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Anvilogic/T5-Typosquat-Training-Dataset | Anvilogic | "2024-11-11T10:07:24Z" | 29 | 0 | [
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---
# Typosquat Dataset
## Dataset Summary
This dataset is intended for typosquatting detection within a domain corpus. It contains 50,000 labeled pairs, categorized as either typosquatted or non-typosquatted.
The data is divided into training and test splits, each maintaining a balanced distribution of positive and negative examples.
## Supported Tasks and Leaderboards
**T5 training**: The primary task is binary classification, specifically detecting typosquatting domains. To do so we define a new task in the T5 format and we prompt the model with both domains.
The dataset can be used to train a cross-encoder or other model types for binary classification.
## Languages
The dataset is multilingual, reflecting the diversity of domain names.
## Dataset Structure
### Data Instances
Each data instance in the dataset consists of two domains and a label indicating if the second domain is a typosquatted version of the first. An example from the training set:
```json
{'text': 'Is the first domain a typosquat of the second: lonlonsoft.com stiltsoft.net',
'response': 'false',
'label': 0}
```
**text**: A prompt string comprised of the task definition as well as the pair of candidate domain and legitimate domain.
**response**: A string representing the expected answer from the model.
**label**: An integer (0 or 1) where 1 indicates a typosquatted domain and 0 indicates no typosquatting.
### Data Splits
The dataset is divided as follows:
| Split | Number of Instances |Positive|Negative|
|----------|---------------------|--------|--------|
| Train | 40000 | 50% | 50% |
| Test | 10000 | 50% | 50% |
## Dataset Creation
### Data Generation
The domain pairs were generated using [ail-typo-squatting](https://github.com/typosquatter/ail-typo-squatting)
Data processing includes balancing positive and negative samples to ensure even representation.
### Dataset usage
This dataset was developed to facilitate large-scale typosquatting detection for cybersecurity applications.
It supports training and evaluating binary classifiers designed to identify domains that may have been intentionally misspelled for malicious purposes.
|
NbAiLab/annotated_distil_raw_ncc_speech_v7_compact1_large_v5 | NbAiLab | "2024-11-08T15:18:13Z" | 29 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_dff54a24-11aa-43f4-a637-23f8bdebb7b6 | argilla-internal-testing | "2024-11-08T15:28:34Z" | 29 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_d9b24f82-b25a-42ec-b2fc-783ea50e2c07 | argilla-internal-testing | "2024-11-08T15:31:43Z" | 29 | 0 | [
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elephantmipt/lmsys-pile-sae-merged | elephantmipt | "2024-11-08T17:10:32Z" | 29 | 0 | [
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DewiBrynJones/evals-cv-whisper-base-ft-cv-cy | DewiBrynJones | "2024-11-08T16:57:18Z" | 29 | 0 | [
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language:
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license: cc0-1.0
tags:
- speech recognition
metrics:
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---
__Model__: DewiBrynJones/whisper-base-ft-cv-cy
__Test Set__: DewiBrynJones/commonvoice_18_0_cy
__Split__: test
------------------------------------------------------------------------------------------------------------------
__WER: 42.680754__
__CER: 14.481497__
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PolyAgent/paraclawl_3m_train_test | PolyAgent | "2024-11-08T17:18:19Z" | 29 | 0 | [
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Anvilogic/Embedder-Typosquat-Training-Dataset | Anvilogic | "2024-11-08T18:07:00Z" | 29 | 0 | [
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---
# Typosquat Embedding Dataset
## Dataset Summary
This dataset is designed for training embedding models to recognize typosquatting within a domain corpus. It consists of pairs of legitimate and typosquatted domains for use in similarity learning, enabling models to identify subtle domain alterations.
The dataset is formatted for embedding-based training, specifically useful for contrastive learning techniques or other tasks where domain similarity is a key factor.
## Supported Tasks and Leaderboards
**Embedding Training**: The primary task supported by this dataset is contrastive learning to create embeddings for typosquatting detection. The dataset can be used to train a similarity model, such as a dual-encoder, where each instance is a pair of legitimate and potentially typosquatted domains.
## Languages
This dataset includes a multilingual set of domains, reflecting the diversity of internet domains globally.
## Dataset Structure
### Data Instances
Each instance in the dataset consists of two domains:
- **anchor**: The legitimate domain.
