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louisbrulenaudet/code-procedures-civiles-execution | louisbrulenaudet | 2023-12-12T19:23:44Z | 0 | 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 procédures civiles d'exécution",
"doi:10.57967/hf/1459",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T19:23:42Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code des procédures civiles d'exécution
source_datasets:
- original
pretty_name: Code des procédures civiles d'exécution
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code des procédures civiles d'exécution, 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 procédures civiles d'exécution, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-procedures-civiles-execution}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-route | louisbrulenaudet | 2023-12-12T19:30:02Z | 0 | 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 de la route",
"doi:10.57967/hf/1461",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T19:30:00Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de la route
source_datasets:
- original
pretty_name: Code de la route
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de la route, 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 de la route, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-route}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-education | louisbrulenaudet | 2023-12-12T19:51:54Z | 0 | 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 de l'éducation",
"doi:10.57967/hf/1462",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T19:51:51Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de l'éducation
source_datasets:
- original
pretty_name: Code de l'éducation
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de l'éducation, 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 de l'éducation, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-education}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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malteos/images | malteos | 2023-12-12T20:01:31Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-12T19:58:30Z | ---
license: apache-2.0
---
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louisbrulenaudet/code-construction-habitation | louisbrulenaudet | 2023-12-12T20:11:09Z | 0 | 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 de la construction et de l'habitation",
"doi:10.57967/hf/1463",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T20:11:06Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de la construction et de l'habitation
source_datasets:
- original
pretty_name: Code de la construction et de l'habitation
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de la construction et de l'habitation, 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 de la construction et de l'habitation, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-construction-habitation}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-mutualite | louisbrulenaudet | 2023-12-12T20:14:27Z | 0 | 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 de la mutualité",
"doi:10.57967/hf/1464",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T20:14:25Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de la mutualité
source_datasets:
- original
pretty_name: Code de la mutualité
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de la mutualité, 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 de la mutualité, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-mutualite}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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vinicm/modelojuniu | vinicm | 2023-12-12T20:36:58Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-12T20:36:08Z | ---
license: openrail
---
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liaad/Bosque_PT-PT | liaad | 2023-12-12T21:45:31Z | 0 | 0 | [
"task_categories:token-classification",
"language:pt",
"license:mit",
"pos",
"pos-tagging",
"part-of-speech",
"region:us"
] | [
"token-classification"
] | 2023-12-12T20:38:00Z | ---
license: mit
dataset_info:
features:
- name: tokens
sequence: string
- name: lemmas
sequence: string
- name: pos_tags
sequence: string
splits:
- name: train
num_bytes: 5033815
num_examples: 9071
- name: test
num_bytes: 286364
num_examples: 576
download_size: 1758940
dataset_size: 5320179
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- token-classification
language:
- pt
tags:
- pos
- pos-tagging
- part-of-speech
pretty_name: Bosque Part of Speech PT-PT
--- | [
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robefernandez/sabatico | robefernandez | 2023-12-12T20:56:23Z | 0 | 0 | [
"license:cc-by-4.0",
"region:us"
] | null | 2023-12-12T20:38:28Z | ---
license: cc-by-4.0
---
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louisbrulenaudet/code-transports | louisbrulenaudet | 2023-12-12T20:51:53Z | 0 | 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 transports",
"doi:10.57967/hf/1465",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T20:51:49Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code des transports
source_datasets:
- original
pretty_name: Code des transports
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code des transports, 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 transports, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-transports}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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jameszhou-gl/gpt-4v-distribution-shift | jameszhou-gl | 2023-12-14T05:22:16Z | 0 | 0 | [
"license:mit",
"arxiv:2312.07424",
"region:us"
] | null | 2023-12-12T20:59:37Z | ---
license: mit
---
## License
This repository is licensed under the MIT License.
## Description
This Hugging Face repository hosts the random case dataset utilized in our research project, detailed in the GitHub repository [gpt-4v-distribution-shift](https://github.com/jameszhou-gl/gpt-4v-distribution-shift).
These datasets are crucial for evaluating the performance of multimodal foundation models under various distribution shift scenarios.
## Using the Dataset
For detailed instructions on how to use this dataset to reproduce the results presented in our research (specifically Tables 1 and 2), please refer to the section "Reproduce Table 1 and 2 in the paper" in our GitHub repository. The direct link to this section is [here](https://github.com/jameszhou-gl/gpt-4v-distribution-shift/tree/master?tab=readme-ov-file#reproduce-table-1-and-2-in-the-paper).
## Additional Resources
- Paper: Access our research paper on Arxiv at https://arxiv.org/pdf/2312.07424.pdf.
