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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
tr416/dataset_20231007_030826 | 2023-10-07T03:08:27.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T03:08:26 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 73672
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231007_030826"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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tr416/dataset_20231007_031407 | 2023-10-07T03:14:08.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T03:14:07 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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sequence: int32
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---
# Dataset Card for "dataset_20231007_031407"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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tr416/dataset_20231007_031447 | 2023-10-07T03:14:48.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T03:14:47 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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sequence: int32
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---
# Dataset Card for "dataset_20231007_031447"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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tr416/dataset_20231007_033121 | 2023-10-07T03:31:22.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T03:31:21 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
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---
# Dataset Card for "dataset_20231007_033121"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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tr416/dataset_20231007_033301 | 2023-10-07T03:33:02.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T03:33:01 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
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num_bytes: 762696.0
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---
# Dataset Card for "dataset_20231007_033301"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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tr416/dataset_20231007_033400 | 2023-10-07T03:34:01.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T03:34:00 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
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num_bytes: 7704.0
num_examples: 3
download_size: 74449
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---
# Dataset Card for "dataset_20231007_033400"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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tr416/dataset_20231007_033716 | 2023-10-07T03:37:17.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T03:37:16 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 73888
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231007_033716"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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tr416/dataset_20231007_033835 | 2023-10-07T03:38:36.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T03:38:35 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 73844
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231007_033835"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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tr416/dataset_20231007_034029 | 2023-10-07T03:40:30.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T03:40:29 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 73744
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231007_034029"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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BangumiBase/hunterxhunter | 2023-10-07T10:42:11.000Z | [
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-07T03:41:55 | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Hunter X Hunter
This is the image base of bangumi Hunter x Hunter, we detected 130 characters, 12906 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 | 3471 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 541 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 306 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 363 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 123 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 154 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 103 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 123 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 52 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 66 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 82 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 29 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 50 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 246 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 31 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 85 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 21 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 45 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 40 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 42 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 61 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 99 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 20 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 118 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 48 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 35 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 142 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 1450 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 43 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 98 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 39 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 42 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 67 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 17 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 27 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 34 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 14 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 15 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 41 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 22 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 24 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 49 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 38 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 19 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 24 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 236 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 57 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 64 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 34 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 62 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 24 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 21 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 12 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 107 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 18 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 745 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 133 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 277 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 33 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 110 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 65 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 24 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 22 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 35 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 65 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 106 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 49 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 21 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 45 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 67 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 50 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 15 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 52 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 32 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 16 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 11 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 21 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 31 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 38 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 15 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 49 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 13 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 15 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 18 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 16 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 122 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 22 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 16 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 57 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 45 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 20 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 10 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 30 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 14 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
| 94 | 134 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 21 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 26 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 69 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 8 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 17 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 18 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 5 | [Download](101/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 102 | 27 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 25 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
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| 105 | 25 | [Download](105/dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 16 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 11 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 25 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 14 | [Download](109/dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 53 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
| 111 | 23 | [Download](111/dataset.zip) |  |  |  |  |  |  |  |  |
| 112 | 21 | [Download](112/dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 12 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 9 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 13 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 47 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 11 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 6 | [Download](118/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 119 | 6 | [Download](119/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 120 | 8 | [Download](120/dataset.zip) |  |  |  |  |  |  |  |  |
| 121 | 54 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 25 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 53 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 8 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 16 | [Download](125/dataset.zip) |  |  |  |  |  |  |  |  |
| 126 | 22 | [Download](126/dataset.zip) |  |  |  |  |  |  |  |  |
| 127 | 6 | [Download](127/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 128 | 57 | [Download](128/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 236 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| 43,223 | [
[
-0.04150390625,
-0.00577545166015625,
0.00833892822265625,
0.01129913330078125,
-0.016448974609375,
-0.002765655517578125,
0.0027561187744140625,
-0.0241851806640625,
0.0413818359375,
0.03179931640625,
-0.059478759765625,
-0.05364990234375,
-0.0419921875,
0.... |
BangumiBase/codegeass | 2023-10-07T09:31:25.000Z | [
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-07T03:43:38 | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Code Geass
This is the image base of bangumi Code Geass, we detected 136 characters, 10361 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 | 37 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 97 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 119 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 187 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 218 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 131 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 77 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 128 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 79 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 42 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 31 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 39 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 13 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 42 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 52 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 89 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 79 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 46 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 75 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 82 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 28 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 21 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 51 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 23 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 26 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 44 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 1363 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 21 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 31 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 109 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 20 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 16 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 178 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 26 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 778 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 16 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 44 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 61 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 71 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 14 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 116 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 20 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 20 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 113 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 298 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 19 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 43 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 141 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 13 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 23 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 48 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 20 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 36 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 19 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 14 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 16 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 17 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 90 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 33 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 17 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 27 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 197 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 19 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 43 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 591 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 44 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 73 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 60 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 151 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 22 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 20 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 74 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 20 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 54 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 26 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 28 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 30 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 14 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 13 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 55 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 12 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 165 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 11 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 185 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 72 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 9 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 32 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 39 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 120 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 126 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 18 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 44 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 10 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 6 | [Download](93/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 94 | 43 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 207 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 12 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 11 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 15 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 17 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 20 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 9 | [Download](101/dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 253 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 10 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
| 104 | 16 | [Download](104/dataset.zip) |  |  |  |  |  |  |  |  |
| 105 | 28 | [Download](105/dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 19 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 9 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 17 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 12 | [Download](109/dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 7 | [Download](110/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 111 | 11 | [Download](111/dataset.zip) |  |  |  |  |  |  |  |  |
| 112 | 20 | [Download](112/dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 17 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 10 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 9 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 22 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 308 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 423 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
| 119 | 19 | [Download](119/dataset.zip) |  |  |  |  |  |  |  |  |
| 120 | 7 | [Download](120/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 121 | 9 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 8 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 114 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 88 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 5 | [Download](125/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 126 | 10 | [Download](126/dataset.zip) |  |  |  |  |  |  |  |  |
| 127 | 14 | [Download](127/dataset.zip) |  |  |  |  |  |  |  |  |
| 128 | 8 | [Download](128/dataset.zip) |  |  |  |  |  |  |  |  |
| 129 | 5 | [Download](129/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 130 | 13 | [Download](130/dataset.zip) |  |  |  |  |  |  |  |  |
| 131 | 14 | [Download](131/dataset.zip) |  |  |  |  |  |  |  |  |
| 132 | 7 | [Download](132/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 133 | 30 | [Download](133/dataset.zip) |  |  |  |  |  |  |  |  |
| 134 | 7 | [Download](134/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 348 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| 45,151 | [
[
-0.042022705078125,
-0.010009765625,
0.01039886474609375,
0.01366424560546875,
-0.0166015625,
-0.0036067962646484375,
-0.0004973411560058594,
-0.023895263671875,
0.038482666015625,
0.0295867919921875,
-0.055908203125,
-0.055206298828125,
-0.0423583984375,
0.... |
ziqin/autotrain-data-test | 2023-10-07T03:52:33.000Z | [
"task_categories:image-classification",
"region:us"
] | ziqin | null | null | 0 | 0 | 2023-10-07T03:45:35 | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: test
## Dataset Description
This dataset has been automatically processed by AutoTrain for project test.
