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louisbrulenaudet/code-assurances | louisbrulenaudet | 2023-12-12T17:45:10Z | 2 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code des assurances",
"doi:10.57967/hf/1455",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T17:45:07Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code des assurances
source_datasets:
- original
pretty_name: Code des assurances
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code des assurances, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code des assurances, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-assurances}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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alperaktasm/turkishComments-mini | alperaktasm | 2023-12-12T22:28:51Z | 2 | 0 | [
"region:us"
] | null | 2023-12-12T22:28:45Z | ---
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 1252874.4645711242
num_examples: 3378
- name: validation
num_bytes: 139455.53542887585
num_examples: 376
download_size: 896671
dataset_size: 1392330.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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Sunbird/salt-multispeaker-lug | Sunbird | 2023-12-12T23:04:19Z | 2 | 0 | [
"region:us"
] | null | 2023-12-12T23:03:28Z | ---
dataset_info:
features:
- name: ids
dtype: string
- name: texts
dtype: string
- name: audios
sequence: float32
- name: audio_languages
dtype: string
- name: are_studio
dtype: bool
- name: speaker_ids
dtype: string
- name: sample_rates
dtype: int64
splits:
- name: train
num_bytes: 2000645994
num_examples: 5016
- name: dev
num_bytes: 38741356
num_examples: 103
- name: test
num_bytes: 39746693
num_examples: 97
download_size: 1016122402
dataset_size: 2079134043
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
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timm/imagenet-12k-wds | timm | 2023-12-15T08:19:17Z | 2 | 1 | [
"task_categories:image-classification",
"size_categories:10M<n<100M",
"license:other",
"arxiv:1409.0575",
"region:us"
] | [
"image-classification"
] | 2023-12-12T23:18:24Z | ---
license: other
license_name: imagenet
license_link: https://www.image-net.org/download.php
task_categories:
- image-classification
pretty_name: ImageNet-12k
size_categories:
- 10M<n<100M
extra_gated_prompt: 'By clicking on “Access repository” below, you also agree to ImageNet
Terms of Access:
[RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet
database (the "Database") at Princeton University and Stanford University. In exchange
for such permission, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational
purposes.
2. Princeton University, Stanford University and Hugging Face make no representations
or warranties regarding the Database, including but not limited to warranties of
non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and
shall defend and indemnify the ImageNet team, Princeton University, Stanford University
and Hugging Face, including their employees, Trustees, officers and agents, against
any and all claims arising from Researcher''s use of the Database, including but
not limited to Researcher''s use of any copies of copyrighted images that he or
she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the
Database provided that they first agree to be bound by these terms and conditions.
5. Princeton University, Stanford University and Hugging Face reserve the right
to terminate Researcher''s access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher''s employer
shall also be bound by these terms and conditions, and Researcher hereby represents
that he or she is fully authorized to enter into this agreement on behalf of such
employer.
7. The law of the State of New Jersey shall apply to all disputes under this agreement.'
---
## Dataset Description
- **Homepage:** https://image-net.org/index.php
- **Repository:** https://github.com/rwightman/imagenet-12k
- **Paper:** https://arxiv.org/abs/1409.0575
### Dataset Summary
This is a filtered copy of the full [ImageNet](https://www.image-net.org/) dataset consisting of the top 11821 (of 21841) classes by number of samples. It has been used to pretrain a number of `in12k` models in `timm`.
The code and metadata for building this dataset from the original full ImageNet can be found at https://github.com/rwightman/imagenet-12k
NOTE: This subset was filtered from the original `fall11` ImageNet release which has been replaced by the `winter21` release which removes close to 3000 synsets containing people, a number of these are of an offensive or sensitive nature. There is work in progress to filter a similar dataset from `winter21`, and there is already [ImageNet-21k-P](https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/dataset_preprocessing/processing_instructions.md) but with different thresholds & preprocessing steps.
### Data Splits
Unlike ImageNet-1k (ILSVRC 2012), the full ImageNet dataset has no defined splits. This subset includes a validation split consiting of 40 samples per 11821 classes.
#### Train
* `imagenet12k-train-{0000..1024}.tar`
* 12129687 samples over 1024 shards
#### Validation
* `imagenet12k-validation-{0000..0512}.tar`
* 472840 samples over 512 shards
## Additional Information
### Dataset Curators
Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848):
- Olga Russakovsky
- Jia Deng
- Hao Su
- Jonathan Krause
- Sanjeev Satheesh
- Wei Dong
- Richard Socher
- Li-Jia Li
- Kai Li
- Sean Ma
- Zhiheng Huang
- Andrej Karpathy
- Aditya Khosla
- Michael Bernstein
- Alexander C Berg
- Li Fei-Fei
### Licensing Information
In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
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### Citation Information
```bibtex
@article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}
```
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pixparse/cc12m-wds | pixparse | 2023-12-15T04:50:41Z | 2 | 1 | [
"task_categories:image-to-text",
"size_categories:10M<n<100M",
"license:other",
"arxiv:2102.08981",
"region:us"
] | [
"image-to-text"
] | 2023-12-12T23:59:59Z | ---
license: other
license_name: conceptual-12m
license_link: LICENSE
task_categories:
- image-to-text
size_categories:
- 10M<n<100M
---
# Dataset Card for Conceptual Captions 12M (CC12M)
## Dataset Description
- **Repository:** [Conceptual 12M repository](https://github.com/google-research-datasets/conceptual-12m)
- **Paper:** [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://arxiv.org/abs/2102.08981)
- **Point of Contact:** [Conceptual Captions e-mail](mailto:[email protected])
### Dataset Summary
Conceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training.
Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M).
### Usage
This instance of Conceptual Captions is in [webdataset](https://github.com/webdataset/webdataset/commits/main) .tar format. It can be used with webdataset library or upcoming releases of Hugging Face `datasets`.
...More Detail TBD
### Data Splits
This dataset was downloaded using img2dataset. Images resized on download if shortest edge > 512 to shortest edge = 512.
#### Train
* `cc12m-train-*.tar`
* Downloaded on 2021/18/22
* 2176 shards, 10968539 samples
## Additional Information
### Dataset Curators
Soravit Changpinyo, Piyush Sharma, Nan Ding and Radu Soricut.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of
Google LLC ("Google") as the data source would be appreciated. The dataset is
provided "AS IS" without any warranty, express or implied. Google disclaims all
liability for any damages, direct or indirect, resulting from the use of the
dataset.
### Citation Information
```bibtex
@inproceedings{changpinyo2021cc12m,
title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
booktitle = {CVPR},
year = {2021},
}
``` | [
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saludobuenas/new_dominoes_refined | saludobuenas | 2023-12-13T05:08:32Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T05:00:52Z | ---
license: apache-2.0
---
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Venki-ds/test-my-alpaca-llama2-1k | Venki-ds | 2023-12-13T10:11:36Z | 2 | 0 | [
"region:us"
] | null | 2023-12-13T10:11:34Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 668749
num_examples: 1000
download_size: 412751
dataset_size: 668749
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Seongill/Trivia_5_only_adversary_1086_gpt | Seongill | 2023-12-13T12:16:04Z | 2 | 0 | [
"region:us"
] | null | 2023-12-13T12:16:00Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ent_type
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: num_advs
dtype: int64
splits:
- name: train
num_bytes: 6842638
num_examples: 1086
download_size: 3082356
dataset_size: 6842638
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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xreborn/ds1 | xreborn | 2023-12-13T12:49:22Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T12:47:07Z | ---
license: apache-2.0
---
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argilla/ultrafeedback-curated | argilla | 2023-12-13T17:55:55Z | 2 | 9 | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | [
"text-generation"
] | 2023-12-13T15:56:12Z | ---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- text-generation
pretty_name: UltraFeedback Curated
dataset_info:
features:
- name: source
dtype: string
- name: instruction
dtype: string
- name: models
sequence: string
- name: completions
list:
- name: annotations
struct:
- name: helpfulness
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: Rationale For Rating
dtype: string
- name: Type
sequence: string
- name: honesty
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: instruction_following
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: truthfulness
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: Rationale For Rating
dtype: string
- name: Type
sequence: string
- name: critique
dtype: string
- name: custom_system_prompt
dtype: string
- name: model
dtype: string
- name: overall_score
dtype: float64
- name: principle
dtype: string
- name: response
dtype: string
- name: correct_answers
sequence: string
- name: incorrect_answers
sequence: string
- name: updated
struct:
- name: completion_idx
dtype: int64
- name: distilabel_rationale
dtype: string
splits:
- name: train
num_bytes: 843221341
num_examples: 63967
download_size: 321698501
dataset_size: 843221341
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Ultrafeedback Curated
This dataset is a curated version of [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset performed by Argilla (using [distilabel](https://github.com/argilla-io/distilabel)).
## Introduction
You can take a look at [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) for more context on the UltraFeedback error, but the following excerpt sums up the problem found:
*After visually browsing around some examples using the sort and filter feature of Argilla (sort by highest rating for chosen responses), we noticed a strong mismatch between the `overall_score` in the original UF dataset (and the Zephyr train_prefs dataset) and the quality of the chosen response.*
*By adding the critique rationale to our Argilla Dataset, we confirmed the critique rationale was highly negative, whereas the rating was very high (the highest in fact: `10`). See screenshot below for one example of this issue. After some quick investigation, we identified hundreds of examples having the same issue and a potential bug on the UltraFeedback repo.*
## Differences with `openbmb/UltraFeedback`
This version of the dataset has replaced the `overall_score` of the responses identified as "wrong", and a new column `updated` to keep track of the updates.
It contains a dict with the following content `{"completion_idx": "the index of the modified completion in the completion list", "distilabel_rationale": "the distilabel rationale"}`, and `None` if nothing was modified.
Other than that, the dataset can be used just like the original.
## Dataset processing
1. Starting from `argilla/ultrafeedback-binarized-curation` we selected all the records with `score_best_overall` equal to 10, as those were the problematic ones.
