Du, Jiachen and Xue, Yun},
booktitle={Natural language understanding and intelligent applications},
pages={907--916},
year={2016},
publisher={Springer}
} | 4 | 8 | 2022-05-19T11:19:12 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- zh
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-analysis
pretty_name: NLPCC Stance
tags:
- stance-detection
---
# Dataset Card for "NLPCC 2016: Stance Detection in Chinese Microblogs"
## 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:** [http://tcci.ccf.org.cn/conference/2016/pages/page05_evadata.html](http://tcci.ccf.org.cn/conference/2016/pages/page05_evadata.html)
- **Repository:**
- **Paper:** [https://link.springer.com/chapter/10.1007/978-3-319-50496-4_85](https://link.springer.com/chapter/10.1007/978-3-319-50496-4_85)
- **Point of Contact:** [Mads Kongsback](https://github.com/mkonxd)
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:**
### Dataset Summary
This is a stance prediction dataset in Chinese.
The data is that from a shared task, stance detection in Chinese microblogs, in NLPCC-ICCPOL 2016. It covers Task A, a mandatory supervised task which detects stance towards five targets of interest with given labeled data.
Some instances of the dataset have been removed, as they were without label.
### Supported Tasks and Leaderboards
* Stance Detection in Chinese Microblogs
### Languages
Chinese, as spoken on the Weibo website (`bcp47:zh`)
## Dataset Structure
### Data Instances
Example instance:
```
{
'id': '0',
'target': 'IphoneSE',
'text': '3月31日，苹果iPhone SE正式开卖，然而这款小屏新机并未出现人们预想的疯抢局面。根据市场分析机构Localytics周一公布的数据，iPhone SE正式上市的这个周末，销量成绩并不算太好。',
'stance': 2
}
```
### Data Fields
* id: a `string` field with a unique id for the instance
* target: a `string` representing the target of the stance
* text: a `string` of the stance-bearing text
* stance: an `int` representing class label -- `0`: AGAINST; `1`: FAVOR; `2`: NONE.
### Data Splits
The training split has 2986 instances
## Dataset Creation
### Curation Rationale
The goal was to create a dataset of microblog text annotated for stance. Six stance targets were selected and data was collected from Sina Weibo for annotation.
### Source Data
#### Initial Data Collection and Normalization
Not specified
#### Who are the source language producers?
Sina Weibo users
### Annotations
#### Annotation process
The stance of each target-microblog pair is duplicated annotated by two students
individually. If these two students provide the same annotation, the stance of this
microblog-target pair is then labeled. If the different annotation is detected, the third
student will be assigned to annotate this pair. Their annotation results will be voted to
obtain the final label.
#### Who are the annotators?
Students in China
### Personal and Sensitive Information
No reflections
## Considerations for Using the Data
### Social Impact of Dataset
The data preserves social media utterances verbatim and so has obviated any right to be forgotten, though usernames and post IDs are not explicitly included in the data.
### Discussion of Biases
There'll be at least a temporal and regional bias to this data, as well as it only representing expressions of stance on six topics.
### Other Known Limitations
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0.
### Citation Information
```
@incollection{xu2016overview,
title={Overview of nlpcc shared task 4: Stance detection in chinese microblogs},
author={Xu, Ruifeng and Zhou, Yu and Wu, Dongyin and Gui, Lin and Du, Jiachen and Xue, Yun},
booktitle={Natural language understanding and intelligent applications},
pages={907--916},
year={2016},
publisher={Springer}
}
```
### Contributions
Added by [@mkonxd](https://github.com/mkonxd), [@leondz](https://github.com/leondz)
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Lehrig/GTZAN-Collection | 2022-06-13T13:54:08.000Z | [
"license:apache-2.0",
"region:us"
] | Lehrig | The dataset consists of 1000 audio tracks each 30 seconds long.
It contains 10 genres, each represented by 100 tracks.
The tracks are all 22050Hz Mono 16-bit audio files in .wav format.
