id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
BangumiBase/akebichannosailorfuku | 2023-10-26T04:32:58.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-26T02:58:08 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Akebi-chan No Sailor-fuku
This is the image base of bangumi Akebi-chan no Sailor-fuku, we detected 36 characters, 3240 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 9 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 18 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 175 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 177 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 154 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 102 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 677 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 147 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 108 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 70 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 71 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 74 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 55 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 53 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 31 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 86 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 142 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 20 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 26 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 32 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 45 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 91 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 47 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 19 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 21 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 10 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 94 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 40 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 269 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 108 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 16 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 19 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 16 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 34 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 160 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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] |
BangumiBase/myheroacademia | 2023-10-30T17:41:10.000Z | [
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-26T02:58:25 | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of My Hero Academia
This is the image base of bangumi My Hero Academia, we detected 146 characters, 15676 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:----------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|
| 0 | 186 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 254 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 105 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 546 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 69 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 739 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 201 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 34 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 129 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 31 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 3091 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 71 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 174 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 211 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 878 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 130 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 75 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 63 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 69 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 540 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 274 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 286 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 140 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 25 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 81 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 30 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 60 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 26 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 41 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 52 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 81 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 26 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 86 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 65 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 32 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 41 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 74 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 259 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 26 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 11 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 26 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 78 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 29 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 24 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 41 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 24 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
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| 58 | 159 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 41 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 14 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 32 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 29 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 388 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 42 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 124 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
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| 67 | 25 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
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| 73 | 26 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 14 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 33 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 21 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
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| 83 | 18 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 331 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 12 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
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| 89 | 813 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 19 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
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| 100 | 25 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
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| 110 | 22 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
| 111 | 7 | [Download](111/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 112 | 67 | [Download](112/dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 23 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 390 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 18 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 10 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 31 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 46 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
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| 121 | 10 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 126 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 48 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 21 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 10 | [Download](125/dataset.zip) |  |  |  |  |  |  |  |  |
| 126 | 52 | [Download](126/dataset.zip) |  |  |  |  |  |  |  |  |
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| 128 | 32 | [Download](128/dataset.zip) |  |  |  |  |  |  |  |  |
| 129 | 28 | [Download](129/dataset.zip) |  |  |  |  |  |  |  |  |
| 130 | 13 | [Download](130/dataset.zip) |  |  |  |  |  |  |  |  |
| 131 | 9 | [Download](131/dataset.zip) |  |  |  |  |  |  |  |  |
| 132 | 57 | [Download](132/dataset.zip) |  |  |  |  |  |  |  |  |
| 133 | 32 | [Download](133/dataset.zip) |  |  |  |  |  |  |  |  |
| 134 | 14 | [Download](134/dataset.zip) |  |  |  |  |  |  |  |  |
| 135 | 138 | [Download](135/dataset.zip) |  |  |  |  |  |  |  |  |
| 136 | 14 | [Download](136/dataset.zip) |  |  |  |  |  |  |  |  |
| 137 | 11 | [Download](137/dataset.zip) |  |  |  |  |  |  |  |  |
| 138 | 6 | [Download](138/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 139 | 33 | [Download](139/dataset.zip) |  |  |  |  |  |  |  |  |
| 140 | 7 | [Download](140/dataset.zip) |  |  |  |  |  |  |  | N/A |
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| 144 | 17 | [Download](144/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 467 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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twodgirl/Fear-and-Frivolity | 2023-10-26T03:41:12.000Z | [
"language:en",
"conversational",
"novel",
"fairseq",
"not-for-all-audiences",
"region:us"
] | twodgirl | null | null | 0 | 0 | 2023-10-26T03:33:04 | ---
language:
- en
tags:
- conversational
- novel
- fairseq
- not-for-all-audiences
---
Take fairseq, a Japanese novel, and make up the dialogues based on the translation.
Translated by: fairseq.
Made by: Mistral-7B-Instruct-5.0bpw.
Theme: Japanese novel.
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parksimon0808/test | 2023-10-26T03:50:08.000Z | [
"region:us"
] | parksimon0808 | null | null | 0 | 0 | 2023-10-26T03:49:59 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 64
num_examples: 2
download_size: 1726
dataset_size: 64
---
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 374 | [
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jannko/fund-sft | 2023-10-26T07:09:12.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-4.0",
"gpt",
"alpaca",
"fine-tune",
"instruct-tune",
"instruction",
"region:us"
] | jannko | null | null | 0 | 0 | 2023-10-26T03:55:21 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 32150579
num_examples: 48818
download_size: 35100559
dataset_size: 32150579
license: cc-by-4.0
language:
- zh
pretty_name: Sft Dataset
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- gpt
- alpaca
- fine-tune
- instruct-tune
- instruction
---
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[More Information Needed] | 4,816 | [
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wolftijuana/staffy | 2023-10-26T03:59:04.000Z | [
"region:us"
] | wolftijuana | null | null | 0 | 0 | 2023-10-26T03:57:56 | Entry not found | 15 | [
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choco9966/results | 2023-10-26T08:44:46.000Z | [
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] | choco9966 | null | null | 0 | 0 | 2023-10-26T04:50:13 | Entry not found | 15 | [
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Jiabin99/GraphGPT-eval-instruction | 2023-10-26T05:22:07.000Z | [
"region:us"
] | Jiabin99 | null | null | 2 | 0 | 2023-10-26T05:03:50 | Entry not found | 15 | [
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mgo1513/UCF-101-split | 2023-10-26T05:21:34.000Z | [
"region:us"
] | mgo1513 | null | null | 0 | 0 | 2023-10-26T05:08:54 | Entry not found | 15 | [
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twwch/summary | 2023-10-26T06:32:33.000Z | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:zh",
"license:apache-2.0",
"region:us"
] | twwch | null | null | 1 | 0 | 2023-10-26T05:11:36 | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 31798343
num_examples: 10352
- name: test
num_bytes: 3617590
num_examples: 1151
download_size: 17798756
dataset_size: 35415933
task_categories:
- summarization
language:
- zh
size_categories:
- 10K<n<100K
---
微调google/mt5-base模型，做文章摘要
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
model_path = "twwch/mt5-base-summary"
model = T5ForConditionalGeneration.from_pretrained(model_path)
tokenizer = T5Tokenizer.from_pretrained(model_path)
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
model.eval()
text = """
什么是Nginx
Nginx是一个开源的高性能HTTP和反向代理服务器。它可以用于处理静态资源、负载均衡、反向代理和缓存等任务。Nginx被广泛用于构建高可用性、高性能的Web应用程序和网站。它具有低内存消耗、高并发能力和良好的稳定性，因此在互联网领域非常受欢迎。
为什么使用Nginx
高性能：Nginx采用事件驱动的异步架构，能够处理大量并发连接而不会消耗过多的系统资源。它的处理能力比传统的Web服务器更高，在高并发负载下表现出色。
高可靠性：Nginx具有强大的容错能力和稳定性，能够在面对高流量和DDoS攻击等异常情况下保持可靠运行。它能通过健康检查和自动故障转移来保证服务的可用性。
负载均衡：Nginx可以作为反向代理服务器，实现负载均衡，将请求均匀分发给多个后端服务器。这样可以提高系统的整体性能和可用性。
静态文件服务：Nginx对静态资源（如HTML、CSS、JavaScript、图片等）的处理非常高效。它可以直接缓存静态文件，减轻后端服务器的负载。
扩展性：Nginx支持丰富的模块化扩展，可以通过添加第三方模块来提供额外的功能，如gzip压缩、SSL/TLS加密、缓存控制等。
如何处理请求