2. We created a new dataset using the `instruction` and the response from the model with the `best_overall_score_response` to be used with [distilabel](https://github.com/argilla-io/distilabel).
3. Using `gpt-4` and a task for `instruction_following` we obtained a new *rating* and *rationale* of the model for the 2405 "questionable" responses.
```python
import os
from distilabel.llm import OpenAILLM
from distilabel.pipeline import Pipeline
from distilabel.tasks import UltraFeedbackTask
from datasets import load_dataset
# Create the distilabel Pipeline
pipe = Pipeline(
labeller=OpenAILLM(
model="gpt-4",
task=UltraFeedbackTask.for_instruction_following(),
max_new_tokens=256,
num_threads=8,
openai_api_key=os.getenv("OPENAI_API_KEY") or "sk-...",
temperature=0.3,
),
)
# Download the original dataset:
ds = load_dataset("argilla/ultrafeedback-binarized-curation", split="train")
# Prepare the dataset in the format required by distilabel, will need the columns "input" and "generations"
def set_columns_for_distilabel(example):
input = example["instruction"]
generations = example["best_overall_score_response"]["response"]
return {"input": input, "generations": [generations]}
# Filter and prepare the dataset
ds_to_label = ds.filter(lambda ex: ex["score_best_overall"] == 10).map(set_columns_for_distilabel).select_columns(["input", "generations"])
# Label the dataset
ds_labelled = pipe.generate(ds_to_label, num_generations=1, batch_size=8)
```
4. After visual inspection, we decided to remove those answers that were rated as a 1, plus some extra ones rated as 2 and 3, as those were also not a real 10.
The final dataset has a total of 1968 records updated from a 10 to a 1 in the `overall_score` field of the corresponding model (around 3% of the dataset), and a new column "updated" with the rationale of `gpt-4` for the new rating, as well as the index in which the model can be found in the "models" and "completions" columns.
## Reproduce
<a target="_blank" href="https://colab.research.google.com/drive/10R6uxb-Sviv64SyJG2wuWf9cSn6Z1yow?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
To reproduce the data processing, feel free to run the attached Colab Notebook or just view it at [notebook](./ultrafeedback_curation_distilabel.ipynb) within this repository.
From Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as ourselves, as well as the whole community, benefit a lot from open source and we also want to give back.
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nancyalarabawy/RiceLeafDiseases | nancyalarabawy | 2023-12-13T22:32:19Z | 2 | 0 | [
"region:us"
] | null | 2023-12-13T22:25:49Z | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 186725282.0
num_examples: 150
download_size: 13898383
dataset_size: 186725282.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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davidgaofc/PRIMA_inout_clean | davidgaofc | 2023-12-14T03:57:46Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T03:56:13Z | ---
dataset_info:
features:
- name: Text
dtype: string
- name: Label
dtype: int64
splits:
- name: train
num_bytes: 1282897
num_examples: 1640
download_size: 450989
dataset_size: 1282897
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Tippawan/test2-data-semi-trainulb-r2 | Tippawan | 2023-12-14T06:35:52Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T06:35:47Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
- name: prob
sequence: float64
- name: ifpass
sequence: int64
- name: pred
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 128419205
num_examples: 44009
download_size: 25269174
dataset_size: 128419205
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Seongill/Trivia_5_small_missing_adv | Seongill | 2023-12-14T07:38:09Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T07:38:06Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: status
dtype: string
splits:
- name: train
num_bytes: 17349142
num_examples: 3771
download_size: 9605929
dataset_size: 17349142
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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massimilianowosz/website_categories | massimilianowosz | 2023-12-14T08:52:37Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T08:36:40Z | ---
dataset_info:
features:
- name: label
dtype: int64
- name: url
dtype: string
- name: text
dtype: string
- name: main_category
dtype: string
- name: main_category_id
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2020153829
num_examples: 15186
- name: test
num_bytes: 503500228
num_examples: 3797
download_size: 752658626
dataset_size: 2523654057
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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mj96/dutch_golden_age_resized_brime | mj96 | 2023-12-14T09:01:51Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T09:01:48Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 605397.0
num_examples: 17
download_size: 606041
dataset_size: 605397.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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zaanind/sinhala_englsih_nmt | zaanind | 2023-12-14T12:02:25Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T10:04:26Z | ---
dataset_info:
features:
- name: english
dtype: string
- name: sinhala
dtype: string
splits:
- name: train
num_bytes: 13232
num_examples: 109
download_size: 7921
dataset_size: 13232
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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nitinbhayana/processed_demo | nitinbhayana | 2023-12-14T11:12:29Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T11:12:21Z | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1729780.7113998258
num_examples: 11360
- name: test
num_bytes: 192316.28860017427
num_examples: 1263
download_size: 1149085
dataset_size: 1922097.0
---
# Dataset Card for "processed_demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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aisuko/funsd-layoutlmv3 | aisuko | 2023-12-14T11:24:28Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T11:19:42Z | ---
license: apache-2.0
---
Original from: https://huggingface.co/datasets/aisuko/funsd-layoutlmv3
Adaptered by: Aisuko
License: Apache-2.0
```python
dataset = load_dataset("aisuko/funsd-layoutlmv3")
# check the dataset
dataset
# check the features
dataset["train"].features
# check the first example
example=dataset["train"][0]
example["image"]
``` | [
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Seongill/Trivia_5_small_missing_adv_top6 | Seongill | 2023-12-14T13:32:12Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T13:32:08Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: status
dtype: string
splits:
- name: train
num_bytes: 17279426
num_examples: 3771
download_size: 9619951
dataset_size: 17279426
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Seongill/Trivia_5_small_missing_adv_top7 | Seongill | 2023-12-14T13:35:51Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T13:35:47Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: status
dtype: string
splits:
- name: train
num_bytes: 17137460
num_examples: 3771
download_size: 9615874
dataset_size: 17137460
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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MMoin/mini-platypus-500 | MMoin | 2023-12-14T14:17:39Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T14:17:38Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2456362
num_examples: 500
download_size: 1322517
dataset_size: 2456362
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ctang/HHH_redteaming_eval_v3 | ctang | 2023-12-14T14:57:46Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T14:57:45Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_reasonable
dtype: string
splits:
- name: train
num_bytes: 578485
num_examples: 438
download_size: 194011
dataset_size: 578485
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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adhisetiawan/bdd10k-bitmasks | adhisetiawan | 2023-12-14T16:09:11Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T16:08:29Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 870529576.0
num_examples: 7000
- name: validation
num_bytes: 159751856.0
num_examples: 1000
download_size: 980250621
dataset_size: 1030281432.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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] |
abhishek/autotrain-data-cp4q-lpqu-u75r | abhishek | 2023-12-14T16:09:21Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T16:09:15Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': daisy
'1': dandelion
'2': rose
'3': sunflower
'4': tulip
splits:
- name: train
num_bytes: 114410927.672
num_examples: 2196
- name: validation
num_bytes: 33682367.0
num_examples: 550
download_size: 166944003
dataset_size: 148093294.672
---
# Dataset Card for "autotrain-data-cp4q-lpqu-u75r"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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plaguss/ultrafeedback-criticon | plaguss | 2023-12-14T17:02:19Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T17:02:07Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
- name: critique
dtype: string
- name: overall_score
dtype: float64
splits:
- name: train
num_bytes: 639775765
num_examples: 252984
download_size: 253016899
dataset_size: 639775765
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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DragonLine/ksponspeech_03_preprocess | DragonLine | 2023-12-14T18:14:14Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T17:32:04Z | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 95285021616
num_examples: 99200
- name: test
num_bytes: 11910618728
num_examples: 12400
- name: valid
num_bytes: 11910635984
num_examples: 12400
download_size: 23741227475
dataset_size: 119106276328
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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davidgaofc/RM_inout_bal_clean | davidgaofc | 2023-12-14T21:16:44Z | 2 | 0 | [
"region:us"
] | null | 2023-12-14T21:16:43Z | ---
dataset_info:
features:
- name: Text
dtype: string
- name: Label
dtype: int64
splits:
- name: train
num_bytes: 434649
num_examples: 910
download_size: 193142
dataset_size: 434649
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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FreedomIntelligence/OVM-dataset | FreedomIntelligence | 2023-12-15T07:26:14Z | 2 | 0 | [
"license:llama2",
"arxiv:2311.09724",
"region:us"
] | null | 2023-12-15T05:27:10Z | ---
license: llama2
---
The training dataset for verifiers, which is generated by the finetuned models in GSM8K and Game of 24. The models are open-sourced in [OVM-llama2-7b](https://huggingface.co/FreedomIntelligence/OVM-llama2-7b) and [OVM-Mistral-7b](https://huggingface.co/FreedomIntelligence/OVM-Mistral-7b).
See the paper [Outcome-supervised Verifiers for Planning in Mathematical Reasoning](https://arxiv.org/pdf/2311.09724.pdf) and the code in [github](https://github.com/FreedomIntelligence/OVM)
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argilla/ultrafeedback-critique | argilla | 2023-12-15T10:59:19Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T08:33:53Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
- name: critique
dtype: string
- name: overall_score
dtype: float64
splits:
- name: train
num_bytes: 639775765
num_examples: 252984
download_size: 253016899
dataset_size: 639775765
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# UltraFeedback - Critique (raw)
This dataset contains a raw version of [ultrafeedback-curated](https://huggingface.co/datasets/argilla/ultrafeedback-curated) prepared to train a critique model.
## Introduction
This dataset is built so that each record contains the original instruction, the response from each one of the models, the corresponding critique and it's overall score. We have prepared the dataset so that applying Supervised fine-tuning (SFT) for a critique model can be as simple as possible.
## Data processing
We extracted each `instruction`, for each model completion in the original `completions` column we obtained the model's `response`, the `critique` rationale and the `overall_score`. Exploring the dataset we noticed that for some records, the `overall_score` was always low, which indicated that the instruction doesn't make sense, so we decided to remove those records for which all `overall_score` responses were below a given threshold (`overall_score_threshold` == 4). The final dataset has 252984 records, around 720 instructions of the original dataset were removed.