The genres are:
* blues
* classical
* country
* disco
* hiphop
* jazz
* metal
* pop
* reggae
* rock
This collection includes the following GTZAN variants:
* raw (original WAV files)
* melspectrograms (from each WAV file, contiguous 2-second windows at 4 random locations are sampled and transformed to Mel Spectrograms, resulting in 8000 Mel Spectrograms) | @ARTICLE{1021072,
author={Tzanetakis, G. and Cook, P.},
journal={IEEE Transactions on Speech and Audio Processing},
title={Musical genre classification of audio signals},
year={2002},
volume={10},
number={5},
pages={293-302},
doi={10.1109/TSA.2002.800560}} | 1 | 8 | 2022-05-25T20:16:44 | ---
license: apache-2.0
---
# Dataset Card for GTZAN Collection
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/derekahuang/Music-Classification
- **Repository:** https://github.com/derekahuang/Music-Classification
- **Paper:** [Musical genre classification of audio signals](https://ieeexplore.ieee.org/document/1021072)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The dataset consists of 1000 audio tracks each 30 seconds long.
It contains 10 genres, each represented by 100 tracks.
The tracks are all 22050Hz Mono 16-bit audio files in .wav format.
The genres are:
* blues
* classical
* country
* disco
* hiphop
* jazz
* metal
* pop
* reggae
* rock
This collection includes the following GTZAN variants:
* raw (original WAV files)
* melspectrograms (from each WAV file, contiguous 2-second windows at 4 random locations are sampled and transformed to Mel Spectrograms, resulting in 8000 Mel Spectrograms)
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | 3,060 | [
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pysentimiento/spanish-targeted-sentiment-headlines | 2022-06-17T21:28:01.000Z | [
"region:us"
] | pysentimiento | null | null | 1 | 8 | 2022-06-10T21:21:22 | Entry not found | 15 | [
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AlekseyKorshuk/mystery-crime-books | 2022-06-11T10:54:38.000Z | [
"region:us"
] | AlekseyKorshuk | null | null | 1 | 8 | 2022-06-11T10:54:36 | Entry not found | 15 | [
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nateraw/rendered-sst2 | 2022-10-25T10:32:21.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|sst2",
"language:en",
"license:unknown",
"region:us"... | nateraw | null | null | 0 | 8 | 2022-06-15T05:32:09 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Rendered SST-2
size_categories:
- 1K<n<10K
source_datasets:
- extended|sst2
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Rendered SST-2
The [Rendered SST-2 Dataset](https://github.com/openai/CLIP/blob/main/data/rendered-sst2.md) from Open AI.
Rendered SST2 is an image classification dataset used to evaluate the models capability on optical character recognition. This dataset was generated by rendering sentences in the Standford Sentiment Treebank v2 dataset.
This dataset contains two classes (positive and negative) and is divided in three splits: a train split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images (444 positive and 428 negative), and a test split containing 1821 images (909 positive and 912 negative). | 979 | [
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EddieChen372/javascript-small | 2022-06-18T09:37:52.000Z | [
"region:us"
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s3prl/mini_voxceleb1 | 2022-06-19T18:49:50.000Z | [
"region:us"
] | s3prl | null | null | 0 | 8 | 2022-06-19T12:06:16 | Entry not found | 15 | [
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scikit-learn/breast-cancer-wisconsin | 2022-06-20T14:28:58.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | scikit-learn | null | null | 0 | 8 | 2022-06-20T14:22:00 | ---
license: cc-by-sa-4.0
---
## Breast Cancer Wisconsin Diagnostic Dataset
Following description was retrieved from [breast cancer dataset on UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)).
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [here](https://pages.cs.wisc.edu/~street/images/).
Separating plane described above was obtained using Multisurface Method-Tree (MSM-T), a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.
The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].
