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BangumiBase/cardcaptorsakura1998
|
BangumiBase
| 2023-11-28T03:36:42Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-27T22:54:09Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Card Captor Sakura (1998)
This is the image base of bangumi Card Captor Sakura (1998), we detected 59 characters, 8455 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 | 2737 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 116 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 111 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 75 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 94 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 261 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 37 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 56 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 943 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 77 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 297 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 195 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 316 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 86 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 62 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 111 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 40 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 47 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 132 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 186 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 16 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 25 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 79 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 296 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 373 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 452 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 37 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 32 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 37 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 72 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 32 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 21 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 8 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 66 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 11 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 96 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 18 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 112 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 28 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 30 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 13 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 10 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 21 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 17 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 20 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 15 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 8 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 67 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 9 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 18 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 11 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 6 | [Download](53/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 54 | 11 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 13 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 8 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 5 | [Download](57/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 345 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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Evanls/minha_voz_mixedmp3_2711_191738.zip
|
Evanls
| 2023-11-27T22:57:06Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-27T22:57:06Z |
---
license: mit
---
|
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djhugg/06myvozmasc06
|
djhugg
| 2023-11-27T23:15:33Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-27T23:12:47Z |
---
license: openrail
---
|
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Idavidrein/gpqa
|
Idavidrein
| 2023-11-30T21:56:58Z | 0 | 2 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"open-domain-qa",
"open-book-qa",
"multiple-choice-qa",
"arxiv:2311.12022",
"region:us"
] |
[
"question-answering",
"text-generation"
] | 2023-11-27T23:18:46Z |
---
license: cc-by-4.0
viewer: true
extra_gated_prompt: >-
You agree to NOT reveal examples from this dataset in plain text or images
online, to reduce the risk of leakage into foundation model training corpora.
extra_gated_fields:
I accept these terms: checkbox
configs:
- config_name: gpqa_extended
data_files: gpqa_extended.csv
- config_name: gpqa_main
data_files: gpqa_main.csv
- config_name: gpqa_diamond
data_files: gpqa_diamond.csv
- config_name: gpqa_experts
data_files: gpqa_experts.csv
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- open-domain-qa
- open-book-qa
- multiple-choice-qa
pretty_name: GPQA
size_categories:
- n<1K
---
# Dataset Card for GPQA
<!-- Provide a quick summary of the dataset. -->
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are "Google-proof"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.
- **Curated by:** David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, Samuel R. Bowman
- **License:** CC BY 4.0
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/idavidrein/gpqa
- **Paper:** https://arxiv.org/abs/2311.12022
## Uses
The dataset is primarily intended to be used for scalable oversight experiments, although it can also be used for more general LLM capabilities benchmarking.
## Dataset Card Contact
David Rein: [email protected]
---
Submit corrections to examples in GPQA via this form: https://forms.gle/iTY4zMETNsPhJq8R9
---
|
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] |
suyuanliu/WinnVOT
|
suyuanliu
| 2023-11-27T23:36:21Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-27T23:36:17Z |
---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 28249935.0
num_examples: 504
download_size: 26349798
dataset_size: 28249935.0
---
# Dataset Card for "WinnVOT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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suyuanliu/Winnf0VOT
|
suyuanliu
| 2023-11-27T23:36:49Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-27T23:36:42Z |
---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 100267569.0
num_examples: 1800
download_size: 93892942
dataset_size: 100267569.0
---
# Dataset Card for "Winnf0VOT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
stanmalkinson199/MikeBirch
|
stanmalkinson199
| 2023-11-27T23:38:59Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-27T23:38:32Z |
---
license: openrail
---
|
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BangumiBase/cardcaptorsakuraclearcardhen
|
BangumiBase
| 2023-11-28T03:22:30Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-27T23:49:26Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Cardcaptor Sakura - Clear Card-hen
This is the image base of bangumi Cardcaptor Sakura - Clear Card-hen, we detected 46 characters, 5120 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 | 1583 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 381 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 26 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 21 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 57 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 47 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 55 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 18 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 24 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 22 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 38 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 381 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 65 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 120 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 81 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 33 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 21 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 19 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 24 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 23 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 14 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 99 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 14 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 46 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 59 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 47 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 129 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 107 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 462 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 64 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 9 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 134 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 90 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 478 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 14 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 21 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 20 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 16 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 29 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 6 | [Download](39/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 40 | 16 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 6 | [Download](41/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 42 | 8 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 23 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 5 | [Download](44/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 165 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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] |
fabsss/WESTFAL
|
fabsss
| 2023-11-28T12:08:44Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T00:19:12Z |
---
license: apache-2.0
---
|
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iliesse/test
|
iliesse
| 2023-11-28T02:18:38Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T02:18:37Z |
---
license: apache-2.0
---
|
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] |
tlittle1/test123
|
tlittle1
| 2023-11-28T02:27:21Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T02:27:20Z |
---
license: apache-2.0
---
|
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] |
ajmangus/qm_alice_easy_2_mixture_1.0e
|
ajmangus
| 2023-11-28T02:42:40Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T02:42:38Z |
---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 201936.0
num_examples: 1809
- name: validation
num_bytes: 18961.666666666668
num_examples: 173
- name: test
num_bytes: 21168.0
num_examples: 194
download_size: 69826
dataset_size: 242065.66666666666
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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] |
ajmangus/qm_alice_hard_4_mixture_1.0e
|
ajmangus
| 2023-11-28T02:43:29Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T02:42:59Z |