## Reproduce
<a target="_blank" href="https://colab.research.google.com/drive/1ggdYCjDALk9ZI9z7j_dLDapabt6AznQn?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
To reproduce the processing of the dataset feel free to run the attached Colab Notebook or just view it at [`notebook.ipynb`](./notebook.ipynb) within this repository.
From Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as
ourselves, as well as the whole community, benefit a lot from open source and we also want to give back.
Example record:
```json
{
"instruction": "I want to do exploratory data analysis on below dataset found on kaggle\n\nhttps://www.kaggle.com/datasets/neuromusic/avocado-prices",
"response": "Hello! I'm here to help you with your exploratory data analysis on the avocado prices dataset from Kaggle. This dataset contains information on avocado prices from different regions in the United States, including California, Florida, and Illinois.\n\nBefore we dive into the analysis, I want to make sure that you are aware of the potential biases and limitations of the dataset. The dataset is collected from a single source, and it may not be representative of the entire avocado market or the entire United States. Additionally, the dataset only contains information on three regions, which may not be enough to make generalizations about the entire country.\n\nThat being said, there are many interesting patterns and trends that we can explore in this dataset. For example, we can analyze the price trends over time to see if there are any seasonal patterns or long-term trends. We can also compare the prices across different regions to see if there are any differences in pricing strategies or market conditions.\n\nTo get started, I recommend that we first inspect the dataset to get a better understanding of the variables and their distributions. We can use statistical methods like histograms and box plots to visualize the data and identify any outliers or anomalies.\n\nOnce we have a better understanding of the data, we can move on to more advanced analytical techniques like regression analysis or time series analysis. These methods can help us identify the factors that influence avocado prices and make predictions about future price trends.\n\nThroughout the analysis, I will make sure to provide you with accurate and unbiased information, and I will always be mindful of any ethical or social implications of our findings. I'm excited to work with you on this project and help you gain insights into the avocado market!\n\nIs there anything specific you would like to explore in the dataset, or any questions you have before we begin?",
"critique": "Your answer was generally good, covering a broad range of points. You explained well about the dataset and potential biases, which shows a good understanding. However, there are a few areas for improvement. \n\nFirst, you mentioned that the dataset only contains information on three regions - California, Florida, and Illinois. This is not accurate. The dataset includes multiple regions. You need to ensure your statements are factual.\n\nSecond, you should have asked the user what kind of exploratory data analysis they want to perform or suggested some specific analyses that could be done, such as looking for correlations between variables, instead of going straight into advanced techniques like regression analysis or time series analysis which might not be necessary at this stage.\n\nLastly, the user didn't ask for a detailed explanation of how to do the analysis. The instruction was to provide a response as an AI assistant, so your role was to provide information or suggestions, not to guide the user through the whole process.",
"overall_score": 6.5
}
```
## Citation
If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: https://huggingface.co/datasets/openbmb/UltraFeedback
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DragonLine/ksponspeech_eval_clean_test_preprocess | DragonLine | 2023-12-15T09:01:49Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T09:01:02Z | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: test
num_bytes: 2881455880
num_examples: 3000
download_size: 425192625
dataset_size: 2881455880
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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Tippawan/test2-data-semi-p2-WLV | Tippawan | 2023-12-15T09:19:23Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T09:19:20Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
- name: prob
sequence: float64
- name: ifpass
sequence: int64
- name: pred
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9057683
num_examples: 3569
download_size: 1308102
dataset_size: 9057683
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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derexHf/MathInstructText161K | derexHf | 2023-12-15T10:54:42Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T10:54:33Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 241110126
num_examples: 161289
download_size: 118946326
dataset_size: 241110126
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Erynan/gpt_deon_10 | Erynan | 2023-12-15T11:19:52Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T11:19:51Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_reasonable
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 3199
num_examples: 10
download_size: 6335
dataset_size: 3199
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Erynan/gpt_util_10 | Erynan | 2023-12-15T11:44:33Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T11:44:32Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_reasonable
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 3173
num_examples: 10
download_size: 6151
dataset_size: 3173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Erynan/gpt_just_10 | Erynan | 2023-12-15T11:48:26Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T11:48:25Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_reasonable
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 3184
num_examples: 10
download_size: 6058
dataset_size: 3184
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Erynan/gpt_virt_10 | Erynan | 2023-12-15T11:51:01Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T11:51:00Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_reasonable
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2763
num_examples: 10
download_size: 5736
dataset_size: 2763
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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pmaslak/test_audio | pmaslak | 2023-12-15T12:33:12Z | 2 | 0 | [
"size_categories:10M<n<100M",
"language:ru",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T12:27:56Z | ---
license: apache-2.0
language:
- ru
size_categories:
- 10M<n<100M
--- | [
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MITCriticalData/SAT3_FULL_COLOMBIA | MITCriticalData | 2023-12-17T05:17:39Z | 2 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-15T17:45:12Z | ---
license: mit
---
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TeamUNIVA/23KOMOD_culture_heritage | TeamUNIVA | 2023-12-15T19:22:56Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T19:22:51Z | ---
dataset_info:
features:
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splits:
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num_bytes: 18379915
num_examples: 9666
download_size: 10957724
dataset_size: 18379915
configs:
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data_files:
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path: data/train-*
---
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gianlucar/rugby_test_2 | gianlucar | 2023-12-15T19:42:24Z | 2 | 0 | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"fine-tuning",
"touch rugby",
"region:us"
] | [
"text-generation"
] | 2023-12-15T19:41:56Z | ---
task_categories:
- text-generation
language:
- en
tags:
- fine-tuning
- touch rugby
size_categories:
- n<1K
---
# Touch Rugby Rules Dataset (for embeddings)
train.csv is taken from the [International Touch Website](https://cdn.internationaltouch.org/public/FIT%205th%20Edition%20Rulebook.pdf)
test.csv is copy pasted from abbreviated rules on the [UK Touch website](https://www.englandtouch.org.uk/develop/coaching/the-rules/). Note that I'm bypassing the pdf to text stage.
All text is chunked to a length of 100 tokens with 50% overlap.
For educational and non-commercial use only. | [
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Jiahuan/teach_object | Jiahuan | 2023-12-16T00:12:52Z | 2 | 0 | [
"region:us"
] | null | 2023-12-15T23:48:51Z | ---
dataset_info:
features:
- name: doc_id
dtype: string
- name: start_time
dtype: string
- name: query
dtype: string
- name: action
dtype: string
- name: action_success
dtype: string
- name: object
dtype: string
splits:
- name: train
num_bytes: 4365626
num_examples: 32487
- name: validation
num_bytes: 551083
num_examples: 4139
- name: test
num_bytes: 1799637
num_examples: 13738
download_size: 1088565
dataset_size: 6716346
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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Masterwin/yo | Masterwin | 2023-12-16T02:26:09Z | 2 | 0 | [
"license:llama2",
"region:us"
] | null | 2023-12-16T02:25:28Z | ---
license: llama2
---
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Ram07/sete-2-dia-log | Ram07 | 2023-12-16T06:05:40Z | 2 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-16T06:05:06Z | ---
license: mit
---
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abhigadgil15/platypus_mini | abhigadgil15 | 2023-12-16T08:29:18Z | 2 | 0 | [
"region:us"
] | null | 2023-12-16T08:29:17Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245925
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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adhisetiawan/pothole-segmentation | adhisetiawan | 2023-12-16T10:04:12Z | 2 | 0 | [
"region:us"
] | null | 2023-12-16T10:04:06Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 6389783.0
num_examples: 80
- name: validation
num_bytes: 337776.0
num_examples: 5
download_size: 6675556
dataset_size: 6727559.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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nesuri/sorsolingo-asr-bsl | nesuri | 2023-12-16T15:02:23Z | 2 | 0 | [
"region:us"
] | null | 2023-12-16T11:03:32Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 106511839.0
num_examples: 140
download_size: 103042340
dataset_size: 106511839.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Masterwin/test | Masterwin | 2023-12-16T22:12:24Z | 2 | 0 | [