Attribute Information:
- ID number
- Diagnosis (M = malignant, B = benign)
Ten real-valued features are computed for each cell nucleus:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
- perimeter
- area
- smoothness (local variation in radius lengths)
- compactness (perimeter^2 / area - 1.0)
- concavity (severity of concave portions of the contour)
- concave points (number of concave portions of the contour)
- symmetry
- fractal dimension ("coastline approximation" - 1)
| 1,626 | [
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knkarthick/samsum | 2022-10-21T03:03:27.000Z | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-nd-4.0",
"conversations-summarization",
"arxiv:1911.12237",
"r... | knkarthick | null | null | 3 | 8 | 2022-06-29T08:24:34 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: samsum-corpus
pretty_name: SAMSum Corpus
tags:
- conversations-summarization
---
# Dataset Card for SAMSum Corpus
## Dataset Description
### Links
- **Homepage:** hhttps://arxiv.org/abs/1911.12237v2
- **Repository:** https://arxiv.org/abs/1911.12237v2
- **Paper:** https://arxiv.org/abs/1911.12237v2
- **Point of Contact:** https://huggingface.co/knkarthick
### Dataset Summary
The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person.
The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0).
### Languages
English
## Dataset Structure
### Data Instances
SAMSum dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people
The first instance in the training set:
{'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"}
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: unique file id of an example.
### Data Splits
- train: 14732
- val: 818
- test: 819
## Dataset Creation
### Curation Rationale
In paper:
In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assistant and a client buying petrol.
As a consequence, we decided to create a chat dialogue dataset by constructing such conversations that would epitomize the style of a messenger app.
### Who are the source language producers?
linguists
### Who are the annotators?
language experts
### Annotation process
In paper:
Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one reference summary.
## Licensing Information
non-commercial licence: CC BY-NC-ND 4.0
## Citation Information
```
@inproceedings{gliwa-etal-2019-samsum,
title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization",
author = "Gliwa, Bogdan and
Mochol, Iwona and
Biesek, Maciej and
Wawer, Aleksander",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5409",
doi = "10.18653/v1/D19-5409",
pages = "70--79"
}
```
## Contributions | 4,358 | [
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PolyAI/evi | 2022-10-25T10:39:33.000Z | [
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:en",
"language:fr",
"language:pl",
"license:cc-by-4.0",
"arxiv:2204.13496",
"region:us"
] | PolyAI | EVI is a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French
that can be used for benchmarking and developing knowledge-based enrolment, identification, and identification
for spoken dialogue systems. | @inproceedings{Spithourakis2022evi,
author = {Georgios P. Spithourakis and Ivan Vuli\'{c} and Micha\l{} Lis and I\~{n}igo Casanueva and Pawe\l{} Budzianowski},
title = {{EVI}: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification},
year = {2022},
note = {Data available at https://github.com/PolyAI-LDN/evi-paper},
url = {https://arxiv.org/abs/2204.13496},
booktitle = {Findings of NAACL (publication pending)}
} | 2 | 8 | 2022-06-30T11:42:45 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
- fr
- pl
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: evi-multilingual-spoken-dialogue-tasks-and-1
language_bcp47:
- en
- en-GB
- fr
- fr-FR
- pl
---
# EVI
## Dataset Description
- **Paper:** [EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification](https://arxiv.org/abs/2204.13496)
- **Repository:** [Github](https://github.com/PolyAI-LDN/evi-paper)
EVI is a challenging spoken multilingual dataset
with 5,506 dialogues in English, Polish, and French
that can be used for benchmarking and developing
knowledge-based enrolment, identification, and identification for spoken dialogue systems.
## Example
EVI can be downloaded and used as follows:
```py
from datasets import load_dataset
evi = load_dataset("PolyAI/evi", "en-GB") # for British English
# to download data from all locales use:
# evi = load_dataset("PolyAI/evi", "all")
# see structure
print(evi)
```
## Dataset Structure
We show detailed information of the example for the `en-GB` configuration of the dataset.
All other configurations have the same structure.