### 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": "<380x254 RGB PIL image>",
"target": 0
},
{
"image": "<339x254 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Mountain', 'sea', 'snow'], 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 | 6 |
| valid | 3 |
| 932 | [
[
-0.042816162109375,
0.00626373291015625,
-0.000133514404296875,
0.0273590087890625,
-0.0290069580078125,
0.0204620361328125,
-0.00695037841796875,
-0.0274200439453125,
-0.0142059326171875,
0.0236053466796875,
-0.047515869140625,
-0.047027587890625,
-0.0316772460... |
alokps/hf-github-issues-comments-cat | 2023-10-07T03:47:00.000Z | [
"region:us"
] | alokps | null | null | 0 | 0 | 2023-10-07T03:46:55 | ---
dataset_info:
features:
- name: html_url
dtype: string
- name: title
dtype: string
- name: comments
dtype: string
- name: body
dtype: string
- name: comment_length
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 23118062
num_examples: 3907
download_size: 5079082
dataset_size: 23118062
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hf-github-issues-comments-cat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 642 | [
[
-0.04644775390625,
-0.0252685546875,
0.013641357421875,
0.034149169921875,
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0.00493621826171875,
-0.001842498779296875,
0.074951171875,
0.035430908203125,
-0.059600830078125,
-0.04022216796875,
-0.038604736328125,
0.010... |
smokiazo/gsgshshsshgsgss | 2023-10-07T03:53:50.000Z | [
"region:us"
] | smokiazo | null | null | 0 | 0 | 2023-10-07T03:51:26 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01497650146484375,
0.05718994140625,
0.02880859375,
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0.046478271484375,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.0170135498046875,
-0.052093505859375,
-0.01497650146484375,
-0.0604248046875,
0.0379028... |
alokps/hf-github-issues-comments-embeddings | 2023-10-07T03:52:15.000Z | [
"region:us"
] | alokps | null | null | 0 | 0 | 2023-10-07T03:52:00 | ---
dataset_info:
features:
- name: html_url
dtype: string
- name: title
dtype: string
- name: comments
dtype: string
- name: body
dtype: string
- name: comment_length
dtype: int64
- name: text
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 35135994
num_examples: 3907
download_size: 18199957
dataset_size: 35135994
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hf-github-issues-comments-embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 693 | [
[
-0.040496826171875,
-0.03143310546875,
0.0209503173828125,
0.0253753662109375,
-0.026763916015625,
0.01202392578125,
-0.005199432373046875,
0.005359649658203125,
0.06890869140625,
0.0227508544921875,
-0.05230712890625,
-0.05340576171875,
-0.0587158203125,
-0... |
toninhodjj/dudola | 2023-10-07T04:05:26.000Z | [
"region:us"
] | toninhodjj | null | null | 0 | 0 | 2023-10-07T04:03:04 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01497650146484375,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046478271484375,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.0170135498046875,
-0.052093505859375,
-0.01497650146484375,
-0.0604248046875,
0.0379028... |
BangumiBase/jojonokimyounabouken | 2023-10-07T13:24:52.000Z | [
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-07T04:30:10 | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Jojo No Kimyou Na Bouken
This is the image base of bangumi JoJo no Kimyou na Bouken, we detected 137 characters, 14828 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 | 68 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 351 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 101 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 188 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 92 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 57 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 161 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 647 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 144 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 821 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 68 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 220 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 942 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 37 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 336 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 274 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 562 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 225 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 151 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 48 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 613 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 132 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 207 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 151 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 58 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 679 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 527 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 86 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 27 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
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| 30 | 47 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 87 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 138 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 48 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
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| 40 | 81 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
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| 42 | 26 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
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| 45 | 15 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
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| 47 | 71 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 20 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 29 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 163 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 172 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 43 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
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| 54 | 63 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
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| 57 | 80 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 38 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 48 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 51 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 33 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 38 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 21 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 39 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 26 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 62 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 27 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 19 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 43 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 87 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
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| 72 | 30 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 52 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 70 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 41 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 39 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 433 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 18 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 12 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 18 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 146 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 19 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 72 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 28 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 29 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 48 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 176 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 63 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 38 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 47 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 87 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 33 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
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| 94 | 63 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 238 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 30 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 47 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 36 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 36 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 26 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 32 | [Download](101/dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 23 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 22 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
| 104 | 31 | [Download](104/dataset.zip) |  |  |  |  |  |  |  |  |
| 105 | 208 | [Download](105/dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 15 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 48 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 18 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 70 | [Download](109/dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 50 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
| 111 | 21 | [Download](111/dataset.zip) |  |  |  |  |  |  |  |  |
| 112 | 9 | [Download](112/dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 70 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 26 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 23 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 21 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 27 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 17 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
| 119 | 368 | [Download](119/dataset.zip) |  |  |  |  |  |  |  |  |
| 120 | 16 | [Download](120/dataset.zip) |  |  |  |  |  |  |  |  |
| 121 | 43 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 30 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 25 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 38 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 42 | [Download](125/dataset.zip) |  |  |  |  |  |  |  |  |
| 126 | 29 | [Download](126/dataset.zip) |  |  |  |  |  |  |  |  |
| 127 | 10 | [Download](127/dataset.zip) |  |  |  |  |  |  |  |  |
| 128 | 23 | [Download](128/dataset.zip) |  |  |  |  |  |  |  |  |
| 129 | 9 | [Download](129/dataset.zip) |  |  |  |  |  |  |  |  |
| 130 | 13 | [Download](130/dataset.zip) |  |  |  |  |  |  |  |  |
| 131 | 10 | [Download](131/dataset.zip) |  |  |  |  |  |  |  |  |
| 132 | 16 | [Download](132/dataset.zip) |  |  |  |  |  |  |  |  |
| 133 | 23 | [Download](133/dataset.zip) |  |  |  |  |  |  |  |  |
| 134 | 10 | [Download](134/dataset.zip) |  |  |  |  |  |  |  |  |
| 135 | 9 | [Download](135/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 382 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| 45,502 | [
[
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0.0401611328125,
0.033843994140625,
-0.055999755859375,
-0.0546875,
-0.04058837890625,
0.033386... |
testing445/HUN | 2023-10-07T05:00:39.000Z | [
"region:us"
] | testing445 | null | null | 0 | 0 | 2023-10-07T05:00:39 | Entry not found | 15 | [
[
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0.0170135498046875,
-0.052093505859375,
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0.0379028... |
BangumiBase/narutomovies | 2023-10-07T06:32:17.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-07T05:03:10 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Naruto [movies]
This is the image base of bangumi NARUTO [Movies], we detected 37 characters, 3111 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 | 1040 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 44 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 34 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 40 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 90 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 120 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 49 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 107 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 19 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 72 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 75 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 86 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 37 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 35 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 75 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 115 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 23 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 44 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 38 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 68 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 158 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 19 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 45 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 45 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 247 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 18 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 9 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 15 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 11 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 6 | [Download](29/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 30 | 60 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 87 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 11 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 35 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 16 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 7 | [Download](35/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 111 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| 12,829 | [
[
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0.031402587890625,
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... |
keshavsudo007/massive | 2023-10-07T05:51:38.000Z | [
"region:us"
] | keshavsudo007 | null | null | 0 | 0 | 2023-10-07T05:51:38 | Entry not found | 15 | [
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0.0170135498046875,
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-0.01497650146484375,
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0.0379028... |
Jaredquek/AuroMiraWorks | 2023-10-07T07:35:47.000Z | [
"task_categories:question-answering",
"task_categories:conversational",
"language:en",
"license:mit",
"philosophy",
"religion",
"spirituality",
"occult",
"indian philosophy",
"hinduism",
"region:us"
] | Jaredquek | null | null | 0 | 0 | 2023-10-07T07:11:33 | ---
license: mit
task_categories:
- question-answering
- conversational
language:
- en
tags:
- philosophy
- religion
- spirituality
- occult
- indian philosophy
- hinduism
---
This 'text completion' dataset (originally in jsonl format) comprises the major prose works of Sri Aurobindo, the Indian philosopher, seer and poet, and his spiritual partner, Mirra Alfassa. The following works have been used:
### Sri Aurobindo:
- Letters on Yoga 1, 2, 3, 4
- Letters on Himself and the Ashram
- The Mother with Letters on the Mother
- The Life Divine
- The Synthesis of Yoga
- The Renaissance in India
- The Secret of the Veda
- Essays Divine and Human
- Essays on the Gita
- Essays in Philosophy and Yoga
- The Future Poetry
- The Human Cycle
- Isha Upanishad
### Mirra (the Mother's):
- Questions and Answers (all volumes)
- Prayers and Meditation
- On Education
- On Thoughts and Aphorisms
- Words of the Mother (all volumes)
The titles of books have been removed to reduce hallucinatory misquotes. We believe this dataset is useful to train AIs to converse on spiritual and philosophical topics, as Sri Aurobindo's writings relate a deep and complex spiritual philosophy to all areas of life and thought.
Anyone interested in datasets by individual books (or in building 'spiritual AIs') - please message me at my Twitter account [@jared_quek](https://twitter.com/jared_quek).
| 1,380 | [
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lakelz/myds2-bpg | 2023-10-07T07:14:30.000Z | [
"region:us"
] | lakelz | null | null | 0 | 0 | 2023-10-07T07:13:56 | This dataset is a subset of the Open Assistant dataset, which you can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
This dataset was used to train Guanaco with QLoRA.
For further information, please see the original dataset.