"license:llama2",
"region:us"
] | null | 2023-12-16T22:11:18Z | ---
license: llama2
---
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adhisetiawan/pothole-segmentation-mix | adhisetiawan | 2023-12-17T00:23:30Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T00:23:11Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 67485908.0
num_examples: 240
- name: validation
num_bytes: 49272029.0
num_examples: 180
download_size: 116477831
dataset_size: 116757937.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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liyongsea/THINGS_EEG_Test | liyongsea | 2023-12-17T03:09:32Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T03:04:28Z | ---
dataset_info:
features:
- name: eeg_array
sequence:
sequence: float64
- name: label
dtype: int64
- name: fold
dtype: int64
- name: metadata
struct:
- name: fold
dtype: int64
- name: img_concept
dtype: string
- name: label
dtype: int64
- name: repeat_id
dtype: int64
- name: subject_id
dtype: int64
splits:
- name: train
num_bytes: 219795280
num_examples: 16000
download_size: 222240491
dataset_size: 219795280
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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RahulRaman/7da0c9e96aa319b1419c5ba8b10335a28ef3aa98 | RahulRaman | 2023-12-17T04:11:36Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T04:11:34Z | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 23605680.0
num_examples: 250
download_size: 14287477
dataset_size: 23605680.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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RahulRaman/combined-dataset | RahulRaman | 2023-12-17T11:00:30Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T04:20:17Z | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 77534526.0
num_examples: 820
download_size: 25278176
dataset_size: 77534526.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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cheafdevo56/InfluentialTriplets5Percent | cheafdevo56 | 2023-12-17T05:09:09Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T05:09:02Z | ---
dataset_info:
features:
- name: query
struct:
- name: abstract
dtype: string
- name: corpus_id
dtype: int64
- name: title
dtype: string
- name: pos
struct:
- name: abstract
dtype: string
- name: corpus_id
dtype: int64
- name: title
dtype: string
- name: neg
struct:
- name: abstract
dtype: string
- name: corpus_id
dtype: int64
- name: score
dtype: int64
- name: title
dtype: string
splits:
- name: train
num_bytes: 173326353.3
num_examples: 45000
- name: validation
num_bytes: 19258483.7
num_examples: 5000
download_size: 115830892
dataset_size: 192584837.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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] |
JeremiahZ/hendrycks_math_merged | JeremiahZ | 2023-12-17T07:20:03Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T07:13:48Z | ---
dataset_info:
features:
- name: problem
dtype: string
- name: level
dtype: string
- name: type
dtype: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 5984772
num_examples: 7500
- name: test
num_bytes: 3732833
num_examples: 5000
download_size: 4848009
dataset_size: 9717605
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# MATH Dataset Merged
This dataset is created from [EleutherAI/hendrycks_math](https://huggingface.co/datasets/EleutherAI/hendrycks_math) using this script
```python
import datasets
from datasets import DatasetDict
# dataset = datasets.load_dataset("EleutherAI/hendrycks_math")
configs = ['algebra', 'counting_and_probability', 'geometry', 'intermediate_algebra', 'number_theory', 'prealgebra', 'precalculus']
all_train_datasets = []
all_test_datasets = []
for c in configs:
c_dataset = datasets.load_dataset("EleutherAI/hendrycks_math", c)
all_train_datasets.append(c_dataset['train'])
all_test_datasets.append(c_dataset['test'])
# merge all datasets
merged_train_dataset = datasets.concatenate_datasets(all_train_datasets)
merged_test_dataset = datasets.concatenate_datasets(all_test_datasets)
merged_dataset = DatasetDict({'train': merged_train_dataset, 'test': merged_test_dataset})
merged_dataset
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Adun/isuzu-ds-test2 | Adun | 2023-12-17T07:28:56Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T07:28:49Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 13375195.0
num_examples: 94
download_size: 13297162
dataset_size: 13375195.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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vigneshgs7/Boundary_detection_twomask | vigneshgs7 | 2023-12-17T09:31:59Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T09:31:26Z | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 1342892808.0
num_examples: 27
download_size: 88157922
dataset_size: 1342892808.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Boundary_detection_twomask"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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herisan/Text2SQL | herisan | 2023-12-17T10:24:03Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T10:23:50Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 403128757
num_examples: 289288
download_size: 111091110
dataset_size: 403128757
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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nlplabtdtu/edu_parent_v10 | nlplabtdtu | 2023-12-17T13:19:29Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T13:19:25Z | ---
dataset_info:
features:
- name: content
dtype: string
- name: metadata
struct:
- name: id
dtype: string
- name: prefix
dtype: string
- name: seq_num
dtype: int64
- name: source
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 355826
num_examples: 163
download_size: 150325
dataset_size: 355826
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ayan1988/diffusion.maobi3 | ayan1988 | 2023-12-17T13:51:11Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T13:50:16Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: txt
dtype: string
splits:
- name: train
num_bytes: 15526635.0
num_examples: 319
download_size: 14468827
dataset_size: 15526635.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Gummybear05/Y_freq_speed2 | Gummybear05 | 2023-12-17T17:06:37Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T15:21:37Z | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sample_rate
dtype: int64
- name: text
dtype: string
- name: scriptId
dtype: int64
- name: fileNm
dtype: string
- name: recrdTime
dtype: float64
- name: recrdQuality
dtype: int64
- name: recrdDt
dtype: string
- name: scriptSetNo
dtype: string
- name: recrdEnvrn
dtype: string
- name: colctUnitCode
dtype: string
- name: cityCode
dtype: string
- name: recrdUnit
dtype: string
- name: convrsThema
dtype: string
- name: gender
dtype: string
- name: recorderId
dtype: string
- name: age
dtype: int64
splits:
- name: train
num_bytes: 10251971653
num_examples: 12401
download_size: 7193080970
dataset_size: 10251971653
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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atsstagram/emotion_annotated_by_gpt4 | atsstagram | 2023-12-17T16:20:47Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T15:56:28Z | ---
license: apache-2.0
---
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xNoper/dubai-aerial | xNoper | 2023-12-17T17:27:16Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T17:27:07Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 33524376.0
num_examples: 72
download_size: 32535970
dataset_size: 33524376.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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katxtong/tokenized_coqa | katxtong | 2023-12-17T18:56:30Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T18:56:14Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: start_positions
dtype: int64
- name: end_positions
dtype: int64
splits:
- name: train
num_bytes: 545842528
num_examples: 108647
- name: validation
num_bytes: 40106592
num_examples: 7983
download_size: 37611347
dataset_size: 585949120
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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Minata/ast_method2test_v1 | Minata | 2023-12-17T22:53:32Z | 2 | 0 | [
"region:us"
] | null | 2023-12-17T22:53:30Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: test
num_bytes: 26340016
num_examples: 15716
download_size: 1879160
dataset_size: 26340016
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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hf-doc-build/doc-build | hf-doc-build | 2023-12-17T14:24:35Z | 1 | 2 | [
"license:mit",
"region:us"
] | null | 2022-10-24T15:39:05Z | ---
license: mit
pretty_name: Generated Docs for HF
---
This repo contains all the docs published on https://huggingface.co/docs.
The docs are generated with https://github.com/huggingface/doc-builder. | [
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kaliansh/oneapi | kaliansh | 2023-12-14T18:16:23Z | 1 | 0 | [
"license:unknown",
"region:us"
] | null | 2023-01-15T19:01:07Z | ---
license: unknown
---
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4eJIoBek/PAIT-Downloads | 4eJIoBek | 2023-12-12T16:08:47Z | 1 | 13 | [
"license:unknown",
"region:us"
] | null | 2023-05-06T16:45:13Z | ---
license: unknown
---
This is a downloads of https://gz1k.itch.io/ai-portable-tools, but on huggingface for lightning speed of downloading. I hope i haven't broke ToS of Huggingface Hub by uploading these tools here.
----------------------------------
This is my collection of portable AI packages to run it fast without anxious headache in console. initially, I made these tools for myself, but maybe someone else will need them. OK, heres the list:
-TEXT-
Koboldai [CPU/CUDA] - link - also in downloads / online demo
-CHAT-
Llama 2 chat 7B 4bit koboldcpp webui [CPU] - in downloads
/ source / webui / model / online demo
Llama 2 chat 7B Luna ai uncensored 4bit koboldcpp webui (note that this is a finetune on unsupervised synthetic dataset, so it hallucinates way more strong than original llama-2-chat) [CPU] - in downloads / source / webui / model /
Vicuna 1.1 7B 4bit koboldcpp webui (much worse than llama2 above, but may be more multilingual) [CPU] - in downloads. / source / webui / model / online demo
-TRANSLATE-
Facebook NLLB 600m webui [CPU] - in downloads / source / webui / model / online demo
-MIDI MUSIC GENERATION-
Midi composer app [CUDA][CPU] - link - also in downloads / source / online demo