### Data Instances
An example of a data instance of the config `en-GB` looks as follows:
```
{
"language": 0,
"dialogue_id": "CA0007220161df7be23f4554704c8720f5",
"speaker_id": "e80e9bdd33eda593f16a1b6f2fb228ff",
"turn_id": 0,
"target_profile_id": "en.GB.608",
"asr_transcription": "w20 a b",
"asr_nbest'": ["w20 a b", "w20 a bee", "w20 a baby"],
"path": "audios/en/CA0007220161df7be23f4554704c8720f5/0.wav",
"audio": {
"path": "/home/georgios/.cache/huggingface/datasets/downloads/extracted/0335ebc25feace53243133b49ba17ba18e26f0f97cb083ffdf4e73dd7427b443/audios/en/CA0007220161df7be23f4554704c8720f5/0.wav",
"array": array([ 0.00024414, 0.00024414, 0.00024414, ..., 0.00024414,
-0.00024414, 0.00024414], dtype=float32),
"sampling_rate": 8000,
}
}
```
### Data Fields
The data fields are the same among all splits.
- **language** (int): ID of language
- **dialogue_id** (str): the ID of the dialogue
- **speaker_id** (str): the ID of the speaker
- **turn_id** (int)": the ID of the turn
- **target_profile_id** (str): the ID of the target profile
- **asr_transcription** (str): ASR transcription of the audio file
- **asr_nbest** (list): n-best ASR transcriptions of the audio file
- **path** (str): Path to the audio file
- **audio** (dict): Audio object including loaded audio array, sampling rate and path of audio
### Data Splits
Every config only has the `"test"` split containing *ca.* 1,800 dialogues.
## Dataset Creation
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
### Citation Information
```
@inproceedings{Spithourakis2022evi,
author = {Georgios P. Spithourakis and
Ivan Vuli\'{c} and
Micha\l{} Lis and
I\~{n}igo Casanueva
and Pawe\l{} Budzianowski},
title = {{EVI}: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification},
year = {2022},
note = {Data available at https://github.com/PolyAI-LDN/evi-paper},
url = {https://arxiv.org/abs/2204.13496},
booktitle = {Findings of NAACL (publication pending)}
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) for helping with adding this dataset | 4,502 | [
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MicPie/unpredictable_full | 2022-08-04T20:07:28.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-02T20:22:21 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-full
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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MicPie/unpredictable_w3-org | 2022-08-04T20:16:53.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-03T11:45:06 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-w3-org
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-w3-org" - 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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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MicPie/unpredictable_sporcle-com | 2022-08-04T20:13:59.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-03T11:58:21 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-sporcle-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-sporcle-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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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MicPie/unpredictable_wiki-openmoko-org | 2022-08-04T20:17:59.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-03T12:06:24 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-wiki-openmoko-org
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-wiki-openmoko-org" - 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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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MicPie/unpredictable_ensembl-org | 2022-08-04T20:06:23.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-03T12:19:43 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-ensembl-org
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-ensembl-org" - 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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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Paul/hatecheck-german | 2022-07-05T10:38:52.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"arxiv:2206.09917",
"regi... | Paul | null | null | 0 | 8 | 2022-07-05T10:36:48 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: German HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. | 3,489 | [
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MicPie/unpredictable_cluster10 | 2022-08-04T19:49:37.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-08T17:18:25 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster10
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster10" - 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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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MicPie/unpredictable_cluster12 | 2022-08-04T19:52:07.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-08T17:20:21 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster12
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster12" - 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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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MicPie/unpredictable_cluster17 | 2022-08-04T19:55:23.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-08T17:33:42 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster17