Nginx处理请求的基本流程如下：
接收请求：Nginx作为服务器软件监听指定的端口，接收客户端发来的请求。
解析请求：Nginx解析请求的内容，包括请求方法（GET、POST等）、URL、头部信息等。
配置匹配：Nginx根据配置文件中的规则和匹配条件，决定如何处理该请求。配置文件定义了虚拟主机、反向代理、负载均衡、缓存等特定的处理方式。
处理请求：Nginx根据配置的处理方式，可能会进行以下操作：
静态文件服务：如果请求的是静态资源文件，如HTML、CSS、JavaScript、图片等，Nginx可以直接返回文件内容，不必经过后端应用程序。
反向代理：如果配置了反向代理，Nginx将请求转发给后端的应用服务器，然后将其响应返回给客户端。这样可以提供负载均衡、高可用性和缓存等功能。
缓存：如果启用了缓存，Nginx可以缓存一些静态或动态内容的响应，在后续相同的请求中直接返回缓存的响应，减少后端负载并提高响应速度。
URL重写：Nginx可以根据配置的规则对URL进行重写，将请求从一个URL重定向到另一个URL或进行转换。
SSL/TLS加密：如果启用了SSL/TLS，Nginx可以负责加密和解密HTTPS请求和响应。
访问控制：Nginx可以根据配置的规则对请求进行访问控制，例如限制IP访问、进行身份认证等。
响应结果：Nginx根据处理结果生成响应报文，包括状态码、头部信息和响应内容。然后将响应发送给客户端。
"""
def _split_text(text, length):
chunks = []
start = 0
while start < len(text):
if len(text) - start > length:
pos_forward = start + length
pos_backward = start + length
pos = start + length
while (pos_forward < len(text)) and (pos_backward >= 0) and (pos_forward < 20 + pos) and (
pos_backward + 20 > pos) and text[pos_forward] not in {'.', '。', '，', ','} and text[
pos_backward] not in {'.', '。', '，', ','}:
pos_forward += 1
pos_backward -= 1
if pos_forward - pos >= 20 and pos_backward <= pos - 20:
pos = start + length
elif text[pos_backward] in {'.', '。', '，', ','}:
pos = pos_backward
else:
pos = pos_forward
chunks.append(text[start:pos + 1])
start = pos + 1
else:
chunks.append(text[start:])
break
# Combine last chunk with previous one if it's too short
if len(chunks) > 1 and len(chunks[-1]) < 100:
chunks[-2] += chunks[-1]
chunks.pop()
return chunks
def summary(text):
chunks = _split_text(text, 300)
chunks = [
"summarize: " + chunk
for chunk in chunks
]
input_ids = tokenizer(chunks, return_tensors="pt",
max_length=512,
padding=True,
truncation=True).input_ids.to(device)
outputs = model.generate(input_ids, max_length=250, num_beams=4, no_repeat_ngram_size=2)
tokens = outputs.tolist()
output_text = [
tokenizer.decode(tokens[i], skip_special_tokens=True)
for i in range(len(tokens))
]
for i in range(len(output_text)):
print(output_text[i])
summary(text)
```
输出：
```
段落内容Nginx是一个开源的高性能HTTP和反向代理服务器,可以用于处理静态资源、负载均衡、反反代理和缓存等任务。它被广泛用于构建高可用性、高性能的Web应用程序和网站,具有低内存消耗、高并发能力和良好的稳定性,因此在互联网领域非常受欢迎。高性能和高可靠性相比传统的Web服务器更高,在高并且发负担下表现出色。高稳定性和容错能力,能够在面对高流量和DDoS攻击等异常情况下保持可靠运行。
段落内容Nginx处理请求的基本流程,包括负载均衡、静态文件服务、扩展性、如何解决请求的流程和如何处理。其中包括接收请求和解析请求,以及对客户端发来的请求进行解析。
段落内容Nginx的配置匹配和处理请求。配置文件定义了虚拟主机、反向代理、负载均衡、缓存等特定的处理方式,并根据配置进行静态文件服务和反面信息处理的操作。通过调用静存来实现高可用性,并且可以提供高可性和缓储等功能。
段落内容主要涉及到缓存静态或动态内容的响应,包括URL重写、SSL/TLS加密、访问控制、响应结果生成和发送给客户端等功能。Nginx可以根据配置的规则对URL进行重写作,将请求从一个URL轻定向到另一个URL或进行转换。 综上所述,Nginx的缓解和响应速度可以快速提高。
``` | 4,448 | [
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] |
pphuc25/trigger_dataset | 2023-10-26T05:18:34.000Z | [
"region:us"
] | pphuc25 | null | null | 0 | 0 | 2023-10-26T05:18:33 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 304886.95362663496
num_examples: 2511
download_size: 130993
dataset_size: 304886.95362663496
---
# Dataset Card for "trigger_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 411 | [
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] |
pphuc25/non_trigger_dataset | 2023-10-26T05:18:36.000Z | [
"region:us"
] | pphuc25 | null | null | 0 | 0 | 2023-10-26T05:18:34 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 307801.04637336504
num_examples: 2535
download_size: 205691
dataset_size: 307801.04637336504
---
# Dataset Card for "non_trigger_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 415 | [
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pphuc25/non_selfie_dataset | 2023-10-26T05:20:38.000Z | [
"region:us"
] | pphuc25 | null | null | 0 | 0 | 2023-10-26T05:20:37 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 151711.86280487804
num_examples: 1670
download_size: 88838
dataset_size: 151711.86280487804
---
# Dataset Card for "non_selfie_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 413 | [
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pphuc25/selfie_dataset | 2023-10-26T05:20:40.000Z | [
"region:us"
] | pphuc25 | null | null | 0 | 0 | 2023-10-26T05:20:38 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 146261.13719512196
num_examples: 1610
download_size: 38650
dataset_size: 146261.13719512196
---
# Dataset Card for "selfie_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 409 | [
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] |
erhwenkuo/coig-pc-zhtw | 2023-10-26T05:33:26.000Z | [
"region:us"
] | erhwenkuo | null | null | 0 | 0 | 2023-10-26T05:27:34 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: split
dtype: string
- name: task_name_in_eng
dtype: string
- name: task_type
struct:
- name: major
sequence: string
- name: minor
sequence: string
- name: domain
sequence: string
- name: other
dtype: string
- name: filename
dtype: string
splits:
- name: valid
num_bytes: 2240096565
num_examples: 2087767
download_size: 1068284005
dataset_size: 2240096565
configs:
- config_name: default
data_files:
- split: valid
path: data/valid-*
---
# Dataset Card for "coig-pc-zhtw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 830 | [
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twodgirl/sacred-parallel-text | 2023-10-26T05:48:48.000Z | [
"task_categories:text-generation",
"language:et",
"language:fi",
"mormon",
"magyar",
"region:us"
] | twodgirl | null | null | 0 | 0 | 2023-10-26T05:29:18 | ---
task_categories:
- text-generation
language:
- et
- fi
tags:
- mormon
- magyar
---
The BoM scripture in Finno-Ugric languages. Modern translations.
Credits: Ben Crowder | 174 | [
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kjappelbaum/chemnlp-uspto | 2023-10-26T06:44:35.000Z | [
"region:us"
] | kjappelbaum | null | null | 0 | 0 | 2023-10-26T05:31:53 | Entry not found | 15 | [
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OpenLLMAI/comparison_data | 2023-10-26T05:40:15.000Z | [
"region:us"
] | OpenLLMAI | null | null | 0 | 0 | 2023-10-26T05:34:33 | Entry not found | 15 | [
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HiraishinEX/propertyguru-en | 2023-10-26T06:10:55.000Z | [
"region:us"
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anonymouse03052002/kannada | 2023-10-26T06:25:11.000Z | [
"region:us"
] | anonymouse03052002 | null | null | 0 | 0 | 2023-10-26T06:24:08 | Entry not found | 15 | [
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masked-spacetime-hashing/Campus | 2023-10-26T10:16:06.000Z | [
"license:apache-2.0",
"novel view synthesis",
"dynamic",
"multi-view video",
"region:us"
] | masked-spacetime-hashing | null | null | 0 | 0 | 2023-10-26T06:25:12 | ---
license: apache-2.0
tags:
- novel view synthesis
- dynamic
- multi-view video
---
# Campus dataset for dynamic novel view synthesis
From Paper [NeurIPS 2023 spotlight] [MSTH: Masked Space-Time Hash Encoding for Efficient Dynamic Scene Reconstruction]()
[Paper]() | [Project Page](https://masked-spacetime-hashing.github.io) | [Code](https://github.com/masked-spacetime-hashing/msth)
## Dataset Description
Each scene contains:
- `<scene_name>_<camera_id>.mp4`: The multi-view videos collected for this scene.
- `transforms_<split>.json`: The camera poses and intrinsics estimated using COLMAP. The format is the same as [nerfstudio](https://docs.nerf.studio/quickstart/data_conventions.html).
- `colmap/`: The sparse point cloud and camera infos obtained during COLMAP pose estimation, could be used for methods starting with a sparse point cloud (like 3D Gaussian Splatting).
## Demos
| 895 | [
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ProjectsbyGaurav/Embeddings_Langchain_QA | 2023-10-26T06:33:37.000Z | [
"region:us"
] | ProjectsbyGaurav | null | null | 0 | 0 | 2023-10-26T06:33:37 | Entry not found | 15 | [
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birdhouse5/finetuning8 | 2023-10-26T07:22:41.000Z | [
"region:us"
] | birdhouse5 | null | null | 0 | 0 | 2023-10-26T07:13:03 | Entry not found | 15 | [
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mesolitica/translated-LLaVA-Instruct-150K | 2023-10-26T07:25:42.000Z | [
"region:us"
] | mesolitica | null | null | 0 | 0 | 2023-10-26T07:24:53 | Entry not found | 15 | [
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open-llm-leaderboard/details_TigerResearch__tigerbot-7b-base | 2023-10-26T07:32:31.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-26T07:32:24 | ---
pretty_name: Evaluation run of TigerResearch/tigerbot-7b-base
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TigerResearch/tigerbot-7b-base](https://huggingface.co/TigerResearch/tigerbot-7b-base)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TigerResearch__tigerbot-7b-base\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-26T07:32:19.507473](https://huggingface.co/datasets/open-llm-leaderboard/details_TigerResearch__tigerbot-7b-base/blob/main/results_2023-10-26T07-32-19.507473.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.008284395973154363,\n\
\ \"em_stderr\": 0.0009282472025612859,\n \"f1\": 0.0630316694630872,\n\
\ \"f1_stderr\": 0.0015568932363703221,\n \"acc\": 0.4022740314737014,\n\
\ \"acc_stderr\": 0.010745030991055528\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.008284395973154363,\n \"em_stderr\": 0.0009282472025612859,\n\
\ \"f1\": 0.0630316694630872,\n \"f1_stderr\": 0.0015568932363703221\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10841546626231995,\n \
\ \"acc_stderr\": 0.008563852506627483\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6961325966850829,\n \"acc_stderr\": 0.012926209475483574\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TigerResearch/tigerbot-7b-base
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_26T07_32_19.507473
path:
- '**/details_harness|drop|3_2023-10-26T07-32-19.507473.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T07-32-19.507473.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_26T07_32_19.507473
path:
- '**/details_harness|gsm8k|5_2023-10-26T07-32-19.507473.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-26T07-32-19.507473.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_26T07_32_19.507473
path:
- '**/details_harness|winogrande|5_2023-10-26T07-32-19.507473.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T07-32-19.507473.parquet'
- config_name: results
data_files:
- split: 2023_10_26T07_32_19.507473
path:
- results_2023-10-26T07-32-19.507473.parquet
- split: latest
path:
- results_2023-10-26T07-32-19.507473.parquet
---
# Dataset Card for Evaluation run of TigerResearch/tigerbot-7b-base
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TigerResearch/tigerbot-7b-base
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TigerResearch/tigerbot-7b-base](https://huggingface.co/TigerResearch/tigerbot-7b-base) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TigerResearch__tigerbot-7b-base",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T07:32:19.507473](https://huggingface.co/datasets/open-llm-leaderboard/details_TigerResearch__tigerbot-7b-base/blob/main/results_2023-10-26T07-32-19.507473.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.008284395973154363,
"em_stderr": 0.0009282472025612859,
"f1": 0.0630316694630872,
"f1_stderr": 0.0015568932363703221,
"acc": 0.4022740314737014,
"acc_stderr": 0.010745030991055528
},
"harness|drop|3": {
"em": 0.008284395973154363,
"em_stderr": 0.0009282472025612859,
"f1": 0.0630316694630872,
"f1_stderr": 0.0015568932363703221
},
"harness|gsm8k|5": {
"acc": 0.10841546626231995,
"acc_stderr": 0.008563852506627483
},
"harness|winogrande|5": {
"acc": 0.6961325966850829,
"acc_stderr": 0.012926209475483574
}
}
```
### 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] | 7,271 | [
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bongoServer2112/splitFour-brela-CM-attachements-archived | 2023-10-26T07:40:54.000Z | [
"region:us"
] | bongoServer2112 | null | null | 0 | 0 | 2023-10-26T07:32:58 | Entry not found | 15 | [
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Narya-ai/summarization-dataset | 2023-10-26T07:35:58.000Z | [
"region:us"
] | Narya-ai | null | null | 0 | 0 | 2023-10-26T07:35:47 | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 1701724
num_examples: 267
download_size: 864891
dataset_size: 1701724
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "summarization-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 489 | [
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dangggngo/ply_test | 2023-10-26T08:15:00.000Z | [
"region:us"
] | dangggngo | null | null | 0 | 0 | 2023-10-26T07:51:18 | Entry not found | 15 | [
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everycoffee/autotrain-data-coffee-bean-quality | 2023-10-27T00:34:12.000Z | [
"task_categories:image-classification",
"region:us"
] | everycoffee | null | null | 0 | 0 | 2023-10-26T07:56:42 | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: coffee-bean-quality
## Dataset Description
This dataset has been automatically processed by AutoTrain for project coffee-bean-quality.