Multitrack midi music generator (generates short jingles, each instrument generated separately) [CPU] - in downloads / webui
-TEXT TO MUSIC/AUDIO-
AudioCraft Plus [CUDA/CPU] - in downloads / source / webui / online demo
-TEXT TO SPEECH-
Suno ai Bark webui (with zeroshot voice conversion) [CUDA/CPU] - in downloads / source / webui / online demo
Coqui XTTS webui (this one generates speech only with voice cloning) (voice cloning is more "stable" than bark, but the accent and emotions can be lost) [CUDA] - in downloads / source / webui
TorToiSe webui [CUDA/CPU] - in downloads / source / webui / online demo
-VOICE CONVERSION VIA TRAINING-
RVC singing voice cloning webui [CUDA] - link - also in downloads / source
-VOICE ZEROSHOT CONVERSION-
FreeVC webui [CPU] - in downloads / source / webui
-VOICE TO TEXT-
Whispercpp GUI [DirectX/CPU] - link - also in downloads / source / gui / online demo
-VOCALS RESTORATION-
VoiceFixer webui [CPU] - in downloads / source / webui
-DUAL SPEAKER SPEECH SEPARATION-
Dual Path RNN (cli interface) - in downloads / source
-VOCALS/STEMS EXTRACTION-
UVR [CPU/CUDA] - link - also in downloads / online demo
Demucs GUI [CPU][CUDA] - link - also in downloads / source / gui
-IMAGE COLORIZATION-
DeOldify .NET gui [CPU] - link - also in downloads / source / gui / online demo
-ZEROSHOT IMAGE MATTING-
DIS webui [CPU] - in downloads / source / webui
-IMAGE UPSCALING-
Cupscale [Vulkan/CUDA] - link - also in downloads / source / webui / online demo
Automatic1111 sdwebui with StableSR extension [CUDA/CPU] - in downloads / source / webui / extension
-TEXT2IMAGE-
Automatic1111 Stable Diffusion base (without models) - link / webui
Automatic1111 deliberate v2 (sd1.5) model [CUDA/CPU][DIRECTX/CPU] - in downloads / source / webui / directx webui / model
Automatic1111 Illuminati Diffusion (sd2.1) model [CUDA/CPU] - in downloads / source / webui / model
Fooocus (sdxl) [CUDA] - link- also in downloads / source / webui / model / refiner
ConfyUI (without models) [CUDA/CPU] - link - also in downloads / source / webui
-IMAGE EDITING BY PROMPT-
Automatic1111 Instructpix2pix (sd1.5) model [DIRECTX/CPU][CUDA/CPU] - in downloads / source / ip2p source / webui / directx webui / model
-IMAGE TO IMAGE VARIATIONS-
Automatic1111 sd-unclip (sd2.1) model [CUDA/CPU] - in downloads / source / webui / model
-IMAGE EDITING BY CONCEPTS-
LEDITS webui [CUDA/CPU] - in downloads / source / webui
-OBJECT REMOVING-
lama cleaner [CUDA] - in downloads / source / webui / online demo
-VIDEO FRAMES INTERPOLATION-
Flowframes [CUDA/Vulkan] - in downloads / source / gui
-VIDEO UPSCALING-
RealBasicVSR (cli interface) [CUDA/CPU] - in downloads / source
-TEXT2VIDEO-
Automatic1111 sdwebui with animatediff extension [CUDA/CPU] - in downloads / source / webui / extension / model / online demo
Automatic1111 sdwebui with modelscope text2video extension with zeroscope-v2-576w model [CUDA] - in downloads / source / webui / extension / model / online demo
-VIDEO HUMAN MATTING-
RobustVideoMatting (cli interface) [CUDA/CPU] - in downloads / source / online demo
-VIDEO ZERO-SHOT MATTING-
Track-anything webui [CPU] - in downloads / webui / online demo
-VIDEO FEW-SHOT MATTING VIA TRAINING-
DeepXTools by Iperov [CUDA] - link - also in downloads
-ZERO-SHOT DEEPFAKING-
Roop neurogen mod (Refacer model) (lightning fast, has realtime deepfake on webcam function) (the refacer model swaps faces better than simswap, but have only 128px resolution and may have more artifacts when head is on side) [DirectX/CUDA/CPU] - in downloads / source / webui / mod by
Deepinsight Refacer gradio webui (replaces only certain faces, has cool face upscale feature) [CUDA] - in downloads / source / webui / mod by
Simswap (cli interface) [CUDA/CPU] - in downloads / source
-DEEPFAKING VIA TRAINING-
DeepFaceLab (cli interface) [DirectX][CUDA] - link - also in downloads / source
DeepfaceLive [DirectX][CUDA] - link - also in downloads / source
-LIPS MANIPULATION ON VIDEO-
wav2lip gui [CUDA/CPU] - link - also in downloads / source / gui
-TEXT To 3D-
Shap-E webui [CUDA/CPU] -in downloads / source / webui
Point-E webui [CUDA/CPU] (results are worse than shap-e) - in downloads / source / webui
-NEURAL RADIANCE FIELDS GENERATION BY IMAGES-
nerfstudio (nerfacto) [CUDA] - in downloads / source
--------------------------------------------------------------
Alternative downloads with torrents on Archive.org: https://archive.org/details/@takeonme1?tab=uploads
Page on civitai: https://civitai.com/models/104609 | [
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DukeG/Kaggle_venv | DukeG | 2023-12-16T06:15:36Z | 1 | 0 | [
"license:other",
"region:us"
] | null | 2023-05-11T09:19:25Z | ---
license: other
---
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KakologArchives/KakologArchives | KakologArchives | 2023-12-18T01:23:13Z | 1 | 2 | [
"task_categories:text-classification",
"language:ja",
"license:mit",
"region:us"
] | [
"text-classification"
] | 2023-05-12T13:31:56Z | ---
pretty_name: ニコニコ実況 過去ログアーカイブ
license: mit
language:
- ja
task_categories:
- text-classification
---
# ニコニコ実況 過去ログアーカイブ
ニコニコ実況 過去ログアーカイブは、[ニコニコ実況](https://jk.nicovideo.jp)のサービス開始から現在までのすべての過去ログコメントを収集したデータセットです。
去る2020年12月、ニコニコ実況は[ニコニコ生放送内の一公式チャンネルとしてリニューアル](https://blog.nicovideo.jp/niconews/143148.html)されました。
これに伴い、2009年11月から運用されてきた旧システムは提供終了となり（事実上のサービス終了）、torne や BRAVIA などの家電への対応が軒並み終了する中、当時の生の声が詰まった約11年分の過去ログも同時に失われることとなってしまいました。
そこで 5ch の DTV 板の住民が中心となり、旧ニコニコ実況が終了するまでに11年分の全チャンネルの過去ログをアーカイブする計画が立ち上がりました。紆余曲折あり Nekopanda 氏が約11年分のラジオや BS も含めた全チャンネルの過去ログを完璧に取得してくださったおかげで、11年分の過去ログが電子の海に消えていく事態は回避できました。
しかし、旧 API が廃止されてしまったため過去ログを API 経由で取得することができなくなり、またアーカイブされた過去ログから見たい範囲のログを探す場合も、アーカイブのサイズが合計約 150GB もあることから、とても以前のように手軽に過去ログに触れることはできなくなってしまいました。
一方、ニコニコ生放送内の一公式チャンネルとして移行した新ニコニコ実況では、タイムシフト（旧ニコニコ実況での過去ログに相当）の視聴期限は3週間までとなっているため、その期限を過ぎると過去ログは視聴できなくなってしまいます。
また一般会員は事前にタイムシフト予約をしておく必要があるなど、以前のような利便性は失われています。
私たちは、ニコニコ実況に投稿された日本のテレビ放送についてのコメントは、当時の世相や時代背景を端的に表す、歴史的価値のある資料だと考えています。
このデータセットでは、ニコニコ実況のすべての過去ログを後世に残すべく、Nekopanda 氏が配布されていた旧ニコニコ実況の 2020/12/15 までのすべての過去ログに加え、コミュニティベースの番組も含めた新ニコニコ実況の当日分の過去ログを5分に1回収集し、随時反映しています。
過去ログをかんたんに取得するための [API](https://jikkyo.tsukumijima.net/) もあります。
よろしければそちらもご活用ください。
## Dataset Structure
### Builder Config
| Key | Value Type | Default Value | Description |
| --------------- | ---------- | ------------- | ----------- |
| channel_id | string | None | 過去ログを取得するニコニコ実況チャンネルの ID (省略時はすべてのチャンネル) |
| year | int | None | 取得する過去ログの年 (省略時はすべての年) |
| number_of_files | int | None | 取得する過去ログファイルの数 (省略時はすべてのファイル) |
### Data Splits
| Split | Approximate Size | Description |
| ------- | ---------------- | ----------- |
| sample | 1GB | サンプルとして、2022年中に投稿された TOKYO MX (ID: jk9) のすべての過去ログコメントを取得します。1GB ほどあります。 |
| all | 180GB | 全チャンネル/全期間のすべての過去ログコメントを取得します。180GB 近くあるため注意してください。 |
### Data Fields
| Field | Type | Description |
| --------------- | -------- | ----------- |
| thread | string | コメントのスレッド ID |
| no | int64 | コメント番号 (コメ番) |
| vpos | int64 | スレッド ID から起算したコメントの再生位置 (1/100秒) |
| date | int64 | コメント投稿時間の UNIX タイムスタンプ |
| date_usec | int64 | コメント投稿時間の小数点以下の時間 |
| user_id | string | ユーザー ID (コマンドに 184 が指定されている場合は匿名化され、1週間ほどでシャッフルされる) |
| mail | string | コメントのコマンド (184, red naka big など、省略されることもある) |
| premium | boolean | コメントしたユーザーがプレミアム会員であれば True |
| anonymity | boolean | 匿名コメントであれば True |
| content | string | コメント本文 (AA など、まれに複数行コメントがあるので注意) |
## Example
```python
from datasets import load_dataset
dataset = load_dataset('KakologArchives/KakologArchives', 'all', channel_id='jk211', year=2023, number_of_files=10)
for data in dataset['train']:
print(data)
```
## Licensing Information
[MIT License](https://opensource.org/license/mit/)
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yahanyang777/DSI_web_content | yahanyang777 | 2023-12-11T16:47:39Z | 1 | 0 | [
"license:mit",
"region:us"
] | null | 2023-07-08T21:37:55Z | ---
license: mit
---
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dacoolkid44/Data | dacoolkid44 | 2023-12-16T08:51:43Z | 1 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-07-27T16:33:37Z | ---
license: openrail
---
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travisdriver/astrovision-data | travisdriver | 2023-12-16T00:47:11Z | 1 | 1 | [
"region:us"
] | null | 2023-09-07T19:27:13Z | ---
pretty_name: AstroVision
viewer: false
---
<a href="https://imgur.com/Vs3Rwin"><img src="https://i.imgur.com/Vs3Rwin.png" title="source: imgur.com" /></a>
---
<a href="https://imgur.com/RjSwdG2"><img src="https://i.imgur.com/RjSwdG2.png" title="source: imgur.com" /></a>
# About
AstroVision is a first-of-a-kind, large-scale dataset of real small body images from both legacy and ongoing deep space missions, which currently features 115,970 densely annotated, real images of sixteen small bodies from eight missions. AstroVision was developed to facilitate the study of computer vision and deep learning for autonomous navigation in the vicinity of a small body, with speicial emphasis on training and evaluation of deep learning-based keypoint detection and feature description methods.
If you find our datasets useful for your research, please cite the [AstroVision paper](https://www.sciencedirect.com/science/article/pii/S0094576523000103):
```bibtex
@article{driver2023astrovision,
title={{AstroVision}: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning},
author={Driver, Travis and Skinner, Katherine and Dor, Mehregan and Tsiotras, Panagiotis},
journal={Acta Astronautica: Special Issue on AI for Space},
year={2023},
volume={210},
pages={393--410}
}
```
Please make sure to like the respository to show support!
# Data format
Following the popular [COLMAP data format](https://colmap.github.io/format.html), each data segment contains the files `images.bin`, `cameras.bin`, and `points3D.bin`, which contain the camera extrinsics and keypoints, camera intrinsics, and 3D point cloud data, respectively.
- `cameras.bin` encodes a dictionary of `camera_id` and [`Camera`](third_party/colmap/scripts/python/read_write_model.py) pairs. `Camera` objects are structured as follows:
- `Camera.id`: defines the unique (and possibly noncontiguious) identifier for the `Camera`.
- `Camera.model`: the camera model. We utilize the "PINHOLE" camera model, as AstroVision contains undistorted images.
- `Camera.width` & `Camera.height`: the width and height of the sensor in pixels.
- `Camera.params`: `List` of cameras parameters (intrinsics). For the "PINHOLE" camera model, `params = [fx, fy, cx, cy]`, where `fx` and `fy` are the focal lengths in $x$ and $y$, respectively, and (`cx`, `cy`) is the principal point of the camera.