License: Apache 2.0 | 395 | [
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0.0078125,
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0.0233612060546875,
0.037811279296875,
-0.06939697265625,
-0.05303955078125,
-0.032623291015625,
-0.012321... |
lighteval/trivia_qa | 2023-10-07T07:35:06.000Z | [
"region:us"
] | lighteval | null | null | 0 | 0 | 2023-10-07T07:31:15 | ---
dataset_info:
- config_name: default
features:
- name: question
dtype: string
- name: question_id
dtype: string
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struct:
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sequence: string
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sequence: string
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dtype: string
- name: normalized_matched_wiki_entity_name
dtype: string
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splits:
- name: train
num_bytes: 106882730
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splits:
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num_bytes: 106882730
num_examples: 138384
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num_bytes: 14059830
num_examples: 17944
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num_bytes: 3667903
num_examples: 17210
download_size: 63926518
dataset_size: 124610463
configs:
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data_files:
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path: data/test-*
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path: data/train-*
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- config_name: rc.nocontext
data_files:
- split: train
path: rc.nocontext/train-*
- split: validation
path: rc.nocontext/validation-*
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path: rc.nocontext/test-*
---
# Dataset Card for "trivia_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 3,108 | [
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CWKSC/common_voice_13_0-zh-HK-whisper-small | 2023-10-07T08:05:00.000Z | [
"region:us"
] | CWKSC | null | null | 1 | 0 | 2023-10-07T07:58:15 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
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download_size: 0
dataset_size: 18835988312
---
# Dataset Card for "common_voice_13_0-zh-HK-whisper-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 637 | [
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106306046derrick/name_of_your_dataset | 2023-10-07T10:18:37.000Z | [
"region:us"
] | 106306046derrick | null | null | 0 | 0 | 2023-10-07T08:52:39 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
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path: data/validation-*
dataset_info:
features:
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num_bytes: 834808939.688
num_examples: 158152
download_size: 11785641447
dataset_size: 11893377825.612
---
# Dataset Card for "name_of_your_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 596 | [
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Antonio49/444 | 2023-10-07T09:04:39.000Z | [
"region:us"
] | Antonio49 | null | null | 0 | 0 | 2023-10-07T09:04:39 | Entry not found | 15 | [
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wuming156/hassakuXLSfwNsfw_alpha05 | 2023-10-07T10:10:26.000Z | [
"region:us"
] | wuming156 | null | null | 0 | 0 | 2023-10-07T10:00:43 | Entry not found | 15 | [
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vladman-25/flickr-30k-romanian-captions | 2023-10-07T10:54:18.000Z | [
"license:unknown",
"region:us"
] | vladman-25 | null | null | 0 | 0 | 2023-10-07T10:39:06 | ---
license: unknown
---
# Dataset Card for Flickr 30k Romanian Captions
### Dataset Summary
This dataset is a translation in romanian of the flickr 30k captions dataset.
This was generated using [nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B), with Hugging face for both tokenization and translation.
Observations:
* the translation keeps the context pretty well.
* there are a few grammatical errors: "Doi tineri sare peste un balustradă"
* some translations are silly: "Un bărbat ţine o jucărie mare de leu împăiat.", "Un bărbat cu barbă care poartă un dulap."
### Languages
romanian | 628 | [
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mrsearchwolf/cricket-wiki | 2023-10-11T04:22:37.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"cricket",
"region:us"
] | mrsearchwolf | null | null | 1 | 0 | 2023-10-07T10:56:33 | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- cricket
size_categories:
- 10K<n<100K
---
# cricket-wiki
```bash
# data preparation steps
pip install wikiextractor
wget -c https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles-multistream.xml.bz2
# extract files using wikiextractor (take a few hours)
python3 -m wikiextractor.WikiExtractor enwiki-latest-pages-articles-multistream.xml.bz2 --json
# get cricket records in a separate file
# take a few minutes
grep -i cricket text/*/* > cricket.jsonl
``` | 562 | [
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LuminanceImagine/SiaFullVocalstems | 2023-10-07T11:09:55.000Z | [
"region:us"
] | LuminanceImagine | null | null | 0 | 0 | 2023-10-07T11:08:46 | Entry not found | 15 | [
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infCapital/vietllama-tiny-envi | 2023-10-09T09:02:41.000Z | [
"task_categories:question-answering",
"language:vi",
"language:en",
"license:apache-2.0",
"region:us"
] | infCapital | null | null | 0 | 0 | 2023-10-07T11:17:24 | ---
license: apache-2.0
task_categories:
- question-answering
language:
- vi
- en
---
+ Instruction dataset for fine-tuning
+ Dataset contains original dataset [lima, orca-mini, alpaca data, alpaca finance, GPTeacher] and their Vietnamese translations
+ Suggested use cases: Fine-tuning Vietnamese LLM | 301 | [
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mjbuehler/Mistral_v102Mistreal | 2023-10-07T11:52:39.000Z | [
"region:us"
] | mjbuehler | null | null | 0 | 0 | 2023-10-07T11:52:36 | ---
dataset_info:
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Mistral_v102Mistreal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 452 | [
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stepkurniawan/test | 2023-10-08T09:34:08.000Z | [
"region:us"
] | stepkurniawan | null | null | 0 | 0 | 2023-10-07T12:07:46 | ---
dataset_info:
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features:
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: starters
data_files:
- split: train
path: starters/train-*
---
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 767 | [
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gjoy/validate_classifier_retry | 2023-10-07T12:20:59.000Z | [
"region:us"
] | gjoy | null | null | 0 | 0 | 2023-10-07T12:18:13 | Entry not found | 15 | [
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autoevaluate/autoeval-eval-ade_corpus_v2-Ade_corpus_v2_classification-d9292a-93577145879 | 2023-10-07T12:39:22.000Z | [
"region:us"
] | autoevaluate | null | null | 0 | 0 | 2023-10-07T12:39:18 | Entry not found | 15 | [
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DGurgurov/mental_disorders_data | 2023-10-07T13:08:58.000Z | [
"region:us"
] | DGurgurov | null | null | 0 | 0 | 2023-10-07T12:48:46 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc/guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
### Dataset Summary
This repository comprises three distinct datasets:
1. **Character Descriptions from 12 Authors across 4 Countries**
Character descriptions authored by a diverse group of writers.
2. **Character Descriptions labeled with Mental Disorders using Cosine Similarity**
Character descriptions annotated with labels corresponding to mental disorders.
3. **Data on Mental Disorders**
Data pertaining to mental disorders.
### Languages
- English
## Dataset Creation
- Character Descriptions datasets were generated by initiating prompts with ChatGPT.
- Character Descriptions were annotated using Cosine Similarity.
- Data on Mental Disorders was extracted through scraping the International Classification of Diseases (ICD).
### Contributors
- Daniil Gurgurov
- Nursulu Sagimbayeva
- Antonia Wächter
- Asmaa Ibrahim
### Additional Details
- Project Website: [Link to Project Website](https://d-gurgurov.github.io/projects/project1.html)
- Deep Learning for Literary Analysis Report: [Link to Report](https://github.com/d-gurgurov/dl_for_text_analysis/blob/main/Deep_Learning_for_Literary_Analysis_Report.pdf)
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erecprime/ErecPrime | 2023-10-07T13:41:57.000Z | [
"region:us"
] | erecprime | null | null | 0 | 0 | 2023-10-07T13:41:27 | Patients undergoing andropause often do not choose these therapies, opting instead to try dietary supplements. As millions of baby boomers are currently experiencing andropause, marketers offer hundreds of products allegedly beneficial in reversing impotence and enhancing male sexual performance.
ErecPrime Male Enhancement
ErecPrime Male Enhancement Pills
ErecPrime Male Enhancement Reviews
Erec Prime Male Enhancement
https://www.supplementz.org/erecprime-male-enhancement/
https://www.supplementz.org/animale-me-capsules/
https://www.supplementz.org/vista-keto-acv-gummies/
https://www.supplementz.org/ | 608 | [
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tr416/tommys_mad_model_dataset_20231007_141121 | 2023-10-07T14:11:22.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T14:11:21 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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sequence: int32
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sequence: int8
splits:
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num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 74087
dataset_size: 770400.0
---
# Dataset Card for "tommys_mad_model_dataset_20231007_141121"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 613 | [
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tr416/test_dataset_20231007_143435 | 2023-10-07T14:34:37.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T14:34:35 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 73765
dataset_size: 770400.0
---
# Dataset Card for "test_dataset_20231007_143435"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 601 | [
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ayoubkirouane/news_summary | 2023-10-07T16:08:54.000Z | [
"region:us"
] | ayoubkirouane | null | null | 0 | 0 | 2023-10-07T16:08:16 | Entry not found | 15 | [
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leharris3/basketball-shooting-video-classification | 2023-10-07T16:14:09.000Z | [
"region:us"
] | leharris3 | null | null | 0 | 0 | 2023-10-07T16:13:26 | Entry not found | 15 | [
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Alfaxad/BioGalacticModels-Zoo | 2023-10-08T14:07:22.000Z | [
"region:us"
] | Alfaxad | null | null | 0 | 0 | 2023-10-07T16:21:05 | 
# 🌌 BioGalacticModels Zoo
## **🔭 Overview**
### **🛰️ Space Biology Datasets And Models Hub**
The frontier of space biology research is vast, with uncharted territories that hide the secrets of life beyond our blue planet. 🌍 With the increasing need for accurate and reliable methods to understand and decode the effects of space on biology, machine learning, particularly transfer learning, emerges as a promising approach. 🧠 This repository serves as a nexus between space biology and computational methodologies, aimed at harnessing the power of transfer learning for space biology applications. 💡
We present to you a comprehensive database of publicly available biomedical datasets and models that can be used to further space-biology research and discovery. 🌠
### **🚀 Purpose and Scope**
This repository is designed to:
1. **Centralize Resources**: 📚 Provide a curated collection of GeneLab datasets tailored for space biology studies, ranging from whole genome sequencing to DNA methylation.
2. **Promote Transfer Learning**: 🎓 Offer pre-trained models suitable for transfer learning.
3. **Streamline Data Processing**: ⚙️ Offer code samples and scripts for efficient dataset management.
4. **Facilitate Collaboration**: 🤝 Foster collaboration amongst researchers in the field.