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster17" - 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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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MicPie/unpredictable_cluster26 | 2022-08-04T20:00:43.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-08T18:38:15 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster26
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster26" - 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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
| 14,797 | [
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MicPie/unpredictable_cluster09 | 2022-08-04T19:48:52.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-08T19:15:21 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster09
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster09" - 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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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-0.046630859375,
0.0... |
MicPie/unpredictable_rated-high | 2022-08-04T20:11:37.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | 0 | 8 | 2022-07-09T08:56:24 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-rated-high
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-rated-high" - 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
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### 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/MicPie/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/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### 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/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### 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
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
| 14,799 | [
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jonathanli/legal-advice-reddit | 2023-02-23T16:39:28.000Z | [
"language:en",
"reddit",
"law",
"region:us"
] | jonathanli | null | null | 5 | 8 | 2022-07-27T20:19:25 | ---
language:
- en
tags:
- reddit
- law
pretty_name: Legal Advice Reddit
---
# Dataset Card for Legal Advice Reddit Dataset
## Dataset Description
- **Paper: [Parameter-Efficient Legal Domain Adaptation](https://aclanthology.org/2022.nllp-1.10/)**
- **Point of Contact: jxl@queensu.ca**
### Dataset Summary
New dataset introduced in [Parameter-Efficient Legal Domain Adaptation](https://aclanthology.org/2022.nllp-1.10) (Li et al., NLLP 2022) from the Legal Advice Reddit community (known as "/r/legaldvice"), sourcing the Reddit posts from the Pushshift
Reddit dataset. The dataset maps the text and title of each legal question posted into one of eleven classes, based on the original Reddit
post's "flair" (i.e., tag). Questions are typically informal and use non-legal-specific language. Per the Legal Advice Reddit rules, posts
must be about actual personal circumstances or situations. We limit the number of labels to the top eleven classes and remove the other
samples from the dataset.
### Citation Information
```
@inproceedings{li-etal-2022-parameter,
title = "Parameter-Efficient Legal Domain Adaptation",
author = "Li, Jonathan and
Bhambhoria, Rohan and
Zhu, Xiaodan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nllp-1.10",
pages = "119--129",
}
``` | 1,537 | [
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0.00788... |
graphs-datasets/ZINC | 2023-02-07T16:37:32.000Z | [
"task_categories:graph-ml",
"license:unknown",
"region:us"
] | graphs-datasets | null | null | 0 | 8 | 2022-08-01T15:11:09 | ---
license: unknown
dataset_info:
features:
- name: node_feat
sequence:
sequence: int64
- name: edge_index
sequence:
sequence: int64
- name: edge_attr
sequence:
sequence: int64
- name: 'y'
sequence: float64
- name: num_nodes
dtype: int64
splits:
- name: train
num_bytes: 376796456
num_examples: 220011
- name: test
num_bytes: 8538528
num_examples: 5000
- name: validation
num_bytes: 41819628
num_examples: 24445
download_size: 20636253
dataset_size: 427154612
task_categories:
- graph-ml
---
# Dataset Card for ZINC
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://zinc15.docking.org/)**
- **[Repository](https://www.dropbox.com/s/feo9qle74kg48gy/molecules.zip?dl=1):**:
- **Paper:**: ZINC 15 – Ligand Discovery for Everyone (see citation)
- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/)
### Dataset Summary
The `ZINC` dataset is a "curated collection of commercially available chemical compounds prepared especially for virtual screening" (Wikipedia).
### Supported Tasks and Leaderboards
`ZINC` should be used for molecular property prediction (aiming to predict the constrained solubility of the molecules), a graph regression task. The score used is the MAE.
The associated leaderboard is here: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-regression-on-zinc).
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 220011 |
| average #nodes | 23.15 |
| average #edges | 49.81 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data comes from the PyGeometric version of the dataset, and follows the provided data splits.
This information can be found back using
```python
from torch_geometric.datasets import ZINC
dataset = ZINC(root = '', split='train') # valid, test
```
## Additional Information
### Licensing Information
The dataset has been released under unknown license. Please open an issue if you know what is the license of this dataset.
