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# Dataset Card for "squad_qa_num_v5_full_no_permute"
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# Dataset Card for "squad_qa_wrong_num_v5_full_first_permute"
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# Dataset Card for "squad_qa_wrong_num_v5_full_last_permute"
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# Dataset Card for "squad_qa_wrong_num_v5_full_no_permute"
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# Dataset Card for "squad_qa_rare_v5_full_last_permute"
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# Dataset Card for "squad_qa_rare_v5_full_no_permute"
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# Dataset Card for "squad_qa_wrong_rare_v5_full_first_permute"
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# Dataset Card for "squad_qa_wrong_rare_v5_full_last_permute"
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# Dataset Card for "squad_qa_wrong_rare_v5_full_no_permute"
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# Dataset Card for "squad_qa_no_id_v5_full_first_permute"
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# Dataset Card for "squad_qa_no_id_v5_full_last_permute"
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# Dataset Card for "squad_qa_no_id_v5_full_no_permute"
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tyzhu/squad_qa_rare_v5_full_first_permute | tyzhu | 2023-11-27T15:13:22Z | 0 | 0 | null | [
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# Dataset Card for "squad_qa_rare_v5_full_first_permute"
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castorini/wura | castorini | 2023-11-28T03:21:57Z | 0 | 0 | null | [
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---
# Dataset Summary
`WURA` is a document-level dataset covering 16 African Languages and 4 high-resource languages widely spoken in Africa (English, French, Arabic and Portuguese). This dataset was created by auditing mC4 and crawling additional verified news sources. It was first used to train AfriTeVa V2.
# Dataset Structure
```
>>> from datasets import load_dataset
```
Although the document-level dataset is loaded by default, you may also optionally load a passage-level dataset as follows
```
>>> data = load_dataset("castorini/wura, "yor", level="passage", verification_mode="no_checks")
```
Note that we must pass `verification_mode="no_checks` to prevent HF from verifying checksums against the document-level checksum infos.
# Citation
```
@article{OladipoBQPD2023EMNLP,
title = "Better Quality Pre-training Data and T5 Models for African Languages",
author = "Oladipo, Akintunde and
Adeyemi, Mofetoluwa and
Ahia, Orevaoghene and
Owodunni, Abraham and
Ogundepo, Odunayo and
Adelani, David and
Lin, Jimmy
",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
publisher = "Association for Computational Linguistics",
year = "2023",
}
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# Dataset Card for "squad_qa_title_v5_full_random_permute_2"
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tyzhu/squad_qa_title_v5_full_random_permute_4 | tyzhu | 2023-11-27T15:27:42Z | 0 | 0 | null | [
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# Dataset Card for "squad_qa_title_v5_full_random_permute_4"
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tyzhu/squad_qa_title_v5_full_random_permute_8 | tyzhu | 2023-11-27T15:28:06Z | 0 | 0 | null | [
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# Dataset Card for "squad_qa_title_v5_full_random_permute_8"
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tyzhu/squad_qa_wrong_title_v5_full_random_permute_2 | tyzhu | 2023-11-27T15:28:42Z | 0 | 0 | null | [
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# Dataset Card for "squad_qa_wrong_title_v5_full_random_permute_2"
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tyzhu/squad_qa_wrong_title_v5_full_random_permute_4 | tyzhu | 2023-11-27T15:29:00Z | 0 | 0 | null | [
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---
# Dataset Card for "squad_qa_wrong_title_v5_full_random_permute_4"
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tyzhu/squad_qa_wrong_title_v5_full_random_permute_8 | tyzhu | 2023-11-27T15:29:22Z | 0 | 0 | null | [
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---
# Dataset Card for "squad_qa_wrong_title_v5_full_random_permute_8"
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---
# Dataset Card for "squad_qa_num_v5_full_random_permute_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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---
# Dataset Card for "squad_qa_num_v5_full_random_permute_2"
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tyzhu/squad_qa_num_v5_full_random_permute_4 | tyzhu | 2023-11-27T15:30:29Z | 0 | 0 | null | [