5. **Reference Architectures**: 🗺️ Navigate through transfer learning architectures with ease.
### **🎯 Intended Audience**
This hub is for:
- **Space Biologists**: 🔬 Integrating computational methodologies.
- **Data Scientists & Machine Learning Enthusiasts**: 💻 Tackling challenges in space biology.
- **Students & Educators**: 📖 Accessing resources for computational space biology.
### **✉️ Contributing and Feedback**
We believe in community-driven science. 💖 Your contributions are warmly welcomed! By joining hands, we can venture further into the mysteries of space biology.
---
🛸 Join us in this interstellar journey of melding computation and space biology, steering the future of life in space.
## 📜 Table of Contents
- [BioGalactic Models](#🌠-biogalactic-models)
- [Datasets](#🧬-datasets)
- [Insights On BioGalacticModels Zoo Usage & Exploration](#💭-insights-on-biogalacticmodels-zoo-usage-&-exploration)
- [Transfer Learning Model Architectures for Space Biology](#🌐-promising-transfer-learning-model-architectures-for-space-biology)
- [Demo: Predicting Viral Host based on Metagenomic Features](#🧪-demo-predicting-viral-host-based-on-metagenomic-features)
---
## 🌠 BioGalactic Models
[BioGalactic Models](https://huggingface.co/spaces/Alfaxad/BioGalacticModels) 🌌 is a dedicated Hugging Face space containing a curated collection of Biology & Biochemistry Foundation Models.
**Significance to the BioGalactic Model Zoo**:
- **Ready-to-use Models**: 🚀 These models are pre-trained, optimized for transfer learning tasks.
- **Diverse Applications**: 🎯 Focused on Biology & Biochemistry, catering to space biology.
- **Continuous Evolution**: 🔄 As space biology progresses, this space will evolve.
**Impacting Space Biology Exploration**:
The models provide insights driving our understanding of life in space conditions. These include:
- Decoding genomic sequences.
- Predicting protein structures and interactions.
- Analyzing metabolic pathways in space.
---
## 🧬 Datasets
Dive into the curated datasets, specifically tailored for space biology studies. These datasets, coming directly from the vaults of NASA's GeneLab, cover a range of biological investigations relevant to space.
### **Whole Genome Sequencing Datasets**
1. [Microbiome profiling of feces from mice flown on the RR-10 mission](https://osdr.nasa.gov/bio/repo/data/studies/OSD-466)
2. [Metagenome profiling of feces from mice flown on the RR-23 mission](https://osdr.nasa.gov/bio/repo/data/studies/OSD-465)
3. [Whole genome sequencing and assembly of Eukaryotic microbes isolated from ISS environmental surface, Kirovograd region soil, Chernobyl Nuclear Power Plant and Chernobyl Exclusion Zone](https://osdr.nasa.gov/bio/repo/data/studies/OSD-132)
4. [Draft Genome Sequences of novel Agrobacterium genomospecies 3 Associated from the International Space Station](https://osdr.nasa.gov/bio/repo/data/studies/OSD-306)
5. [Metagenomic analysis of feces from mice flown on the RR-6 mission](https://osdr.nasa.gov/bio/repo/data/studies/OSD-249)
6. [Insta-Deep's Multi-species genome dataset](https://huggingface.co/datasets/InstaDeepAI/multi_species_genomes)
### **DNA Methylation Datasets**
1. [Changes in DNA Methylation in Arabidopsis thaliana Plants Exposed Over Multiple Generations to Gamma Radiation](https://osdr.nasa.gov/bio/repo/data/studies/OSD-520)
2. [Characterization of Epigenetic Regulation in an Extraterrestrial Environment: The Arabidopsis Spaceflight Methylome](https://osdr.nasa.gov/bio/repo/data/studies/OSD-217)
3. [Ionizing radiation induces transgenerational effects of DNA methylation in zebrafish](https://osdr.nasa.gov/bio/repo/data/studies/OSD-524)
4. [Methylome Analysis of Arabidopsis Seedlings Exposed to Microgravity](https://osdr.nasa.gov/bio/repo/data/studies/OSD-220)
For an exhaustive list of datasets and other resources, explore [NASA's Open Science Data Repository (OSDR)](https://osdr.nasa.gov/bio/repo/search?q=&data_source=cgene,alsda&data_type=study).
## **Bulk Downloading GeneLab Datasets with genelab-utils**
### **Quick Usage Guide**
# GeneLab utils
Some helper programs for [NASA GeneLab](https://genelab.nasa.gov/), such as `GL-download-GLDS-data` for downloading files from a specific OSD or GLDS ID, and `GL-get-workflow` for downloading workflows used by [GeneLab for processing datasets](https://github.com/nasa/GeneLab_Data_Processing).
## Conda install
The genelab-utils package should be installed with conda/mamba. If you are not familiar with conda, you can find an introduction [here](https://astrobiomike.github.io/unix/conda-intro) if wanted, and if you are not familiar with mamba, there is a super-short introduction on that same page [here](https://astrobiomike.github.io/unix/conda-intro#bonus-mamba-no-5) if wanted – it's definitely worth using mamba if you use conda at all :+1:
```bash
conda install -c conda-forge -n base mamba
mamba create -n genelab-utils -c conda-forge -c bioconda -c defaults -c astrobiomike genelab-utils
conda activate genelab-utils
```
All programs are prefixed with `GL-` and have a help menu accessible with `-h`. Version info can be accessed with `GL-version`.
## Some example pages
- Programmatically downloading [GLDS data](https://genelab-data.ndc.nasa.gov/genelab/)
- [`GL-download-GLDS-data`](https://hackmd.io/@astrobiomike/using-genelab-utils-to-download-GLDS-data)
- Downloading GeneLab workflows
- [`GL-get-workflow`](https://hackmd.io/@astrobiomike/using-genelab-utils-to-download-workflows)
---
## 💭 Insights On BioGalacticModels Zoo Usage & Exploration
### **1. Preprocessing**
For transfer learning these biomedical datasets may require various preprocessing steps depending on their source and format:
- **Data Cleaning:** Removing noise and inconsistencies.
- **Normalization:** Scaling features to a standard range.
- **Data Augmentation:** Especially for image datasets, augmenting data can help improve model robustness.
- **Feature Selection/Extraction:** Especially in genomics, where dimensionality can be very high.
- **Handling Imbalances:** In some datasets, certain classes may be underrepresented.
- **Format Conversion:** Datasets might need to be converted to formats compatible with machine learning frameworks.
### **3. Potential Multimodal Data Combinations for Space Biology Knowledge Gain**
Combining different types of datasets, like genomic, proteomic, and transcriptomic data, can provide a holistic view of biological systems.
Additionally, integrating imaging data with molecular data can enhance our understanding of spatial-temporal patterns.
Multi-modal datasets can help discover patterns or signals that might not be evident when analyzing data types in isolation.
#### a. **Genomic & Transcriptomic Data**:
- **Why**: While genomic data (like Whole Genome Sequencing) provides the blueprint of life, transcriptomic data offers insights into gene expression under specific conditions. Combining both can help in understanding the genetic basis of responses to space environments and how genes are expressed differently in space.
#### b. **Proteomic & Metabolomic Data**:
- **Why**: Proteomic data tells us about the proteins produced, while metabolomic data provides information on the small molecules in an organism. Together, they can offer insights into the functional state of cells in space, revealing which proteins are active and what metabolic pathways they're influencing.
#### c. **Transcriptomic & Metabolomic Data**:
- **Why**: This combination can correlate gene expression with metabolic changes. It can be particularly insightful to understand how gene expression changes influence metabolic responses in space conditions.
#### d. **Genomic & Phenotypic Data**:
- **Why**: Connecting the genetic makeup with observable traits (phenotypes) can help in predicting how specific genetic variations might influence an organism's ability to thrive in space.
#### e. **Imaging & Transcriptomic Data**:
- **Why**: While transcriptomic data reveals gene expression, imaging (like MRI or microscopy) can show structural or functional changes in tissues or cells. Combined, they can link gene expression patterns with visual manifestations.
#### f. **Epigenomic & Transcriptomic Data**:
- **Why**: Epigenomic data, like DNA Methylation, reveals changes in gene activity not caused by DNA sequence changes. By combining it with transcriptomic data, one can understand how space conditions might epigenetically influence gene expression.
#### g. **Genomic & Proteomic Data**:
- **Why**: This combination can be used to understand the translation of genes to proteins under space conditions, offering insights into post-transcriptional modifications in space.
#### h. **Environmental Data & Any Biological Data**:
- **Why**: Combining data on the space environment (like radiation levels or microgravity conditions) with any biological dataset can help correlate external conditions with biological responses.
The task of organizing multimodal datasets may face the following challenges:
1. **Data Integration**: Combining data from different sources and modalities can be challenging due to differences in scale, resolution, and format.
2. **Interpretability**: While multi-modal data can provide richer insights, it can also make interpretations complex.
3. **Computational Needs**: Integrating and analyzing multi-modal data often requires robust computational resources and specialized algorithms.