### Citation Information
```bibtex
@article{doi:10.1021/acs.jcim.5b00559,
author = {Sterling, Teague and Irwin, John J.},
title = {ZINC 15 – Ligand Discovery for Everyone},
journal = {Journal of Chemical Information and Modeling},
volume = {55},
number = {11},
pages = {2324-2337},
year = {2015},
doi = {10.1021/acs.jcim.5b00559},
note ={PMID: 26479676},
URL = {
https://doi.org/10.1021/acs.jcim.5b00559
},
eprint = {
https://doi.org/10.1021/acs.jcim.5b00559
}
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. | 4,177 | [
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cjvt/sentinews | 2022-08-17T06:28:13.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:sl",
"license:cc-by-sa-4.0",
"slovenian sentiment",
"news articles",
"region:us"
] | cjvt | SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their
sentiment by between 2 and 6 annotators. The news articles contain political, business, economic and financial content
from the Slovenian news portals 24ur, Dnevnik, Finance, Rtvslo, and Žurnal24. The texts were annotated using the
five-level Lickert scale (1 – very negative, 2 – negative, 3 – neutral, 4 – positive, and 5 – very positive) on three
levels of granularity, i.e. on the document, paragraph, and sentence level. The final sentiment is determined using
the following criterion: negative (if average of scores ≤ 2.4); neutral (if average of scores is between 2.4 and 3.6);
positive (average of annotated scores ≥ 3.6). | @article{buvcar2018annotated,
title={Annotated news corpora and a lexicon for sentiment analysis in Slovene},
author={Bu{\v{c}}ar, Jo{\v{z}}e and {\v{Z}}nidar{\v{s}}i{\v{c}}, Martin and Povh, Janez},
journal={Language Resources and Evaluation},
volume={52},
number={3},
pages={895--919},
year={2018},
publisher={Springer}
} | 1 | 8 | 2022-08-15T08:32:30 | ---
annotations_creators:
- crowdsourced
language:
- sl
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: SentiNews
size_categories: []
source_datasets:
- original
tags:
- slovenian sentiment
- news articles
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for SentiNews
## Dataset Description
- **Homepage:** https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene
- **Paper:** Bučar, J., Žnidaršič, M. & Povh, J. Annotated news corpora and a lexicon for sentiment analysis in Slovene. Lang Resources & Evaluation 52, 895–919 (2018). https://doi.org/10.1007/s10579-018-9413-3
### Dataset Summary
SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their sentiment by between two and six annotators.
It is annotated at three granularities:
- document-level (config `document_level`, 10 427 documents),
- paragraph-level (config `paragraph_level`, 89 999 paragraphs), and
- sentence-level (config `sentence_level`, 168 899 sentences).
### Supported Tasks and Leaderboards
Sentiment classification, three classes (negative, neutral, positive).
### Languages
Slovenian.
## Dataset Structure
### Data Instances
A sample instance from the sentence-level config:
```
{
'nid': 2,
'content': 'Vilo Prešeren je na dražbi ministrstva za obrambo kupilo nepremičninsko podjetje Condor Real s sedežem v Lescah.',
'sentiment': 'neutral',
'pid': 1,
'sid': 1
}
```
### Data Fields
The data fields are similar among all three configs, with the only difference being the IDs.
- `nid`: a uint16 containing a unique ID of the news article (document).
- `content`: a string containing the body of the news article
- `sentiment`: the sentiment of the instance
- `pid`: a uint8 containing the consecutive number of the paragraph inside the current news article, **not unique** (present in the configs `paragraph_level` and `sentence_level`)
- `sid`: a uint8 containing the consecutive number of the sentence inside the current paragraph, **not unique** (present in the config `sentence_level`)
## Additional Information
### Dataset Curators
Jože Bučar, Martin Žnidaršič, Janez Povh.