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---
# Dataset Card for "squad_qa_num_v5_full_random_permute_4"
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tyzhu/squad_qa_num_v5_full_random_permute_8 | tyzhu | 2023-11-27T15:30:51Z | 0 | 0 | null | [
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---
# Dataset Card for "squad_qa_num_v5_full_random_permute_8"
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tyzhu/squad_qa_wrong_num_v5_full_random_permute_1 | tyzhu | 2023-11-27T15:31:11Z | 0 | 0 | null | [
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---
# Dataset Card for "squad_qa_wrong_num_v5_full_random_permute_1"
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tyzhu/squad_qa_wrong_num_v5_full_random_permute_2 | tyzhu | 2023-11-27T15:31:32Z | 0 | 0 | null | [
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---
# Dataset Card for "squad_qa_wrong_num_v5_full_random_permute_2"
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tyzhu/squad_qa_wrong_num_v5_full_random_permute_4 | tyzhu | 2023-11-27T15:31:52Z | 0 | 0 | null | [
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---
# Dataset Card for "squad_qa_wrong_num_v5_full_random_permute_4"
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tyzhu/squad_qa_wrong_num_v5_full_random_permute_8 | tyzhu | 2023-11-27T15:32:14Z | 0 | 0 | null | [
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---
# Dataset Card for "squad_qa_wrong_num_v5_full_random_permute_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ArtifactAI/arxiv_nlp_intstruct | ArtifactAI | 2023-11-28T01:33:23Z | 0 | 1 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
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"language:en",
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"region:us"
] | 2023-11-28T01:33:23Z | 2023-11-27T15:32:21.000Z | 2023-11-27T15:32:21 | ---
annotations_creators:
- no-annotation
language:
- en
license:
- cc0-1.0
multilinguality:
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pretty_name: arxiv_nlp_intstruct
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
---
# Dataset Card for "arxiv_nlp_intstruct"
### Dataset Summary
The "ArtifactAI/arxiv_nlp_intstruct" dataset consists of question-answer pairs derived from ArXiv abstracts from the cs.CL category".
Questions and answers are generated using [GPT-3.5-turbo model](https://openai.com/chatgpt)
### Languages
English
## Dataset Structure
### Data Instances
#### train
- **Size of downloaded dataset files:** 38.4 MB
An example of 'train' looks as follows.
{
"question": "What is the role of the light-weight cross-attention mechanism in MixEncoder?",
"answer": "In the MixEncoder, the lightweight cross-attention mechanism plays a crucial role
in capturing cross-modal dependencies between different modalities (such as text and image).
The purpose of cross-attention is to align features from one modality with features from another modality,
allowing the model to understand the correlation between them. Specifically, the lightweight
cross-attention mechanism in MixEncoder enables the model to attend to relevant information in the other modality
when processing a specific modality. This helps the model to exploit important semantic relationships between
different modalities and improve the overall representation learning process. By incorporating cross-attention,
the MixEncoder provides a way for different modalities to interact and co-adapt during training, enabling the model
to effectively understand and build representations that capture the underlying relationships between text and image inputs.
This boosts the performance of multimodal models in tasks such as image captioning, visual question answering,
and multimodal sentiment analysis, among others."
}
### Data Fields
The data fields present in the dataset are as follows:
- question: a string feature representing the question.
- answer: a string feature representing the answer.
### Data Splits
train: 36,894 question answer pairs
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
Question-answer pairs derived from [ArXiv](https://arxiv.org/) abstracts.
#### Initial Data Collection and Normalization
Questions and answers are generated using [GPT-3.5-turbo model](https://openai.com/chatgpt) from ArXiv abstracts from the cs.CL category".
### Annotations
The dataset doesn't contain annotations.
### Personal and Sensitive Information
None
#### Notice policy
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
Clearly identify the copyrighted work claimed to be infringed.
Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
And contact us at the following email address: matt at artifactai.com and datasets at huggingface.co
#### Take down policy
The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus.
Hugging Face will also update this repository accordingly.