- **positive**: A version of the domain with minor alterations that may represent typosquatting.
An example from the dataset is as follows:
```json
{
"anchor": "e-volution.ai",
"positive": "e-volutiọn.ai"
}
```
The anchor and positive columns are both strings representing domains.
The "positive" domain is a variation created by intentional typosquatting techniques (e.g., homoglyphs or character substitution).
### Data Splits
The dataset is structured to be used for embedding model training and evaluation:
- Split: Train
Number of Instances: 43,447
- Split: Test
Number of Instances: 10,881
## Dataset Creation
### Data Generation
The domain pairs were generated using [ail-typo-squatting](https://github.com/typosquatter/ail-typo-squatting)
Data processing includes balancing positive and negative samples to ensure even representation.
### Dataset Usage
This dataset is suitable for cybersecurity applications focusing on typosquatting detection. It can be used to train and evaluate embedding-based models designed to identify domains that may have been manipulated for malicious purposes, supporting efforts in online safety and domain monitoring.
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nphuc/ben-thang-cuoc-huy-duc_v4 | nphuc | "2024-11-08T18:36:21Z" | 29 | 0 | [
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arinzeo/ru_en_txt | arinzeo | "2024-11-08T20:38:39Z" | 29 | 0 | [
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pbarde1/moma-ppo | pbarde1 | "2024-11-08T20:14:09Z" | 29 | 0 | [
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bitmind/ffhq-256___15554-to-23330___RealVisXL_V4.0 | bitmind | "2024-11-08T20:34:55Z" | 29 | 0 | [
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bitmind/ffhq-256___62216-to-69999___RealVisXL_V4.0 | bitmind | "2024-11-08T20:42:26Z" | 29 | 0 | [
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Nayana-cognitivelab/Nayana-DocOCR-in-14k-v1-arxiv | Nayana-cognitivelab | "2024-11-08T21:30:47Z" | 29 | 0 | [
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tkdrnjs0621/hotpotqa_processed_pv2_ner_v2 | tkdrnjs0621 | "2024-11-10T08:34:56Z" | 29 | 0 | [
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TrevorJS/us-addresses-synthetic-v3-llm-context | TrevorJS | "2024-11-09T01:40:19Z" | 29 | 0 | [
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falan42/data_qa_med_test2 | falan42 | "2024-11-09T10:33:32Z" | 29 | 0 | [
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sam749/dolly15k-sharegpt | sam749 | "2024-11-09T15:29:11Z" | 29 | 0 | [
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caarenas97/testData | caarenas97 | "2024-11-13T13:54:46Z" | 29 | 0 | [
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amuvarma/luna-3-1.3k-lower-case-emotions-mapped-tagged-processed | amuvarma | "2024-11-09T18:29:32Z" | 29 | 0 | [
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harsh-7070/COCO-Wholebody-annotated | harsh-7070 | "2024-11-09T23:58:57Z" | 29 | 0 | [
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pxyyy/RLHFlow_mixture_clean_empty_round_with_dart-math_downsampled-600k | pxyyy | "2024-11-09T21:57:54Z" | 29 | 0 | [
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violetxi/NUMINA-V1-Clean-Blocks-1600_1800-100_200 | violetxi | "2024-11-11T06:45:36Z" | 29 | 0 | [
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binisha/lisa-voice | binisha | "2024-11-10T04:56:17Z" | 29 | 1 | [
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Bhargav1127/ring-dataset-with-captions | Bhargav1127 | "2024-11-10T04:57:27Z" | 29 | 0 | [
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GitBag/llama3-ultrafeedback-reasoning-iter_2-1731041913-armo-tokenized | GitBag | "2024-11-10T05:13:11Z" | 29 | 0 | [
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ieeeeeH/svm-apple-1 | ieeeeeH | "2024-11-10T05:25:57Z" | 29 | 0 | [
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license: apache-2.0
---
|
ICT2214Team7/GPT_Generated_Dataset_Fold5_2000 | ICT2214Team7 | "2024-11-10T05:32:10Z" | 29 | 0 | [
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nuyyep81/Synthetic_DepressionStatement | nuyyep81 | "2024-11-11T07:51:18Z" | 29 | 0 | [
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] | [
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mingye94/ultrafeedback_binarized_with_soft_lable | mingye94 | "2024-11-13T22:02:41Z" | 29 | 0 | [