### 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": "<224x224 RGB PIL image>",
"feat_width": 224,
"feat_height": 224,
"target": 1,
"feat_xmin": 42,
"feat_ymin": 84,
"feat_xmax": 174,
"feat_ymax": 202
},
{
"image": "<224x224 RGB PIL image>",
"feat_width": 224,
"feat_height": 224,
"target": 0,
"feat_xmin": 80,
"feat_ymin": 90,
"feat_xmax": 186,
"feat_ymax": 150
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"feat_width": "Value(dtype='int64', id=None)",
"feat_height": "Value(dtype='int64', id=None)",
"target": "ClassLabel(names=['defect', 'good'], id=None)",
"feat_xmin": "Value(dtype='int64', id=None)",
"feat_ymin": "Value(dtype='int64', id=None)",
"feat_xmax": "Value(dtype='int64', id=None)",
"feat_ymax": "Value(dtype='int64', 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 | 4185 |
| valid | 400 |
| 1,515 | [
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KendrickPham/csv-transactions-sample | 2023-10-26T07:59:51.000Z | [
"region:us"
] | KendrickPham | null | null | 0 | 0 | 2023-10-26T07:59:51 | Entry not found | 15 | [
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chunpingvi/dataset_tokenized | 2023-10-26T08:04:19.000Z | [
"region:us"
] | chunpingvi | null | null | 0 | 0 | 2023-10-26T08:04:07 | Entry not found | 15 | [
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toilaluan/t2i_topic_comparision_db | 2023-10-26T14:39:42.000Z | [
"region:us"
] | toilaluan | null | null | 0 | 0 | 2023-10-26T08:16:26 | ---
dataset_info:
features:
- name: image
dtype: image
- name: topic
dtype: string
- name: prompt
dtype: string
- name: request_id
dtype: int64
- name: model_type
dtype: string
splits:
- name: train
num_bytes: 251342211.0
num_examples: 5000
download_size: 489446445
dataset_size: 251342211.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "t2i_topic_comparision_db"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 608 | [
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UMCU/SNLI_Dutch_translated_with_Marianmt | 2023-10-26T11:24:51.000Z | [
"task_categories:sentence-similarity",
"size_categories:100K<n<1M",
"language:nl",
"license:cc-by-sa-4.0",
"generic",
"sentence similarity",
"arxiv:1910.13461",
"doi:10.57967/hf/1268",
"region:us"
] | UMCU | null | null | 0 | 0 | 2023-10-26T08:19:14 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 67523459
num_examples: 538896
- name: test
num_bytes: 1285789
num_examples: 9792
- name: validation
num_bytes: 1295645
num_examples: 9792
download_size: 20806553
dataset_size: 70104893
license: cc-by-sa-4.0
language:
- nl
tags:
- generic
- sentence similarity
pretty_name: Dutch translation of SNLI corpus with Maria NMT
size_categories:
- 100K<n<1M
task_categories:
- sentence-similarity
---
Information on the dataset:
```
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 67523459
num_examples: 538896
- name: test
num_bytes: 1285789
num_examples: 9792
- name: validation
num_bytes: 1295645
num_examples: 9792
download_size: 20806553
dataset_size: 70104893
```
# Dataset Card for "SNLI_Dutch_translated_with_Marianmt"
Translation of the **English** corpus [Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/),
to **Dutch** using an [Maria NMT model](https://marian-nmt.github.io/), trained by [Helsinki NLP](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl).
Note, for reference: Maria NMT is based on [BART](https://huggingface.co/docs/transformers/model_doc/bart), described [here](https://arxiv.org/abs/1910.13461).
A complete description of the dataset is given [here](https://huggingface.co/datasets/snli).
# Attribution
If you use this dataset please use the following to credit the creators of SNLI:
```citation
@inproceedings{snli:emnlp2015,
Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
Publisher = {Association for Computational Linguistics},
Title = {A large annotated corpus for learning natural language inference},
Year = {2015}
}
```
The creators of the OPUS-MT models:
```
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
```
and
```
@misc {van_es_2023,
author = { {Bram van Es} },
title = { SNLI_Dutch_translated_with_Marianmt (Revision 9ad7971) },
year = 2023,
url = { https://huggingface.co/datasets/UMCU/SNLI_Dutch_translated_with_Marianmt },
doi = { 10.57967/hf/1268 },
publisher = { Hugging Face }
}
```
# License
For both the Maria NMT model and the original [Helsinki NLP](https://twitter.com/HelsinkiNLP) [Opus MT model](https://huggingface.co/Helsinki-NLP)
we did **not** find a license, if this was in error please let us know and we will add the appropriate licensing promptly.
We adopt the licensing of the SNLI corpus: a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 3,794 | [
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TrainingDataPro/ct-of-the-spine-scoliosis | 2023-10-26T10:18:48.000Z | [
"task_categories:image-classification",
"task_categories:image-to-image",
"language:en",
"license:cc-by-nc-nd-4.0",
"medical",
"code",
"region:us"
] | TrainingDataPro | null | null | 1 | 0 | 2023-10-26T09:05:25 | ---
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- image-to-image
language:
- en
tags:
- medical
- code
---
# Computed Tomography (CT) of the Spine - Scoliosis
The dataset consists of CT spine scans of people with **scoliosis**. images that aid in the assessment and diagnosis of scoliosis. Each scan consists of multiple slices capturing various sections of the spine, including the **cervical (neck), thoracic (upper back), and lumbar (lower back) regions**. The data are presented in 2 different formats: **.jpg and .dcm**.
The dataset of CT spine scans is valuable for research in **automated scoliosis detection, scoliosis segmentation and scoliosis classification**.
This dataset may contribute to the development of *treatment planning techniques, surgical interventions, and monitoring strategies* for patients with scoliosis.
# Get the Dataset
## This is just an example of the data
Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/spine-x-ray-image?utm_source=huggingface&utm_medium=cpc&utm_campaign=ct-of-the-spine) to discuss your requirements, learn about the price and buy the dataset
# Content
### The folder "files" includes 4 folders:
- corresponding to each person with scoliosis
- including spine scans in 2 different formats: **.jpg and .dcm**.
### File with the extension .csv includes the following information for each media file:
- **dcm**: link to access the .dcm file,
- **jpg**: link to access the .jpg file,
# Medical data might be collected in accordance with your requirements.
## [TrainingData](https://trainingdata.pro/data-market/spine-x-ray-image?utm_source=huggingface&utm_medium=cpc&utm_campaign=ct-of-the-spine) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/trainingdata-pro**
*keywords: scoliosis detection, scoliosis segmentation, scoliosis classification, scoliosis image dataset, medical imaging, radiology dataset, vertebral segmentation dataset, spine deformity dataset. thoracic spine, lumbar spine, abnormal spinal curvature, adolescent idiopathic scoliosis, congenital scoliosis, juvenile scoliosis, cobb angle ct, vertebral rotation, scoliotic curve dataset, posterior-anterior projection, lateral projection, spondylolisthesis* | 2,612 | [
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] |
ISCA-IUB/HateSpeechAndBias | 2023-10-26T09:09:13.000Z | [
"language:en",
"hate speech",
"bias",
"racism",
"asians",
"blacks",
"jews",
"latinos",
"muslims",
"region:us"
] | ISCA-IUB | null | null | 0 | 0 | 2023-10-26T09:06:24 | ---
language:
- en
tags:
- hate speech
- bias
- racism
- asians
- blacks
- jews
- latinos
- muslims
---
### Institute for the Study of Contemporary Antisemitism (ISCA) at Indiana University Dataset on bias against Asians, Blacks, Jews, Latines, and Muslims
The ISCA project compiled this dataset using an annotation portal, which was used to label tweets as either biased or non-biased, among other labels. Note that the annotation was done on live data, including images and context, such as threads. The original data comes from annotationportal.com. They include representative samples of live tweets from the years 2020 and 2021 with the keywords "Asians, Blacks, Jews, Latinos, and Muslims".