### Citation Information
```
@misc{arxiv_nlp_intstruct,
title={arxiv_nlp_intstruct},
author={Matthew Kenney},
year={2023}
}
```
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tyzhu/squad_qa_rare_v5_full_random_permute_1 | tyzhu | 2023-11-27T15:32:41Z | 0 | 0 | null | [
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# Dataset Card for "squad_qa_rare_v5_full_random_permute_2"
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# Dataset Card for "squad_qa_rare_v5_full_random_permute_4"
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# Dataset Card for "squad_qa_rare_v5_full_random_permute_8"
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# Dataset Card for "squad_qa_wrong_rare_v5_full_random_permute_1"
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# Dataset Card for "squad_qa_wrong_rare_v5_full_random_permute_2"
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# Dataset Card for "squad_qa_wrong_rare_v5_full_random_permute_4"
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# Dataset Card for "squad_qa_wrong_rare_v5_full_random_permute_8"
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# Dataset Card for "squad_qa_baseline_v5_full_random_permute_1"
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# Dataset Card for "squad_qa_baseline_v5_full_random_permute_2"
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# Dataset Card for "squad_qa_baseline_v5_full_random_permute_4"
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# Dataset Card for "squad_qa_context_v5_full_random_permute_1"
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---
# Dataset Card for "squad_qa_context_v5_full_random_permute_2"
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tyzhu/squad_qa_context_v5_full_random_permute_4 | tyzhu | 2023-11-27T15:37:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T15:37:56Z | 2023-11-27T15:37:48.000Z | 2023-11-27T15:37:48 | ---
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---
# Dataset Card for "squad_qa_context_v5_full_random_permute_4"
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tyzhu/squad_qa_context_v5_full_random_permute_8 | tyzhu | 2023-11-27T15:38:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T15:38:18Z | 2023-11-27T15:38:09.000Z | 2023-11-27T15:38:09 | ---
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---
# Dataset Card for "squad_qa_context_v5_full_random_permute_8"
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tyzhu/squad_qa_no_id_v5_full_random_permute_1 | tyzhu | 2023-11-27T15:38:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T15:38:37Z | 2023-11-27T15:38:30.000Z | 2023-11-27T15:38:30 | ---
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---
# Dataset Card for "squad_qa_no_id_v5_full_random_permute_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tyzhu/squad_qa_no_id_v5_full_random_permute_2 | tyzhu | 2023-11-27T15:38:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T15:38:56Z | 2023-11-27T15:38:49.000Z | 2023-11-27T15:38:49 | ---
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---
# Dataset Card for "squad_qa_no_id_v5_full_random_permute_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tyzhu/squad_qa_no_id_v5_full_random_permute_4 | tyzhu | 2023-11-27T15:39:21Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T15:39:21Z | 2023-11-27T15:39:11.000Z | 2023-11-27T15:39:11 | ---
configs:
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download_size: 1347848
dataset_size: 6992594.427453769
---
# Dataset Card for "squad_qa_no_id_v5_full_random_permute_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tyzhu/squad_qa_no_id_v5_full_random_permute_8 | tyzhu | 2023-11-27T15:39:39Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T15:39:39Z | 2023-11-27T15:39:33.000Z | 2023-11-27T15:39:33 | ---
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---
# Dataset Card for "squad_qa_no_id_v5_full_random_permute_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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fatcatmilo/KGText | fatcatmilo | 2023-11-27T15:50:50Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-27T15:50:50Z | 2023-11-27T15:50:50.000Z | 2023-11-27T15:50:50 | ---
license: mit
---
| [
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Trollator/shad | Trollator | 2023-11-27T15:57:13Z | 0 | 0 | null | [
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"region:us"
] | 2023-11-27T15:57:13Z | 2023-11-27T15:56:41.000Z | 2023-11-27T15:56:41 | ---
license: openrail
---
| [
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Gabriel1322/juao | Gabriel1322 | 2023-11-27T15:59:38Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-27T15:59:38Z | 2023-11-27T15:58:17.000Z | 2023-11-27T15:58:17 | ---
license: openrail
---
| [
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ami-iit/paper_Mohamed_2023_humanoids_nonlinear-ft-calibration_dataset | ami-iit | 2023-11-27T16:28:32Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | 2023-11-27T16:28:32Z | 2023-11-27T16:03:11.000Z | 2023-11-27T16:03:11 | ---
license: bsd-3-clause
---
<h1 align="center">
Nonlinear In-situ Calibration of Strain-Gauge Force/Torque Sensors for Humanoid Robots
</h1>
<div align="center">
Hosameldin Awadalla Omer Mohamed, Gabriele Nava, Punith Reddy Vanteddu, Francesco Braghin and Daniele Pucci
</div>
<div align="center">
2023 IEEE-RAS International Conference on Humanoid Robots (Humanoids)
</div>
<section>
<h2>📂 Dataset</h2>
<p>
The dataset is collected from the robot <code>iRonCub-Mk3</code> performing movements to calibrated the F/T sensors mounted on the upper arms. Each folder is holds some <code>.mat</code> files containing the measurements collected from the robot's sensors with <a href="https://github.com/ami-iit/bipedal-locomotion-framework/tree/master/devices/YarpRobotLoggerDevice"><b>YarpRobotLoggerDevice</b></a>.