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ieeeeeH/svm-youtube-1 | ieeeeeH | "2024-11-10T06:12:09Z" | 29 | 0 | [
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license: apache-2.0
---
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nuprl-staging/pointer-aliasing-v5 | nuprl-staging | "2024-11-10T06:43:03Z" | 29 | 0 | [
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ieeeeeH/svm-apple-01 | ieeeeeH | "2024-11-10T07:10:10Z" | 29 | 0 | [
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license: apache-2.0
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ieeeeeH/svm_mal_bing01 | ieeeeeH | "2024-11-10T07:43:39Z" | 29 | 0 | [
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license: apache-2.0
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ieeeeeH/svm_mal_wikipedia01 | ieeeeeH | "2024-11-10T07:47:58Z" | 29 | 0 | [
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license: apache-2.0
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MewtwoX23/ade20k | MewtwoX23 | "2024-11-10T15:34:27Z" | 29 | 0 | [
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ieeeeeH/svm-bing | ieeeeeH | "2024-11-10T09:54:43Z" | 29 | 0 | [
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license: apache-2.0
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|
ieeeeeH/svm-google | ieeeeeH | "2024-11-10T09:55:36Z" | 29 | 0 | [
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license: apache-2.0
---
|
Treza12/DataImTestnew | Treza12 | "2024-11-10T10:39:42Z" | 29 | 0 | [
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ieeeeeH/svm_mal_bing | ieeeeeH | "2024-11-10T10:42:59Z" | 29 | 0 | [
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license: apache-2.0
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ieeeeeH/svm_mal_wikipedia | ieeeeeH | "2024-11-10T10:45:40Z" | 29 | 0 | [
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license: apache-2.0
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ihughes15234/ttt_dpo_phi3_5_v3 | ihughes15234 | "2024-11-10T12:01:55Z" | 29 | 0 | [
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midrees2806/gptneo_dataset | midrees2806 | "2024-11-10T12:17:56Z" | 29 | 0 | [
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tkdrnjs0621/2wikimultihopqa_processed_pv2_ner | tkdrnjs0621 | "2024-11-10T13:57:52Z" | 29 | 0 | [
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|
slugroom/rjochtwurd-dataset | slugroom | "2024-11-10T14:46:58Z" | 29 | 0 | [
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|
SkyDiff/G2A-3 | SkyDiff | "2024-11-15T06:30:22Z" | 29 | 0 | [
"license:mit",
"modality:image",
"region:us"
] | null | "2024-11-10T14:56:11Z" | ---
license: mit
---
# G2A-3
<!-- Provide a quick summary of the dataset. -->
## Dataset Introduction
![1731315043833](image/README/1731315043833.png)
We introduce Ground2Aerial-3, a multi-task cross-view synthesis dataset designed to explore the performance of cross-view synthesis methods in several novel scenarios. G2A-3 contains over 10,000 pairs of street-view and aerial images, covering disaster scene aerial image synthesis, historical high-resolution satellite image synthesis, and low-altitude UAV image synthesis. The ground street-view images are sized at 1024×512, with true north aligned at the center, and the aerial images are sized at 512×512, aligned with the center of the street-view images.
## Task presentation
The following figure shows some qualitative visualization Results on the G2A-3 Dataset.
![1731650930323](image/README/1731650930323.jpg) |
zhehuderek/YESBUT_Benchmark | zhehuderek | "2024-11-25T08:16:12Z" | 29 | 0 | [
"license:cc-by-nc-nd-4.0",
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"arxiv:2405.19088",
"region:us"
] | null | "2024-11-10T15:06:57Z" | ---
license: cc-by-nc-nd-4.0
---
This repository provides the official *data* of our paper:
**Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions**, NeurIPS (Oral), 2024.
Zhe Hu, Tuo Liang, Jing Li, Yiren Lu, Yunlai Zhou, Yiran Qiao, Jing Ma, Yu Yin
### [[Paper](https://neurips.cc/virtual/2024/oral/97967)] [[Arxiv](https://arxiv.org/pdf/2405.19088)] [[Webpage](https://vulab-ai.github.io/YESBUT_Homepage/)][[GitHub](https://github.com/Tuo-Liang/YESBUT/)]
### Download
- Image Download: Download the associated images by running the following command:
```
python download_images.py --json_file='yesbut_benchmark.json' --save_folder='./YesBut_images'
```
This will save the images to the specified `./YesBut_images` folder.