---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 20101703.0
num_examples: 166263
- name: validation
num_bytes: 2026424.3333333333
num_examples: 16758
- name: test
num_bytes: 2012512.6666666667
num_examples: 16650
download_size: 6344627
dataset_size: 24140640.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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] |
ajmangus/qm_bob_mixture_1.0e
|
ajmangus
| 2023-11-28T02:44:27Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T02:43:52Z |
---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 24063561.333333332
num_examples: 199999
- name: validation
num_bytes: 2403257.0
num_examples: 19999
- name: test
num_bytes: 2407978.6666666665
num_examples: 19999
download_size: 7603113
dataset_size: 28874797.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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] |
ajmangus/qm_bob_easy_2_mixture_1.0e
|
ajmangus
| 2023-11-28T02:44:30Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T02:44:27Z |
---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 201936.0
num_examples: 1809
- name: validation
num_bytes: 18961.666666666668
num_examples: 173
- name: test
num_bytes: 21168.0
num_examples: 194
download_size: 70020
dataset_size: 242065.66666666666
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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] |
ajmangus/qm_bob_hard_4_mixture_1.0e
|
ajmangus
| 2023-11-28T02:45:17Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T02:44:48Z |
---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 20101703.0
num_examples: 166263
- name: validation
num_bytes: 2026424.3333333333
num_examples: 16758
- name: test
num_bytes: 2012512.6666666667
num_examples: 16650
download_size: 6306313
dataset_size: 24140640.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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] |
ajmangus/qm_charlie_mixture_1.0e
|
ajmangus
| 2023-11-28T02:45:59Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T02:45:40Z |
---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 24063561.333333332
num_examples: 199999
- name: validation
num_bytes: 2403257.0
num_examples: 19999
- name: test
num_bytes: 2407978.6666666665
num_examples: 19999
download_size: 7673676
dataset_size: 28874797.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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0.04487833380699158,
-0.6869441866874695
] |
ajmangus/qm_charlie_easy_2_mixture_1.0e
|
ajmangus
| 2023-11-28T02:46:02Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T02:46:00Z |
---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 201936.0
num_examples: 1809
- name: validation
num_bytes: 18961.666666666668
num_examples: 173
- name: test
num_bytes: 21168.0
num_examples: 194
download_size: 69643
dataset_size: 242065.66666666666
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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] |
ajmangus/qm_charlie_hard_4_mixture_1.0e
|
ajmangus
| 2023-11-28T02:46:20Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T02:46:06Z |
---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 20101703.0
num_examples: 166263
- name: validation
num_bytes: 2026424.3333333333
num_examples: 16758
- name: test
num_bytes: 2012512.6666666667
num_examples: 16650
download_size: 6375040
dataset_size: 24140640.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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] |
Raphael04/julio
|
Raphael04
| 2023-11-28T05:13:27Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T03:48:06Z |
---
license: openrail
---
|
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furry-br/blitz
|
furry-br
| 2023-11-28T04:01:52Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T04:01:38Z |
---
license: openrail
---
|
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caiosoares26/vozdocoxinhz
|
caiosoares26
| 2023-11-28T05:07:21Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T05:07:21Z |
---
license: openrail
---
|
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] |
caiosoares26/vozdocoxinha
|
caiosoares26
| 2023-11-28T05:08:43Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T05:07:42Z |
---
license: openrail
---
|
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malaysia-ai/mosaic-yi
|
malaysia-ai
| 2023-11-28T08:15:38Z | 0 | 0 |
[
"language:ms",
"region:us"
] | null | 2023-11-28T05:10:59Z |
---
language:
- ms
---
# Mosaic format for filtered combine dataset to finetune Yi models
This repository is to store dataset shards using mosaic format.
1. prepared at https://github.com/malaysia-ai/dedup-text-dataset/blob/main/yi/combine-dataset.ipynb
2. using tokenizer https://huggingface.co/01-ai/Yi-6B
3. 4096 context length.
## how-to
1. git clone,
```bash
git lfs clone https://huggingface.co/datasets/malaysia-ai/mosaic-yi
```
2. load it,
```python
from streaming import LocalDataset
import numpy as np
from streaming.base.format.mds.encodings import Encoding, _encodings
class UInt16(Encoding):
def encode(self, obj) -> bytes:
return obj.tobytes()
def decode(self, data: bytes):
return np.frombuffer(data, np.uint16)
_encodings['uint16'] = UInt16
dataset = LocalDataset('mosaic-yi')
len(dataset)
```
|
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worldboss/2021-ghana-population-housing-census
|
worldboss
| 2023-11-28T05:14:06Z | 0 | 0 |
[
"license:afl-3.0",
"region:us"
] | null | 2023-11-28T05:14:06Z |
---
license: afl-3.0
---
|
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] |
benayas/banking_llm_v2
|
benayas
| 2023-11-28T05:18:25Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T05:18:19Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 21973863
num_examples: 10003
- name: test
num_bytes: 6745410
num_examples: 3080
download_size: 2573578
dataset_size: 28719273
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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] |
benayas/atis_llm_v2
|
benayas
| 2023-11-28T05:19:00Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T05:18:58Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 3200088
num_examples: 4455
- name: test
num_bytes: 980787
num_examples: 1373
download_size: 449523
dataset_size: 4180875
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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] |
benayas/massive_llm_v2
|
benayas
| 2023-11-28T05:23:01Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T05:22:54Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: locale
dtype: string
- name: partition
dtype: string
- name: scenario
dtype:
class_label:
names:
'0': social
'1': transport
'2': calendar
'3': play
'4': news
'5': datetime
'6': recommendation
'7': email
'8': iot
'9': general
'10': audio
'11': lists
'12': qa
'13': cooking
'14': takeaway
'15': music
'16': alarm
'17': weather
- name: intent
dtype:
class_label:
names:
'0': datetime_query
'1': iot_hue_lightchange
'2': transport_ticket
'3': takeaway_query
'4': qa_stock
'5': general_greet
'6': recommendation_events
'7': music_dislikeness
'8': iot_wemo_off
'9': cooking_recipe
'10': qa_currency
'11': transport_traffic
'12': general_quirky
'13': weather_query
'14': audio_volume_up
'15': email_addcontact
'16': takeaway_order
'17': email_querycontact
'18': iot_hue_lightup
'19': recommendation_locations
'20': play_audiobook
'21': lists_createoradd
'22': news_query
'23': alarm_query
'24': iot_wemo_on
'25': general_joke
'26': qa_definition
'27': social_query
'28': music_settings
'29': audio_volume_other
'30': calendar_remove
'31': iot_hue_lightdim
'32': calendar_query
'33': email_sendemail
'34': iot_cleaning
'35': audio_volume_down
'36': play_radio
'37': cooking_query
'38': datetime_convert
'39': qa_maths
'40': iot_hue_lightoff
'41': iot_hue_lighton
'42': transport_query
'43': music_likeness
'44': email_query
'45': play_music
'46': audio_volume_mute
'47': social_post
'48': alarm_set
'49': qa_factoid
'50': calendar_set
'51': play_game
'52': alarm_remove
'53': lists_remove
'54': transport_taxi
'55': recommendation_movies
'56': iot_coffee
'57': music_query
'58': play_podcasts
'59': lists_query
- name: utt
dtype: string
- name: annot_utt
dtype: string
- name: worker_id
dtype: string
- name: slot_method
sequence:
- name: slot
dtype: string
- name: method
dtype: string
- name: judgments
sequence:
- name: worker_id
dtype: string
- name: intent_score
dtype: int8
- name: slots_score
dtype: int8
- name: grammar_score
dtype: int8
- name: spelling_score
dtype: int8
- name: language_identification
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 17839343
num_examples: 11514
- name: validation
num_bytes: 3144099
num_examples: 2033
- name: test
num_bytes: 4598528
num_examples: 2974
download_size: 2975433
dataset_size: 25581970
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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PixArt-alpha/data_toy
|
PixArt-alpha
| 2023-11-28T06:43:26Z | 0 | 0 |
[
"license:openrail++",
"text-to-image",
"Pixart-α",
"region:us"
] | null | 2023-11-28T06:22:46Z |
---
license: openrail++
tags:
- text-to-image
- Pixart-α
---
Images are from [JourneyDB](https://journeydb.github.io/)
|
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sam-mosaic/iv4-chatml-16k
|
sam-mosaic
| 2023-11-28T06:43:19Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T06:41:35Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 2334144256.136339
num_examples: 384809
- name: test
num_bytes: 349214193.311071
num_examples: 57116
download_size: 1227729872
dataset_size: 2683358449.44741
---
# Dataset Card for "iv4-chatml-16k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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BangumiBase/deathparade
|
BangumiBase
| 2023-11-28T08:41:17Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-28T07:01:59Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Death Parade
This is the image base of bangumi Death Parade, we detected 20 characters, 1332 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 186 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 28 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 57 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 45 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 59 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 70 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 31 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 72 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 117 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 46 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 40 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 63 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 15 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 22 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 214 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 60 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 49 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 13 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 47 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 98 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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BangumiBase/ariatheanimation