</p>
</section>
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sorasleep/conceptVN | sorasleep | 2023-11-27T16:24:48Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T16:24:48Z | 2023-11-27T16:24:48.000Z | 2023-11-27T16:24:48 | Entry not found | [
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nguyenthanhdo/ultrachat-aem-v2.1 | nguyenthanhdo | 2023-11-28T12:33:04Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T12:33:04Z | 2023-11-27T16:25:17.000Z | 2023-11-27T16:25:17 | ```python
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("minhbui/viettel_v3.2")
def token_count(example):
conv = example["data"]
first_instruction = conv[0]
first_response = conv[1]
first_instruction_num_tokens = len(tokenizer.encode(first_instruction))
first_response_num_tokens = len(tokenizer.encode(first_response))
result = dict(
first_instruction_num_tokens=first_instruction_num_tokens,
first_response_num_tokens=first_response_num_tokens
)
return result
def get_medium_length(example):
first_response_num_tokens = example["first_response_num_tokens"]
flag = False
if (first_response_num_tokens <= 350) and \
(first_response_num_tokens >= 100):
flag = True
return flag
def answer_wo_a_question(example):
flag = False
if "?" not in example["data"][1]:
flag = True
return flag
ultra_aem = load_dataset("nguyenthanhdo/ultrachat-aem-v2.0", split="train")
ultra_aem = ultra_aem.map(token_count)
ultra_aem_medium = ultra_aem.filter(get_medium_length)
ultra_aem_question = ultra_aem_medium.filter(answer_wo_a_question)
ultra_aem_question.push_to_hub("nguyenthanhdo/ultrachat-aem-v2.1")
``` | [
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kenken6696/folio_by_ccg2lambda | kenken6696 | 2023-11-28T05:44:37Z | 0 | 0 | null | [
"task_categories:text-generation",
"license:cc-by-4.0",
"region:us"
] | 2023-11-28T05:44:37Z | 2023-11-27T17:00:57.000Z | 2023-11-27T17:00:57 | ---
license: cc-by-4.0
task_categories:
- text-generation
dataset_info:
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dtype: string
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configs:
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data_files:
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path: data/train-*
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path: data/valid-*
---
## This dataset is being developed now. | [
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ancerlop/crowdsourced-calculator-demo | ancerlop | 2023-11-27T20:41:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T20:41:13Z | 2023-11-27T17:05:09.000Z | 2023-11-27T17:05:09 | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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aciborowska/customers-complaints-train-eval | aciborowska | 2023-11-27T17:11:53Z | 0 | 0 | null | [
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] | 2023-11-27T17:11:53Z | 2023-11-27T17:11:46.000Z | 2023-11-27T17:11:46 | ---
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---
# Dataset Card for "customers-complaints-train-eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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X2FD/LVIS-Instruct4V-Nodetail-mix619k | X2FD | 2023-11-28T06:53:43Z | 0 | 0 | null | [
"arxiv:2311.07574",
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This is a mixture of our LVIS-Instruct4V dataset (no detail and only conversation) with the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG.
Please refer to the Sec. 3 and pseudo-code in [our paper](https://arxiv.org/abs/2311.07574) for the clarification of ***detail*** and ***conversation*** in our dataset. | [
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"region:us"
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license: apache-2.0
---
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---
# Dataset Card for "customers-complaints-eval"
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ancerlop/MistralAI | ancerlop | 2023-11-28T12:54:59Z | 0 | 0 | null | [
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---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
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- **Curated by:** [More Information Needed]
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## Dataset Structure
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### Source Data
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#### Data Collection and Processing
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[More Information Needed]