### Annotated data format
- One sample has the format such as following.
```
{
"image_file": "00001.jpg",
"description": "The comic is divided into two panels, each presenting a contradictory perspective of the same object\u2014a mug. In the first panel, the mug is illustrated as an adorable fox with closed eyes, giving off a serene and cute vibe. It's an object that one would admire or find endearing. However, in the second panel, we see a person drinking from this fox-shaped mug. The contradiction lies in the mug's impracticality: its ears and head protrude awkwardly, obstructing the person's ability to sip comfortably. Despite its endearing appearance, the mug fails its primary function as a practical vessel for beverages.",
"caption": "The comic is divided into two panels, each presenting a contradictory perspective of the same object\u2014a mug. In the first panel, the mug is illustrated as an adorable fox with closed eyes, giving off a serene and cute vibe. It's an object that one would admire or find endearing. However, the second panel reveals a practical issue: a person attempts to drink from the fox-shaped mug, but its design\u2014featuring protruding ears and head\u2014awkwardly interferes, complicating the act of sipping comfortably.",
"contradiction": "The comic illustrates a contradiction where a mug designed as an adorable fox is charming to look at but proves impractical to use due to its awkwardly protruding ears and head that hinder drinking.",
"moral": "The illustration critiques the clash between aesthetics and usability, emphasizing the need for a balanced consideration of both to ensure a harmonious and practical experience in any aspect of life.",
"title": "Charming Design, Prickly Reality: The Fox Mug's Surprise",
"neg_title": [
"A Toast to Vulpine Grace",
"Harmony in a Sip",
"Enchanting Elixir: The Fox's Secret Brew"
],
"neg_moral": [
"The comic shows that adding more decorative elements to an object will enhance its value and enjoyment, when in fact, the opposite is true in this case.",
"The illustration suggests that the initial charming appearance of an item will always lead to a positive overall experience, disregarding any practical complications that arise later.",
"The image shows enduring inconvenience is a worthwhile sacrifice for the sake of owning something that looks unique or cute."
],
"category": "the theme of expectation versus reality",
"moral_mcq": "A. The comic shows that adding more decorative elements to an object will enhance its value and enjoyment, when in fact, the opposite is true in this case.\nB. The illustration critiques the clash between aesthetics and usability, emphasizing the need for a balanced consideration of both to ensure a harmonious and practical experience in any aspect of life.\nC. The illustration suggests that the initial charming appearance of an item will always lead to a positive overall experience, disregarding any practical complications that arise later.\nD. The image shows enduring inconvenience is a worthwhile sacrifice for the sake of owning something that looks unique or cute.",
"moral_mcq_answer": "B",
"title_mcq": "A. A Toast to Vulpine Grace\nB. Charming Design, Prickly Reality: The Fox Mug's Surprise\nC. Enchanting Elixir: The Fox's Secret Brew\nD. Harmony in a Sip",
"title_mcq_answer": "B",
"url": "https://pbs.twimg.com/media/F9Y1i8zXIAEtKy-?format=jpg&name=medium",
"bounding_box": [
[
[
270,
12
],
[
919,
529
]
],
[
[
270,
551
],
[
919,
1068
]
]
]
}
```
### Acknowledgement
All data samples collected are sourced from publicly available content on social media platforms. We ensure compliance with copyright by utilizing original links to comics without infringement. In addition, we obtained permission from the author artist (e.g., {Anton Gudim, Liz Climo}) to conduct our benchmark using these public images. Additionally, we commit to open-sourcing our annotated benchmark, providing corresponding links to each comic image. We diligently review samples, filtering out potentially offensive or harmful content.