|
BangumiBase
| 2023-11-28T10:55:37Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-28T07:03:51Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Aria The Animation
This is the image base of bangumi Aria The Animation, we detected 50 characters, 5059 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 | 1592 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 18 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 29 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 25 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 20 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 22 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 30 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 19 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 42 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 58 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 58 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 22 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 48 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 106 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 15 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 18 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 87 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 25 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 26 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 13 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 41 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 183 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 43 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 387 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 336 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 30 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 12 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 34 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 18 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 106 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 26 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 7 | [Download](32/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 33 | 19 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 488 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 87 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 19 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 395 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 46 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 15 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 17 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 24 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 7 | [Download](42/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 43 | 13 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 8 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 133 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 6 | [Download](46/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 47 | 15 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 15 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 229 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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tyzhu/ds_combined_try_lora_merge
|
tyzhu
| 2023-11-28T07:50:02Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T07:21:23Z |
---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 2088.495238095238
num_examples: 20
- name: validation
num_bytes: 2088.495238095238
num_examples: 20
download_size: 5988
dataset_size: 4176.990476190476
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "ds_combined_try_lora_merge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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cyy0/BMTL
|
cyy0
| 2023-11-28T07:22:23Z | 0 | 0 |
[
"license:agpl-3.0",
"region:us"
] | null | 2023-11-28T07:22:22Z |
---
license: agpl-3.0
---
|
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] |
Stein-Fun/ddpm-butterflies-128
|
Stein-Fun
| 2023-11-28T07:33:53Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T07:33:51Z |
---
license: mit
---
|
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0.26214760541915894,
0.04487833380699158,
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] |
Mousaicv/gpt4_reward_train
|
Mousaicv
| 2023-11-28T07:50:09Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T07:48:14Z |
---
license: apache-2.0
---
|
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] |
CHEN0312/fyefu
|
CHEN0312
| 2023-11-28T08:17:45Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T08:17:43Z |
---
license: apache-2.0
---
|
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rmatousek/ics
|
rmatousek
| 2023-11-29T12:58:49Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T08:17:55Z |
---
dataset_info:
features:
- name: code
dtype: string
- name: class_en
dtype: string
- name: comment_en
dtype: string
- name: length
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 213219
num_examples: 1382
download_size: 103809
dataset_size: 213219
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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zicsx/Hindi-YouTubeCC
|
zicsx
| 2023-11-28T08:48:07Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T08:27:42Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6962453685
num_examples: 161248
download_size: 2554893278
dataset_size: 6962453685
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Hindi-YouTubeCC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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edzhu/binance_eth_bnb_btc_usdt_marketdata
|
edzhu
| 2023-11-28T09:27:50Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T09:21:53Z |
---
license: mit
---
|
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BangumiBase/princesstutu
|
BangumiBase
| 2023-11-28T11:12:28Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-28T09:30:22Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Princess Tutu
This is the image base of bangumi Princess Tutu, we detected 23 characters, 2179 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 | 190 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 536 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 67 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 21 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 288 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 20 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 19 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 22 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 250 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 352 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 23 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 35 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 22 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 19 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 38 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 13 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 10 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 16 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 67 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 14 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 107 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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] |
IliyanGochev/common_voice_13_0_bg_pseudo_labelled
|
IliyanGochev
| 2023-11-28T10:27:00Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T09:42:38Z |
---
dataset_info:
config_name: bg
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 92489982.56
num_examples: 3385
- name: validation
num_bytes: 79482559.912
num_examples: 2358
- name: test
num_bytes: 84919243.036
num_examples: 2463
download_size: 265321822
dataset_size: 256891785.50800002
configs:
- config_name: bg
data_files:
- split: train
path: bg/train-*
- split: validation
path: bg/validation-*
- split: test
path: bg/test-*
---
|
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] |
Andron00e/CIFAR100-custom
|
Andron00e
| 2023-11-28T10:00:58Z | 0 | 0 |
[
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] |
[
"image-classification"
] | 2023-11-28T09:51:12Z |
---
dataset_info:
features:
- name: image_file_path
dtype: string
- name: image
dtype: image
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 59505360
num_examples: 60000
download_size: 27123594
dataset_size: 59505360
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- image-classification
language:
- en
size_categories:
- 10K<n<100K
---
Example of usage:
```python
from datasets import load_dataset
dataset = load_dataset("Andron00e/CIFAR100-custom")
splitted_dataset = dataset["train"].train_test_split(test_size=0.2)
```
|
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] |
Rami/sketch_to_hed
|
Rami
| 2023-11-28T09:51:46Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T09:51:40Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': test
'1': train
'2': validation
splits:
- name: train
num_bytes: 400653.0
num_examples: 10
- name: validation
num_bytes: 363040.0
num_examples: 12
- name: test
num_bytes: 1224181.0
num_examples: 40
download_size: 1727421
dataset_size: 1987874.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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] |
Sefaria/english_library
|
Sefaria
| 2023-11-28T10:21:12Z | 0 | 0 |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-11-28T09:58:25Z |
---
license: gpl-3.0
---
# Description
Export of Sefaria's English library data. This data represents over version in the library marked as English.
## Schema
| Field | Description |
| --- | --- |
| text | The text of a single segment in the library. A segment is the smallest chunk of test, usually representing a paragraph. |
| metadata | Dictionary of metadata. See below for schema. |
### Metadata Schema
| Field | Description |
| --- | --- |
| url | URL to this segment in Sefaria |
| ref | Canonical Ref to this segment. Refs are a human readable ID that is unique independent of version. Different versions of a segment all share the same Ref. |
| versionTitle | Version title of the version this segment came from. |
| lang | two letter language code. |
| docCategory | Category for this segment. This corresponds to where the segment's book is located in Sefaria's table of contents. |
| dataQuality | Estimate of the quality of the text. This can be either "professional" or "user". |
| pagerank | Pagerank for this segment calculated using Sefaria's internal link graph. Higher values indicate the segment is more centrally cited by sources. |
|
[
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Kuro0911/phishing_url_llama
|
Kuro0911
| 2023-11-28T09:59:28Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T09:59:24Z |
---
license: mit
---
|
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] |
Sefaria/hebrew_library
|
Sefaria
| 2023-11-28T10:47:51Z | 0 | 0 |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-11-28T10:23:03Z |
---
license: gpl-3.0
---
# Description
Export of Sefaria's Hebrew library data. This data represents over version in the library marked as Hebrew.