#### Who are the source data producers?
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[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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[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]
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[More Information Needed]
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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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## Dataset Card Contact
[More Information Needed] | [
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MMMU/MMMU | MMMU | 2023-11-28T02:33:20Z | 0 | 2 | null | [
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"ac... | 2023-11-28T02:33:20Z | 2023-11-27T17:52:01.000Z | 2023-11-27T17:52:01 | ---
license: apache-2.0
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path: Accounting/dev-*
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path: Accounting/validation-*
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path: Accounting/test-*
- config_name: Agriculture
data_files:
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path: Agriculture/dev-*
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path: Agriculture/validation-*
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path: Agriculture/test-*
- config_name: Architecture_and_Engineering
data_files:
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path: Architecture_and_Engineering/dev-*
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path: Architecture_and_Engineering/validation-*
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path: Architecture_and_Engineering/test-*
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path: Art/dev-*
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path: Art/validation-*
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path: Art/test-*
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path: Art_Theory/dev-*
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path: Art_Theory/validation-*
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path: Art_Theory/test-*
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path: Basic_Medical_Science/dev-*
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path: Basic_Medical_Science/validation-*
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path: Basic_Medical_Science/test-*
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path: Biology/dev-*
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path: Biology/validation-*
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path: Biology/test-*
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data_files:
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path: Chemistry/dev-*
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path: Chemistry/validation-*
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path: Chemistry/test-*
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data_files:
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path: Clinical_Medicine/dev-*
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path: Clinical_Medicine/validation-*
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path: Clinical_Medicine/test-*
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path: Computer_Science/dev-*
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path: Computer_Science/validation-*
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path: Computer_Science/test-*
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data_files:
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path: Design/dev-*
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path: Design/validation-*
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path: Design/test-*
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data_files:
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path: Diagnostics_and_Laboratory_Medicine/dev-*
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path: Diagnostics_and_Laboratory_Medicine/validation-*
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path: Diagnostics_and_Laboratory_Medicine/test-*
- config_name: Economics
data_files:
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path: Economics/dev-*
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path: Economics/validation-*
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path: Economics/test-*
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data_files:
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path: Electronics/dev-*
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path: Electronics/validation-*
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path: Electronics/test-*
- config_name: Energy_and_Power
data_files:
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path: Energy_and_Power/dev-*
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path: Energy_and_Power/validation-*
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path: Energy_and_Power/test-*
- config_name: Finance
data_files:
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path: Finance/dev-*
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path: Finance/validation-*
- split: test
path: Finance/test-*
- config_name: Geography
data_files:
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path: Geography/dev-*
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path: Geography/validation-*
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path: Geography/test-*
- config_name: History
data_files:
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path: History/dev-*
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path: History/validation-*
- split: test
path: History/test-*
- config_name: Literature
data_files:
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path: Literature/dev-*
- split: validation
path: Literature/validation-*
- split: test
path: Literature/test-*
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data_files:
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path: Manage/dev-*
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path: Manage/validation-*
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path: Manage/test-*
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data_files:
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path: Marketing/dev-*
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path: Marketing/validation-*
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path: Marketing/test-*
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data_files:
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path: Materials/dev-*
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path: Materials/validation-*
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path: Materials/test-*
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data_files:
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path: Math/dev-*
- split: validation
path: Math/validation-*
- split: test
path: Math/test-*
- config_name: Mechanical_Engineering
data_files:
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path: Mechanical_Engineering/dev-*
- split: validation
path: Mechanical_Engineering/validation-*
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path: Mechanical_Engineering/test-*
- config_name: Music
data_files:
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path: Music/dev-*
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path: Music/validation-*
- split: test
path: Music/test-*
- config_name: Pharmacy
data_files:
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path: Pharmacy/dev-*
- split: validation
path: Pharmacy/validation-*
- split: test
path: Pharmacy/test-*
- config_name: Physics
data_files:
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path: Physics/dev-*
- split: validation
path: Physics/validation-*
- split: test
path: Physics/test-*
- config_name: Psychology
data_files:
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path: Psychology/dev-*
- split: validation
path: Psychology/validation-*
- split: test
path: Psychology/test-*
- config_name: Public_Health
data_files:
- split: dev
path: Public_Health/dev-*
- split: validation
path: Public_Health/validation-*
- split: test
path: Public_Health/test-*
- config_name: Sociology
data_files:
- split: dev
path: Sociology/dev-*
- split: validation
path: Sociology/validation-*
- split: test
path: Sociology/test-*
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- question-answering
- visual-question-answering
- multiple-choice
tags:
- biology
- medical
- finance
- chemistry
- music
- art
- art_theory
- design
- music
- business
- accounting
- economics
- finance
- manage
- marketing
- health
- medicine
- basic_medical_science
- clinical
- pharmacy
- public_health
- humanities
- social_science
- history
- literature
- sociology
- psychology
- science
- biology
- chemistry
- geography
- math
- physics
- engineering
- agriculture
- architecture
- computer_science
- electronics
- energy_and_power
- materials
- mechanical_engineering
pretty_name: mmmu
---
# MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)
[**🤗 Dataset**](https://arxiv.org/abs/XX) | [**📖 Paper**](https://arxiv.org/abs/XX) | [**GitHub**](https://github.com/MMMU-Benchmark/MMMU) | [**🌐 Homepage**](https://mmmu-benchmark.github.io/)
## Dataset Details
### Dataset Description
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes **11.5K meticulously collected multimodal questions** from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span **30 subjects** and **183 subfields**, comprising **30 highly heterogeneous image types**, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI).