- GitHub Repository: For more details about our project and source code, visit https://github.com/jameszhou-gl/gpt-4v-distribution-shift. | [
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louisbrulenaudet/code-urbanisme | louisbrulenaudet | 2023-12-12T21:03:39Z | 0 | 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 de l'urbanisme",
"doi:10.57967/hf/1466",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T21:03:36Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de l'urbanisme
source_datasets:
- original
pretty_name: Code de l'urbanisme
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de l'urbanisme, 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 de l'urbanisme, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-urbanisme}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-general-fonction-publique | louisbrulenaudet | 2023-12-12T21:11:04Z | 0 | 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 général de la fonction publique",
"doi:10.57967/hf/1467",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T21:11:01Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code général de la fonction publique
source_datasets:
- original
pretty_name: Code général de la fonction publique
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code général de la fonction publique, 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 général de la fonction publique, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-general-fonction-publique}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-forestier | louisbrulenaudet | 2023-12-12T21:18:23Z | 0 | 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 forestier",
"doi:10.57967/hf/1468",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T21:18:21Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code forestier
source_datasets:
- original
pretty_name: Code forestier
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code forestier, 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 forestier, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-forestier}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-justice-administrative | louisbrulenaudet | 2023-12-12T21:26:03Z | 0 | 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 de justice administrative",
"doi:10.57967/hf/1469",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T21:26:00Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de justice administrative
source_datasets:
- original
pretty_name: Code de justice administrative
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de justice administrative, 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 de justice administrative, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-justice-administrative}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-postes-communications-electroniques | louisbrulenaudet | 2023-12-12T21:31:03Z | 0 | 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 postes et des communications électroniques",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T21:31:00Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code des postes et des communications électroniques
source_datasets:
- original
pretty_name: Code des postes et des communications électroniques
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code des postes et des communications électroniques, 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 postes et des communications électroniques, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-postes-communications-electroniques}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-relations-public-administration | louisbrulenaudet | 2023-12-12T21:35:24Z | 0 | 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 relations entre le public et l'administration",
"doi:10.57967/hf/1470",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T21:35:22Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code des relations entre le public et l'administration
source_datasets:
- original
pretty_name: Code des relations entre le public et l'administration
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code des relations entre le public et l'administration, 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 relations entre le public et l'administration, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-relations-public-administration}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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marcones/esportefla | marcones | 2023-12-12T21:37:46Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-12T21:37:32Z | ---
license: openrail
---
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liaad/Harem | liaad | 2023-12-12T22:06:13Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-12T21:46:21Z | ---
license: mit
---
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jxm/nfcorpus__gtr_base__dpr | jxm | 2023-12-12T23:14:36Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:16:43Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 27811436
num_examples: 3633
download_size: 30257196
dataset_size: 27811436
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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jxm/ag_news__gtr_base__dpr | jxm | 2023-12-12T22:20:12Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:20:10Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 48573874
num_examples: 7600
download_size: 57917827
dataset_size: 48573874
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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jxm/arxiv__gtr_base__dpr | jxm | 2023-12-12T22:21:21Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:21:18Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 45790865
num_examples: 6436
download_size: 51768923
dataset_size: 45790865
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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louisbrulenaudet/code-rural-peche-maritime | louisbrulenaudet | 2023-12-12T22:21:23Z | 0 | 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 rural et de la pêche maritime",
"doi:10.57967/hf/1471",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T22:21:20Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code rural et de la pêche maritime
source_datasets:
- original
pretty_name: Code rural et de la pêche maritime
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code rural et de la pêche maritime, 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 rural et de la pêche maritime, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-rural-peche-maritime}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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jxm/python_code_alpaca__gtr_base__dpr | jxm | 2023-12-12T22:22:48Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:22:42Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 116403502
num_examples: 18612
download_size: 139008748
dataset_size: 116403502
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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jxm/wikibio__gtr_base__dpr | jxm | 2023-12-12T22:28:15Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:27:55Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 486666297
num_examples: 72831
download_size: 562415111
dataset_size: 486666297
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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jxm/arguana__gtr_base__dpr | jxm | 2023-12-12T22:29:14Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:29:12Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 62186663
num_examples: 8674
download_size: 69549764
dataset_size: 62186663
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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jxm/climate-fever__gtr_base__dpr | jxm | 2023-12-12T22:36:15Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:35:50Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 653714421
num_examples: 100000
download_size: 758665016
dataset_size: 653714421
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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louisbrulenaudet/code-securite-interieure | louisbrulenaudet | 2023-12-12T22:37:00Z | 0 | 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 de la sécurité intérieure",
"doi:10.57967/hf/1472",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T22:36:58Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de la sécurité intérieure
source_datasets:
- original
pretty_name: Code de la sécurité intérieure
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de la sécurité intérieure, 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 de la sécurité intérieure, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-securite-interieure}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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hayden-donnelly/db-sfw-512-crop-dataset-v1 | hayden-donnelly | 2023-12-13T21:22:40Z | 0 | 3 | [
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"region:us"
] | [
"image-segmentation"
] | 2023-12-12T22:43:47Z | ---
task_categories:
- image-segmentation
size_categories:
- 1K<n<10K
---
# Danbooru SFW 512 Crop Dataset V1
This dataset includes image and bounding box pairs where the images are samples from
Danbooru 2021 SFW and the bounding boxes are crops of the central character(s). It can
be used to train a model that automatically crops the base dataset in order to create a cleaner
and more character-centric version. Importantly, the crops aim to remove artifacts such as
horizontal/vertical bars that could negatively impact performance on image generation tasks.
## Bounding Boxes
The base images are all 512x512, the bounding boxes represent crops with a maximium size of
512x512 and a minimum size of 128x128. Each bounding box is represented as a 3-dimensional
vector [x, y, s] where x is the left coordinate of the box, y is the top coordinate of the box,
and s is the side length of the box. These elements have been converted to floats and
normalized according to their maximum possible values (512). For example, [16, 28, 256] would
have been converted to [16/512, 28/512, 256/512], or [0.03125, 0.0546875, 0.5]. Using this
conversion, the minimum value of s is 128/512, or 0.25.
## Dataset Structure
db_sfw_512_crop_dataset_v1.zip contains a metadata.csv file and an images directory.
The metadata.csv file contains the bounding box labels in the "crop" column and paths
to the corresponding images in the "file_name" column:
```
file_name crop
0 images/3613000.jpg [0.03125, 0.0, 0.474609375]
1 images/2128000.jpg [0.314453125, 0.17578125, 0.548828125]
2 images/2200000.jpg [0.1796875, 0.041015625, 0.32421875]
3 images/1856000.jpg [0.109375, 0.115234375, 0.548828125]
4 images/4352000.jpg [0.224609375, 0.0078125, 0.548828125]
... ... ...