## Schema
| Field | Description |
| --- | --- |
| text | The text of a single segment in the library. A segment is the smallest chunk of test, usually representing a paragraph. |
| metadata | Dictionary of metadata. See below for schema. |
### Metadata Schema
| Field | Description |
| --- | --- |
| url | URL to this segment in Sefaria |
| ref | Canonical Ref to this segment. Refs are a human readable ID that is unique independent of version. Different versions of a segment all share the same Ref. |
| versionTitle | Version title of the version this segment came from. |
| lang | two letter language code. |
| docCategory | Category for this segment. This corresponds to where the segment's book is located in Sefaria's table of contents. |
| dataQuality | Estimate of the quality of the text. This can be either "professional" or "user". |
| pagerank | Pagerank for this segment calculated using Sefaria's internal link graph. Higher values indicate the segment is more centrally cited by sources. |
|
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NobodyExistsOnTheInternet/turbotoconvert
|
NobodyExistsOnTheInternet
| 2023-11-28T10:38:02Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T10:35:49Z |
---
license: mit
---
|
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] |
imone/FLAN_NIv2_NoExplanation
|
imone
| 2023-11-28T10:56:20Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T10:48:30Z |
---
license: mit
---
# FLAN NIv2 without explanation
This is a subset of [FLAN NIv2](https://huggingface.co/datasets/Open-Orca/FLAN). We removed all examples with explanations in the few-shot template, as the final answers also don't have explanations.
|
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Sefaria/links
|
Sefaria
| 2023-11-28T11:04:51Z | 0 | 0 |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-11-28T10:52:14Z |
---
license: gpl-3.0
---
|
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] |
James332/tt3
|
James332
| 2023-11-28T11:31:31Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T11:02:14Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: question_type
dtype: string
- name: confidence
dtype: int32
- name: answers
sequence: string
- name: answers_original
list:
- name: answer
dtype: string
- name: raw_answer
dtype: string
- name: answer_confidence
dtype: string
- name: answer_id
dtype: int64
- name: id_image
dtype: int64
- name: answer_type
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B
sequence: string
- name: blip_caption_beam_5
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes
list:
- name: attribute
dtype: string
- name: box
sequence: float32
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float32
- name: size
dtype: string
- name: tag
dtype: string
- name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random
list:
- name: attribute
dtype: string
- name: box
sequence: float64
- name: captions_module
sequence: string
- name: captions_module_filter
sequence: string
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float64
- name: size
dtype: string
- name: tag
dtype: string
splits:
- name: train
num_bytes: 1686555802.0
num_examples: 9009
download_size: 1572400067
dataset_size: 1686555802.0
---
# Dataset Card for "OK-VQA_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Shawt/liz
|
Shawt
| 2023-11-28T11:26:54Z | 0 | 0 |
[
"license:openrail",
"art",
"lizz",
"region:us"
] | null | 2023-11-28T11:12:58Z |
---
license: openrail
tags:
- art
- lizz
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
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- **Paper [optional]:** [More Information Needed]
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### Direct Use
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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<!-- This section describes the people or systems who created the annotations. -->
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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|
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bashmanxx/llama37train
|
bashmanxx
| 2023-11-28T11:16:10Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T11:16:00Z |
---
dataset_info:
features:
- name: text
struct:
- name: text
struct:
- name: text
dtype: string
splits:
- name: train
num_bytes: 495601
num_examples: 420
download_size: 44997
dataset_size: 495601
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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indiejoseph/commoncrawl_cantonese
|
indiejoseph
| 2023-11-28T11:57:17Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T11:57:15Z |
---
license: mit
---
|
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OpenGVLab/MVBench
|
OpenGVLab
| 2023-12-01T15:17:30Z | 0 | 2 |
[
"task_categories:visual-question-answering",
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"arxiv:2311.17005",
"region:us"
] |
[
"visual-question-answering",
"question-answering",
"conversational"
] | 2023-11-28T12:03:30Z |
---
license: mit
extra_gated_prompt: >-
You agree to not use the dataset to conduct experiments that cause harm to
human subjects. Please note that the data in this dataset may be subject to
other agreements. Before using the data, be sure to read the relevant
agreements carefully to ensure compliant use. Video copyrights belong to the
original video creators or platforms and are for academic research use only.
task_categories:
- visual-question-answering
- question-answering
- conversational
extra_gated_fields:
Name: text
Company/Organization: text
Country: text
E-Mail: text
configs:
- config_name: action_sequence
data_files: json/action_sequence.json
- config_name: moving_count
data_files: json/moving_count.json
- config_name: action_prediction
data_files: json/action_prediction.json
- config_name: episodic_reasoning
data_files: json/episodic_reasoning.json
- config_name: action_antonym
data_files: json/action_antonym.json
- config_name: action_count
data_files: json/action_count.json
- config_name: scene_transition
data_files: json/scene_transition.json
- config_name: object_shuffle
data_files: json/object_shuffle.json
- config_name: object_existence
data_files: json/object_existence.json
- config_name: fine_grained_pose
data_files: json/fine_grained_pose.json
- config_name: unexpected_action
data_files: json/unexpected_action.json
- config_name: moving_direction
data_files: json/moving_direction.json
- config_name: state_change
data_files: json/state_change.json
- config_name: object_interaction
data_files: json/object_interaction.json
- config_name: character_order
data_files: json/character_order.json
- config_name: action_localization
data_files: json/action_localization.json
- config_name: counterfactual_inference
data_files: json/counterfactual_inference.json
- config_name: fine_grained_action
data_files: json/fine_grained_action.json
- config_name: moving_attribute
data_files: json/moving_attribute.json
- config_name: egocentric_navigation
data_files: json/egocentric_navigation.json
language:
- en
size_categories:
- 1K<n<10K
---
# MVBench
## Dataset Description
- **Repository:** [MVBench](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb)
- **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005)
- **Point of Contact:** mailto:[kunchang li]([email protected])
We introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then **automatically transform public video annotations into multiple-choice QA** for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual intervention while ensuring evaluation fairness through ground-truth video annotations and avoiding biased LLM scoring. The **20** temporal task examples are as follows.
## Evaluation
An evaluation example is provided in [mvbench.ipynb](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb). Please follow the pipeline to prepare the evaluation code for various MLLMs.
- **Preprocess**: We preserve the raw video (high resolution, long duration, etc.) along with corresponding annotations (start, end, subtitles, etc.) for future exploration; hence, the decoding of some raw videos like Perception Test may be slow.