🎯 **We have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.**
The development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. To submit your model's predictions for the **test set**, please [contact](#Contact) us. An automated submission system and leaderboard are upcoming. Stay tuned!

### Dataset Creation
MMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning.
The data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials.
- **Content:** The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects.
- **Image Types:** The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text.

## 🏆 Mini-Leaderboard
We show a mini-leaderboard here and please find more information in our paper or [**homepage**](https://mmmu-benchmark.github.io/).
| Model | Val (900) | Test (10.5K) |
|----------------------------|:---------:|:------------:|
| GPT-4V(ision) (Playground) | **56.8** | **55.7** |
| BLIP-2 FLAN-T5-XXL | 35.4 | 34.0 |
| InstructBLIP-T5-XXL | 35.7 | 33.8 |
| LLaVA-1.5-13B | 36.4 | 33.6 |
| Qwen-VL-7B | 35.9 | 32.9 |
| BLIP-2 FLAN-T5-XL | 34.4 | 31.0 |
| InstructBLIP-T5-XL | 32.9 | 30.6 |
| CogVLM | 32.1 | 30.1 |
| Otter | 32.2 | 29.1 |
| LLaMA-Adapter2-7B | 29.8 | 27.7 |
| MiniGPT4-Vicuna-13B | 26.8 | 27.6 |
| Fuyu-8B | 27.9 | 27.4 |
| mPLUG-OWL2 | 28.0 | 26.8 |
| Kosmos2 | 24.4 | 26.6 |
| OpenFlamingo2-9B | 28.7 | 26.3 |
| Frequent Choice | 22.1 | 23.9 |
| Random Choice | 26.8 | 25.8 |
## Limitations
Despite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases.
And the focus on college-level subjects might not fully be a sufficient test for Expert AGI.
However, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities.
In future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI.
## Disclaimers
The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution.
Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed.
## Contact
- Xiang Yue: xiangyue@in.ai
- Yu Su: su.809@osu.edu
- Wenhu Chen: wenhuchen@uwaterloo.ca
## Citation
**BibTeX:**
```bibtex
@article{yue2023mmmu,
title={MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI},
author={Yue, Xiang and Ni, Yuansheng and Zhang, Kai and Zheng, Tianyu and Liu, Ruoqi and Zhang, Ge and Jiang, Dongfu and Ren, Weiming and Yang, Zhenzhu and Sun, Renliang and Zheng, Boyuan and Stevens, Samuel and Yuan, Ruibin and Yu, Botao and Sun, Yuxuan and Liu, Yibo and Wei, Cong and Yin, Ming and Huang, Wenhao and Sun, Huan and Su, Yu and Chen, Wenhu},
journal={arXiv preprint Link},
year={2023},
} | [
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# Dataset Card for "processed_demo"
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license: mit
tags:
- art
size_categories:
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---
# Bangumi Image Base of The Dangers In My Heart
This is the image base of bangumi The Dangers in My Heart, we detected 18 characters, 1703 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 | 561 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 83 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 78 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 44 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 27 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 33 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 70 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 21 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 261 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 117 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 123 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 36 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 29 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 10 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 13 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 51 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 119 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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license: other
license_name: other
license_link: LICENSE
---
This is dataset based on airoboros 2.2.1 with removed orca and gptslop samples.
Models trained on this datasets are likely to hallucinate more than base airoboros since I also removed a lot of samples that made the model aware that it's not a human but an ai and it doesn't have physical body.