However, the potential insights gained from such combinations, especially in understanding the complex biological responses to space conditions, can be invaluable.
Leveraging transfer learning with models pretrained on diverse biomedical datasets and refined on space biology datasets can significantly boost the knowledge derived from these multi-modal combinations.
---
## 🌐 Promising Transfer Learning Model Architectures for Space Biology
The deep learning domain has birthed numerous architectures tailor-made for transfer learning. These models, having trained on expansive datasets, excel at grasping general features, which can be specialized for niche tasks, such as those in space biology. Here's a selection of architectures ripe for exploration in this challenge:
### 1. **Convolutional Neural Networks (CNNs)**:
Primarily efficient for image-centric data.
- **VGG (e.g., VGG16, VGG19)**: Crafted by the Visual Geometry Group, it's a staple for image recognition.
- **ResNet**: Features skip connections, countering the vanishing gradient dilemma in deep structures.
- **Inception (or GoogLeNet)**: Employs varied convolution sizes for multi-scale detail capture.
- **DenseNet**: Innovatively links each layer to every subsequent one in a feed-forward manner.
### 2. **Transformers**:
Originally for NLP, but have branched out to other areas like imagery.
- **BERT**: Tailored for NLP, it's versatile for text-oriented tasks.
- **ViT (Vision Transformer)**: Modifies the transformer design for visual tasks.
### 3. **Recurrent Neural Networks (RNNs)**:
Best suited for sequences such as time-series or biological sequences.
- **LSTM**: Counters the standard RNN's vanishing gradient issue.
- **GRU**: A streamlined LSTM variant.
### 4. **Autoencoders**:
For unsupervised learning, adept at feature extraction from unlabeled content.
- **Variational Autoencoders (VAEs)**: Introduces a probabilistic layer to autoencoders, frequently in generative scenarios.
### 5. **Generative Adversarial Networks (GANs)**:
Ideal for dataset augmentation, synthesizing data resembling the original distribution.
### 6. **U-Net**:
Conceived for biomedical image segmentation, amalgamating a context-capturing contractive route with a precision-centric expanding one.
### 7. **Capsule Networks**:
Navigates the spatial hierarchy between simple and intricate objects in visuals, potentially invaluable for intricate biological imaging.
### 8. **EfficientNet**:
Balances network breadth, depth, and clarity using fixed scaling coefficients, creating potentially smaller yet more precise models.
### 9. **BioBERT**:
A BERT variant pre-trained on biomedical datasets, apt for biology-centered tasks.
### 10. **AlphaFold**:
By DeepMind, it revolutionizes protein structure prediction, a seminal biological conundrum.
### **Recommendations**:
- For the unique aspects of space biology, initiating with biomedically proven architectures like U-Net could be fruitful.
- LSTMs or GRUs, being RNN derivatives, could be promising for genomic or other sequential datasets.
- GANs might be instrumental for data augmentation or crafting synthetic examples to enrich datasets.
- For challenges surrounding protein structures or other molecular biology facets, models like AlphaFold are worthy contenders.
---
## 🧪 Demo: Predicting Viral Host based on Metagenomic Features
In this repository,we also explore a demo using metagenomic features extracted from viral genomes to predict the virus host. Features include Genome size, GC%, and count of CDS. These serve as the independent variables to predict the viral host.
An SVM (Support Vector Machine) model is used, achieving an accuracy rate of 86%. Dive deeper into the methods, data preprocessing, and results [here](https://huggingface.co/datasets/Alfaxad/Space-Biology-Model-Zoo/blob/main/viral_host_demo/predict-viral-host-based-on-meta-genomic-features.ipynb).
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tr416/test_dataset_20231007_171958 | 2023-10-07T17:20:00.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T17:19:58 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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sequence: int32
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download_size: 73618
dataset_size: 770400.0
---
# Dataset Card for "test_dataset_20231007_171958"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 601 | [
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tr416/test2_dataset_20231007_172035 | 2023-10-07T17:20:36.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T17:20:35 | ---
configs:
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data_files:
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path: data/train-*
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dataset_size: 770400.0
---
# Dataset Card for "test2_dataset_20231007_172035"
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BangumiBase/freeeternalsummer | 2023-10-07T19:53:45.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-07T18:29:01 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Free! -eternal Summer-
This is the image base of bangumi Free! -Eternal Summer-, we detected 24 characters, 2471 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 | 411 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 274 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 32 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 105 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 215 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 37 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 23 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 45 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 284 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 36 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 54 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 36 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 9 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 238 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 19 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 306 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 11 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 118 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 12 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 14 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 14 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 37 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 5 | [Download](22/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 136 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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Hack90/ncbi_genbank_part_73 | 2023-10-07T18:51:59.000Z | [
"region:us"
] | Hack90 | null | null | 0 | 0 | 2023-10-07T18:39:17 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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dtype: string
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dtype: string
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dtype: int64
splits:
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num_bytes: 31446287535
num_examples: 1129212
download_size: 14015101306
dataset_size: 31446287535
---
# Dataset Card for "ncbi_genbank_part_73"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 645 | [
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BangumiBase/ilsolepenetraleillusioni | 2023-10-07T19:58:38.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-07T18:40:21 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Il Sole Penetra Le Illusioni
This is the image base of bangumi il sole penetra le illusioni, we detected 26 characters, 1875 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 | 82 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 144 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 47 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 26 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 23 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 27 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 41 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 18 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 12 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 11 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 73 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 17 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 152 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 11 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 14 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 75 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 12 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 206 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 60 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 16 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 12 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 28 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 53 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 8 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 29 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 678 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| 9,401 | [
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Hack90/ncbi_genbank_part_46 | 2023-10-07T19:46:27.000Z | [
"region:us"
] | Hack90 | null | null | 0 | 0 | 2023-10-07T19:05:52 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: sequence
dtype: string
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dtype: int64
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dtype: int64
splits:
- name: train
num_bytes: 45473431595
num_examples: 198370
download_size: 20050383599
dataset_size: 45473431595
---
# Dataset Card for "ncbi_genbank_part_46"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 644 | [
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Hack90/ncbi_genbank_part_74 | 2023-10-07T19:24:02.000Z | [
"region:us"
] | Hack90 | null | null | 0 | 0 | 2023-10-07T19:09:37 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: sequence
dtype: string
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dtype: string
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dtype: int64
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dtype: int64
splits:
- name: train
num_bytes: 33100376103
num_examples: 414925
download_size: 14899366001
dataset_size: 33100376103
---
# Dataset Card for "ncbi_genbank_part_74"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 644 | [
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0.... |
ummagumm-a/colorization_dataset | 2023-10-07T19:22:39.000Z | [
"region:us"
] | ummagumm-a | null | null | 0 | 0 | 2023-10-07T19:13:33 | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
sequence:
sequence:
sequence: uint8
- name: text
dtype: string
splits:
- name: train
num_bytes: 333261193.0
num_examples: 1000
download_size: 127051514
dataset_size: 333261193.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "colorization_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 574 | [
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M-A-D/Mixed-Arabic-Dataset-Main-Test | 2023-10-07T19:17:56.000Z | [
"region:us"
] | M-A-D | null | null | 1 | 0 | 2023-10-07T19:17:51 | ---
dataset_info:
features:
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dtype: int64
- name: SubId
dtype: int64
- name: DatasetName
dtype: string
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- name: MetaData
struct:
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- name: AboutBook
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splits:
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num_bytes: 96491917
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Mixed-Arabic-Dataset-Main-Test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 2,172 | [
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BangumiBase/zombielandsagarevenge | 2023-10-07T20:45:00.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-07T19:19:50 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Zombie Land Saga Revenge
This is the image base of bangumi Zombie Land Saga Revenge, we detected 36 characters, 2401 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 | 127 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 86 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 40 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 80 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 18 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 12 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 61 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 60 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 35 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 40 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 61 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 58 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 31 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 43 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 22 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 10 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 13 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 5 | [Download](17/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 18 | 217 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 46 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 229 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 40 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 87 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 18 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 20 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 57 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 21 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 13 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 196 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 49 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 30 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 92 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 184 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 8 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 8 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 284 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| 12,533 | [
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BySwax/JeanPormanove | 2023-10-07T19:21:00.000Z | [
"region:us"
] | BySwax | null | null | 0 | 0 | 2023-10-07T19:21:00 | Entry not found | 15 | [
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ccore/rhetoric-saint-thomas-aquinas | 2023-10-07T19:25:07.000Z | [
"license:mit",
"region:us"
] | ccore | null | null | 0 | 0 | 2023-10-07T19:22:48 | ---
license: mit
---
Whether God Is Composed of Matter and Form?
Objection 1: It seems that God is composed of matter and form. For
whatever has a soul is composed of matter and form; since the soul is
the form of the body. But Scripture attributes a soul to God; for it
is mentioned in Hebrews (Heb. 10:38), where God says: "But My just man
liveth by faith; but if he withdraw himself, he shall not please My
soul." Therefore God is composed of matter and form.