- **Prompt**: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction.
## Leadrboard
While an [Online leaderboard]() is under construction, the current standings are as follows:
|
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] |
BangorAI/exl2-wiki-calibration-set-cy
|
BangorAI
| 2023-11-28T16:41:25Z | 0 | 0 |
[
"license:cc-by-sa-3.0",
"region:us"
] | null | 2023-11-28T12:14:53Z |
---
license: cc-by-sa-3.0
---
### Data Calibro Exl2
Detholiad o [Cofnod y Cynulliad](https://huggingface.co/datasets/techiaith/cofnodycynulliad_en-cy) Cymraeg i'w ddefnyddio yng ngham calibro ExLlama 2 wrth drosi modelau i fformat exl2.
|
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] |
fabsss/westfalmelado
|
fabsss
| 2023-11-28T12:30:03Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T12:29:18Z |
---
license: apache-2.0
---
|
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] |
yimhuang/guanaco-llama2-1k
|
yimhuang
| 2023-11-28T12:38:21Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T12:30:09Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Guanaco-1k: Lazy Llama 2 Formatting
This is a subset (1000 samples) of the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing).
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.
|
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ctoraman/BilTweetNews-sentiment-analysis
|
ctoraman
| 2023-11-29T11:09:56Z | 0 | 0 |
[
"task_categories:text-classification",
"language:tr",
"license:cc-by-nc-sa-4.0",
"sentiment analysis",
"text classification",
"tweets",
"social media",
"turkish",
"sarcasm",
"sarcastic",
"region:us"
] |
[
"text-classification"
] | 2023-11-28T12:41:24Z |
---
license: cc-by-nc-sa-4.0
language:
- tr
task_categories:
- text-classification
tags:
- sentiment analysis
- text classification
- tweets
- social media
- turkish
- sarcasm
- sarcastic
---
# Turkish Sentiment Analysis Tweet Dataset: BilTweetNews
The dataset contains tweets related to six major events from Turkish news sources between May 4, 2015
and Jan 8, 2017.
The dataset covers 6 major events:
- May 25, 2015 One of the popular football clubs in Turkey, Galatasaray, wins the 2015
Turkish Super League.
- Sep 6, 2015 A terrorist group, called PKK, attacked to soldiers in Dağlıca, a village in
southeastern Turkey.
- Oct 7, 2015 A Turkish scientist, Aziz Sancar, won the 2015 Nobel Chemistry prize with
his studies on DNA repair.
- May 27, 2016 A local football club of Alanya promoted to the Turkish Super League for
the first time in their history.
- Jun 17, 2016 A traditional anthem that is mostly played by secularists in Turkey, called
the 10th Year Anthem, was forbidden in schools by the director of national
education in the Black Sea province of Bolu.
- Oct 17, 2016 A magazine programmer confused that Madonna in a Fur Coat, a book written
in 1943 by a Turkish celebrated writer, Sabahattin Ali, was about popstar
Madonna’s life. The book tells a story between a Turkish student and German
singer after the World War I.
- Not related to any news topic
For each event, 100 related-candidate and 60 unrelated-candidate tweets are selected. Lastly, we randomly select 40 tweets that are potentially not related at all, 5 of them are
removed due to detecting near-duplicates later. The dataset has 995 tweets in total.
There are 4 sentiment classes:
- Positive
- Negative
- Neutral
- Sarcastic
All tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 4 sentiment classes. We also provide the majority sentiment class at the last column. If there are multiple classes with highest scores, then we set "Multi" as majority.
Github Repo: https://github.com/BilkentInformationRetrievalGroup/BilTweetNews2017
# If you would like to use any material in this repository, please cite the following papers:
- Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017.
- Toraman, C. Event-related microblog retrieval in Turkish. Turkish Journal of Electrical Engineering and Computer Sciences. 2021. DOI: 10.3906/elk-2108-167
****
|
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] |
Jaspernl/common_voice_13_0_nl_pseudo_labelled
|
Jaspernl
| 2023-11-28T13:50:06Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T12:45:19Z |
---
dataset_info:
config_name: nl
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 887412317.796
num_examples: 31906
- name: validation
num_bytes: 355862437.37
num_examples: 10930
- name: test
num_bytes: 402683280.568
num_examples: 10936
download_size: 1643910548
dataset_size: 1645958035.734
configs:
- config_name: nl
data_files:
- split: train
path: nl/train-*
- split: validation
path: nl/validation-*
- split: test
path: nl/test-*
---
|
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] |
jotasax/sulit
|
jotasax
| 2023-11-28T13:47:25Z | 0 | 0 |
[
"language:es",
"license:gpl",
"region:us"
] | null | 2023-11-28T13:14:00Z |
---
license: gpl
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- es
pretty_name: s
---
|
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mano-wii/blender_duplicates
|
mano-wii
| 2023-11-30T01:44:14Z | 0 | 0 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] |
[
"text-classification"
] | 2023-11-28T13:16:30Z |
---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- code
pretty_name: Blender Duplicates
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
Contains reduced description of issues reported at https://projects.blender.org/blender/blender/issues and points to duplicate issues in order to categorize similarity.
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
Each report has been shortened by removing frequently repeated texts such as **System Information**, **Blender Version**, **Short description of error**.
This dataset was used to train https://huggingface.co/mano-wii/BAAI_bge-base-en-v1.5-tunned-for-blender-issues
- **Curated by:** @mano-wii
- **Funded by:** @mano-wii
- **Shared by:** @mano-wii
- **Language(s) (NLP):** English
- **License:** https://mano-wii-tools.hf.space/api/v1/static/privace.txt
## Uses
With this dataset, we can train a model considering terms and utilities used in Blender and the relation with problems with specific hardware.
### Direct Use
Creation of embeddings for 3D software technical reports
## Dataset Structure
At this dataset we can see the main issue in the first column, unrecognized issues in the second column ('neg') and duplicates in the third column ('pos').
## Dataset Creation
### Curation Rationale
This dataset was created to train a model for creating embeddings to search for semantic similarity of reports in Blender and thus allow the WEB Extension
[Blender Find Related Issues](https://chromewebstore.google.com/detail/blender-find-related-issu/gppmbbnfhiajghdannflpoieilidjpnf) to work
### Source Data
https://projects.blender.org/blender/blender/issues
#### Data Collection and Processing
The date was automatically collected in Python when fetching reports categorized as duplicates. These reports were then filtered by similarity testing using other AI models.
#### Who are the source data producers?
These reports are produced by Blender users around the world who are interested in reporting bugs in order to improve the quality of the software.