7052 images/2559002.jpg [0.314453125, 0.08203125, 0.474609375]
7053 images/2858002.jpg [0.203125, 0.08984375, 0.548828125]
7054 images/1775002.jpg [0.076171875, 0.087890625, 0.849609375]
7055 images/4116002.jpg [0.130859375, 0.267578125, 0.3984375]
7056 images/1313002.jpg [0.185546875, 0.037109375, 0.625]
```
## Original Dataset Citation
```bibtex
@misc{danbooru2021,
author={Anonymous and Danbooru community and Gwern Branwen},
title={Danbooru2021: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset},
howpublished={\url{https://gwern.net/danbooru2021}},
url={https://gwern.net/danbooru2021},
type={dataset},
year={2022},
month={January},
timestamp={2022-01-21},
note={Accessed: 2023-12-06}
}
``` | [
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jxm/cqadupstack__gtr_base__dpr | jxm | 2023-12-12T22:44:15Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:43:50Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 703439722
num_examples: 100000
download_size: 789472189
dataset_size: 703439722
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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IndicRAGware/mistral-rag-4bit-test | IndicRAGware | 2023-12-12T22:48:01Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-12T22:48:01Z | ---
license: apache-2.0
---
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jxm/fever__gtr_base__dpr | jxm | 2023-12-12T22:55:15Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:54:50Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 653714421
num_examples: 100000
download_size: 758665016
dataset_size: 653714421
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Simaosantos/cremosos | Simaosantos | 2023-12-12T22:58:43Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-12T22:58:02Z | ---
license: openrail
---
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jxm/fiqa__gtr_base__dpr | jxm | 2023-12-12T22:59:50Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T22:59:34Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 399096008
num_examples: 57638
download_size: 454567065
dataset_size: 399096008
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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lordvader31/movieclip | lordvader31 | 2023-12-13T00:02:08Z | 0 | 0 | [
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"contrastive",
"image_captioning",
"knowledge_graph",
"region:us"
] | null | 2023-12-12T22:59:53Z | ---
license: apache-2.0
language:
- en
tags:
- contrastive
- image_captioning
- knowledge_graph
size_categories:
- 100K<n<1M
---
# Movieclip Dataset
A dataset of 180,000 movie scene images from over 3000 movies spanning 80 years of cinema. The current dataset is a demo dataset with only 180 images. I will add the rest soon.
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jxm/hotpotqa__gtr_base__dpr | jxm | 2023-12-12T23:06:57Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T23:06:31Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 648412365
num_examples: 100000
download_size: 757993666
dataset_size: 648412365
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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jxm/nq__gtr_base__dpr | jxm | 2023-12-12T23:21:55Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T23:21:26Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 667377118
num_examples: 100000
download_size: 770237710
dataset_size: 667377118
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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vinicm/modelomichelle | vinicm | 2023-12-12T23:23:21Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-12T23:22:41Z | ---
license: openrail
---
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jxm/scidocs__gtr_base__dpr | jxm | 2023-12-12T23:25:00Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T23:24:51Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 187036558
num_examples: 25657
download_size: 206524641
dataset_size: 187036558
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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jxm/scifact__gtr_base__dpr | jxm | 2023-12-12T23:25:47Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T23:25:45Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 39174749
num_examples: 5183
download_size: 42913676
dataset_size: 39174749
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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cheeusus94/Pronunciament | cheeusus94 | 2023-12-12T23:32:38Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-12T23:31:54Z | ---
license: openrail
---
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jxm/trec-covid__gtr_base__dpr | jxm | 2023-12-12T23:33:59Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T23:33:34Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 719847373
num_examples: 100000
download_size: 732191015
dataset_size: 719847373
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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eitanturok/toxic-pile-small | eitanturok | 2023-12-12T23:36:01Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T23:35:58Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: texts
sequence: string
- name: meta
struct:
- name: pile_set_name
dtype: string
- name: scores
sequence: float64
- name: avg_score
dtype: float64
- name: num_sents
dtype: int64
splits:
- name: train
num_bytes: 5791634
num_examples: 953
download_size: 3316229
dataset_size: 5791634
---
# Dataset Card for "toxic-pile-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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jxm/webis-touche2020__gtr_base__dpr | jxm | 2023-12-12T23:43:29Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T23:42:55Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 801147912
num_examples: 100000
download_size: 841200496
dataset_size: 801147912
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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n3gflash/duda | n3gflash | 2023-12-12T23:49:37Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-12T23:48:44Z | ---
license: apache-2.0
---
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jxm/trec-news__gtr_base__dpr | jxm | 2023-12-12T23:57:57Z | 0 | 0 | [
"region:us"
] | null | 2023-12-12T23:57:18Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 1054505434
num_examples: 100000
download_size: 1013356324
dataset_size: 1054505434
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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marcones/wendelsoares | marcones | 2023-12-13T00:02:23Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-13T00:02:03Z | ---
license: openrail
---