A random sample of 600 tweets per year was drawn for each of the keywords. This includes retweets. Due to a sampling error, the sample for the year 2021 for the keyword "Jews" has only 453 tweets from 2021 and 147 from the first eight months of 2022 and it includes some tweets from the query with the keyword "Israel." The tweets were divided into six samples of 100 tweets, which were then annotated by three to seven students in the class "Researching White Supremacism and Antisemitism on Social Media" taught by Gunther Jikeli, Elisha S. Breton, and Seth Moller at Indiana University in the fall of 2022, see this report. Annotators used a scale from 1 to 5 (confident not biased, probably not biased, don't know, probably biased, confident biased). The definitions of bias against each minority group used for annotation are also included in the report.
If a tweet called out or denounced bias against the minority in question, it was labeled as "calling out bias."
The labels of whether a tweet is biased or calls out bias are based on a 75% majority vote. We considered "probably biased" and "confident biased" as biased and "confident not biased," "probably not biased," and "don't know" as not biased.
The types of stereotypes vary widely across the different categories of prejudice. While about a third of all biased tweets were classified as "hate" against the minority, the stereotypes in the tweets often matched common stereotypes about the minority. Asians were blamed for the Covid pandemic. Blacks were seen as inferior and associated with crime. Jews were seen as powerful and held collectively responsible for the actions of the State of Israel. Some tweets denied the Holocaust. Hispanics/Latines were portrayed as being in the country illegally and as "invaders," in addition to stereotypical accusations of being lazy, stupid, or having too many children. Muslims, on the other hand, were often collectively blamed for terrorism and violence, though often in conversations about Muslims in India.
# Content:
This dataset contains 5880 tweets that cover a wide range of topics common in conversations about Asians, Blacks, Jews, Latines, and Muslims. 357 tweets (6.1 %) are labeled as biased and 5523 (93.9 %) are labeled as not biased. 1365 tweets (23.2 %) are labeled as calling out or denouncing bias.
1180 out of 5880 tweets (20.1 %) contain the keyword "Asians," 590 were posted in 2020 and 590 in 2021. 39 tweets (3.3 %) are biased against Asian people. 370 tweets (31,4 %) call out bias against Asians.
1160 out of 5880 tweets (19.7%) contain the keyword "Blacks," 578 were posted in 2020 and 582 in 2021. 101 tweets (8.7 %) are biased against Black people. 334 tweets (28.8 %) call out bias against Blacks.
1189 out of 5880 tweets (20.2 %) contain the keyword "Jews," 592 were posted in 2020, 451 in 2021, and ––as mentioned above––146 tweets from 2022. 83 tweets (7 %) are biased against Jewish people. 220 tweets (18.5 %) call out bias against Jews.
1169 out of 5880 tweets (19.9 %) contain the keyword "Latinos," 584 were posted in 2020 and 585 in 2021. 29 tweets (2.5 %) are biased against Latines. 181 tweets (15.5 %) call out bias against Latines.
1182 out of 5880 tweets (20.1 %) contain the keyword "Muslims," 593 were posted in 2020 and 589 in 2021. 105 tweets (8.9 %) are biased against Muslims. 260 tweets (22 %) call out bias against Muslims.
# File Description:
The dataset is provided in a csv file format, with each row representing a single message, including replies, quotes, and retweets. The file contains the following columns:
'TweetID': Represents the tweet ID.
'Username': Represents the username who published the tweet (if it is a retweet, it will be the user who retweetet the original tweet.
'Text': Represents the full text of the tweet (not pre-processed).
'CreateDate': Represents the date the tweet was created.
'Biased': Represents the labeled by our annotators if the tweet is biased (1) or not (0).
'Calling_Out': Represents the label by our annotators if the tweet is calling out bias against minority groups (1) or not (0).
'Keyword': Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username.
# Licences
Data is published under the terms of the "Creative Commons Attribution 4.0 International" licence (https://creativecommons.org/licenses/by/4.0)
# Acknowledgements
We are grateful for the technical collaboration with Indiana University's Observatory on Social Media (OSoMe). We thank all class participants for the annotations and contributions, including Kate Baba, Eleni Ballis, Garrett Banuelos, Savannah Benjamin, Luke Bianco, Zoe Bogan, Elisha S. Breton, Aidan Calderaro, Anaye Caldron, Olivia Cozzi, Daj Crisler, Jenna Eidson, Ella Fanning, Victoria Ford, Jess Gruettner, Ronan Hancock, Isabel Hawes, Brennan Hensler, Kyra Horton, Maxwell Idczak, Sanjana Iyer, Jacob Joffe, Katie Johnson, Allison Jones, Kassidy Keltner, Sophia Knoll, Jillian Kolesky, Emily Lowrey, Rachael Morara, Benjamin Nadolne, Rachel Neglia, Seungmin Oh, Kirsten Pecsenye, Sophia Perkovich, Joey Philpott, Katelin Ray, Kaleb Samuels, Chloe Sherman, Rachel Weber, Molly Winkeljohn, Ally Wolfgang, Rowan Wolke, Michael Wong, Jane Woods, Kaleb Woodworth, and Aurora Young.
This work used Jetstream2 at Indiana University through allocation HUM200003 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296. | 6,355 | [
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samiesam/snd_t | 2023-10-26T09:08:08.000Z | [
"region:us"
] | samiesam | null | null | 0 | 0 | 2023-10-26T09:08:08 | Entry not found | 15 | [
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bongoServer2112/splitFive-brela-CM-attachements-archived | 2023-10-26T10:07:20.000Z | [
"region:us"
] | bongoServer2112 | null | null | 0 | 0 | 2023-10-26T09:59:40 | Entry not found | 15 | [
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easyleafzcbdgummiesdf/EasyLeafzCBDGummies | 2023-10-26T10:06:50.000Z | [
"region:us"
] | easyleafzcbdgummiesdf | null | null | 0 | 0 | 2023-10-26T10:06:50 | Entry not found | 15 | [
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open-llm-leaderboard/details_HWERI__Llama2-7b-sharegpt4 | 2023-10-26T10:08:34.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-26T10:08:27 | ---
pretty_name: Evaluation run of HWERI/Llama2-7b-sharegpt4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [HWERI/Llama2-7b-sharegpt4](https://huggingface.co/HWERI/Llama2-7b-sharegpt4)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HWERI__Llama2-7b-sharegpt4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-26T10:08:23.331981](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__Llama2-7b-sharegpt4/blob/main/results_2023-10-26T10-08-23.331981.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001572986577181208,\n\
\ \"em_stderr\": 0.0004058451132417743,\n \"f1\": 0.06141988255033573,\n\
\ \"f1_stderr\": 0.0014263478827371335,\n \"acc\": 0.369226585159047,\n\
\ \"acc_stderr\": 0.008577465355756637\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001572986577181208,\n \"em_stderr\": 0.0004058451132417743,\n\
\ \"f1\": 0.06141988255033573,\n \"f1_stderr\": 0.0014263478827371335\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.026535253980288095,\n \
\ \"acc_stderr\": 0.00442704598726516\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7119179163378059,\n \"acc_stderr\": 0.012727884724248115\n\
\ }\n}\n```"
repo_url: https://huggingface.co/HWERI/Llama2-7b-sharegpt4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_26T10_08_23.331981
path:
- '**/details_harness|drop|3_2023-10-26T10-08-23.331981.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T10-08-23.331981.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_26T10_08_23.331981
path:
- '**/details_harness|gsm8k|5_2023-10-26T10-08-23.331981.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-26T10-08-23.331981.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_26T10_08_23.331981
path:
- '**/details_harness|winogrande|5_2023-10-26T10-08-23.331981.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T10-08-23.331981.parquet'
- config_name: results
data_files:
- split: 2023_10_26T10_08_23.331981
path:
- results_2023-10-26T10-08-23.331981.parquet
- split: latest
path:
- results_2023-10-26T10-08-23.331981.parquet
---
# Dataset Card for Evaluation run of HWERI/Llama2-7b-sharegpt4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/HWERI/Llama2-7b-sharegpt4
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [HWERI/Llama2-7b-sharegpt4](https://huggingface.co/HWERI/Llama2-7b-sharegpt4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_HWERI__Llama2-7b-sharegpt4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T10:08:23.331981](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__Llama2-7b-sharegpt4/blob/main/results_2023-10-26T10-08-23.331981.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001572986577181208,
"em_stderr": 0.0004058451132417743,
"f1": 0.06141988255033573,
"f1_stderr": 0.0014263478827371335,
"acc": 0.369226585159047,
"acc_stderr": 0.008577465355756637
},
"harness|drop|3": {
"em": 0.001572986577181208,
"em_stderr": 0.0004058451132417743,
"f1": 0.06141988255033573,
"f1_stderr": 0.0014263478827371335
},
"harness|gsm8k|5": {
"acc": 0.026535253980288095,
"acc_stderr": 0.00442704598726516
},
"harness|winogrande|5": {
"acc": 0.7119179163378059,
"acc_stderr": 0.012727884724248115
}
}
```
### 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] | 7,213 | [
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amylguardofficial/amylguardofficial | 2023-10-26T10:40:19.000Z | [
"region:us"
] | amylguardofficial | null | null | 0 | 0 | 2023-10-26T10:39:36 | **[Amyl Guard Supplement](https://www.glitco.com/get-amyl-guard) is one of the best solutions to help you lose weight fast, and easily, and helps to support a healthy digestive system without side effects. Read this review all about ingredients, side effects, complaints, price, pros, cons, and more.**
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* Best Value: Buy six bottles of **[Amyl Guard](https://www.glitco.com/get-amyl-guard)** for $29 each and Save $228 + Free Bonus and FREE SHIPPING. | 4,840 | [
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abhi200110/fessfg | 2023-10-26T11:01:42.000Z | [
"region:us"
] | abhi200110 | null | null | 0 | 0 | 2023-10-26T11:01:42 | Entry not found | 15 | [
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] |
zhongshupeng/dataset_4090_1 | 2023-10-26T11:54:50.000Z | [
"region:us"
] | zhongshupeng | null | null | 0 | 0 | 2023-10-26T11:12:20 | # Disclaimer:
this dataset is curated for NeurIPS 2023 LLM efficiency challange, and currently work in progress. Please use at your own risk.