Objection 2: Further, anger, joy and the like are passions of the
composite. But these are attributed to God in Scripture: "The Lord was
exceeding angry with His people" (Ps. 105:40). Therefore God is
composed of matter and form.
Objection 3: Further, matter is the principle of individualization.
But God seems to be individual, for He cannot be predicated of many.
Therefore He is composed of matter and form.
Contrary: Whatever is composed of matter and form is a body;
for dimensive quantity is the first property of matter. But God is not
a body as proved in the preceding Article; therefore He is not
composed of matter and form.
Response: It is impossible that matter should exist in God.
First, because matter is in potentiality. But we have shown (Q. 2, A. 3)
that God is pure act, without any potentiality. Hence it is
impossible that God should be composed of matter and form. Secondly,
because everything composed of matter and form owes its perfection and
goodness to its form; therefore its goodness is participated, inasmuch
as matter participates the form. Now the first good and the
best--viz. God--is not a participated good, because the essential
good is prior to the participated good. Hence it is impossible that
God should be composed of matter and form. Thirdly, because every
agent acts by its form; hence the manner in which it has its form is
the manner in which it is an agent. Therefore whatever is primarily
and essentially an agent must be primarily and essentially form. Now
God is the first agent, since He is the first efficient cause. He is
therefore of His essence a form; and not composed of matter and form.
Reply Objection 1: A soul is attributed to God because His acts
resemble the acts of a soul; for, that we will anything, is due to our
soul. Hence what is pleasing to His will is said to be pleasing to His
soul.
Reply Objection 2: Anger and the like are attributed to God on
account of a similitude of effect. Thus, because to punish is properly
the act of an angry man, God's punishment is metaphorically spoken of
as His anger.
Reply Objection 3: Forms which can be received in matter are
individualized by matter, which cannot be in another as in a subject
since it is the first underlying subject; although form of itself,
unless something else prevents it, can be received by many. But that
form which cannot be received in matter, but is self-subsisting, is
individualized precisely because it cannot be received in a subject;
and such a form is God. Hence it does not follow that matter exists in
God.
_______________________ | 3,050 | [
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BangumiBase/sekaisaikounoansatsushaisekaikizokunitenseisuru | 2023-10-07T20:43:37.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-07T19:30:47 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Sekai Saikou No Ansatsusha, Isekai Kizoku Ni Tensei Suru
This is the image base of bangumi Sekai Saikou no Ansatsusha, Isekai Kizoku ni Tensei Suru, we detected 32 characters, 1510 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 | 118 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 40 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 27 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 23 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 17 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 20 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 270 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 9 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 98 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 91 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 20 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 29 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 23 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 16 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 86 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 11 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 15 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 13 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 14 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 16 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 10 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 6 | [Download](22/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 23 | 39 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 150 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 38 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 70 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 15 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 10 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 11 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 9 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 169 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| 11,341 | [
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Vinisasasasas/gleemercedes | 2023-10-07T19:34:33.000Z | [
"region:us"
] | Vinisasasasas | null | null | 0 | 0 | 2023-10-07T19:31:34 | Entry not found | 15 | [
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ai-habitat/hab3_episodes | 2023-10-19T22:26:11.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | ai-habitat | null | null | 0 | 0 | 2023-10-07T19:36:55 | ---
viewer: false
license: cc-by-nc-4.0
---
# Habitat v0.3.x Episode Datasets
Episode datasets for Social Navigation and Social Rearrangement tasks. The training dataset has 37k episodes and the evaluation dataset has 1.2k episodes.
# License Notes:
HSSD assets and episodes are provided under cc-by-nc license as a subset of the dataset described here: https://3dlg-hcvc.github.io/hssd/ | 392 | [
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Hack90/ncbi_genbank_part_75 | 2023-10-07T19:53:04.000Z | [
"region:us"
] | Hack90 | null | null | 0 | 0 | 2023-10-07T19:39:00 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
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features:
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splits:
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num_bytes: 35009212242
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download_size: 15493347795
dataset_size: 35009212242
---
# Dataset Card for "ncbi_genbank_part_75"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 643 | [
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Misterjo/Jo | 2023-10-07T19:39:01.000Z | [
"region:us"
] | Misterjo | null | null | 0 | 0 | 2023-10-07T19:39:01 | Entry not found | 15 | [
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tr416/catholic_model_v2_dataset_20231007_194934 | 2023-10-07T19:49:35.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-07T19:49:34 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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num_examples: 3
download_size: 52253
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---
# Dataset Card for "catholic_model_v2_dataset_20231007_194934"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 614 | [
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PocketDoc/Floyd-Text-Adventures | 2023-10-14T23:37:07.000Z | [
"task_categories:conversational",
"language:en",
"not-for-all-audiences",
"region:us"
] | PocketDoc | null | null | 2 | 0 | 2023-10-07T20:02:05 | ---
tags:
- not-for-all-audiences
task_categories:
- conversational
language:
- en
pretty_name: Floyd Text Adventures
---
This is the 'Floyd' text adventure dataset converted to a chat format with system messages. The system messages were randomly constructed from a table of phrases and templates. The original data can be found in the .7z archive.
**Credits:**
Thank you to VE Forbryderne from KoboldAI for scraping the dataset. | 432 | [
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Hack90/ncbi_genbank_part_76 | 2023-10-07T20:21:23.000Z | [
"region:us"
] | Hack90 | null | null | 0 | 0 | 2023-10-07T20:08:38 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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download_size: 13887863083
dataset_size: 31427190646
---
# Dataset Card for "ncbi_genbank_part_76"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 644 | [
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BangumiBase/ishuzokureviewers | 2023-10-07T21:23:49.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-07T20:14:37 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Ishuzoku Reviewers
This is the image base of bangumi Ishuzoku Reviewers, we detected 37 characters, 1196 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 | 148 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 25 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 24 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 11 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 12 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 8 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 201 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 9 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 15 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 9 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 6 | [Download](10/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 11 | 14 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 202 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 18 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 7 | [Download](14/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 15 | 19 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 11 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 7 | [Download](17/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 18 | 59 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 11 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 9 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 7 | [Download](21/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 22 | 49 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 14 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 11 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 13 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 9 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 7 | [Download](27/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 28 | 9 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 7 | [Download](29/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 30 | 6 | [Download](30/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 31 | 6 | [Download](31/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 32 | 5 | [Download](32/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 33 | 21 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 7 | [Download](34/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 35 | 5 | [Download](35/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 195 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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rain4242/nva-emma | 2023-10-10T13:14:07.000Z | [
"region:us"
] | rain4242 | null | null | 0 | 0 | 2023-10-07T20:21:03 | Entry not found | 15 | [
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Hack90/ncbi_genbank_part_77 | 2023-10-07T20:46:41.000Z | [
"region:us"
] | Hack90 | null | null | 0 | 0 | 2023-10-07T20:35:16 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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dataset_size: 29897565069
---
# Dataset Card for "ncbi_genbank_part_77"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 645 | [
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Drcx989/Erick | 2023-10-07T21:27:55.000Z | [
"region:us"
] | Drcx989 | null | null | 0 | 0 | 2023-10-07T21:27:55 | Entry not found | 15 | [
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Jellywibble/20231007_chai_prize_model_feedback_all | 2023-10-08T00:14:05.000Z | [
"region:us"
] | Jellywibble | null | null | 0 | 0 | 2023-10-08T00:13:48 | ---
dataset_info:
features:
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dtype: string
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dtype: string
- name: user_id
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- name: thumbs_up
dtype: bool
- name: feedback
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splits:
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num_bytes: 242533107