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@article{buvcar2018annotated,
title={Annotated news corpora and a lexicon for sentiment analysis in Slovene},
author={Bu{\v{c}}ar, Jo{\v{z}}e and {\v{Z}}nidar{\v{s}}i{\v{c}}, Martin and Povh, Janez},
journal={Language Resources and Evaluation},
volume={52},
number={3},
pages={895--919},
year={2018},
publisher={Springer}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
| 2,812 | [
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0.018051147460... |
allenai/multinews_sparse_oracle | 2022-11-12T00:15:42.000Z | [
"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"
] | allenai | null | null | 1 | 8 | 2022-08-17T22:44:40 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: 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
---
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__: `"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 `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8775 | 0.7480 | 0.7480 | 0.7480 | | 1,361 | [
[
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-0.02703857421875,
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-0.0390625,
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0.000894... |
pinecone/movielens-recent-ratings | 2022-08-23T10:00:17.000Z | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"movielens",
"recommendation",
"collaborative filtering",
"region:us"
] | pinecone | This dataset streams recent user ratings from the MovieLens 25M dataset and adds poster URLs. | @InProceedings{huggingface:dataset,
title = {MovieLens Ratings},
author={Ismail Ashraq, James Briggs},
year={2022}
} | 1 | 8 | 2022-08-22T16:42:11 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license: []
multilinguality:
- monolingual
pretty_name: MovieLens User Ratings
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- movielens
- recommendation
- collaborative filtering
task_categories: []
task_ids: []
---
# MovieLens User Ratings
This dataset contains ~1M user ratings, consisting of ~10k of the most recent movies from the MovieLens 25M dataset, for which over 30k unique users have rated. The dataset is streamed from the MovieLens 25M dataset, filters for the recent movies, and returns the user ratings for those. After a few joins and checks, we get this dataset. Included are the URLs of the respective movie posters.
The dataset is part of an example on [building a movie recommendation engine](https://www.pinecone.io/docs/examples/movie-recommender-system/) with vector search. | 910 | [
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unpredictable/unpredictable_support-google-com | 2022-08-28T18:25:26.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | unpredictable | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | null | 0 | 8 | 2022-08-28T18:12:13 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-support-google-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-support-google-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/AnonCodeShare/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/unpredictable/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/unpredictable/unpredictable_support-google-com)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Licensing Information
Apache 2.0 | 8,248 | [
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Osaleh/ArSAS | 2022-09-05T07:09:56.000Z | [
"region:us"
] | Osaleh | null | null | 0 | 8 | 2022-09-05T05:13:23 | Entry not found | 15 | [
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0.0379... |
ashgorithm/movie_review_prediction_3000 | 2022-09-13T12:43:51.000Z | [
"region:us"
] | ashgorithm | null | null | 0 | 8 | 2022-09-13T12:43:26 | Entry not found | 15 | [
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0.0379... |
abidlabs/celeb-dataset | 2022-10-02T20:23:09.000Z | [
"region:us"
] | abidlabs | null | null | 0 | 8 | 2022-10-02T19:15:52 | Entry not found | 15 | [
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Drewd/lex_fridman_podcast_transcripts | 2022-10-05T01:41:30.000Z | [
"annotations_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"podcast",
"ai",
"interviews",
"region:us"
] | Drewd | This new dataset is meant to fine tune a model on how lex would talk. It's meant
to support Q+A style models as well as encoders. | null | 0 | 8 | 2022-10-04T03:42:55 | ---
annotations_creators:
- found
language:
- en
language_creators:
- machine-generated
license: []
multilinguality:
- monolingual
pretty_name: The transcripts from Lex Fridman podcast episodes on Youtube.