# Data composition:
All data were derived from the training set portion of the open source dataset.
**gsm8k_dolly15k_cnnadd8k_mmlulog1.7w_bbqabc8k.json**：
-gsm8k_8000: https://huggingface.co/datasets/gsm8k
-dolly_15000: https://huggingface.co/datasets/databricks/databricks-dolly-15k
-cnn_dailymail_8000: https://huggingface.co/datasets/cnn_dailymail
-mmlu_17000: https://huggingface.co/datasets/cais/mmlu
-bbq_8000: https://huggingface.co/datasets/tasksource/bigbench
**lima_4kall.json**
-lima_1000: https://huggingface.co/datasets/GAIR/lima
-3000 of gsm8k_dolly15k_cnnadd8k_mmlulog1.7w_bbqabc8k.json | 756 | [
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CLEAR-Global/kanuri-books-corpus | 2023-10-26T11:33:18.000Z | [
"size_categories:10K<n<100K",
"language:kr",
"license:cc-by-4.0",
"region:us"
] | CLEAR-Global | null | null | 0 | 0 | 2023-10-26T11:27:04 | ---
license: cc-by-4.0
language:
- kr
size_categories:
- 10K<n<100K
---
Randomized sentences from books collected from Kanuri authors: Dr. Baba Kura Alkali Gazali, Lawan Dalama, Kaka Gana Abba, Lawan Hassan.
Corpus size:
- 10,281 sentences
- 90,706 words
The sentences alone are copyrighted to the authors. The compiled corpus is licensed with Attribution 4.0 International (CC BY 4.0)
This corpus was compiled for the creation of open-source language technology. To download you need to agree our terms that prohibits harmful use. If you use this corpus you must give attribution to CLEAR Global and the authors.
For citation you can use this even though this corpus was created later than the paper:
```
Alp Öktem, Muhannad Albayk Jaam, Eric DeLuca, Grace Tang
Gamayun – Language Technology for Humanitarian Response
In: 2020 IEEE Global Humanitarian Technology Conference (GHTC)
2020 October 29 - November 1; Virtual.
``` | 932 | [
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] |
CLEAR-Global/Gamayun-kits | 2023-10-26T11:44:10.000Z | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:ha",
"language:kr",
"language:en",
"language:fr",
"language:sw",
"language:swc",
"language:ln",
"language:nnd",
"language:rhg",
"language:ti",
"region:us"
] | CLEAR-Global | null | null | 0 | 0 | 2023-10-26T11:35:12 | ---
task_categories:
- translation
language:
- ha
- kr
- en
- fr
- sw
- swc
- ln
- nnd
- rhg
- ti
size_categories:
- 10K<n<100K
pretty_name: Gamayun kits
---
# Gamayun Language Data Kits
There are more than 7,000 languages in the world, yet only a small proportion of them have language data presence in public. CLEAR Global's Gamayun kits are a starting point for developing audio and text corpora for languages without pre-existing data resources. We create parallel data for a language by translating a pre-compiled set of general-domain sentences in English. If audio data is needed, these translated sentences are recorded by native speakers.
To scale corpus production, we offer four dataset versions:
- Mini-kit of 5,000 sentences (`kit5k`)
- Small-kit of 10,000 sentences (`kit10k`)
- Medium-kit of 15,000 sentences (`kit15k`)
- Large-kit of 30,000 sentences (`kit30k`)
For audio corpora developed using these kits refer to the official initiative website [Gamayun portal](https://gamayun.translatorswb.org/data/).
## Source sentences (`core`)
Sentences in `core` directory are in English, French and Spanish and are sourced from the [Tatoeba repository](https://tatoeba.org). Sentence selection algorithm ensures representation of most frequently used words in the language. For more information, please refer to [corepus-gen repository](https://github.com/translatorswb/corepus-gen). `etc` directories contain sentence id's as used in the Tatoeba corpus.
## Parallel corpora (`parallel`)
Translations of the kits are performed by professionals and volunteers of TWB's translator community. A complete list of translated sentences are:
| Language | Pair | # Segments | Source |
|------|--------|--------|--------|
| Hausa | English | 15,000 | Tatoeba |
| Kanuri | English | 5,000 | Tatoeba |
| Nande | French | 15,000 | Tatoeba |
| Rohingya | English | 5,000 | Tatoeba |
| Swahili (Coastal) | English | 5,000 | Tatoeba |
| Swahili (Congolese) | French | 25,302 | Tatoeba |
## Reference
More on [Gamayun, language equity initiative](https://translatorswithoutborders.org/gamayun/)
Gamayun kits are officially published in the [Gamayun portal](https://gamayun.translatorswb.org/data/). Conditions for use are described in `LICENSE.txt`.
If you need to cite Gamayun kits:
```
Alp Öktem, Muhannad Albayk Jaam, Eric DeLuca, Grace Tang
Gamayun – Language Technology for Humanitarian Response
In: 2020 IEEE Global Humanitarian Technology Conference (GHTC)
2020 October 29 - November 1; Virtual.
Link: https://ieeexplore.ieee.org/document/9342939
``` | 2,569 | [
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Gewo556/Prime-numbers | 2023-10-26T15:33:11.000Z | [
"region:us"
] | Gewo556 | null | null | 0 | 0 | 2023-10-26T11:43:14 | Entry not found | 15 | [
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MyRebRIc/mcignegao | 2023-10-26T12:19:23.000Z | [
"region:us"
] | MyRebRIc | null | null | 0 | 0 | 2023-10-26T11:54:31 | Entry not found | 15 | [
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bongoServer2112/splitSix-brela-CM-attachements-archived | 2023-10-26T12:11:59.000Z | [
"region:us"
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juanberasategui/masterdata | 2023-10-26T12:15:04.000Z | [
"region:us"
] | juanberasategui | null | null | 0 | 0 | 2023-10-26T12:15:04 | Entry not found | 15 | [
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re2panda/click_bate_article | 2023-10-26T12:39:09.000Z | [
"region:us"
] | re2panda | null | null | 0 | 0 | 2023-10-26T12:36:53 | Entry not found | 15 | [
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] |
Ryan20/hotel_dataset_pushed | 2023-10-27T08:50:48.000Z | [
"region:us"
] | Ryan20 | null | null | 0 | 0 | 2023-10-26T12:50:32 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: answers
sequence: string
- name: context
dtype: string
- name: questions
sequence: string
splits:
- name: train
num_bytes: 4634
num_examples: 7
download_size: 7932
dataset_size: 4634
---
# Dataset Card for "hotel_dataset_pushed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 519 | [
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johannes-garstenauer/structs_token_size_4_labelled_eval_ | 2023-10-26T13:14:08.000Z | [
"region:us"
] | johannes-garstenauer | null | null | 0 | 0 | 2023-10-26T13:13:42 | ---
dataset_info:
features:
- name: struct
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1323561990
num_examples: 5451270
download_size: 384897245
dataset_size: 1323561990
---
# Dataset Card for "structs_token_size_4_labelled_eval_"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 423 | [
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Facico/test | 2023-10-27T06:48:02.000Z | [
"region:us"
] | Facico | null | null | 0 | 0 | 2023-10-26T13:20:59 | Entry not found | 15 | [
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cawoylel/whisper-tiny-new_embeddings | 2023-10-26T13:24:34.000Z | [
"region:us"
] | cawoylel | null | null | 0 | 0 | 2023-10-26T13:24:30 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: embedding
sequence: float64
splits:
- name: train
num_bytes: 37008000
num_examples: 12000
download_size: 19589183
dataset_size: 37008000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "whisper-tiny-new_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 498 | [
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open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3 | 2023-10-26T13:43:48.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-26T13:43:39 | ---
pretty_name: Evaluation run of pszemraj/pythia-6.9b-HC3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [pszemraj/pythia-6.9b-HC3](https://huggingface.co/pszemraj/pythia-6.9b-HC3) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-26T13:43:34.818170](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3/blob/main/results_2023-10-26T13-43-34.818170.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.00010486577181208053,\n\
\ \"em_stderr\": 0.00010486577181208242,\n \"f1\": 0.022344798657718254,\n\
\ \"f1_stderr\": 0.0006975774134342648,\n \"acc\": 0.30386740331491713,\n\
\ \"acc_stderr\": 0.006861200231000444\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.00010486577181208053,\n \"em_stderr\": 0.00010486577181208242,\n\
\ \"f1\": 0.022344798657718254,\n \"f1_stderr\": 0.0006975774134342648\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6077348066298343,\n\
\ \"acc_stderr\": 0.013722400462000888\n }\n}\n```"
repo_url: https://huggingface.co/pszemraj/pythia-6.9b-HC3
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_26T13_43_34.818170
path:
- '**/details_harness|drop|3_2023-10-26T13-43-34.818170.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T13-43-34.818170.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_26T13_43_34.818170
path:
- '**/details_harness|gsm8k|5_2023-10-26T13-43-34.818170.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-26T13-43-34.818170.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_26T13_43_34.818170
path:
- '**/details_harness|winogrande|5_2023-10-26T13-43-34.818170.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T13-43-34.818170.parquet'
- config_name: results
data_files:
- split: 2023_10_26T13_43_34.818170
path:
- results_2023-10-26T13-43-34.818170.parquet
- split: latest
path:
- results_2023-10-26T13-43-34.818170.parquet
---
# Dataset Card for Evaluation run of pszemraj/pythia-6.9b-HC3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/pszemraj/pythia-6.9b-HC3
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [pszemraj/pythia-6.9b-HC3](https://huggingface.co/pszemraj/pythia-6.9b-HC3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T13:43:34.818170](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3/blob/main/results_2023-10-26T13-43-34.818170.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.00010486577181208053,
"em_stderr": 0.00010486577181208242,
"f1": 0.022344798657718254,
"f1_stderr": 0.0006975774134342648,
"acc": 0.30386740331491713,
"acc_stderr": 0.006861200231000444
},
"harness|drop|3": {
"em": 0.00010486577181208053,
"em_stderr": 0.00010486577181208242,
"f1": 0.022344798657718254,
"f1_stderr": 0.0006975774134342648
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.6077348066298343,
"acc_stderr": 0.013722400462000888
}
}
```
### 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] | 7,150 | [
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lingjoor/lingjoor-dataset | 2023-10-26T14:07:17.000Z | [
"region:us"
] | lingjoor | null | null | 0 | 0 | 2023-10-26T14:06:45 | Entry not found | 15 | [
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bongoServer2112/splitSeven-brela-CM-attachements-archived | 2023-10-26T14:23:08.000Z | [
"region:us"
] | bongoServer2112 | null | null | 0 | 0 | 2023-10-26T14:16:24 | Entry not found | 15 | [
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] |
zion84006/tencent_data_encodec | 2023-10-26T21:43:04.000Z | [
"region:us"
] | zion84006 | null | null | 0 | 0 | 2023-10-26T14:18:56 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: file_id
dtype: int64
- name: instruction
dtype: string
- name: transcription
dtype: string
- name: src_encodec_0
sequence: int64
- name: src_encodec_1
sequence: int64
- name: src_encodec_2
sequence: int64
- name: src_encodec_3
sequence: int64
- name: src_encodec_4
sequence: int64
- name: src_encodec_5
sequence: int64
- name: src_encodec_6
sequence: int64
- name: src_encodec_7
sequence: int64
- name: tgt_encodec_0
sequence: int64
- name: tgt_encodec_1
sequence: int64
- name: tgt_encodec_2
sequence: int64
- name: tgt_encodec_3
sequence: int64
- name: tgt_encodec_4
sequence: int64
- name: tgt_encodec_5
sequence: int64
- name: tgt_encodec_6
sequence: int64
- name: tgt_encodec_7
sequence: int64
splits:
- name: train
num_bytes: 18586613483
num_examples: 266780
- name: valid
num_bytes: 527894882
num_examples: 7620
- name: test
num_bytes: 508453304
num_examples: 7620
download_size: 472185815
dataset_size: 19622961669
---
# Dataset Card for "tencent_data_encodec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,453 | [
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] |
JWBickel/Concordances_And_Cross_References | 2023-11-01T21:52:34.000Z | [
"language:en",
"region:us"
] | JWBickel | null | null | 0 | 0 | 2023-10-26T14:21:14 | ---
language:
- en
---
The phrases in Phrasal Concordance are chunked by Strongs numbers. The Cross References have THREE levels: carat (columns), comma (references), and percent (votes).