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download_size: 127593487
dataset_size: 242533107
---
# Dataset Card for "20231007_chai_prize_model_feedback_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 658 | [
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tr416/christianGPTv2_dataset_20231008_001740 | 2023-10-08T00:17:40.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-08T00:17:40 | Entry not found | 15 | [
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tr416/fullv2_dataset_20231008_001946 | 2023-10-08T00:19:47.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-08T00:19:46 | Entry not found | 15 | [
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tr416/test | 2023-10-08T00:20:28.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-08T00:20:28 | Entry not found | 15 | [
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tr416/v2_dataset_20231008_002216 | 2023-10-08T00:22:19.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-08T00:22:16 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
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num_bytes: 75203880.0
num_examples: 29285
- name: test
num_bytes: 760128.0
num_examples: 296
download_size: 12799490
dataset_size: 75964008.0
---
# Dataset Card for "v2_dataset_20231008_002216"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 612 | [
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tr416/v2_dataset_20231008_002613 | 2023-10-08T00:26:15.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-08T00:26:13 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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sequence: int32
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num_examples: 296
download_size: 12818386
dataset_size: 75964008.0
---
# Dataset Card for "v2_dataset_20231008_002613"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 612 | [
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tr416/v2_dataset_20231008_002916 | 2023-10-08T00:29:27.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-08T00:29:24 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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sequence: int32
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num_examples: 296
download_size: 12811954
dataset_size: 75964008.0
---
# Dataset Card for "v2_dataset_20231008_002916"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 612 | [
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tr416/v2_dataset_20231008_003113 | 2023-10-08T00:31:15.000Z | [
"region:us"
] | tr416 | null | null | 0 | 0 | 2023-10-08T00:31:13 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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sequence: int32
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sequence: int8
splits:
- name: train
num_bytes: 75203880.0
num_examples: 29285
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num_bytes: 760128.0
num_examples: 296
download_size: 12796324
dataset_size: 75964008.0
---
# Dataset Card for "v2_dataset_20231008_003113"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 612 | [
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nanoshinonomecom/RVC | 2023-10-08T00:48:29.000Z | [
"region:us"
] | nanoshinonomecom | null | null | 0 | 0 | 2023-10-08T00:46:57 | Entry not found | 15 | [
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metric-space/test-images | 2023-10-15T04:50:51.000Z | [
"region:us"
] | metric-space | null | null | 0 | 0 | 2023-10-08T00:51:50 | Entry not found | 15 | [
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-0.06036376953125,
0.0... |
katryo/jeneral-stb | 2023-10-08T01:05:51.000Z | [
"region:us"
] | katryo | null | null | 0 | 0 | 2023-10-08T01:05:51 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01494598388671875,
0.05718994140625,
0.0288543701171875,
-0.0350341796875,
0.046478271484375,
0.052490234375,
0.005062103271484375,
0.051361083984375,
0.0170135498046875,
-0.05206298828125,
-0.01494598388671875,
-0.06036376953125,
0.0... |
daishen/legal-er | 2023-10-14T07:34:30.000Z | [
"region:us"
] | daishen | null | null | 0 | 0 | 2023-10-08T01:17:14 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01494598388671875,
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0.0170135498046875,
-0.05206298828125,
-0.01494598388671875,
-0.06036376953125,
0.0... |
daishen/legal-cr | 2023-10-14T07:39:01.000Z | [
"region:us"
] | daishen | null | null | 0 | 0 | 2023-10-08T01:19:57 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01494598388671875,
0.05718994140625,
0.0288543701171875,
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0.0170135498046875,
-0.05206298828125,
-0.01494598388671875,
-0.06036376953125,
0.0... |
ZelaAI/lex_encodec | 2023-10-08T02:08:11.000Z | [
"region:us"
] | ZelaAI | null | null | 0 | 0 | 2023-10-08T01:38:08 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
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0.052520751953125,
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0.016998291015625,
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-0.060394287109375,
0.0379... |
Sanjay19tsh/fastFood | 2023-10-08T01:53:46.000Z | [
"region:us"
] | Sanjay19tsh | null | null | 0 | 0 | 2023-10-08T01:41:31 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Fraol/1ColDedupedRefDatasetWMetricFinal | 2023-10-08T03:23:35.000Z | [
"region:us"
] | Fraol | null | null | 0 | 0 | 2023-10-08T01:42:07 | ---
dataset_info:
features:
- name: source
dtype: string
- name: path_name
dtype: string
- name: file_name
dtype: string
- name: ref_type
dtype: string
- name: hash
dtype: string
- name: class_name
dtype: string
- name: method_name
dtype: string
- name: row_number
dtype: int64
- name: cbo
dtype: float64
- name: wmc
dtype: float64
- name: lcom*
dtype: float64
- name: loc
dtype: float64
- name: source_after
dtype: string
- name: cbo_after
dtype: float64
- name: wmc_after
dtype: float64
- name: lcom*_after
dtype: float64
- name: loc_after
dtype: float64
- name: issue_name
dtype: string
- name: issue_localize
dtype: string
splits:
- name: train
num_bytes: 476226598
num_examples: 37325
download_size: 0
dataset_size: 476226598
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "1ColDedupedRefDatasetWMetricFinal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,140 | [
[
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0.049774169921875,
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-0.03375244140625,
-0.00727... |
gabrielcava/GabrielV2 | 2023-10-08T16:21:13.000Z | [
"region:us"
] | gabrielcava | null | null | 0 | 0 | 2023-10-08T02:01:59 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
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0.0513916015625,
0.016998291015625,
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-0.014984130859375,
-0.060394287109375,
0.0379... |
pytc/zebrafinch-j0126 | 2023-10-08T02:22:42.000Z | [
"region:us"
] | pytc | null | null | 0 | 0 | 2023-10-08T02:09:05 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
AiForTheChurch/catholic_denomination_300 | 2023-10-08T02:46:20.000Z | [
"region:us"
] | AiForTheChurch | null | null | 0 | 0 | 2023-10-08T02:46:19 | ---
dataset_info:
features:
- name: user
dtype: string
- name: llm
dtype: string
splits:
- name: train
num_bytes: 172156
num_examples: 300
download_size: 91806
dataset_size: 172156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "catholic_denomination_300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 480 | [
[
-0.042572021484375,
-0.005519866943359375,
0.02081298828125,
0.03857421875,
-0.01134490966796875,
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0.0092010498046875,
0.006649017333984375,
0.044921875,
0.03704833984375,
-0.053375244140625,
-0.049102783203125,
-0.037445068359375,
-0.00957... |
asgaardlab/GamePhysicsDailyDump | 2023-11-03T01:04:37.000Z | [
"task_categories:video-classification",
"language:en",
"license:mit",
"game",
"game-physics",
"game-bug",
"video-understanding",
"region:us"
] | asgaardlab | null | null | 1 | 0 | 2023-10-08T03:05:20 | ---
license: mit
task_categories:
- video-classification
language:
- en
tags:
- game
- game-physics
- game-bug
- video-understanding
pretty_name: GamePhysics
---
# GamePhysics Dataset (Daily Dump)
| 200 | [
[
-0.00569915771484375,
-0.00870513916015625,
0.0006413459777832031,
0.03558349609375,
-0.0016574859619140625,
-0.015228271484375,
0.0274658203125,
-0.0018291473388671875,
0.00841522216796875,
0.063720703125,
-0.052459716796875,
-0.0496826171875,
-0.03326416015625... |
BangumiBase/narutoshippuden | 2023-10-08T15:11:06.000Z | [
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-08T03:05:35 | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Naruto Shippuden
This is the image base of bangumi Naruto Shippuden, we detected 196 characters, 36722 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 | 2958 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 726 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 1111 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 442 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 132 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 1913 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 80 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 719 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 7149 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 71 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 946 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 159 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 1667 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 109 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 158 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 94 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 1473 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 1392 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 88 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 70 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 333 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 178 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 628 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 139 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 418 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 1193 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 287 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 142 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 45 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 49 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 356 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 172 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 85 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 122 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 292 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 115 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 103 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 96 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 190 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 49 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 22 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 65 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 643 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 59 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 162 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 347 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 55 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 122 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 45 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 179 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 68 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 88 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 32 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 33 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 148 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 228 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 170 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 112 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 234 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 29 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 106 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 