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jxm/robust04__gtr_base__dpr | jxm | 2023-12-13T00:09:19Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T00:08:45Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 922326096
num_examples: 100000
download_size: 906189329
dataset_size: 922326096
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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jxm/bioasq__gtr_base__dpr | jxm | 2023-12-13T00:18:13Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T00:17:44Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 766198450
num_examples: 100000
download_size: 823733480
dataset_size: 766198450
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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BarkDross/AITube_Commercials | BarkDross | 2023-12-13T00:39:47Z | 0 | 0 | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-12-13T00:28:31Z | ---
license: cc-by-nc-sa-4.0
---
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TCurran4589/LessonPlans | TCurran4589 | 2023-12-13T00:49:07Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-13T00:49:07Z | ---
license: mit
---
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mnoukhov/compare_test | mnoukhov | 2023-12-13T20:40:01Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T01:40:06Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 28710
num_examples: 16
download_size: 30368
dataset_size: 28710
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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FancyJay/test_12_13 | FancyJay | 2023-12-13T02:22:48Z | 0 | 0 | [
"task_categories:text-generation",
"language:en",
"region:us"
] | [
"text-generation"
] | 2023-12-13T01:46:05Z | ---
task_categories:
- text-generation
language:
- en
pretty_name: test 12 13
---
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worldboss/ghana-national-identification-auth-faq | worldboss | 2023-12-13T02:15:03Z | 0 | 0 | [
"license:afl-3.0",
"region:us"
] | null | 2023-12-13T02:05:30Z | ---
license: afl-3.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 30312
num_examples: 66
download_size: 21362
dataset_size: 30312
---
### Description
- This is a dataset collected from Ghana National Identification Authority website. Can be used for Question-Answering Bot
### Install
```shell
pip install datasets
```
### Usage
```python
from datasets import load_dataset
faq = load_dataset("worldboss/ghana-national-identification-auth-faq", split="train")
pd.DataFrame(faq).head()
```
source: NIA, collected by Theophilus Siameh
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Vinnyyw/Stilesvoices | Vinnyyw | 2023-12-13T02:06:16Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-13T02:05:30Z | ---
license: openrail
---
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] |
IBM-DTT/starcoder_telefonica_blueprism_finetuned_v3 | IBM-DTT | 2023-12-13T02:06:50Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T02:06:49Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: messages
dtype: string
splits:
- name: train
num_bytes: 501909
num_examples: 372
download_size: 73399
dataset_size: 501909
---
# Dataset Card for "starcoder_telefonica_blueprism_finetuned_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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seablue/DiDi_GAIA_dataset_jsonl | seablue | 2023-12-13T03:13:42Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T03:11:39Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: most_similar_instructions
struct:
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- name: 下面是一段可以自动化为你的代码添加注释的工具，请你根据这个工具的描述，使用它为你的代码添加注释。
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- name: 下面的代码可能会让其他人感到困惑，请为每一行添加注释以便于其他人理解。
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- name: 下面的代码需要添加注释以解释代码实现的逻辑，请您为其添加注释。
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- name: 下面的代码需要添加注释来解释代码的目的，请你给出相应的注释。
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- name: 下面这段代码需要加入一些注释以便后续使用，请你帮忙补充一下。
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- name: 下面这段代码需要添加注释以解释其中的细节和处理过程，请帮我添加注释。
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- name: 下面这段代码需要添加注释以解释其在整个项目中的作用，请您为其添加注释。
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- name: 下面这段代码需要补充注释来解释变量和函数的用途和功能，请你为其添加注释。
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- name: 你能帮我解释一下以下代码的作用吗？
dtype: float64
- name: 可以为下面的代码添加注释，以便于其他人更好地理解吗？
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- name: 在下面的代码中添加注释，以便阅读代码时更加容易理解和使用。
dtype: float64
- name: 在下面这段代码中添加注释，使得代码更加易读、易用。
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- name: 帮我增加一些注释，让下面这个代码片段更好理解。
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- name: 根据下面的代码实现，请为其添加注释以便更好地了解其实现思路。
dtype: float64
- name: 根据下面的代码逻辑，请为其添加注释，以方便更好地理解代码。
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- name: 根据下面的代码，为每一行添加注释以解释其含义。
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- name: 根据下面的代码，请为每行添加注释来描述其作用。
dtype: float64
- name: 给下面这段代码添加注释，让其他人了解代码的实现细节和使用方法。
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- name: 能不能编写一段代码来自动生成注释呢？
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- name: 能否为下面的代码添加注释，以便于我和其他人更好地理解和使用？
dtype: float64
- name: 能否为下面的代码添加注释，使得其他人更容易了解代码和使用方法？
dtype: float64
- name: 能否为下面的代码添加注释，使得我们更容易理解代码的逻辑和实现方法？
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- name: 能否为下面的代码添加注释，描述代码的主要作用和输入输出。
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- name: 能否为下面的函数添加注释，以方便阅读和理解代码？
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- name: 能否为下面的类添加注释，以便更好地理解其属性和方法？
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- name: 能否为下面这段代码添加注释，让其他人更好地了解代码的功能和使用方法？
dtype: float64
- name: 能否为这个开源项目中的代码添加注释，以便新的开发者更快地了解其功能？
dtype: float64
- name: 能否为这段代码添加注释，解释各个变量和函数的作用？
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- name: 能否为这段代码编写注释，以便于初学者理解代码逻辑和实现方式？
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- name: 能否为这段代码自动生成注释，描述代码的主要功能和用途？
dtype: float64
- name: 能否帮我添加下面这段代码的注释，使得代码更加容易阅读和理解。
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- name: 能否编写代码，自动识别出变量和函数的作用，然后为它们添加注释？
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- name: 能否请你为下面这段代码增加一些注释，使得别人也能够看懂？
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- name: 能否请你在下面的代码中为每一行添加一些注释？
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- name: 能帮我给下面代码加上注释吗？
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- name: 请为下面的代码增加注释，以便在以后代码需要维护时更好地理解和更改代码。
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- name: 请为下面的代码添加注释，以便于后续的调试和维护。
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- name: 请为下面的代码添加注释，以便于我们更好地理解代码的实现和功能。
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- name: 请为下面的代码添加注释，以解释每一行代码的用途。
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- name: 请为下面的代码编写简要的注释，方便阅读和理解。
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- name: 请为下面这段代码添加注释来解释各个变量和函数的作用。