BLB (Blue Letter Bible) Greek
-
GroupID ^ ReferenceID ^ Reference
BLB Hebrew
-
GroupID ^ ReferenceID ^ Reference
Chained Phrasal Concordances
-
Reference ^ StrongsChunkedPhraseChain
Cross References
-
RowID ^ ReferencedPassage ^ ReferencingPassagesWithVoteCounts
Phrasal Concordance
-
Phrase ^ Reference ^ Count | 505 | [
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Pixelatory/ZINC-20 | 2023-10-26T15:55:08.000Z | [
"size_categories:1B<n<10B",
"chemistry",
"biology",
"region:us"
] | Pixelatory | null | null | 0 | 0 | 2023-10-26T14:46:31 | ---
tags:
- chemistry
- biology
size_categories:
- 1B<n<10B
---
1,863,903,840 samples.
Contains only the unique, RDKit canonicalized SMILES molecules in a CSV format (after extracting), from the ZINC-20 dataset found at https://zinc20.docking.org/tranches/home/ with react set to standard and purchasability set to annotated. ZINC-20 compounds collected in 17 August 2023. | 374 | [
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] |
JWBickel/Synonyms | 2023-10-29T18:01:06.000Z | [
"language:en",
"region:us"
] | JWBickel | null | null | 0 | 0 | 2023-10-26T14:48:04 | ---
language:
- en
pretty_name: KJV Synonyms
---
There are 3 files. MobyIndex is a CSV file with a carat as the delimiter and a comma as an INTERNAL DELIMITER.
KJVSynonyms uses carats, but there's only one column, so it's like an internal delimiter, breaking up each group of synonyms.
Synonyms Index has a one to many relationship between words and group numbers. It's similar to KJVSynonyms in structure, but it has two columns where the FIRST carat separates the text from group numbers.
MobyIndex
----
Synonym ^ IDs,
KJVSynonyms
-----------
Synonym1 ^ Synonym2 ^ ..
SynonymsIndex
------------
Synonym ^ Group1 ^ Group2 ^ ...
| 636 | [
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yangcong/sd_config | 2023-10-26T14:53:34.000Z | [
"region:us"
] | yangcong | null | null | 0 | 0 | 2023-10-26T14:52:54 | Entry not found | 15 | [
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JWBickel/Lexicons | 2023-10-26T16:05:54.000Z | [
"language:en",
"region:us"
] | JWBickel | null | null | 0 | 0 | 2023-10-26T15:05:32 | ---
language:
- en
---
There are 4 separate lexicons, each in a CSV file. All the files are delimited by carats. Two of the files also include internal delimiters.
Blue Letter Bible (BLB) Hebrew
------------------------------
LexicalID ^ Total Translated ^ Connection ^ Gender ^ POS ^ Pronunciation ^ Transliteration ^ TWOT Number ^ IsAramaic ^ IsRoot ^ ~Translation Counts% ^ ~Lexical Hierarchy%
BLB Greek
---------
LexicalID ^ IsRoot ^ Total Translated ^ Connection ^ Gender ^ POS ^ Pronunciation ^ Transliteration ^ TWOT Number ^ ~Conjugated% ^ ~Translation Counts% ^ ~Extra TDNT Information% ^ ~Lexical Hierarchy%
STEPBible Hebrew
----------------
Line Number ^ Part Number ^ Parse ^ Strongs Number ^ Word Order ID ^ Translation Order ID ^ Translation ^ Translation Alternate ^ Translation Alternate 2 ^ Translation Semantic
NT Words
--------
Gloss ^ Book Number ^ Chapter Number ^ Verse Number ^ Word ID ^ Word Function ^ Clause Level ^ Clause ^ Clause Function ^ Subclause ^ Subclause Function ^ Greek Clause Level ^ Greek ID ^ Strongs ^ Morphology ^ Greek Word
| 1,074 | [
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] |
JWBickel/StrongsChunked_English_Phrase_Counts | 2023-10-26T15:53:22.000Z | [
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | JWBickel | null | null | 0 | 0 | 2023-10-26T15:14:50 | ---
language:
- en
size_categories:
- 10K<n<100K
---
These are KJV phrases and their counts, chunked by Strong's.
It's a CSV file, delimited by carats.
-------------------------------------
RowID ^ StrongsChunkedPhrase ^ Count
_____________________________________
Note that the first record is nonsense - it's just a space. Taking it out would have thrown off the Row IDs. Don't overlook it (but overlook my flaw). | 419 | [
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] |
JWBickel/Strongs | 2023-10-26T15:46:41.000Z | [
"language:en",
"region:us"
] | JWBickel | null | null | 0 | 0 | 2023-10-26T15:15:49 | ---
language:
- en
pretty_name: Strongs
---
There are 3 files
-----------------
Two are CSV files, delimited by a carat. The other one is a text file which also has carats, but only the first one is a normal column delimiter. The other carats separate the words in each verse.
Strongs Connections and Derivations.csv
---------------------------------------
Strongs Number ^ Derived From ^ Derived By
MultipleStrongs with Gloss and Counts.csv
-----------------------------------------
PhraseID ^ ComplexPhraseStrongsSequence ^ ComplexPhraseEnglishText ^ PhraseCount
KJV Strongs-Chunked Alternate Glosses.txt
-----------------------------------------
VerseID ^ word1-gloss1, word1-gloss2, .. ^ word2-gloss1, ..
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mesolitica/translated-router-switch-instruct | 2023-10-26T15:17:36.000Z | [
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JWBickel/KJVWordCounts | 2023-10-26T15:34:15.000Z | [
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | JWBickel | null | null | 0 | 0 | 2023-10-26T15:19:53 | ---
language:
- en
pretty_name: KJV Word Counts
size_categories:
- 10K<n<100K
---
# Dataset Card for KJVWordCounts
This is a simple list of every word and their total counts in the King James version of the Bible.
## Dataset Structure
It's a CSV file, delimited with a carat.
Word ^ Count
## Dataset Card Author
Jeremy Bickel
## Dataset Card Contact
JeremyWBickel@gmail.com | 381 | [
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Maxlinn/dalle3-paper-images | 2023-10-26T15:24:55.000Z | [
"region:us"
] | Maxlinn | null | null | 0 | 0 | 2023-10-26T15:22:53 | # DALLE3 Paper Images
Extracted examples shown in dalle-3 paper without any compression: https://cdn.openai.com/papers/dall-e-3.pdf
Used https://pdfcandy.com/extract-images.html to extract images from dalle-3 paper.