247 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 37 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 66 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 43 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 34 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 36 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 36 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 38 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 12 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 65 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 81 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 33 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 16 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 315 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 15 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 56 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 50 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 60 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 48 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 115 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 15 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 163 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 36 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 237 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 20 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 1991 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 36 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 62 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 63 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 28 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 57 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 48 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 54 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
| 94 | 17 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 60 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 69 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 36 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 33 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 67 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 128 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 34 | [Download](101/dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 11 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 114 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
| 104 | 63 | [Download](104/dataset.zip) |  |  |  |  |  |  |  |  |
| 105 | 22 | [Download](105/dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 15 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 53 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 88 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 26 | [Download](109/dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 26 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
| 111 | 50 | [Download](111/dataset.zip) |  |  |  |  |  |  |  |  |
| 112 | 26 | [Download](112/dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 99 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 29 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 67 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 18 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 8 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 34 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
| 119 | 21 | [Download](119/dataset.zip) |  |  |  |  |  |  |  |  |
| 120 | 15 | [Download](120/dataset.zip) |  |  |  |  |  |  |  |  |
| 121 | 22 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 26 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 32 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 16 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 22 | [Download](125/dataset.zip) |  |  |  |  |  |  |  |  |
| 126 | 45 | [Download](126/dataset.zip) |  |  |  |  |  |  |  |  |
| 127 | 12 | [Download](127/dataset.zip) |  |  |  |  |  |  |  |  |
| 128 | 40 | [Download](128/dataset.zip) |  |  |  |  |  |  |  |  |
| 129 | 28 | [Download](129/dataset.zip) |  |  |  |  |  |  |  |  |
| 130 | 55 | [Download](130/dataset.zip) |  |  |  |  |  |  |  |  |
| 131 | 22 | [Download](131/dataset.zip) |  |  |  |  |  |  |  |  |
| 132 | 53 | [Download](132/dataset.zip) |  |  |  |  |  |  |  |  |
| 133 | 30 | [Download](133/dataset.zip) |  |  |  |  |  |  |  |  |
| 134 | 18 | [Download](134/dataset.zip) |  |  |  |  |  |  |  |  |
| 135 | 35 | [Download](135/dataset.zip) |  |  |  |  |  |  |  |  |
| 136 | 31 | [Download](136/dataset.zip) |  |  |  |  |  |  |  |  |
| 137 | 60 | [Download](137/dataset.zip) |  |  |  |  |  |  |  |  |
| 138 | 52 | [Download](138/dataset.zip) |  |  |  |  |  |  |  |  |
| 139 | 16 | [Download](139/dataset.zip) |  |  |  |  |  |  |  |  |
| 140 | 17 | [Download](140/dataset.zip) |  |  |  |  |  |  |  |  |
| 141 | 41 | [Download](141/dataset.zip) |  |  |  |  |  |  |  |  |
| 142 | 49 | [Download](142/dataset.zip) |  |  |  |  |  |  |  |  |
| 143 | 37 | [Download](143/dataset.zip) |  |  |  |  |  |  |  |  |
| 144 | 14 | [Download](144/dataset.zip) |  |  |  |  |  |  |  |  |
| 145 | 26 | [Download](145/dataset.zip) |  |  |  |  |  |  |  |  |
| 146 | 31 | [Download](146/dataset.zip) |  |  |  |  |  |  |  |  |
| 147 | 32 | [Download](147/dataset.zip) |  |  |  |  |  |  |  |  |
| 148 | 21 | [Download](148/dataset.zip) |  |  |  |  |  |  |  |  |
| 149 | 28 | [Download](149/dataset.zip) |  |  |  |  |  |  |  |  |
| 150 | 15 | [Download](150/dataset.zip) |  |  |  |  |  |  |  |  |
| 151 | 21 | [Download](151/dataset.zip) |  |  |  |  |  |  |  |  |
| 152 | 33 | [Download](152/dataset.zip) |  |  |  |  |  |  |  |  |
| 153 | 26 | [Download](153/dataset.zip) |  |  |  |  |  |  |  |  |
| 154 | 17 | [Download](154/dataset.zip) |  |  |  |  |  |  |  |  |
| 155 | 14 | [Download](155/dataset.zip) |  |  |  |  |  |  |  |  |
| 156 | 27 | [Download](156/dataset.zip) |  |  |  |  |  |  |  |  |
| 157 | 15 | [Download](157/dataset.zip) |  |  |  |  |  |  |  |  |
| 158 | 12 | [Download](158/dataset.zip) |  |  |  |  |  |  |  |  |
| 159 | 21 | [Download](159/dataset.zip) |  |  |  |  |  |  |  |  |
| 160 | 31 | [Download](160/dataset.zip) |  |  |  |  |  |  |  |  |
| 161 | 21 | [Download](161/dataset.zip) |  |  |  |  |  |  |  |  |
| 162 | 11 | [Download](162/dataset.zip) |  |  |  |  |  |  |  |  |
| 163 | 13 | [Download](163/dataset.zip) |  |  |  |  |  |  |  |  |
| 164 | 32 | [Download](164/dataset.zip) |  |  |  |  |  |  |  |  |
| 165 | 8 | [Download](165/dataset.zip) |  |  |  |  |  |  |  |  |
| 166 | 16 | [Download](166/dataset.zip) |  |  |  |  |  |  |  |  |
| 167 | 16 | [Download](167/dataset.zip) |  |  |  |  |  |  |  |  |
| 168 | 19 | [Download](168/dataset.zip) |  |  |  |  |  |  |  |  |
| 169 | 22 | [Download](169/dataset.zip) |  |  |  |  |  |  |  |  |
| 170 | 8 | [Download](170/dataset.zip) |  |  |  |  |  |  |  |  |
| 171 | 21 | [Download](171/dataset.zip) |  |  |  |  |  |  |  |  |
| 172 | 9 | [Download](172/dataset.zip) |  |  |  |  |  |  |  |  |
| 173 | 14 | [Download](173/dataset.zip) |  |  |  |  |  |  |  |  |
| 174 | 8 | [Download](174/dataset.zip) |  |  |  |  |  |  |  |  |
| 175 | 24 | [Download](175/dataset.zip) |  |  |  |  |  |  |  |  |
| 176 | 43 | [Download](176/dataset.zip) |  |  |  |  |  |  |  |  |
| 177 | 27 | [Download](177/dataset.zip) |  |  |  |  |  |  |  |  |
| 178 | 11 | [Download](178/dataset.zip) |  |  |  |  |  |  |  |  |
| 179 | 18 | [Download](179/dataset.zip) |  |  |  |  |  |  |  |  |
| 180 | 26 | [Download](180/dataset.zip) |  |  |  |  |  |  |  |  |
| 181 | 26 | [Download](181/dataset.zip) |  |  |  |  |  |  |  |  |
| 182 | 33 | [Download](182/dataset.zip) |  |  |  |  |  |  |  |  |
| 183 | 8 | [Download](183/dataset.zip) |  |  |  |  |  |  |  |  |
| 184 | 17 | [Download](184/dataset.zip) |  |  |  |  |  |  |  |  |
| 185 | 12 | [Download](185/dataset.zip) |  |  |  |  |  |  |  |  |
| 186 | 10 | [Download](186/dataset.zip) |  |  |  |  |  |  |  |  |
| 187 | 17 | [Download](187/dataset.zip) |  |  |  |  |  |  |  |  |
| 188 | 11 | [Download](188/dataset.zip) |  |  |  |  |  |  |  |  |
| 189 | 5 | [Download](189/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 190 | 24 | [Download](190/dataset.zip) |  |  |  |  |  |  |  |  |
| 191 | 23 | [Download](191/dataset.zip) |  |  |  |  |  |  |  |  |
| 192 | 9 | [Download](192/dataset.zip) |  |  |  |  |  |  |  |  |
| 193 | 14 | [Download](193/dataset.zip) |  |  |  |  |  |  |  |  |
| 194 | 17 | [Download](194/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 148 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| 64,543 | [
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Elriggs/pythia-6.9-rm | 2023-10-08T05:22:15.000Z | [
"region:us"
] | Elriggs | null | null | 0 | 0 | 2023-10-08T05:22:15 | Entry not found | 15 | [
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m-aliabbas1/test_ner | 2023-10-08T05:35:00.000Z | [
"region:us"
] | m-aliabbas1 | null | null | 0 | 0 | 2023-10-08T05:34:26 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: tokens
sequence: string
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sequence: string
splits:
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num_bytes: 40184.8938547486
num_examples: 304
- name: test
num_bytes: 7138.106145251397
num_examples: 54
download_size: 8540
dataset_size: 47323.0
---
# Dataset Card for "test_ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 593 | [
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Hyder12/Fine-tuning-gpt-3.5-Dataset | 2023-10-08T05:38:45.000Z | [
"region:us"
] | Hyder12 | null | null | 0 | 0 | 2023-10-08T05:36:35 | Entry not found | 15 | [
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datazeit/gpt_target_group_v1-2 | 2023-10-08T07:03:50.000Z | [
"region:us"
] | datazeit | null | null | 0 | 0 | 2023-10-08T06:06:58 | ---
dataset_info:
features:
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splits:
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num_bytes: 2879849
num_examples: 1984
download_size: 1125328
dataset_size: 2879849
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "gpt_target_group_v1-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 595 | [
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yyy1227/test_public | 2023-10-08T06:19:38.000Z | [
"region:us"
] | yyy1227 | null | null | 0 | 0 | 2023-10-08T06:19:38 | Entry not found | 15 | [
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dmarx/whats-in-a-name_v0.1_embeds_clip-b32 | 2023-10-08T06:31:14.000Z | [
"region:us"
] | dmarx | null | null | 0 | 0 | 2023-10-08T06:23:37 | ---
dataset_info:
features:
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dtype: int64
- name: name
dtype: string
- name: root
dtype: string
- name: image_id
dtype: string
- name: embed_type
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sequence: float32
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sequence: float32
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
- name: DIV@24
dtype: float64
splits:
- name: train
num_bytes: 149815296
num_examples: 34200
download_size: 72810192
dataset_size: 149815296
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "whats-in-a-name_v0.1_embeds_clip-b32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,046 | [
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Asdiansyah/eimirdad-test | 2023-10-08T06:43:26.000Z | [
"region:us"
] | Asdiansyah | null | null | 0 | 0 | 2023-10-08T06:41:50 | Entry not found | 15 | [
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autoevaluate/autoeval-eval-ade_corpus_v2-Ade_corpus_v2_classification-6ee4d3-93701145894 | 2023-10-08T07:00:09.000Z | [
"region:us"
] | autoevaluate | null | null | 0 | 0 | 2023-10-08T07:00:05 | Entry not found | 15 | [
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autoevaluate/autoeval-eval-ade_corpus_v2-Ade_corpus_v2_classification-b12b80-93702145895 | 2023-10-08T07:00:13.000Z | [
"region:us"
] | autoevaluate | null | null | 0 | 0 | 2023-10-08T07:00:09 | Entry not found | 15 | [
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Psychxy/autotrain-data-athiba-man | 2023-10-08T08:24:08.000Z | [
"region:us"
] | Psychxy | null | null | 0 | 0 | 2023-10-08T07:10:03 | Entry not found | 15 | [
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0.03790... |
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