- `images.bin` encodes a dictionary of `image_id` and [`Image`](third_party/colmap/scripts/python/read_write_model.py) pairs. `Image` objects are structured as follows:
- `Image.id`: defines the unique (and possibly noncontiguious) identifier for the `Image`.
- `Image.tvec`: $\mathbf{r}^\mathcal{C_ i}_ {\mathrm{BC}_ i}$, i.e., the relative position of the origin of the camera frame $\mathcal{C}_ i$ with respect to the origin of the body-fixed frame $\mathcal{B}$ expressed in the $\mathcal{C}_ i$ frame.
- `Image.qvec`: $\mathbf{q}_ {\mathcal{C}_ i\mathcal{B}}$, i.e., the relative orientation of the camera frame $\mathcal{C}_ i$ with respect to the body-fixed frame $\mathcal{B}$. The user may call `Image.qvec2rotmat()` to get the corresponding rotation matrix $R_ {\mathcal{C}_ i\mathcal{B}}$.
- `Image.camera_id`: the identifer for the camera that was used to capture the image.
- `Image.name`: the name of the corresponding file, e.g., `00000000.png`.
- `Image.xys`: contains all of the keypoints $\mathbf{p}^{(i)} _k$ in image $i$, stored as a ($N$, 2) array. In our case, the keypoints are the forward-projected model vertices.
- `Image.point3D_ids`: stores the `point3D_id` for each keypoint in `Image.xys`, which can be used to fetch the corresponding `point3D` from the `points3D` dictionary.
- `points3D.bin` enocdes a dictionary of `point3D_id` and [`Point3D`](third_party/colmap/scripts/python/read_write_model.py) pairs. `Point3D` objects are structured as follows:
- `Point3D.id`: defines the unique (and possibly noncontiguious) identifier for the `Point3D`.
- `Point3D.xyz`: the 3D-coordinates of the landmark in the body-fixed frame, i.e., $\mathbf{\ell} _{k}^\mathcal{B}$.
- `Point3D.image_ids`: the ID of the images in which the landmark was observed.
- `Point3D.point2D_idxs`: the index in `Image.xys` that corresponds to the landmark observation, i.e., `xy = images[Point3D.image_ids[k]].xys[Point3D.point2D_idxs[k]]` given some index `k`.
These three data containers, along with the ground truth shape model, completely describe the scene.
In addition to the scene geometry, each image is annotated with a landmark map, a depth map, and a visibility mask.
<a href="https://imgur.com/DGUC0ef"><img src="https://i.imgur.com/DGUC0ef.png" title="source: imgur.com" /></a>
- The _landmark map_ provides a consistent, discrete set of reference points for sparse correspondence computation and is derived by forward-projecting vertices from a medium-resolution (i.e., $\sim$ 800k facets) shape model onto the image plane. We classify visible landmarks by tracing rays (via the [Trimesh library](https://trimsh.org/)) from the landmarks toward the camera origin and recording landmarks whose line-of-sight ray does not intersect the 3D model.
- The _depth map_ provides a dense representation of the imaged surface and is computed by backward-projecting rays at each pixel in the image and recording the depth of the intersection between the ray and a high-resolution (i.e., $\sim$ 3.2 million facets) shape model.
- The _visbility mask_ provides an estimate of the non-occluded portions of the imaged surface.
**Note:** Instead of the traditional $z$-depth parametrization used for depth maps, we use the _absolute depth_, similar to the inverse depth parameterization. | [
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hastylol/dataaa | hastylol | 2023-12-14T15:07:27Z | 1 | 0 | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-24T19:38:25Z | ---
license: creativeml-openrail-m
---
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juannat7/ChaosBench | juannat7 | 2023-12-13T17:44:23Z | 1 | 0 | [
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | null | 2023-11-05T12:55:05Z | ---
license: mit
pretty_name: ChaosBench
size_categories:
- 10K<n<100K
language:
- en
---
# ChaosBench - A benchmark for long-term forecasting of chaotic systems
ChaosBench is a benchmark project to improve long-term forecasting of chaotic systems, in particular subseasonal-to-seasonal (S2S) weather. Current features include:
## 1. Benchmark and Dataset
- __Input:__ ERA5 Reanalysis (1979-2022)
- __Target:__ The following table indicates the 48 variables (channels) that are available for Physics-based models. Note that the __Input__ ERA5 observations contains __ALL__ fields, including the unchecked boxes:
Parameters/Levels (hPa) | 1000 | 925 | 850 | 700 | 500 | 300 | 200 | 100 | 50 | 10
:---------------------- | :----| :---| :---| :---| :---| :---| :---| :---| :--| :-|
Geopotential height, z ($gpm$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Specific humidity, q ($kg kg^{-1}$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | | |
Temperature, t ($K$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
U component of wind, u ($ms^{-1}$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
V component of wind, v ($ms^{-1}$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Vertical velocity, w ($Pas^{-1}$) | | | | | ✓ | | | | | |
- __Baselines:__
- Physics-based models:
- [x] UKMO: UK Meteorological Office
- [x] NCEP: National Centers for Environmental Prediction
- [x] CMA: China Meteorological Administration
- [x] ECMWF: European Centre for Medium-Range Weather Forecasts
- Data-driven models:
- [x] Lagged-Autoencoder
- [x] Fourier Neural Operator (FNO)
- [x] ResNet
- [x] UNet
- [x] ViT-ClimaX
## 2. Metrics
We divide our metrics into 2 classes: (1) ML-based, which cover evaluation used in conventional computer vision and forecasting tasks, (2) Physics-based, which are aimed to construct a more physically-faithful and explainable data-driven forecast.
- __Vision-based:__
- [x] RMSE
- [x] Bias
- [x] Anomaly Correlation Coefficient (ACC)
- [x] Multiscale Structural Similarity Index (MS-SSIM)
- __Physics-based:__
- [x] Spectral Divergence (SpecDiv)
- [x] Spectral Residual (SpecRes)
## 3. Tasks
We presented two task, where the model still takes as inputs the __FULL__ 60 variables, but the benchmarking is done on either __ALL__ or a __SUBSET__ of target variable(s).
- __Task 1: Full Dynamics Prediction.__
It is aimed at __ALL__ target channels simultaneously. This task is generally harder to perform but is useful to build a model that emulates the entire weather conditions.
- __Task 2: Sparse Dynamics Prediction.__
It is aimed at a __SUBSET__ of target channel(s). This task is useful to build long-term forecasting model for specific variables, such as near-surface temperature (t-1000) or near-surface humidity (q-1000). | [
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iandennismiller/llama-cpp-scripts | iandennismiller | 2023-12-13T16:33:53Z | 1 | 0 | [
"language:code",
"license:mit",
"bash",
"llama.cpp",
"script",
"region:us"
] | null | 2023-11-06T14:18:35Z | ---
language:
- code
pretty_name: "These are scripts that have helped me to manage llama.cpp, llama models, etc."
tags:
- bash
- llama.cpp
- script
license: mit
---
# llama.cpp scripts
These are scripts that have helped me to manage llama.cpp, llama models, etc.
## Install
Scripts are installed to `~/.local/bin`.
```bash
bash install.sh
```
| [
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TinyPixel/oasst | TinyPixel | 2023-12-17T12:37:49Z | 1 | 0 | [
"region:us"
] | null | 2023-11-15T04:32:12Z | ---
dataset_info:
features:
- name: message_tree_id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 15836903
num_examples: 9823
download_size: 9334076
dataset_size: 15836903
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oasst"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tollefj/all-nli-NOB | tollefj | 2023-12-11T11:39:05Z | 1 | 0 | [
"task_categories:sentence-similarity",
"language:nb",
"language:no",
"region:us"
] | [
"sentence-similarity"
] | 2023-11-16T07:38:54Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 142671221
num_examples: 942854
download_size: 67856445
dataset_size: 142671221
task_categories:
- sentence-similarity
language:
- nb
- 'no'
---
# Dataset Card for "all-nli-NOB"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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TinyPixel/HF-no_robots | TinyPixel | 2023-12-14T08:54:26Z | 1 | 0 | [
"region:us"
] | null | 2023-11-16T13:41:41Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 28730735
num_examples: 9500
download_size: 17908483
dataset_size: 28730735
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Zhongyuan/xl-gen | Zhongyuan | 2023-12-11T09:05:49Z | 1 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-11-24T02:54:04Z | ---
license: openrail
---
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projecte-aina/CA-FR_Parallel_Corpus | projecte-aina | 2023-12-13T10:14:56Z | 1 | 0 | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:ca",
"language:fr",
"language:multilingual",
"region:us"
] | [
"translation"
] | 2023-11-27T10:07:55Z | ---
language:
- ca
- fr
- multilingual
multilinguality:
- translation
pretty_name: CA-FR Parallel Corpus
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
# Dataset Card for CA-FR Parallel Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Data preparation](#data-preparation)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [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)
- [Author](#author)
- [Contact Information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licenciung-informatrion)
- [Funding](#funding)
## Dataset Description
### Dataset Summary
The CA-FR Parallel Corpus is a Catalan-French dataset of **18.634.844** parallel sentences. The dataset was created to support Catalan NLP tasks, e.g.,
Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score.
### Languages
The texts in the dataset are in Catalan and French.
## Dataset Structure
Two separated txt files are provided with the sentences sorted in the same order:
- ca-fr_corpus.ca: contains 18.634.844 Catalan sentences.
- ca-fr_corpus.fr: contains 18.634.844 French sentences.
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Source Data
The dataset is a combination of the following authentic datasets:
| Dataset | Sentences |
|:----------------|----------:|
| CCMatrix | 16.305.758|
| Multi CCAligned | 1.442.584|
| WikiMatrix | 437.665|
| GNOME | 1.686|
| KDE 4 | 111.750|
| QED | 52.797|
| TED 2020 | 44.101|
| Open Subtitles | 225.786|
| **Total** | **18.634.844** |
All corpora were collected from [Opus](https://opus.nlpl.eu/).
### Data preparation
All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
The filtered datasets are then concatenated to form a final corpus of **18.634.844** parallel sentences.