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- name: 请为下面这段代码添加注释，注释中需要说明代码的执行步骤以及相关函数的作用。
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- name: 请为下面这段代码添加注释，注释中需要说明每个函数的功能和用途。
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- name: 请为下面这段代码添加注释，注释中需要说明该代码的入参和出参以及相关算法的实现。
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- name: 请为以下代码添加注释，描述代码的主要实现思路。
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- name: 请你为下面的代码添加注释，解释代码的主要思路和逻辑。
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- name: 请你写一段代码，并为你所写的代码加上适当的注释。
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- name: 请你在下面的代码中找出难懂的地方并加上相应的注释。
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- name: 请你帮忙把下面的代码加上适当的注释，以便于其他人更好地了解代码。
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- name: 请你根据下面的代码，给出一份详细的代码注释，让其他人更好地理解代码。
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- name: 请写一个生成类方法注释的函数。
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- name: 请写一个生成自然语言描述的代码注释的代码。
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- name: 请写一段代码，使其能够为一个包含多个函数的脚本文件自动添加注释。
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- name: 请写一段代码，使其能够根据代码段的逻辑结构生成注释。
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- name: 请写一段代码，使其能够根据变量和函数的命名规则来自动生成注释。
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- name: 请写一段代码，使其能够自动为一个特定函数生成相应的注释。
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- name: 请写一段代码，根据函数的输入和输出来自动生成注释。
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- name: 请写一段代码，生成注释，描述一个函数的作用和输入输出格式。
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- name: 请写一段代码，生成注释，描述一个函数的输入输出和用法实例。
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- name: 请写一段代码，生成注释，描述一个程序中的类的功能和属性。
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- name: 请写一段代码，自动为一个Python模块里的所有函数和类生成注释。
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- name: 请对下面的代码添加适当的注释，以便其他人更好地理解。
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- name: 请帮我为下面的代码片段添加注释，方便其他人更好地理解代码。
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- name: 请帮我为这个循环添加注释，以便更好地理解它。
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- name: 请帮我给下面这段代码添加注释，以便于其他人了解代码的使用方法和功能。
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- name: 请您给下面的代码加上注释，以便于后续代码维护和开发。
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- name: 请根据下面的代码描述每一行的功能，然后加上注释。
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- name: 请根据下面的代码，为代码添加注释以便于其他人理解。
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- name: 请根据以下代码的功能，为其加上注释。
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- name: 请根据你的理解为下面的代码片段增加注释。
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- name: 请生成一组注释，描述下面这段代码的主要功能。
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- name: 请给下面的代码加上注释。
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- name: 请给下面的代码添加注释来解释代码的执行顺序。
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- name: 请给下面的代码添加注释，以便于其他人更好地了解代码的使用方法和注意事项。
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- name: 请给下面这段代码添加注释，以便于我们更好地了解代码的功能和用法。
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- name: 请问你能为下面的代码添加注释吗？
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- name: 请阅读下面的代码并为其添加注释以指明该段代码的功能。
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- name: 请阅读下面这段代码，并为它添加必要的注释，以便理解。
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- name: 请阅读以下代码，给代码加上相应的注释。
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- name: 这是一段优化后的代码，请你为每一行代码添加注释。
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- name: 这段代码可能有一些复杂，你能不能帮我添加一些注释来让它更加易读？
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- name: 这段代码有些复杂，请你给每个函数和循环写上注释，以便于读者理解代码逻辑。
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- name: 这里是一段写好的代码，你能帮我为它添加注释吗？
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- name: 这里是一段没有注释的代码，你能帮我添加一些注释，使其更易读吗？
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- name: 麻烦你为这些变量和函数写一些注释，以便于别人理解代码。
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- name: avg_similarity_score
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splits:
- name: train
num_bytes: 62883
num_examples: 73
download_size: 128943
dataset_size: 62883
---
# Dataset Card for "DiDi_GAIA_dataset_jsonl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mriosqu/landing_pages_02_dataset | mriosqu | 2023-12-13T03:31:42Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T03:17:45Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 64770191.0
num_examples: 89
download_size: 63485786
dataset_size: 64770191.0
---
# Dataset Card for "landing_pages_02_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Tippawan/semi-pre-psd | Tippawan | 2023-12-13T03:30:59Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T03:30:57Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
- name: prob
sequence: float64
- name: comb
sequence:
sequence: float64
- name: ifpass
sequence: int64
- name: pred
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 17452187
num_examples: 7083
download_size: 2901416
dataset_size: 17452187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Margaret-mmh/mini-platypus | Margaret-mmh | 2023-12-13T03:35:02Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T03:35:00Z | ---
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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hayden-donnelly/db-sfw-512-character-filter-dataset-v1 | hayden-donnelly | 2023-12-13T20:30:33Z | 0 | 1 | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"region:us"
] | [
"image-classification"
] | 2023-12-13T04:42:02Z | ---
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---
# Danbooru SFW 512 Character Filter Dataset V1
This dataset is meant to be used for training a simple binary classifier that can filter the
Danbooru SFW 2021 dataset. It is similar to [db-sfw-512-general-filter-dataset-v1](https://huggingface.co/datasets/hayden-donnelly/db-sfw-512-general-filter-dataset-v1)
but it has different class criteria. Just like the general dataset there are two
classes: "accepted" and "rejected", with "accepted" representing samples that should pass
through the filter and "rejected" representing samples that should not.
To be accepted, a sample should meet the following criteria:
1. Focused on a character or set of characters. This includes non-humans like animals, humanoid
mechs, and anthropomorphized robots, but not vehicles and other non-anthropomorphized machines.
For example, pokemon, gundams, and r2-d2 all meet this criterion, but not tanks, robotic arms,
and helicopter drones.
3. Character(s) are clear and not overwhelmed by the background. This means the character(s)
should not be tiny compared to a large background. They should also have enough contrast with
the background that they don't blend into it.