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
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] |
ceadar-ie/FinTalk-19k
|
ceadar-ie
| 2023-11-29T22:58:02Z | 0 | 0 |
[
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"finance",
"LLM",
"finetuning",
"region:us"
] |
[
"text-generation",
"question-answering"
] | 2023-11-28T13:39:30Z |
---
license: apache-2.0
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- finance
- LLM
- finetuning
pretty_name: FinTalk-19K
size_categories:
- 10K<n<100K
---
# Dataset Card for FinTalk-19k
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contact Information](#contact-information)
## Dataset Description
### Dataset Summary
FinTalk-19k is a domain-specific dataset designed for the fine-tuning of Large Language Models (LLMs) with a focus on financial conversations. Extracted from public Reddit conversations, this dataset is tagged with categories like "Personal Finance", "Financial Information", and "Public Sentiment". It consists of more than 19,000 entries, each representing a conversation about financial topics.
### Supported Tasks and Leaderboards
- `language-modeling`: The dataset can be used to train models for language modeling in the context of financial discussions.
- `text-generation`: Suitable for generating responses in financial conversations.
### Languages
The dataset is primarily in English.
## Dataset Structure
### Data Instances
Each data instance in FinTalk-19k includes a financial conversation, comprising an `instruction` (question or topic), a `response`, additional `context`, and a categorizing `tag`. For example, a data instance may have an `instruction` about seeking job ideas for a person with limited skills, a `response` suggesting truck driving, `context` explaining the person's situation, and a `tag` like "Personal Finance".
### Data Fields
- `instruction`: The question or topic of the conversation.
- `response`: The answer or information provided in response.
- `context`: Background or additional details about the conversation.
- `tag`: Category label for the conversation, e.g., "Personal Finance".
## Considerations for Using the Data
### Social Impact of Dataset
This dataset can improve AI's understanding of financial topics, aiding in the development of more informed and contextually aware financial chatbots or assistants.
### Discussion of Biases
- The dataset reflects public opinion from Reddit and may contain subjective views and Reddit-specific language.
- The dataset's focus on Reddit conversations may limit its applicability to broader financial discourse.
### License/Attribution
Copyright © 2023 CeADAR Connect Group. Developed by CeADAR (ceadar.ie), its use is governed by the Apache 2.0 license.
### Feedback
For any questions or feedback related to the dataset, please direct your communications to [email protected]
### Disclaimer
This dataset is provided "as is" without any guarantees or warranty. Although the data has been processed with care, CeADAR Connect Group is not responsible for any errors, omissions, or discrepancies within the data. Users are advised to use this dataset at their discretion and assume any risks associated with its use.
|
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davanstrien/blbooks
|
davanstrien
| 2023-11-28T14:23:09Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T13:48:05Z |
---
dataset_info:
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configs:
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data_files:
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path: 1500_1899/train-*
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data_files:
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path: 1510_1699/train-*
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data_files:
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path: 1700_1799/train-*
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data_files:
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path: 1800_1899/train-*
---
|
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] |
idleheroevich2/Jodl
|
idleheroevich2
| 2023-11-28T14:15:04Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-28T14:02:05Z |
---
license: unknown
---
|
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] |
Hackpk/TEST
|
Hackpk
| 2023-11-28T14:17:44Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T14:17:44Z |
---
license: apache-2.0
---
|
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] |
Datasaur/Mongabay-sentiment-classifiction
|
Datasaur
| 2023-11-28T15:56:05Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-28T14:20:59Z |
---
license: unknown
---
|
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PipocaClipes/Ruy2
|
PipocaClipes
| 2023-11-28T14:23:13Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-28T14:21:27Z |
---
license: unknown
---
|
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viethq188/instruct-math-zalo2023
|
viethq188
| 2023-11-28T14:39:58Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T14:39:57Z |
---
license: mit
---
|
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skaltenp/evalworld_level3
|
skaltenp
| 2023-11-28T14:44:43Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T14:43:40Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: labeled
dtype: bool
- name: z8_file_path
dtype: string
- name: id
dtype: string
- name: json_file_path
dtype: string
- name: demonstration
sequence:
sequence: string
- name: split
dtype: string
- name: ulx_file_path
dtype: string
splits:
- name: train
num_bytes: 393193494
num_examples: 17128
- name: valid
num_bytes: 18439096
num_examples: 872
- name: test
num_bytes: 18168034
num_examples: 868
download_size: 52906615
dataset_size: 429800624
---
# Dataset Card for "evalworld_level3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Apinapi/Lucas
|
Apinapi
| 2023-11-28T14:46:14Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T14:45:08Z |
---
license: openrail
---
|
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NextDayAI/MultipleResponsesChat_spicy1_20231115_20231127
|
NextDayAI
| 2023-11-28T14:48:49Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T14:45:52Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 6120129840.69
num_examples: 1990000
- name: valid
num_bytes: 15377210.655
num_examples: 5000
- name: test
num_bytes: 15377210.655
num_examples: 5000
download_size: 3862745010
dataset_size: 6150884261.999999
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
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] |
Hackpk/nrr
|
Hackpk
| 2023-11-28T14:48:10Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T14:48:10Z |
---
license: openrail
---
|
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oliverjthomas2000/finetune
|
oliverjthomas2000
| 2023-11-28T14:58:28Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T14:58:26Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 8756
num_examples: 199
download_size: 1363
dataset_size: 8756
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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vilsonrodrigues/identification-in-football
|
vilsonrodrigues
| 2023-11-30T05:03:07Z | 0 | 0 |
[
"size_categories:n<1K",
"license:cc-by-4.0",
"region:us"
] | null | 2023-11-28T14:59:48Z |
---
license: cc-by-4.0
pretty_name: Identification in Football
size_categories:
- n<1K
---
Football player datasets, labeled by name. Made for facial identification experiments.