Do not include three images that caption may not be correct. | 279 | [
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] |
mekaneeky/Synthetic_Ateso_MMS | 2023-10-26T15:42:31.000Z | [
"region:us"
] | mekaneeky | null | null | 0 | 0 | 2023-10-26T15:26:54 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: eng
dtype: string
- name: lug
dtype: string
- name: ach
dtype: string
- name: teo
dtype: string
- name: lgg
dtype: string
- name: nyn
dtype: string
- name: ID
dtype: string
- name: teo_tts
sequence:
sequence: float32
splits:
- name: train
num_bytes: 15356273984
num_examples: 23947
- name: dev
num_bytes: 311338572
num_examples: 500
- name: test
num_bytes: 326319368
num_examples: 500
download_size: 16002788236
dataset_size: 15993931924
---
# Dataset Card for "Synthetic_Atest_MMS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 903 | [
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RyanTheAvian/loona | 2023-10-28T06:28:29.000Z | [
"region:us"
] | RyanTheAvian | null | null | 0 | 0 | 2023-10-26T15:28:32 | Entry not found | 15 | [
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YL95/partical_fpml_indx30 | 2023-10-26T15:55:53.000Z | [
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chitanda/wiki_erica_path_v9.1 | 2023-10-27T03:06:17.000Z | [
"region:us"
] | chitanda | null | null | 0 | 0 | 2023-10-26T16:04:25 | Entry not found | 15 | [
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Classacre/Tye_Sheridan_SDXL | 2023-10-26T16:04:41.000Z | [
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bongoServer2112/splitEight-brela-CM-attachements-archived | 2023-10-26T16:20:27.000Z | [
"region:us"
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jsrdhher/voice02 | 2023-10-26T18:28:22.000Z | [
"region:us"
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royson/train_splits_helm | 2023-10-26T16:27:29.000Z | [
"license:apache-2.0",
"region:us"
] | royson | null | null | 0 | 0 | 2023-10-26T16:17:46 | ---
license: apache-2.0
---
Contains the following train split from datasets in [helm](https://github.com/stanford-crfm/helm):
- big bench
- mmlu
- TruthfulQA
- cnn/dm
- gsm
- bbq
- boolq
- NarrativeQA
- QuAC
- math
- bAbI
Each prompt has <= 5 in-context samples along with a sample, all of which from the train set of the respective datasets. | 346 | [
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] |
mekaneeky/Synthetic_Ateso_VITS_22.5k | 2023-10-26T16:53:16.000Z | [
"region:us"
] | mekaneeky | null | null | 0 | 0 | 2023-10-26T16:44:32 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: eng
dtype: string
- name: lug
dtype: string
- name: ach
dtype: string
- name: teo
dtype: string
- name: lgg
dtype: string
- name: nyn
dtype: string
- name: ID
dtype: string
- name: teo_tts
sequence:
sequence: float32
splits:
- name: train
num_bytes: 12491742528
num_examples: 23947
- name: dev
num_bytes: 260929100
num_examples: 500
- name: test
num_bytes: 264178952
num_examples: 500
download_size: 13028184575
dataset_size: 13016850580
---
# Dataset Card for "Synthetic_Ateso_VITS_22.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 910 | [
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pythainlp/wisesight_sentiment_prompt | 2023-10-31T20:04:47.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:th",
"license:cc0-1.0",
"instruct-fellow",
"region:us"
] | pythainlp | null | null | 0 | 0 | 2023-10-26T16:50:41 | ---
license: cc0-1.0
task_categories:
- text-generation
- text2text-generation
language:
- th
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 12676755
num_examples: 21628
- name: validation
num_bytes: 1410232
num_examples: 2404
- name: test
num_bytes: 1569570
num_examples: 2671
download_size: 3621794
dataset_size: 15656557
tags:
- instruct-fellow
pretty_name: i
---
wisesight_sentiment_prompt is the instruct fellow dataset for sentiment Thai text by prompt. It can use fine-tuning model.
- inputs: Prompt
- targets: Text targets that AI should answer.
**Template**
```
Inputs: จำแนกประโยคต่อไปนี้เป็นคำถามหรือข้อความเชิงบวก/เป็นกลาง/เชิงลบ:\n{text}
targets: ประโยคที่กำหนดสามารถจำแนกข้อความได้เป็นข้อความ{category}
```
category
- คำถาม: question
- เชิงบวก: positive
- เป็นกลาง: neutral
- เชิงลบ: negative
Notebook that used create this dataset: [https://github.com/PyThaiNLP/support-aya-datasets/blob/main/sentiment-analysis/wisesight_sentiment.ipynb](https://github.com/PyThaiNLP/support-aya-datasets/blob/main/sentiment-analysis/wisesight_sentiment.ipynb)
Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)
* Released to public domain under Creative Commons Zero v1.0 Universal license.
* Size: 26,737 messages
* Language: Central Thai
* Style: Informal and conversational. With some news headlines and advertisement.
* Time period: Around 2016 to early 2019. With small amount from other period.
* Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs.
See more: [wisesight_sentiment](https://huggingface.co/datasets/wisesight_sentiment).
PyThaiNLP | 1,871 | [
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nlplabtdtu/multi-choices-text | 2023-10-27T06:39:43.000Z | [
"region:us"
] | nlplabtdtu | null | null | 0 | 0 | 2023-10-26T17:25:47 | ---
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: 183847576
num_examples: 89447
download_size: 98876693
dataset_size: 183847576
---
# Dataset Card for "multi-choices-text"
## Preprocess
- Filter out **<img** in _solution_ and _question_
- Remove "English" subject
- Remove **all options above** option
- Remove **Đáp án cần chọn.*[ABCD]** in _solution_
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,057 | [
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ahmedsali/aboutme | 2023-10-26T17:45:59.000Z | [
"region:us"
] | ahmedsali | null | null | 0 | 0 | 2023-10-26T17:45:59 | Entry not found | 15 | [
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] |
vira-chatbot/vira-intents-mod | 2023-10-31T03:54:15.000Z | [
"region:us"
] | vira-chatbot | null | null | 0 | 0 | 2023-10-26T18:04:54 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 509234
num_examples: 7047
- name: validation
num_bytes: 213834
num_examples: 2971
download_size: 329146
dataset_size: 723068
---
# Dataset Card for "vira-intents-mod"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 454 | [
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bongoServer2112/splitNine-brela-CM-attachements-archived | 2023-10-26T18:22:40.000Z | [
"region:us"
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HiraishinEX/Full_edX | 2023-10-26T19:33:16.000Z | [
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jorgeortizv/Bestande | 2023-10-26T19:40:39.000Z | [
"language:de",
"region:us"
] | jorgeortizv | null | null | 0 | 0 | 2023-10-26T19:33:27 | ---
language:
- de
---
Bestande [1] was an academic review and rating platform which was very popular within UZH and ETH student communities.
Bestande terminated its service in 2022 and with the hope of someone taking over, the founder disclosed the database over 2016 and 2022 on Bestande.ch. Therefore, we took it without any data scraping and built our project on it. The raw data is a json file.
The provided files offer a translation of reviews from different comments to German, where reviews of a course are given, as well as the score of the course, up-votes and down-votes that the review received and labeling that states if a review is useful or not.
For the binary dataset:
- 0 : neutral review
- 1 : useful review
For the multiclass dataset:
- 0 : neutral review
- 1 : slightly useful review
- 2 : useful review
- 3 : extremely useful review
[1] J. Burger. Bestande. [Accessed 21-09-2023]. [Online]. Available: http://www.
bestande.ch | 951 | [
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Starkate/Friday | 2023-10-26T19:50:08.000Z | [
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] | Starkate | null | null | 0 | 0 | 2023-10-26T19:49:45 | Entry not found | 15 | [
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bongoServer2112/splitTen-brela-CM-attachements-archived | 2023-10-26T21:15:53.000Z | [
"region:us"
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yunhaol/briar-train | 2023-10-26T22:20:57.000Z | [
"region:us"
] | yunhaol | null | null | 0 | 0 | 2023-10-26T20:05:11 | Entry not found | 15 | [
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] |
mickume/harry_potter_tiny_tk | 2023-10-26T20:17:33.000Z | [
"region:us"
] | mickume | null | null | 0 | 0 | 2023-10-26T20:17:30 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 1114248.0
num_examples: 34
- name: test
num_bytes: 131088.0
num_examples: 4
download_size: 539624
dataset_size: 1245336.0
---
# Dataset Card for "harry_potter_tiny_tk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 553 | [
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] |
jessica-ecosia/gpdr-dpr-dataset | 2023-10-26T20:27:22.000Z | [
"region:us"
] | jessica-ecosia | null | null | 0 | 0 | 2023-10-26T20:27:04 | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: embeddings
sequence:
sequence: float64
splits:
- name: train
num_bytes: 4191740
num_examples: 620
download_size: 0
dataset_size: 4191740
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "gpdr-dpr-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 561 | [
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furkanemircecover/Cover | 2023-10-26T20:31:19.000Z | [
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DuckyPolice/Mark-Rober-Voice | 2023-10-27T20:14:24.000Z | [
"language:en",
"license:wtfpl",
"region:us"
] | DuckyPolice | null | null | 0 | 0 | 2023-10-26T20:40:52 | ---
license: wtfpl
language:
- en
pretty_name: Mark Rober Voice Dataset
---
this is where i put mark rober voice clips | 118 | [
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berrrrr/Espanol_HD | 2023-10-26T20:58:41.000Z | [
"region:us"
] | berrrrr | null | null | 0 | 0 | 2023-10-26T20:42:05 | Entry not found | 15 | [
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dilith/RPA | 2023-10-26T20:48:34.000Z | [
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] | dilith | null | null | 0 | 0 | 2023-10-26T20:46:27 | Entry not found | 15 | [
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amandlek/mimicgen_datasets | 2023-10-27T01:21:47.000Z | [
"license:cc-by-nc-sa-4.0",
"arxiv:2310.17596",
"region:us"
] | amandlek | null | null | 0 | 0 | 2023-10-26T20:47:14 | ---
license: cc-by-nc-sa-4.0
---
# Dataset Card for MimicGen Datasets
## Dataset Summary
This repository contains the official release of datasets for the [CoRL 2023](https://www.corl2023.org/) paper "MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations".