4. Character(s) are not presented in a noisy comic strip. Some comic strips are allowed if they
are simple and the central character(s) are easy to extract via cropping. Note that the second part of
this criterion is a bit inconsistent and generally errs on the side of rejecting all comic
strips.
## Original Dataset Citation
```bibtex
@misc{danbooru2021,
author={Anonymous and Danbooru community and Gwern Branwen},
title={Danbooru2021: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset},
howpublished={\url{https://gwern.net/danbooru2021}},
url={https://gwern.net/danbooru2021},
type={dataset},
year={2022},
month={January},
timestamp={2022-01-21},
note={Accessed: 2023-12-06}
}
``` | [
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yfyeung/libritts_dcp | yfyeung | 2023-12-13T04:58:41Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T04:57:05Z | ---
license: apache-2.0
---
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contentlingo/assets_post | contentlingo | 2023-12-13T05:37:40Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T05:02:44Z | ---
license: apache-2.0
---
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lorinma/Moss003-zh-sharegpt-10k | lorinma | 2023-12-13T05:47:22Z | 0 | 0 | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:zh",
"region:us"
] | [
"text-generation",
"conversational"
] | 2023-12-13T05:18:07Z | ---
task_categories:
- text-generation
- conversational
language:
- zh
size_categories:
- 10K<n<100K
---
因为原生的Moss003数量太大，所以进行了简单的去重。
去重方法大致为，只选择中文的对话，使用bert-base-chinese将第一个问题转换为embedding，使用类knn的方法抽取了1万条。并转换成了sharegpt格式。 | [
-0.2396407425403595,
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JohnMelwin18/Traceability_Instruction_Tuning_Data_v1 | JohnMelwin18 | 2023-12-13T06:49:27Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-13T05:20:33Z | ---
license: mit
---
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Vinnyyw/Moranguinhovoice | Vinnyyw | 2023-12-13T05:23:56Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-13T05:23:20Z | ---
license: openrail
---
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Ethan0927/Clone-voiceone | Ethan0927 | 2023-12-13T05:26:29Z | 0 | 0 | [
"license:agpl-3.0",
"region:us"
] | null | 2023-12-13T05:25:47Z | ---
license: agpl-3.0
---
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acmalpha/earth-stones | acmalpha | 2023-12-13T06:00:49Z | 0 | 0 | [
"license:cc",
"region:us"
] | null | 2023-12-13T05:59:16Z | ---
license: cc
---
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Kazabian/voxxuxa | Kazabian | 2023-12-13T06:30:53Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-13T06:28:13Z | ---
license: openrail
---
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zjwfufu/gsplat_view | zjwfufu | 2023-12-13T09:25:00Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-13T06:34:29Z | ---
license: mit
---
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JohnMelwin18/dataset_2_more | JohnMelwin18 | 2023-12-13T06:35:06Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-13T06:34:43Z | ---
license: mit
---
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sungmoon22/asr1 | sungmoon22 | 2023-12-13T06:42:24Z | 0 | 0 | [
"license:other",
"region:us"
] | null | 2023-12-13T06:42:24Z | ---
license: other
license_name: asr
license_link: LICENSE
---
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JohnMelwin18/new_dataset_3 | JohnMelwin18 | 2023-12-13T06:47:36Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-13T06:45:11Z | ---
license: mit
---
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debajyotidasgupta/MathOlympiadTextbooks | debajyotidasgupta | 2023-12-13T09:53:27Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T06:56:08Z | ---
license: apache-2.0
---
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VIRBHADRa/Ywhwh | VIRBHADRa | 2023-12-13T07:17:04Z | 0 | 0 | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-12-13T07:17:04Z | ---
license: bigscience-openrail-m
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useSword/runpod_dataset | useSword | 2023-12-13T07:18:05Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T07:18:05Z | ---
license: apache-2.0
---
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useSword/runpod_Lora_Style | useSword | 2023-12-13T07:35:37Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T07:27:01Z | ---
license: apache-2.0
---
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JLGY/regreal | JLGY | 2023-12-13T07:55:51Z | 0 | 1 | [
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-12-13T07:53:49Z | ---
license: cc-by-sa-4.0
---
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BangumiBase/crossangetenshitoryuunorondo | BangumiBase | 2023-12-13T13:21:55Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-12-13T08:05:26Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Cross Ange - Tenshi To Ryuu No Rondo
This is the image base of bangumi Cross Ange - Tenshi to Ryuu no Rondo, we detected 67 characters, 4478 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 40 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 111 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 30 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 22 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 229 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 64 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 117 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 67 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 179 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 40 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 38 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 40 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 24 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 32 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 79 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 69 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 21 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 28 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 201 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 90 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 601 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 121 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 95 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 56 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 96 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 25 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 32 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 18 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 13 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 37 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 13 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 12 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 24 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 156 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 16 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 74 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 19 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 14 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 80 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 33 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 23 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 8 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 24 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 21 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 23 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 195 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 9 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 23 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 15 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 42 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 62 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 24 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 8 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 16 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 279 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 42 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 7 | [Download](56/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 57 | 115 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 19 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 20 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 8 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 18 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 6 | [Download](62/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 63 | 9 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 5 | [Download](64/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 65 | 6 | [Download](65/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 395 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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wenge-research/yayi_uie_sft_data | wenge-research | 2023-12-14T13:52:33Z | 0 | 0 | [
"size_categories:1M<n<10M",
"language:zh",
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T08:27:48Z | ---
license: apache-2.0
language:
- zh
- en
size_categories:
- 1M<n<10M
---
## 训练数据/Training Data
百万级语料中文54%，英文46%；其中其中数据集包括**12**个领域包括金融，社会，生物，商业，工业制造，化学，车辆，科学，疾病医疗，个人生活，安全和通用。覆盖数百个使用场景
- NER：中文覆盖**28**个实体类型包括人物，地缘政治，组织，身体部位，药物等，英文覆盖**130**个实体类型包括Animal, Weapon, Conference, Book等。
- RE：中文覆盖**232**种关系包括买资，增持，重组，国籍，别名，亲属，入股，转让，导致，发生地点，制造商等，英文覆盖**236**种关系包括founded by，state or province of headquarters，employee of，occupation，creator等。
- EE：中文覆盖**84**种事件类型,包括中标，高管变动，产品行为-发布，公司上市等，和**203**种论元，英文覆盖**45**种事件类型，包括Born, Demonstrate, Meet, End Organization, Divorce等，和**62**种论元。
In the corpus of over a million entries, 54% are in Chinese and 46% in English. The dataset encompasses 12 fields including finance, society, biology, business, industrial manufacturing, chemistry, vehicles, science, disease and medicine, personal life, security, and general topics, covering hundreds of scenarios:
- NER: In Chinese, it covers **28** types of entities including individuals, geopolitics, organizations, body parts, drugs, etc., while in English, it covers 130 types of entities such as Animals, Weapons, Conferences, Books, etc.