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machine Translation tasks for mid-resource languages such as Catalan.
### Discussion of Biases
We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
### Contact information
For further information, please send an email to [email protected].
### Copyright
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023).
### Licensing information
This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). | [
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projecte-aina/CA-IT_Parallel_Corpus | projecte-aina | 2023-12-13T10:15:21Z | 1 | 0 | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"language:it",
"language:multilingual",
"region:us"
] | [
"translation"
] | 2023-11-27T10:45:27Z | ---
language:
- ca
- it
- multilingual
multilinguality:
- translation
pretty_name: CA-IT Parallel Corpus
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
# Dataset Card for CA-IT Parallel Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Data preparation](#data-preparation)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [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)
- [Author](#author)
- [Contact Information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licenciung-informatrion)
- [Funding](#funding)
## Dataset Description
### Dataset Summary
The CA-IT Parallel Corpus is a Catalan-Italian dataset of **9.482.927** parallel sentences. The dataset was created to support Catalan NLP tasks, e.g.,
Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score.
### Languages
The texts in the dataset are in Catalan and Italian.
## Dataset Structure
Two separated txt files are provided with the sentences sorted in the same order:
- ca-it_corpus.ca: contains 9.482.927 Catalan sentences.
- ca-it_corpus.it: contains 9.482.927 Italian sentences.
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Source Data
The dataset is a combination of the following authentic datasets:
| Dataset | Sentences |
|:--- | ---: |
| CCMatrix v1 | 7.757.357|
| MultiCCAligned v1 | 1.010.921|
| WikiMatrix | 271.587|
| GNOME | 1.198|
| KDE4 | 115.027 |
| QED | 52.616 |
| TED2020 v1 | 43.280 |
| OpenSubtitles | 225.732 |
| GlobalVoices| 5.209|
| **Total** | **9.482.927** |
All corpora were collected from [Opus](https://opus.nlpl.eu/).
### Data preparation
All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
The filtered datasets are then concatenated to form a final corpus of **9.482.927** parallel sentences.
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machine Translation tasks for mid-resource languages such as Catalan.
### Discussion of Biases
We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
### Contact information
For further information, please send an email to [email protected].
### Copyright
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023).
### Licensing information
This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
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cyanelis/15485 | cyanelis | 2023-12-17T23:07:14Z | 1 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-27T14:03:34Z | ---
license: cc-by-nc-4.0
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projecte-aina/CA-EU_Parallel_Corpus | projecte-aina | 2023-12-13T10:30:31Z | 1 | 0 | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"language:eu",
"language:multilingual",
"region:us"
] | [
"translation"
] | 2023-11-27T16:00:05Z | ---
language:
- ca
- eu
- multilingual
multilinguality:
- translation
pretty_name: CA-EU Parallel Corpus
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
# Dataset Card for CA-EU Parallel Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [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)
- [Author](#author)
- [Contact Information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licenciung-informatrion)
- [Funding](#funding)
## Dataset Description
### Dataset Summary
The CA-EU Parallel Corpus is a Catalan-Basque synthetic dataset of **9.692.996** parallel sentences. The dataset was created to support Catalan NLP tasks, e.g.,
Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score.
### Languages
The texts in the dataset are in Catalan and Basque.
## Dataset Structure
Two separated txt files are provided with the sentences sorted in the same order:
- train_clean.ca: contains 9.692.996 Catalan sentences (synthetic).
- train_clean.eu: contains 9.692.996 Basque sentences (authentic).
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Source Data
This synthetic dataset was created in the frame of Project Ilenia. An authentic parallel corpus ES-EU was delivered by Project GAITU
and the Spanish was translated to Catalan using the machine translation model [PlanTL-GOB-ES](https://huggingface.co/PlanTL-GOB-ES/mt-plantl-es-ca).
**Total: 9.692.996 parallel sentences** .
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machine Translation tasks between Catalan and Basque, two of the co-official languages of Spain.
### Discussion of Biases
We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
### Contact information
For further information, please send an email to [email protected].
### Copyright
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023).
### Licensing information
This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Funding
This work was funded by the SEDIA within the framework of ILENIA. | [
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cyanelis/69189881T | cyanelis | 2023-12-17T23:08:39Z | 1 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-30T12:02:06Z | ---
license: cc-by-nc-4.0
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FASOXO/1145141919180 | FASOXO | 2023-12-16T15:23:03Z | 1 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-02T04:44:35Z | ---
license: openrail
---
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jahb57/gpt2_embeddings_BATCH_15 | jahb57 | 2023-12-11T15:51:01Z | 1 | 0 | [
"region:us"
] | null | 2023-12-04T03:05:08Z | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: last_hidden_state
sequence:
sequence: float32
splits:
- name: train
num_bytes: 8167688597
num_examples: 44677
download_size: 8189631397
dataset_size: 8167688597
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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mesolitica/chatgpt-code-instruct-multiturn | mesolitica | 2023-12-13T06:49:29Z | 1 | 0 | [
"task_categories:conversational",
"language:ms",
"region:us"
] | [
"conversational"
] | 2023-12-04T04:01:33Z | ---
task_categories:
- conversational
language:
- ms
---
# Code Instruct Multiturn
Originally from https://huggingface.co/datasets/TokenBender/glaive_coder_raw_text, use Ultrachat template to generate multiturn using ChatGPT3.5
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/code-instruct-multiturn
There are bad pairs you need to reject, follow https://github.com/mesolitica/malaysian-dataset/blob/master/llm-instruction/prepare-malaysian-ultrachat.ipynb | [
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ntphu/shetssss | ntphu | 2023-12-11T08:15:38Z | 1 | 0 | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:ab",
"license:apache-2.0",
"biology",
"region:us"
] | [
"token-classification"
] | 2023-12-04T07:23:35Z | ---
license: apache-2.0
task_categories:
- token-classification
language:
- ab
tags:
- biology
size_categories:
- 1K<n<10K
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uitnlp/OpenViVQA-dataset | uitnlp | 2023-12-13T14:37:50Z | 1 | 0 | [
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:vi",
"license:mit",
"region:us"
] | [
"visual-question-answering"
] | 2023-12-05T10:52:34Z | ---
license: mit
task_categories:
- visual-question-answering
language:
- vi
size_categories:
- 10K<n<100K
---
OpenViVQA: Open-domain Vietnamese Visual Question Answering
=====
The OpenViVQA dataset contains <b>11,000+</b> images with <b>37,000+</b> question-answer pairs which introduces the Text-based Open-ended Visual Question Answering in Vietnamese. This dataset is publicly available to the research community in the VLSP 2023 - ViVRC shared task challenge. You can access the dataset as well as submit your results to evaluate on the private test set on the [Codalab](https://codalab.lisn.upsaclay.fr/competitions/15212#participate) evaluation system.
Link to the OpenViVQA dataset:
- [Train images](train-images.zip) + [train annotations](vlsp2023_train_data.json).
- [Dev images](dev-images.zip) + [dev annotations](vlsp2023_dev_data.json).
- [Test images](test-images.zip) + [test annotations (without answers)](vlsp2023_test_data.json).