The plus of that is that non-llama model trained on it should very rarely if ever issue a refusal.
It also should sound more like a person than a sterile gpt-4.
I can't guarantee for that to happen with llama 2 base models since they are pre-trained with gptslop and refusals.
If you see a model that was trained on this dataset generating refusals, let me know and I will try to fix that.
I removed jokes from airoboros 2.2.1 that I used as base and put in jokes from airoboros 2.2, as jokes from 2.2.1 were really lame.
I will probably release fine-tunes of Yi-34B and Llama 1 33B on this dataset soon, Mistral finetune is available already.
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# Dataset Card for "cai-conversation-prod"
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0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
haroldim/treino-voz-haroldo-para-narrar | haroldim | 2023-11-27T19:34:45Z | 0 | 0 | null | [
"license:openrail++",
"region:us"
] | 2023-11-27T19:34:45Z | 2023-11-27T19:34:02.000Z | 2023-11-27T19:34:02 | ---
license: openrail++
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/xxxholic | BangumiBase | 2023-11-27T21:21:54Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T21:21:54Z | 2023-11-27T19:54:38.000Z | 2023-11-27T19:54:38 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Xxxholic
This is the image base of bangumi xxxHOLiC, we detected 36 characters, 3967 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 | 2265 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 70 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 20 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 189 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 20 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 23 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 11 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 16 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 27 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 9 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 59 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 94 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 20 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 67 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 33 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 48 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 543 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 29 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 66 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 16 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 26 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 11 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 30 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 29 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 31 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 9 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 12 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 7 | [Download](27/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 28 | 6 | [Download](28/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 29 | 8 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 39 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 23 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 7 | [Download](32/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 33 | 14 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 21 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 69 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
-0.6741213798522949,
-0.09443873167037964,
0.13315771520137787,
0.2210688292980194,
-0.2655628025531769,
-0.04864473268389702,
-0.05255403742194176,
-0.40229174494743347,
0.6359435319900513,
0.5342600345611572,
-0.9425720572471619,
-0.8797690868377686,
-0.6487733721733093,
0.51477885246276... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/kamisamakiss | BangumiBase | 2023-11-27T21:41:48Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T21:41:48Z | 2023-11-27T19:55:35.000Z | 2023-11-27T19:55:35 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Kamisama Kiss
This is the image base of bangumi Kamisama Kiss, we detected 50 characters, 2686 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 | 11 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 65 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 40 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 24 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 59 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 63 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 50 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 20 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 122 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 122 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 544 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 176 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 18 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 33 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 29 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 22 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 32 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 16 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 27 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 257 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 31 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 14 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 41 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 10 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 25 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 17 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 106 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 12 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 86 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 16 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 9 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 10 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 51 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 26 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 25 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 8 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 10 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 28 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 40 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 9 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 10 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 9 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 11 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 16 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 12 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 32 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 8 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 6 | [Download](48/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 267 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
-0.6923245191574097,
-0.14908438920974731,
0.15869002044200897,
0.20909883081912994,
-0.2510540187358856,
-0.10676027089357376,
-0.03020106814801693,
-0.3733265697956085,
0.6577488780021667,
0.5410845279693604,
-0.9637501239776611,
-0.8579371571540833,
-0.6969646215438843,
0.51340025663375... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
remyxai/ffmperative_dpo | remyxai | 2023-11-27T20:08:00Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T20:08:00Z | 2023-11-27T20:00:21.000Z | 2023-11-27T20:00:21 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 1178298
num_examples: 1500
download_size: 356757
dataset_size: 1178298
---
# Dataset Card for "ffmperative_dpo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5124567151069641,
-0.17184925079345703,
0.34174081683158875,
0.45840415358543396,
-0.30223605036735535,
0.09660742431879044,
0.4273108243942261,
-0.06050236150622368,
0.5427504777908325,
0.5256507992744446,
-0.8799238204956055,
-0.5577808618545532,
-0.5793954133987427,