size_categories:
- n<1K
source_datasets: []
tags:
- podcast
- ai
- interviews
task_categories: []
task_ids: []
---
# Dataset Card for Lex Fridman Podcast Transcripts
## Table of Contents
- [Dataset Card for Lex Fridman Podcast Transcripts](#dataset-card-for-lex-fridman-podcast-transcripts)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://karpathy.ai/lexicap/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [@drewdresser](https://twitter.com/drewdresser)
### Dataset Summary
These are transcripts from the Lex Fridman podcast. The podcast is hosted by Lex Fridman, a computer scientist at MIT. The podcast is a mix of interviews with researchers in AI and other fields, and discussions of current events in AI. The transcripts are generated using [OpenAI Whisper](https://github.com/openai/whisper), then made available on [Karpathy AI](https://karpathy.ai/lexicap/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
~325
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | 3,670 | [
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YaYaB/onepiece-blip-captions | 2022-10-05T10:08:34.000Z | [
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:YaYaB/onepiece-blip-captions",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | YaYaB | null | null | 6 | 8 | 2022-10-05T08:53:42 | ---
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
pretty_name: 'One Piece BLIP captions'
size_categories:
- n<1K
source_datasets:
- YaYaB/onepiece-blip-captions
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Disclaimer
This was inspired from https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions
# Dataset Card for One Piece BLIP captions
_Dataset used to train [One Piece text to image model](https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning)_
BLIP generated captions for One piece images collected from the web. Original images were obtained from [Anime Characters](https://www.animecharactersdatabase.com) and captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided.
## Examples
> a man in a straw hat
> a man in a green coat holding two swords
> a man with red hair and a black coat
## Citation
If you use this dataset, please cite it as:
```
@misc{yayab2022onepiece,
author = {YaYaB},
title = {One Piece BLIP captions},
year={2022},
howpublished= {\url{https://huggingface.co/datasets/YaYaB/onepiece-blip-captions/}}
}
``` | 1,672 | [
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arbml/arabic_text_diacritization | 2022-11-03T13:33:33.000Z | [
"region:us"
] | arbml | null | null | 1 | 8 | 2022-10-05T13:05:16 | Entry not found | 15 | [
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brendenc/celeb-identities | 2022-10-09T02:33:12.000Z | [
"region:us"
] | brendenc | null | null | 0 | 8 | 2022-10-09T02:31:19 | This is a small dataset containing celebrity faces. This dataset was created for educational purposes and is far too small for any sort of model training. However, these images can be used for demo examples or other educational purposes. | 237 | [
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joey234/nan-nli | 2022-10-13T23:18:18.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"negation",
"regi... | joey234 | null | null | 0 | 8 | 2022-10-13T23:16:18 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: nan-nli
size_categories:
- n<1K
source_datasets:
- original
tags:
- negation
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
Natural Language Inference
Text Classification
### Languages
en
## Dataset Structure
### Data Instances
### Data Fields
premise:
hypothesis:
label:
### Data Splits
Evaluation: 258 samples
## Dataset Creation
### Curation Rationale
Extracting samples corresponding to different linguistics constructions of negation.
### Source Data
Geoffrey K. Pullum and Rodney Huddleston. 2002. Negation, chapter 9. Cambridge University Press.
#### 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?
The annotators are the authors of the papers, one of whom holds a graduate degree in linguistics.
### 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 [@joey234](https://github.com/joey234) for adding this dataset. | 2,995 | [
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0.020... |
TheoTsio/Health_Misinfo | 2023-08-28T21:51:26.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"health_misinformation, credibility",
"region:us"
] | TheoTsio | null | null | 0 | 8 | 2022-10-19T12:45:11 | ---
task_categories:
- text-classification
language:
- en
tags:
- health_misinformation, credibility
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The health misinfo dataset is an English Document dataset containing just over 6k unique articles related to health issues from web. This dataset was created in an effort to detect the misinformation in health documents. This dataset was created from the relevance judgment of the TREC health misinformation
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 1,739 | [
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0.03460693359375,
0.041595458984375,
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-0.052520751953... |
ChristophSchuhmann/aesthetic-logo-ratings | 2022-11-06T15:48:48.000Z | [
"region:us"
] | ChristophSchuhmann | null | null | 4 | 8 | 2022-11-06T15:42:12 | ~ 15k logo images from LAION-5B have been rated for aesthetic preference ( preference_average ) and for how professional the design look ( professionalism_average ).
---
license: apache-2.0
---
| 196 | [
[
-0.0531005859375,
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0.029998779296875,
-0.037506103515625,
-0.052154541015625,
-0.03369140625,
0.029617... |
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