The datasets contain over 48,000 task demonstrations across 12 tasks, grouped into the following categories:
- **source**: 120 human demonstrations across 12 tasks used to automatically generate the other datasets
- **core**: 26,000 task demonstrations across 12 tasks (26 task variants)
- **object**: 2000 task demonstrations on the Mug Cleanup task with different mugs
- **robot**: 16,000 task demonstrations across 4 different robot arms on 2 tasks (4 task variants)
- **large_interpolation**: 6000 task demonstrations across 6 tasks that pose significant challenges for modern imitation learning methods
For more information please see the [website](https://mimicgen.github.io), the [paper](https://arxiv.org/abs/2310.17596), and the [code](https://github.com/NVlabs/mimicgen_environments).
## Dataset Structure
Each dataset is an hdf5 file that is readily compatible with [robomimic](https://robomimic.github.io/) --- the structure is explained [here](https://robomimic.github.io/docs/datasets/overview.html#dataset-structure).
As described in the paper, each task has a default reset distribution (D_0). Source human demonstrations (usually 10 demos) were collected on this distribution and MimicGen was subsequently used to generate large datasets (usually 1000 demos) across different task reset distributions (e.g. D_0, D_1, D_2), objects, and robots.
The datasets are split into different types:
- **source**: source human datasets used to generate all data -- this generally consists of 10 human demonstrations collected on the D_0 variant for each task.
- **core**: datasets generated with MimicGen for different task reset distributions. These correspond to the core set of results in Figure 4 of the paper.
- **object**: datasets generated with MimicGen for different objects. These correspond to the results in Appendix G of the paper.
- **robot**: datasets generated with MimicGen for different robots. These correspond to the results in Appendix F of the paper.
- **large_interpolation**: datasets generated with MimicGen using much larger interpolation segments. These correspond to the results in Appendix H in the paper.
**Note**: We found that the large_interpolation datasets pose a significant challenge for imitation learning, and have substantial room for improvement.
## Citation
Please cite the [MimicGen paper](https://arxiv.org/abs/2310.17596) if you use these datasets in your work:
```bibtex
@inproceedings{mandlekar2023mimicgen,
title={MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations},
author={Mandlekar, Ajay and Nasiriany, Soroush and Wen, Bowen and Akinola, Iretiayo and Narang, Yashraj and Fan, Linxi and Zhu, Yuke and Fox, Dieter},
booktitle={7th Annual Conference on Robot Learning},
year={2023}
}
``` | 3,105 | [
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bloyal/uniref100 | 2023-10-26T21:39:10.000Z | [
"region:us"
] | bloyal | UniRef100 contains all UniProt Knowledgebase records plus selected UniParc records (see below). It is generated by clustering identical sequences and subfragments. | The UniProt Consortium
UniProt: the Universal Protein Knowledgebase in 2023
Nucleic Acids Res. 51:D523–D531 (2023) | 0 | 0 | 2023-10-26T21:00:15 | Entry not found | 15 | [
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Cam1234K/caze | 2023-10-26T21:01:03.000Z | [
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] |
MaxReynolds/TestUpload | 2023-10-26T21:10:24.000Z | [
"region:us"
] | MaxReynolds | null | null | 0 | 0 | 2023-10-26T21:10:22 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1216137.0
num_examples: 10
download_size: 1217696
dataset_size: 1216137.0
---
# Dataset Card for "TestUpload"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 473 | [
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PDBEurope/protein_structure_NER_independent_val_set | 2023-11-01T09:47:21.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | PDBEurope | null | null | 0 | 0 | 2023-10-26T21:41:13 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
---
## Overview
This data was used to evaluate the two models below to decide whether convergence was reached.
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v3.1
There are 20 different entity types in this dataset:
"bond_interaction", "chemical", "complex_assembly", "evidence", "experimental_method", "gene",
"mutant", "oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC5173035 | 885 | 885 | 195 |
| PMC4993997 | 1052 | 1051 | 217 |
| PMC5014086 | 676 | 676 | 136 |
| PMC5063996 | 1048 | 1046 | 243 |
| PMC4980666 | 669 | 669 | 164 |
| PMC4817029 | 897 | 897 | 180 |
| PMC5012862 | 2203 | 2202 | 438 |
| PMC4981400 | 570 | 570 | 121 |
| PMC4806292 | 760 | 760 | 167 |
| PMC5603727 | 1353 | 1353 | 240 |
| total | 10113 | 10109 | 2101 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat (https://www.teamtat.org/). More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB". There is one file for each
document in the dataset and they all follow the naming "unique PubMedCentral ID".tsv.
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
```
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] |
mevol/protein_structure_NER_independent_val_set | 2023-11-01T10:28:29.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | mevol | null | null | 0 | 0 | 2023-10-26T21:46:25 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
---
## Overview
This data was used to evaluate the two models below to decide whether convergence was reached.
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v3.1
There are 20 different entity types in this dataset:
"bond_interaction", "chemical", "complex_assembly", "evidence", "experimental_method", "gene",
"mutant", "oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC5173035 | 885 | 885 | 195 |
| PMC4993997 | 1052 | 1051 | 217 |
| PMC5014086 | 676 | 676 | 136 |
| PMC5063996 | 1048 | 1046 | 243 |
| PMC4980666 | 669 | 669 | 164 |
| PMC4817029 | 897 | 897 | 180 |
| PMC5012862 | 2203 | 2202 | 438 |
| PMC4981400 | 570 | 570 | 121 |
| PMC4806292 | 760 | 760 | 167 |
| PMC5603727 | 1353 | 1353 | 240 |
| total | 10113 | 10109 | 2101 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat (https://www.teamtat.org/). More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB". There is one file for each
document in the dataset and they all follow the naming "unique PubMedCentral ID".tsv.
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
```
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OneSimmer/CamilaLoures | 2023-10-26T21:52:57.000Z | [
"region:us"
] | OneSimmer | null | null | 0 | 0 | 2023-10-26T21:52:11 | Entry not found | 15 | [
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shanchen/OncQA | 2023-10-30T14:24:19.000Z | [
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"license:cc-by-sa-4.0",
"medical",
"arxiv:2310.17703",
"region:us"
] | shanchen | null | null | 1 | 0 | 2023-10-26T21:56:33 | ---
license: cc-by-sa-4.0
task_categories:
- conversational
- text2text-generation
language:
- en
tags:
- medical
---
## OncQA: The Impact of Using an AI Chatbot to Respond to Patient Messages
### Importance
Documentation burden is a major factor contributing to clinician burnout, which is increasing across the country and threatens our capacity to provide patient care in the U.S. While AI chatbots show potential in reducing this burden by aiding in documentation and are being incorporated into electronic health record systems, their influence on clinical decision-making remains understudied for this purpose.
### Objective
Investigate the acceptability, safety, and potential human factors issues when utilizing an AI-powered chatbot to draft responses to patients' inquiries.
### Design
- A 2-stage cross-sectional study was designed around 100 synthetic cancer patient scenarios couples with patient messages.
- These questions emulate realistic oncology scenarios.
- **Stage 1: Manual Reponse**: Six oncologists were randomly allocated 26 questions for response.
- **Stage 2: AI-Assisted Response**: The same oncologists received 26 new questions, alongside GPT-4 generated responses for editing.
- Informed consent was obtained.
- Participants were blinded to the source of the drafts.
- Surveys were undertaken for every scenario/response.
### About this repo
The dataset shows here is the complete stage2 parsed data with all physician edits.
If you wish to see the full data for stage1 and others please visit https://github.com/AIM-Harvard/OncQA/
### Settings
This research was conducted at the Brigham and Women’s Hospital, Boston, MA in 2023.
### Participants
Six board-certified oncologists participated.
### Intervention
Employment of GPT-4, an AI chatbot, for drafting responses to patient inquiries.
### Main Outcomes & Measures
- Evaluate the impact and utility of an AI chatbot in assisting responses to patient messages.
- Impact was determined by comparing response length and readability, using the Flesch reading ease score, and content.
- Utility was ascertained through physician feedback on surveys regarding acceptability, potential harm, and efficiency of chatbot-crafted drafts.
### Results
- On average, manual responses were more concise than those by GPT-4 or AI-assisted (34 vs. 169 vs. 160 words, p<0.001).
- Manual responses were more readable than GPT-4 or AI-assisted messages (Flesch score 67 vs. 45 vs. 46, p<0.001).
- About 58% of GPT-4 drafts were immediately acceptable, with 82% posing a low risk of harm.
- Utilizing the GPT-4 draft enhanced documentation efficiency in 77% of replies.
- Surprisingly, 31% of GPT-4 responses were perceived to be human-written, despite being AI-generated.
- 7.7% of survey responses felt unedited GPT-4 drafts could lead to severe harm or death.
- Among 56 dual-annotated responses, annotation agreement was low for manual responses (Cohen's kappa 0.10), but improved for AI-assisted responses (Cohen's kappa 0.52).
- AI-assistance led to differences in clinical content in the responses (p=0.001).
- Manual replies were more likely to advise direct clinical actions, while GPT-4 drafts often provided educational and self-management suggestions.
- AI-aided replies closely mirrored GPT-4 drafts but introduced some direct clinical actions.
### Conclusions & Relevance
AI-generated chatbot responses, while lengthier and less accessible, were overall safe and improved efficiency. AI-assistance altered the nature of physician feedback and reduced variability. AI chatbots are a promising avenue to address physician burnout and could improve patient care, however interactions between humans and AI might affect clinical decisions in unexpected ways. Addressing these interactions is vital for the safe incorporation of such technologies.
**Note**: It's imperative to delve deeper into human-AI interactions and their potential impact on outcomes.
# Citation:
```
@misc{chen2023impact,
title={The impact of using an AI chatbot to respond to patient messages},
author={Shan Chen and Marco Guevara and Shalini Moningi and Frank Hoebers and Hesham Elhalawani and Benjamin H. Kann and Fallon E. Chipidza and Jonathan Leeman and Hugo J. W. L. Aerts and Timothy Miller and Guergana K. Savova and Raymond H. Mak and Maryam Lustberg and Majid Afshar and Danielle S. Bitterman},
year={2023},
eprint={2310.17703},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 4,498 | [
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