## Citation
If you find our work useful, please cite:
```
@inproceedings{
hu2024cracking,
title={Cracking the Code of Juxtaposition: Can {AI} Models Understand the Humorous Contradictions},
author={Zhe Hu and Tuo Liang and Jing Li and Yiren Lu and Yunlai Zhou and Yiran Qiao and Jing Ma and Yu Yin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=bCMpdaQCNW}
}
```
|
VictorCarr02/EcuadorContext | VictorCarr02 | "2024-11-10T21:17:07Z" | 29 | 0 | [
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|
gili-ibm/constrained-lmsys-chat-1m | gili-ibm | "2024-11-24T14:00:45Z" | 29 | 0 | [
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|
NONAME0924/processed_cifar10 | NONAME0924 | "2024-11-10T16:29:42Z" | 29 | 0 | [
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|
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|
rostislavfisher/ksy | rostislavfisher | "2024-11-12T08:36:35Z" | 29 | 1 | [
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KSY dataset |
supergoose/buzz_sources_002_Select_Stack | supergoose | "2024-11-10T17:51:31Z" | 29 | 0 | [
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supergoose/buzz_sources_069_processed_unified_poetry_2_song | supergoose | "2024-11-10T18:07:58Z" | 29 | 0 | [
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supergoose/buzz_sources_077_csharp | supergoose | "2024-11-10T18:08:12Z" | 29 | 0 | [
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|
supergoose/buzz_sources_082_medical_meadow_health_advice | supergoose | "2024-11-10T18:08:20Z" | 29 | 0 | [
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supergoose/buzz_sources_087_know_medical_dialogue_v2 | supergoose | "2024-11-10T18:08:29Z" | 29 | 0 | [
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supergoose/buzz_sources_115_medical_meadow_mediqa | supergoose | "2024-11-10T18:09:08Z" | 29 | 0 | [
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|
amuvarma/luna-day3-facodecced-amu | amuvarma | "2024-11-10T18:19:56Z" | 29 | 0 | [
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SaniyaEmpire/quality_dataset | SaniyaEmpire | "2024-11-10T18:53:34Z" | 29 | 0 | [
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amuvarma/luna-day1and2-facodecced-amu-tagged-processed | amuvarma | "2024-11-10T19:03:54Z" | 29 | 0 | [
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|
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|
wolfayoub/edmdataset | wolfayoub | "2024-11-10T20:45:44Z" | 29 | 0 | [
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|
alesi12/context_HEADQA_2c_formatted | alesi12 | "2024-11-10T20:54:34Z" | 29 | 0 | [
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|
Suraponn/Llama-3.1-8B-Instruct_training_eval | Suraponn | "2024-11-11T00:22:49Z" | 29 | 0 | [
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|
hoonikoo/emo2 | hoonikoo | "2024-11-11T01:51:53Z" | 29 | 0 | [
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-11-11T01:51:43Z" | ---
license: apache-2.0
---
|
Lorenzoym/Meu_Modelo | Lorenzoym | "2024-11-11T02:05:39Z" | 29 | 0 | [
"license:openrail",
"region:us"
] | null | "2024-11-11T02:05:07Z" | ---
license: openrail
---
|
gswamy/pythia-1.4B-tldr-temp | gswamy | "2024-11-11T03:14:42Z" | 29 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-11-11T03:14:21Z" | ---
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: response0_policy
dtype: string
- name: query_response0
dtype: string
- name: query_response0_token
sequence: int64
- name: query_response0_token_len
dtype: int64
- name: query_response0_token_response_label
sequence: int64
- name: response1
dtype: string
- name: response1_token
sequence: int64
- name: response1_token_len
dtype: int64
- name: response1_policy
dtype: string
- name: query_response1
dtype: string
- name: query_response1_token
sequence: int64
- name: query_response1_token_len
dtype: int64
- name: query_response1_token_response_label
sequence: int64
- name: query_token_len
dtype: int64
- name: policies
dtype: string
- name: iter_1_query_response_0
sequence: int64
- name: iter_1_mask_0
sequence: int64
- name: iter_1_query_response_1
sequence: int64
- name: iter_1_mask_1
sequence: int64
splits:
- name: train
num_bytes: 4840303203
num_examples: 92858
download_size: 189653116
dataset_size: 4840303203
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
1231czx/epoch1_base | 1231czx | "2024-11-11T03:23:34Z" | 29 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-11-11T03:23:24Z" | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: solution
dtype: string
- name: type
dtype: string
- name: my_solu
sequence: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 509538314
num_examples: 249972
download_size: 220495755
dataset_size: 509538314
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|