the images were taken from the web, they are all CC protected, so the license has been maintained
|
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] |
davanstrien/EMVD
|
davanstrien
| 2023-11-28T15:06:33Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T15:05:43Z |
---
dataset_info:
features:
- name: file_name
dtype: string
- name: singer_id
dtype: int64
- name: type
dtype: string
- name: name
dtype: string
- name: range
dtype: string
- name: vowel
dtype: string
- name: authors_rank
dtype: string
- name: duration(s)
dtype: string
- name: audio
dtype: audio
- name: metadata
struct:
- name: BlackShriek_High
dtype: int64
- name: BlackShriek_Mid
dtype: int64
- name: ClearVoice_High
dtype: int64
- name: ClearVoice_Low
dtype: int64
- name: ClearVoice_Mid
dtype: int64
- name: DAW
dtype: string
- name: DeathGrowl_Low
dtype: int64
- name: DeathGrowl_Mid
dtype: int64
- name: DeepGutturals
dtype: int64
- name: GrindInhale
dtype: int64
- name: HardcoreScream_High
dtype: int64
- name: HardcoreScream_Low
dtype: int64
- name: HardcoreScream_Mid
dtype: int64
- name: PigSqueal
dtype: int64
- name: TunnelThroat
dtype: int64
- name: audio_interface
dtype: string
- name: distance_to_microphone(cm)
dtype: int64
- name: gender
dtype: string
- name: microphone
dtype: string
- name: recording
dtype: string
- name: status
dtype: string
splits:
- name: train
num_bytes: 1170551421.788
num_examples: 1068
download_size: 1152832290
dataset_size: 1170551421.788
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
NextDayAI/MultipleResponsesChat_spicy1_20231123_20231127
|
NextDayAI
| 2023-11-28T15:19:23Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T15:16:49Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 6220954598.0925
num_examples: 1995000
- name: valid
num_bytes: 7795682.45375
num_examples: 2500
- name: test
num_bytes: 7795682.45375
num_examples: 2500
download_size: 3918561564
dataset_size: 6236545962.999999
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
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VisualsAPI/coco-clip-vit-l-14
|
VisualsAPI
| 2023-11-28T16:01:04Z | 0 | 0 |
[
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:sentence-similarity",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"image-classification",
"feature-extraction",
"sentence-similarity"
] | 2023-11-28T15:22:07Z |
---
license: cc-by-4.0
task_categories:
- image-classification
- feature-extraction
- sentence-similarity
language:
- en
pretty_name: COCO Dataset Processed with CLIP ViT-L/14
size_categories:
- 100K<n<1M
---
# COCO Dataset Processed with CLIP ViT-L/14
## Overview
This dataset represents a processed version of the '2017 Unlabeled images' subset of the COCO dataset ([COCO Dataset](https://cocodataset.org/#home)), utilizing the CLIP ViT-L/14 model from OpenAI. The original dataset comprises 123K images, approximately 19GB in size, which have been processed to generate 786-dimensional vectors. These vectors can be utilized for various applications like semantic search systems, image similarity assessments, and more.
Direct download link for the original dataset: [COCO 2017 Unlabeled Images](http://images.cocodataset.org/zips/unlabeled2017.zip)
## Dataset Description
The output of the processing is a parquet file containing the path of each file along with its corresponding embedding. No normalization was applied to the model output; the embeddings are direct results from the OpenAI CLIP model.
### Processing Details
We aimed to produce the same image vectors from the script below.
Our approach utilizes a core CLIP model from OpenAI, similar to this sample:
```python
import torch
import clip
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-L/14", device=device)
image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)
text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
with torch.no_grad():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
logits_per_image, logits_per_text = model(image, text)
probs = logits_per_image.softmax(dim=-1).cpu().numpy()
print("Label probs:", probs)
```
## Applications
The dataset is suitable for various AI-driven applications, including but not limited to:
- Semantic Search Systems
- Image Similarity Detection
- Enhanced Image Categorization
## About Visuals API
This dataset was processed by Visuals API, specialists in Computer Vision and AI technologies. Visuals API offers robust solutions for image/video tagging, content moderation, and NSFW detection. For more information about our services and solutions, visit our website: [Visuals API](https://visualsapi.com/).
|
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Kitare/peach
|
Kitare
| 2023-11-28T15:24:27Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T15:23:33Z |
---
license: openrail
---
|
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codys12/PRM800K
|
codys12
| 2023-11-28T15:36:46Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T15:36:07Z |
---
license: mit
---
|
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-0.6869441866874695
] |
NextDayAI/MultipleResponsesChat_ch13brope_20231021_20231031
|
NextDayAI
| 2023-11-28T15:43:17Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T15:43:03Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 335517419.3886027
num_examples: 114186
- name: valid
num_bytes: 7345852.805698656
num_examples: 2500
- name: test
num_bytes: 7345852.805698656
num_examples: 2500
download_size: 217392731
dataset_size: 350209124.99999994
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
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DZN222/dzn
|
DZN222
| 2023-11-28T16:13:52Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T16:13:21Z |
---
license: openrail
---
|
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andrecoelho/diogoia
|
andrecoelho
| 2023-11-28T17:16:34Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-28T17:14:11Z |
---
license: unknown
---
|
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] |
EdirOC/nandopinheiro1
|
EdirOC
| 2023-11-28T17:23:13Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T17:20:23Z |
---
license: openrail
---
|
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nguyenthanhdo/orca-cqa-v1.0
|
nguyenthanhdo
| 2023-11-28T18:38:00Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T17:46:40Z |
---
configs:
- config_name: default
data_files:
- split: gpt3.5
path: data/gpt3.5-*
- split: gpt4
path: data/gpt4-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
- name: question_word_count
dtype: int64
- name: response_word_count
dtype: int64
splits:
- name: gpt3.5
num_bytes: 721493280.2090137
num_examples: 426091
- name: gpt4
num_bytes: 199603726.6207845
num_examples: 110053
download_size: 1074570511
dataset_size: 921097006.8297982
---
```py
from datasets import load_dataset
cqa_signs = [
"based on the context",
"based on the given",
"based on the article",
# "given the following article",
"following article",
"above article",
"given the following passage",
"is inside the article"
]
mqa_signs = [
"Options:",
"Choose from:",
"Write an article based on this summary",
"select the best answer"
]
orca = load_dataset("Open-Orca/OpenOrca", data_files={
"gpt3.5": "3_5M-GPT3_5-Augmented.parquet",
"gpt4": "1M-GPT4-Augmented.parquet"
})
def word_count(example):
question, response = example["question"], example["response"]
result = dict(
question_word_count=len(question.split()),
response_word_count=len(response.split())
)
return result
orca = orca.map(word_count)
keywords = ["article", "context", "passage", "summa", "paragraph"]
def get_closed_qa(example):
input = example["question"].lower()
flag = False
if sum([1 for k in keywords if k in input]):
flag = True
return flag
orca1 = orca.filter(get_closed_qa)
def get_closed_qa2(example):
"""
Exclude multiple choice questions and filter by length.
"""
mqa_signs = [
"Options:",
"OPTIONS:",
"[+]", "[-]",
"Choose from:",
"Choices:",
"select the best answer",
"Pick from",
]
flag = False
qwc, rwc = example["question_word_count"], example["response_word_count"]
if qwc > 200 and rwc < 300:
flag = True
if qwc < rwc:
flag = False
if any([sign in example["question"] for sign in mqa_signs]):
flag = False
return flag
orca2 = orca1.filter(get_closed_qa2)
orca2.push_to_hub("nguyenthanhdo/orca-cqa-v1.0")
```
|
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] |
jaozindacdd/chiquinho
|
jaozindacdd
| 2023-11-28T18:15:36Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T17:47:03Z |
---
license: openrail
---
|
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] |
martymukherjee/poisson2d
|
martymukherjee
| 2023-11-28T17:58:07Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T17:58:07Z |
---
license: mit
---
|
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] |
Oscaraandersson/reveal
|
Oscaraandersson
| 2023-11-28T18:08:03Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T18:07:57Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 25247683.954605438
num_examples: 18187
- name: valid
num_bytes: 3155439.909210874
num_examples: 2273
- name: test
num_bytes: 3156828.13618369
num_examples: 2274
download_size: 11897933
dataset_size: 31559952.000000004
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
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MITCriticalData/cloud2cloudless_dataset_5_municipalities
|
MITCriticalData
| 2023-11-29T14:12:25Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-28T18:42:07Z |
---
license: mit
---
# Creating Cloud-Cloudless Paired Dataset
To generate the Cloud-Cloudless Paired Dataset, we utilize an existing dataset that encompasses imagery from five municipalities in Colombia. This dataset is structured with each municipality containing 165 images, acquired through the satellite_extractor API and based on SentinelHub, spanning across 12 different channels. Within each municipality, we have meticulously identified the optimal cloudless image and stored the corresponding names in a dictionary called `cloudless_groundtruths`.