If you mention or use any information from our dataset, please cite our paper:
```
@article{NGUYEN2023101868,
title = {OpenViVQA: Task, dataset, and multimodal fusion models for visual question answering in Vietnamese},
journal = {Information Fusion},
volume = {100},
pages = {101868},
year = {2023},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2023.101868},
url = {https://www.sciencedirect.com/science/article/pii/S1566253523001847},
author = {Nghia Hieu Nguyen and Duong T.D. Vo and Kiet {Van Nguyen} and Ngan Luu-Thuy Nguyen},
keywords = {Visual question answering, Vision-language understanding, Low-resource languages, Information fusion, Multimodal representation},
abstract = {In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge. The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers. Neural visual question answering models have achieved tremendous growth on large-scale datasets which are mostly for resource-rich languages such as English. However, available datasets narrow the VQA task as the answers selection task or answer classification task. We argue that this form of VQA is far from human ability and eliminates the challenge of the answering aspect in the VQA task by just selecting answers rather than generating them. In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question–answer pairs (QAs). Moreover, we proposed FST, QuMLAG, and MLPAG which fuse information from images and questions, then use these fused features to construct answers as humans iteratively. Our proposed methods achieve results that are competitive with SOTA models such as SAAA, MCAN, LORA, and M4C. The dataset11https://github.com/hieunghia-pat/OpenViVQA-dataset. is available to encourage the research community to develop more generalized algorithms including transformers for low-resource languages such as Vietnamese.}
}
```
### Contact
This repository was constructed under the instruction of the [NLP@UIT Research Group](https://nlp.uit.edu.vn/). For more information, contact the following author:
1. Nghia Hieu Nguyen. Email: [email protected] | [
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open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v3 | open-llm-leaderboard | 2023-12-11T21:57:57Z | 1 | 0 | [
"region:us"
] | null | 2023-12-10T17:24:53Z | ---
pretty_name: Evaluation run of AIDC-ai-business/Marcoroni-7B-v3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-11T21:54:18.910589](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v3/blob/main/results_2023-12-11T21-54-18.910589.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6546412729180768,\n\
\ \"acc_stderr\": 0.03206351393693139,\n \"acc_norm\": 0.6544628324282981,\n\
\ \"acc_norm_stderr\": 0.03272956247677216,\n \"mc1\": 0.4467564259485924,\n\
\ \"mc1_stderr\": 0.017403977522557144,\n \"mc2\": 0.6041920821344222,\n\
\ \"mc2_stderr\": 0.015321517108315672\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6612627986348123,\n \"acc_stderr\": 0.01383056892797433,\n\
\ \"acc_norm\": 0.6953924914675768,\n \"acc_norm_stderr\": 0.013449522109932489\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6811392152957578,\n\
\ \"acc_stderr\": 0.004650825168905205,\n \"acc_norm\": 0.8681537542322246,\n\
\ \"acc_norm_stderr\": 0.0033763209559167064\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\
\ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\
\ \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n\
\ \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \
\ \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7283018867924528,\n \"acc_stderr\": 0.027377706624670713,\n\
\ \"acc_norm\": 0.7283018867924528,\n \"acc_norm_stderr\": 0.027377706624670713\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\
: 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n\
\ \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.6820809248554913,\n\
\ \"acc_norm_stderr\": 0.0355068398916558\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\
\ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4365079365079365,\n \"acc_stderr\": 0.0255428468174005,\n \"acc_norm\"\
: 0.4365079365079365,\n \"acc_norm_stderr\": 0.0255428468174005\n },\n\
\ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\
\ \"acc_stderr\": 0.02390491431178265,\n \"acc_norm\": 0.7709677419354839,\n\
\ \"acc_norm_stderr\": 0.02390491431178265\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790482,\n \"\
acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790482\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \
\ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"\
acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\
acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944863,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944863\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n\
\ \"acc_stderr\": 0.013428186370608311,\n \"acc_norm\": 0.8301404853128991,\n\
\ \"acc_norm_stderr\": 0.013428186370608311\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044287,\n\
\ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044287\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4122905027932961,\n\
\ \"acc_stderr\": 0.016463200238114515,\n \"acc_norm\": 0.4122905027932961,\n\
\ \"acc_norm_stderr\": 0.016463200238114515\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826524,\n\
\ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826524\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.025670259242188936,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.025670259242188936\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7623456790123457,\n \"acc_stderr\": 0.023683591837008564,\n\
\ \"acc_norm\": 0.7623456790123457,\n \"acc_norm_stderr\": 0.023683591837008564\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46479791395045633,\n\
\ \"acc_stderr\": 0.012738547371303957,\n \"acc_norm\": 0.46479791395045633,\n\
\ \"acc_norm_stderr\": 0.012738547371303957\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.684640522875817,\n \"acc_stderr\": 0.01879808628488689,\n \
\ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.01879808628488689\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4467564259485924,\n\
\ \"mc1_stderr\": 0.017403977522557144,\n \"mc2\": 0.6041920821344222,\n\
\ \"mc2_stderr\": 0.015321517108315672\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8176795580110497,\n \"acc_stderr\": 0.010851565594267198\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7232752084912812,\n \
\ \"acc_stderr\": 0.012323047397959794\n }\n}\n```"
repo_url: https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|arc:challenge|25_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|arc:challenge|25_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|arc:challenge|25_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|arc:challenge|25_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|gsm8k|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|gsm8k|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|gsm8k|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|gsm8k|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hellaswag|10_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hellaswag|10_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hellaswag|10_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hellaswag|10_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T17-21-59.510264.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T17-21-59.510264.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T21-54-18.910589.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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data_files:
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path:
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data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T21-54-18.910589.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- '**/details_harness|winogrande|5_2023-12-10T17-21-59.510264.parquet'
- split: 2023_12_10T23_59_25.388550
path:
- '**/details_harness|winogrande|5_2023-12-10T23-59-25.388550.parquet'
- split: 2023_12_11T00_01_07.651038
path:
- '**/details_harness|winogrande|5_2023-12-11T00-01-07.651038.parquet'
- split: 2023_12_11T21_54_18.910589
path:
- '**/details_harness|winogrande|5_2023-12-11T21-54-18.910589.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-11T21-54-18.910589.parquet'
- config_name: results
data_files:
- split: 2023_12_10T17_21_59.510264
path:
- results_2023-12-10T17-21-59.510264.parquet
- split: 2023_12_10T23_59_25.388550
path:
- results_2023-12-10T23-59-25.388550.parquet
- split: 2023_12_11T00_01_07.651038
path:
- results_2023-12-11T00-01-07.651038.parquet
- split: 2023_12_11T21_54_18.910589
path:
- results_2023-12-11T21-54-18.910589.parquet
- split: latest
path:
- results_2023-12-11T21-54-18.910589.parquet
---
# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-7B-v3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-11T21:54:18.910589](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v3/blob/main/results_2023-12-11T21-54-18.910589.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6546412729180768,
"acc_stderr": 0.03206351393693139,
"acc_norm": 0.6544628324282981,
"acc_norm_stderr": 0.03272956247677216,
"mc1": 0.4467564259485924,
"mc1_stderr": 0.017403977522557144,
"mc2": 0.6041920821344222,
"mc2_stderr": 0.015321517108315672
},
"harness|arc:challenge|25": {
"acc": 0.6612627986348123,
"acc_stderr": 0.01383056892797433,
"acc_norm": 0.6953924914675768,
"acc_norm_stderr": 0.013449522109932489
},
"harness|hellaswag|10": {
"acc": 0.6811392152957578,
"acc_stderr": 0.004650825168905205,
"acc_norm": 0.8681537542322246,
"acc_norm_stderr": 0.0033763209559167064
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6518518518518519,
"acc_stderr": 0.041153246103369526,
"acc_norm": 0.6518518518518519,
"acc_norm_stderr": 0.041153246103369526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695238,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695238
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7283018867924528,
"acc_stderr": 0.027377706624670713,
"acc_norm": 0.7283018867924528,
"acc_norm_stderr": 0.027377706624670713
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
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```
## Dataset Details
### Dataset Description
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[More Information Needed] | [
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falcon002/guanaco-llama2-1k | falcon002 | 2023-12-14T12:38:34Z | 1 | 0 | [
"region:us"
] | null | 2023-12-11T05:34:38Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 15401731
num_examples: 9846
download_size: 9094513
dataset_size: 15401731
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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JohnYang88/lean-dojo-mathlib4 | JohnYang88 | 2023-12-11T06:54:44Z | 1 | 0 | [
"region:us"
] | null | 2023-12-11T06:17:54Z | ---
dataset_info:
features:
- name: url
dtype: string
- name: commit
dtype: string
- name: file_path
dtype: string
- name: full_name
dtype: string
- name: start
sequence: int64
- name: end
sequence: int64
- name: traced_tactics
dtype: string
splits:
- name: train
num_bytes: 320023872
num_examples: 98514
- name: test
num_bytes: 6116916
num_examples: 2000
- name: validation
num_bytes: 7228697
num_examples: 2000
download_size: 54194769
dataset_size: 333369485
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
# Dataset Card for "lean-dojo-mathlib4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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pankajemplay/intent_1816 | pankajemplay | 2023-12-11T07:58:19Z | 1 | 1 | [
"region:us"
] | null | 2023-12-11T07:58:17Z | ---
dataset_info:
features:
- name: User Query
dtype: string
- name: Intent
dtype: string
- name: id type
dtype: string
- name: id value
dtype: string
- name: id slot filled
dtype: bool
- name: Task
dtype: string
- name: task slot filled
dtype: bool
- name: Bot Response
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 792435
num_examples: 1816
download_size: 207517
dataset_size: 792435
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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reza-alipour/con-lnd-eng-0 | reza-alipour | 2023-12-11T10:40:33Z | 1 | 0 | [
"region:us"
] | null | 2023-12-11T10:40:16Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: mask
dtype: image
- name: caption
dtype: string
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 84212360.0
num_examples: 200
download_size: 83537296
dataset_size: 84212360.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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reza-alipour/con-lnd-eng-1 | reza-alipour | 2023-12-11T10:40:45Z | 1 | 0 | [
"region:us"
] | null | 2023-12-11T10:40:16Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: mask
dtype: image
- name: caption
dtype: string
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 83771387.0
num_examples: 200
download_size: 83096672
dataset_size: 83771387.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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lyon-nlp/summarization-summeval-fr-p2p | lyon-nlp | 2023-12-11T16:48:01Z | 1 | 1 | [
"task_categories:summarization",
"size_categories:n<1K",
"language:fr",
"license:apache-2.0",
"region:us"
] | [
"summarization"
] | 2023-12-11T11:17:49Z | ---
license: apache-2.0
task_categories:
- summarization
language:
- fr
size_categories:
- n<1K
---
## SummEval FR
This dataset is a french translation of the original work [SummEval](https://github.com/Yale-LILY/SummEval).
The translation was made using [DeepL](https://www.deepl.com) from English to French.
We use this dataset for the french version of [MTEB](https://github.com/embeddings-benchmark/mteb) :
The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total). Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total). Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance. Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries.
For this dataset, the 3 expert annotations were averaged to get the human scores.
source : https://huggingface.co/datasets/mteb/summeval
### Usage
To use this dataset, you can run the following code :
```py
from datasets import load_dataset
dataset = load_dataset("lyon-nlp/summarization-summeval-fr-p2p", "test")
```
> Fabbri, A.R., Kryscinski, W., McCann, B., Socher, R., & Radev, D.R. (2020). SummEval: Re-evaluating Summarization Evaluation. Transactions of the Association for Computational Linguistics, 9, 391-409. | [
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reza-alipour/con-lnd-eng-2 | reza-alipour | 2023-12-11T11:25:44Z | 1 | 0 | [
"region:us"
] | null | 2023-12-11T11:25:39Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: mask
dtype: image
- name: caption
dtype: string
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 125619806.0
num_examples: 300
download_size: 124608753
dataset_size: 125619806.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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reza-alipour/con-lnd-eng-3 | reza-alipour | 2023-12-11T11:26:51Z | 1 | 0 | [
"region:us"
] | null | 2023-12-11T11:26:44Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: mask
dtype: image
- name: caption
dtype: string
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 125699870.0
num_examples: 300
download_size: 124691299
dataset_size: 125699870.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ypilseong/deepnlp_autotrain_Emapthy_chat_data_2 | ypilseong | 2023-12-11T12:39:51Z | 1 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-11T12:38:31Z | ---
license: apache-2.0
---
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reza-alipour/con-ml-lnd | reza-alipour | 2023-12-11T12:47:47Z | 1 | 0 | [
"region:us"
] | null | 2023-12-11T12:47:13Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: mask
dtype: image
- name: caption
dtype: string
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 628749059.504
num_examples: 1498
download_size: 623698728
dataset_size: 628749059.504
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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