-0.02846205420792... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/shinsekaiyori | BangumiBase | 2023-11-27T21:35:09Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T21:35:09Z | 2023-11-27T20:16:23.000Z | 2023-11-27T20:16:23 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Shinsekai Yori
This is the image base of bangumi Shinsekai Yori, we detected 31 characters, 1618 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 | 431 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 90 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 66 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 19 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 24 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 18 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 13 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 19 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 21 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 14 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 45 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 14 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 119 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 29 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 21 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 69 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 198 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 33 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 46 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 15 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 12 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 12 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 25 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 40 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 34 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 12 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 25 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 8 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 8 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 5 | [Download](29/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 133 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
-0.7077954411506653,
-0.14336353540420532,
0.17307211458683014,
0.194778710603714,
-0.24891960620880127,
-0.07900189608335495,
-0.0380915068089962,
-0.39861223101615906,
0.6580953001976013,
0.5097334980964661,
-0.9567834138870239,
-0.8589252829551697,
-0.6766756176948547,
0.523193776607513... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
siddanshchawla/arithmetic_model2_super_v1 | siddanshchawla | 2023-11-27T20:19:36Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T20:19:36Z | 2023-11-27T20:18:50.000Z | 2023-11-27T20:18:50 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pavitemple/Accident-Multiple-Labels-Single-Video | pavitemple | 2023-11-27T20:36:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T20:36:27Z | 2023-11-27T20:25:10.000Z | 2023-11-27T20:25:10 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
breno30/PauloCarvalhoVoice | breno30 | 2023-11-27T20:26:41Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-27T20:26:41Z | 2023-11-27T20:26:13.000Z | 2023-11-27T20:26:13 | ---
license: openrail
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hsienchen/CORD2 | hsienchen | 2023-11-27T20:46:57Z | 0 | 0 | null | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:ab",
"license:mit",
"biology",
"region:us"
] | 2023-11-27T20:46:57Z | 2023-11-27T20:34:15.000Z | 2023-11-27T20:34:15 | ---
license: mit
task_categories:
- text-generation
language:
- ab
tags:
- biology
pretty_name: CORD2
size_categories:
- 1K<n<10K
--- | [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/vanitasnokarte | BangumiBase | 2023-11-27T22:15:06Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T22:15:06Z | 2023-11-27T20:36:56.000Z | 2023-11-27T20:36:56 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Vanitas No Karte
This is the image base of bangumi Vanitas no Karte, we detected 31 characters, 2212 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 | 446 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 58 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 47 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 21 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 20 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 31 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 102 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 14 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 13 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 42 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 16 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 11 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 46 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 38 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 12 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 481 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 67 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 94 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 40 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 64 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 19 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 9 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 40 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 55 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 39 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 55 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 32 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 5 | [Download](27/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 28 | 8 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 10 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 277 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
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-0.6720170974731445,
0.552771866321563... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ikhandel/watermelon | ikhandel | 2023-11-28T19:06:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T19:06:16Z | 2023-11-27T20:36:58.000Z | 2023-11-27T20:36:58 | Entry not found | [
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0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Lam-ia/Wikipedia-Euskera | Lam-ia | 2023-11-27T20:46:31Z | 0 | 0 | null | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:eu",
"license:apache-2.0",
"region:us"
] | 2023-11-27T20:46:31Z | 2023-11-27T20:41:21.000Z | 2023-11-27T20:41:21 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- eu
pretty_name: tiny-basque-demo
size_categories:
- n<1K
--- | [
-0.12853392958641052,
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-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hsienchen/CORD-T1 | hsienchen | 2023-11-27T21:38:17Z | 0 | 0 | null | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:ab",
"license:apache-2.0",
"medical",
"region:us"
] | 2023-11-27T21:38:17Z | 2023-11-27T20:57:00.000Z | 2023-11-27T20:57:00 | ---
license: apache-2.0
task_categories:
- token-classification
language:
- ab
tags:
- medical
pretty_name: CORD2
size_categories:
- 1K<n<10K
---
# CORD-T1
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 57404876
num_examples: 21
download_size: 57334757
dataset_size: 57404876
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- token-classification
language:
- ak
tags:
- medical
pretty_name: CORD-T1
size_categories:
- 1K<n<10K
--- | [
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-0.08481329679489... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sandeep12345/stack_dataset | sandeep12345 | 2023-11-27T21:03:08Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T21:03:08Z | 2023-11-27T21:01:25.000Z | 2023-11-27T21:01:25 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Blucky/Rimuru_4.5 | Blucky | 2023-11-27T21:10:07Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T21:10:07Z | 2023-11-27T21:09:06.000Z | 2023-11-27T21:09:06 | Entry not found | [
-0.32276472449302673,
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0.2563222646713257,
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-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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