The primary objective is to subtract this specific cloudless image from the set of 165 images, resulting in 164 images per municipality. Subsequently, each of these 164 images will be paired with the previously identified cloudless image. Consequently, this process creates a total of `164 * 2 * NUM_MUNICIPALITIES`, yielding 1640 images or 820 image pairs in total.
To facilitate this dataset creation, we have introduced the class `Cloud2CloudlesDataset`. This class replicates each corresponding ground truth for the 164 images in each municipality, storing every paired set in a newly designated folder named `DATASET`.
Originally, the images were formatted as `image_DD%%MM%%YY`. As part of the dataset creation process, we will rename these images to `image_DD%%MM%%YY_gt` for the ground truth image and `image_DD%%MM%%YY_cloud` for the image with clouds.
Upon initialization, the class requires the path to the source dataset, which contains raw images for each municipality organized in N folders, and the final path where the new dataset will be stored.
The implementation includes thorough testing to verify the number of images, ensuring that the final count aligns with the total number of images encountered in the source folder path. Additionally, one of the functions within this class ensures the existence of each folder in the specified destination path, guaranteeing a well-organized and comprehensive Cloud-Cloudless Paired Dataset.
Github code [here](https://github.com/sebasmos/satellite.extractor/blob/main/notebooks/create_Cloud2CloudlesDataset.ipynb):
|
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] |
ramosramon433/adsa
|
ramosramon433
| 2023-11-28T22:16:07Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T19:51:55Z |
---
license: apache-2.0
---
|
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] |
DZN222/joaocaetano
|
DZN222
| 2023-11-28T19:57:01Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T19:53:12Z |
---
license: openrail
---
|
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BangumiBase/nodamecantabile
|
BangumiBase
| 2023-11-28T22:32:30Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-28T19:55:05Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Nodame Cantabile
This is the image base of bangumi Nodame Cantabile, we detected 31 characters, 5634 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 | 281 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 181 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 1138 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 217 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 1479 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 373 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 553 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 126 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 116 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 158 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 56 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 35 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 36 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 130 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 73 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 54 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 28 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 52 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 71 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 39 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 44 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 76 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 46 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 60 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 19 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 16 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 31 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 18 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 17 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 7 | [Download](29/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 104 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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BangumiBase/mahoushoujoprettysammy
|
BangumiBase
| 2023-11-29T03:23:04Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-28T19:56:15Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Mahou Shoujo Pretty Sammy
This is the image base of bangumi Mahou Shoujo Pretty Sammy, we detected 40 characters, 2878 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 | 1023 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 72 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 17 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 19 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 15 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 56 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 18 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 58 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 168 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 39 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 107 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 22 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 20 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 12 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 22 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 12 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 46 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 60 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 22 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 19 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 7 | [Download](20/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 21 | 15 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 34 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 5 | [Download](23/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 24 | 35 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 12 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 98 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 169 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 34 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 18 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 60 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 27 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 10 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 8 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 17 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 76 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 271 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 26 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 5 | [Download](38/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 124 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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] |
Gabriel1322/minimessi
|
Gabriel1322
| 2023-11-28T20:01:58Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-28T20:01:45Z |
---
license: openrail
---
|
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] |
ThanhMai/Clips_dataset
|
ThanhMai
| 2023-11-28T20:57:41Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T20:06:17Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': nok
'1': ok
splits:
- name: train
num_bytes: 952667.0
num_examples: 313
download_size: 880767
dataset_size: 952667.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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BangumiBase/hinamatsuri
|
BangumiBase
| 2023-11-28T21:12:18Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-28T20:08:48Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Hinamatsuri
This is the image base of bangumi Hinamatsuri, we detected 23 characters, 1820 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 | 107 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 93 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 11 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 342 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 216 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 40 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 27 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 90 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 39 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 24 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 28 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 64 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 30 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 284 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 51 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 217 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 28 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 25 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 9 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 30 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 8 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 43 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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vwxyzjn/summarize_from_feedback_oai_preprocessing_gpt2_153
|
vwxyzjn
| 2023-11-28T20:18:30Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T20:17:54Z |
---
dataset_info:
features:
- name: info
struct:
- name: id
dtype: string
- name: post
dtype: string
- name: title
dtype: string
- name: subreddit
dtype: string
- name: site
dtype: string
- name: article
dtype: string
- name: summaries
list:
- name: text
dtype: string
- name: policy
dtype: string
- name: note
dtype: string
- name: choice
dtype: int32
- name: worker
dtype: string
- name: batch
dtype: string
- name: split
dtype: string
- name: extra
struct:
- name: confidence
dtype: int32
- name: query_token
sequence: int64
- name: query
dtype: string
- name: response0
dtype: string
- name: response0_token
sequence: int64
- name: response0_token_len
dtype: int64
- name: response1
dtype: string
- name: response1_token
sequence: int64
- name: response1_token_len
dtype: int64
- name: response0_policy
dtype: string
- name: response1_policy
dtype: string
- name: policies
dtype: string
splits:
- name: train
num_bytes: 946647458
num_examples: 92858
- name: validation
num_bytes: 887905078
num_examples: 86086
download_size: 125420707
dataset_size: 1834552536
---
# Dataset Card for "summarize_from_feedback_oai_preprocessing_gpt2_153"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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l4zy0n3/stripe_metrics_cyber_monday_23
|
l4zy0n3
| 2023-11-28T20:45:40Z | 0 | 0 |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-11-28T20:30:04Z |
---
license: gpl-3.0
---
|
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] |
benayas/snips_llm_v4
|
benayas
| 2023-11-28T21:19:28Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T21:19:26Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 7164970
num_examples: 13084
- name: test
num_bytes: 768070
num_examples: 1400
download_size: 900859
dataset_size: 7933040
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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] |
benayas/atis_llm_v4
|
benayas
| 2023-11-28T21:20:47Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-28T21:20:46Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 3200088
num_examples: 4455
- name: test
num_bytes: 980787
num_examples: 1373
download_size: 449831
dataset_size: 4180875
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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