- RE: In Chinese, it includes **232** types of relations like acquisitions, stake increases, restructurings, nationality, aliases, relatives, buying shares, transfers, causes, locations of occurrence, manufacturers, etc., and in English, 236 types of relations such as founded by, state or province of headquarters, employee of, occupation, creator, etc.
- EE: Chinese covers **84** types of events including winning a bid, executive changes, product actions - launches, company listings, etc., and **203** types of arguments, whereas English covers **45** types of events such as Birth, Demonstration, Meeting, End of Organization, Divorce, etc., and **62** types of arguments.
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useSword/runpod_Negative_Embeds | useSword | 2023-12-13T09:02:32Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T09:02:01Z | ---
license: apache-2.0
---
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Pipper/SolFuncs | Pipper | 2023-12-14T06:30:10Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T09:09:43Z | ---
dataset_info:
features:
- name: file_name
dtype: string
- name: comments
dtype: string
- name: code_string
dtype: string
- name: __index_level_0__
dtype: int64
splits:
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num_bytes: 15153841436.646095
num_examples: 591941
- name: test
num_bytes: 1894239779.676952
num_examples: 73993
- name: valid
num_bytes: 1894239779.676952
num_examples: 73993
download_size: 5432099769
dataset_size: 18942320996.0
---
# Dataset Card for "SolFuncs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Hunzla/xls-r-crema-d-300-each-val | Hunzla | 2023-12-13T09:48:20Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T09:48:18Z | ---
dataset_info:
features:
- name: emotionG_label
dtype: string
- name: gender
dtype: string
- name: emotion
dtype: string
- name: path
dtype: string
- name: label
sequence: int64
- name: input_values
sequence:
sequence: float32
- name: input_length
dtype: int64
splits:
- name: train
num_bytes: 46119146
num_examples: 144
download_size: 40688936
dataset_size: 46119146
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Venki-ds/test-my-guanaco-llama2-1k | Venki-ds | 2023-12-13T09:50:01Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T09:50:00Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966694
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Narmadat21/tes1-my-guanaco-llama2-1k | Narmadat21 | 2023-12-13T09:59:32Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T09:59:31Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966694
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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chaoscodes/xp3 | chaoscodes | 2023-12-13T10:12:46Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T10:10:47Z | ---
license: apache-2.0
---
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Narmadat21/tes1-my-Alpaca-llama2-1k | Narmadat21 | 2023-12-13T10:18:53Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T10:18:51Z | ---
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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Narmadat21/tes2-my-Alpaca-llama2-1k | Narmadat21 | 2023-12-13T10:19:55Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T10:19:54Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 672749
num_examples: 1000
download_size: 413015
dataset_size: 672749
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] |
open-llm-leaderboard/details_rishiraj__smol-3b | open-llm-leaderboard | 2023-12-13T10:30:17Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T10:29:34Z | ---
pretty_name: Evaluation run of rishiraj/smol-3b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rishiraj/smol-3b](https://huggingface.co/rishiraj/smol-3b) 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 1 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_rishiraj__smol-3b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-13T10:26:39.414520](https://huggingface.co/datasets/open-llm-leaderboard/details_rishiraj__smol-3b/blob/main/results_2023-12-13T10-26-39.414520.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\": 0.4630403817848583,\n\
\ \"acc_stderr\": 0.03471311735087895,\n \"acc_norm\": 0.467024043140488,\n\
\ \"acc_norm_stderr\": 0.035458188509216476,\n \"mc1\": 0.32313341493268055,\n\
\ \"mc1_stderr\": 0.0163718362864546,\n \"mc2\": 0.5073211090135596,\n\
\ \"mc2_stderr\": 0.015470937650792245\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.42662116040955633,\n \"acc_stderr\": 0.014453185592920293,\n\
\ \"acc_norm\": 0.46331058020477817,\n \"acc_norm_stderr\": 0.014572000527756998\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.510157339175463,\n\
\ \"acc_stderr\": 0.004988751698341138,\n \"acc_norm\": 0.6823341963752241,\n\
\ \"acc_norm_stderr\": 0.004646172373100999\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\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04063302731486671,\n\
\ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04063302731486671\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.43,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5245283018867924,\n \"acc_stderr\": 0.030735822206205608,\n\
\ \"acc_norm\": 0.5245283018867924,\n \"acc_norm_stderr\": 0.030735822206205608\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5347222222222222,\n\
\ \"acc_stderr\": 0.04171115858181618,\n \"acc_norm\": 0.5347222222222222,\n\
\ \"acc_norm_stderr\": 0.04171115858181618\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4161849710982659,\n\
\ \"acc_stderr\": 0.03758517775404948,\n \"acc_norm\": 0.4161849710982659,\n\
\ \"acc_norm_stderr\": 0.03758517775404948\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\
\ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n\
\ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3446808510638298,\n \"acc_stderr\": 0.031068985963122145,\n\
\ \"acc_norm\": 0.3446808510638298,\n \"acc_norm_stderr\": 0.031068985963122145\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\
\ \"acc_stderr\": 0.04339138322579861,\n \"acc_norm\": 0.30701754385964913,\n\
\ \"acc_norm_stderr\": 0.04339138322579861\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\
\ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2804232804232804,\n \"acc_stderr\": 0.023135287974325625,\n \"\
acc_norm\": 0.2804232804232804,\n \"acc_norm_stderr\": 0.023135287974325625\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3253968253968254,\n\
\ \"acc_stderr\": 0.04190596438871136,\n \"acc_norm\": 0.3253968253968254,\n\
\ \"acc_norm_stderr\": 0.04190596438871136\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.532258064516129,\n\
\ \"acc_stderr\": 0.02838474778881333,\n \"acc_norm\": 0.532258064516129,\n\
\ \"acc_norm_stderr\": 0.02838474778881333\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.37438423645320196,\n \"acc_stderr\": 0.03405155380561952,\n\
\ \"acc_norm\": 0.37438423645320196,\n \"acc_norm_stderr\": 0.03405155380561952\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\
: 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6060606060606061,\n \"acc_stderr\": 0.03815494308688931,\n\
\ \"acc_norm\": 0.6060606060606061,\n \"acc_norm_stderr\": 0.03815494308688931\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5303030303030303,\n \"acc_stderr\": 0.03555804051763929,\n \"\
acc_norm\": 0.5303030303030303,\n \"acc_norm_stderr\": 0.03555804051763929\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.6373056994818653,\n \"acc_stderr\": 0.03469713791704371,\n\
\ \"acc_norm\": 0.6373056994818653,\n \"acc_norm_stderr\": 0.03469713791704371\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4282051282051282,\n \"acc_stderr\": 0.025088301454694827,\n\
\ \"acc_norm\": 0.4282051282051282,\n \"acc_norm_stderr\": 0.025088301454694827\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2222222222222222,\n \"acc_stderr\": 0.025348097468097845,\n \
\ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.025348097468097845\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.4327731092436975,\n \"acc_stderr\": 0.03218358107742613,\n \
\ \"acc_norm\": 0.4327731092436975,\n \"acc_norm_stderr\": 0.03218358107742613\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.618348623853211,\n \"acc_stderr\": 0.020828148517022596,\n \"\
acc_norm\": 0.618348623853211,\n \"acc_norm_stderr\": 0.020828148517022596\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.3287037037037037,\n \"acc_stderr\": 0.03203614084670058,\n \"\
acc_norm\": 0.3287037037037037,\n \"acc_norm_stderr\": 0.03203614084670058\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5931372549019608,\n \"acc_stderr\": 0.03447891136353382,\n \"\
acc_norm\": 0.5931372549019608,\n \"acc_norm_stderr\": 0.03447891136353382\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6244725738396625,\n \"acc_stderr\": 0.03152256243091156,\n \
\ \"acc_norm\": 0.6244725738396625,\n \"acc_norm_stderr\": 0.03152256243091156\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.4663677130044843,\n\
\ \"acc_stderr\": 0.033481800170603065,\n \"acc_norm\": 0.4663677130044843,\n\
\ \"acc_norm_stderr\": 0.033481800170603065\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5343511450381679,\n \"acc_stderr\": 0.043749285605997376,\n\
\ \"acc_norm\": 0.5343511450381679,\n \"acc_norm_stderr\": 0.043749285605997376\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6033057851239669,\n \"acc_stderr\": 0.04465869780531009,\n \"\
acc_norm\": 0.6033057851239669,\n \"acc_norm_stderr\": 0.04465869780531009\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5185185185185185,\n\
\ \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.5185185185185185,\n\
\ \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.5705521472392638,\n \"acc_stderr\": 0.03889066619112723,\n\
\ \"acc_norm\": 0.5705521472392638,\n \"acc_norm_stderr\": 0.03889066619112723\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\
\ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \
\ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6310679611650486,\n \"acc_stderr\": 0.0477761518115674,\n\
\ \"acc_norm\": 0.6310679611650486,\n \"acc_norm_stderr\": 0.0477761518115674\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6965811965811965,\n\
\ \"acc_stderr\": 0.03011821010694265,\n \"acc_norm\": 0.6965811965811965,\n\
\ \"acc_norm_stderr\": 0.03011821010694265\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5351213282247765,\n\
\ \"acc_stderr\": 0.017835798806290642,\n \"acc_norm\": 0.5351213282247765,\n\
\ \"acc_norm_stderr\": 0.017835798806290642\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.49710982658959535,\n \"acc_stderr\": 0.02691864538323901,\n\
\ \"acc_norm\": 0.49710982658959535,\n \"acc_norm_stderr\": 0.02691864538323901\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\
\ \"acc_stderr\": 0.014333522059217892,\n \"acc_norm\": 0.2424581005586592,\n\
\ \"acc_norm_stderr\": 0.014333522059217892\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.0285803410651383,\n\
\ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.0285803410651383\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5080385852090032,\n\
\ \"acc_stderr\": 0.028394421370984538,\n \"acc_norm\": 0.5080385852090032,\n\
\ \"acc_norm_stderr\": 0.028394421370984538\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.027777777777777797,\n\
\ \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.027777777777777797\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.31560283687943264,\n \"acc_stderr\": 0.027724989449509317,\n \
\ \"acc_norm\": 0.31560283687943264,\n \"acc_norm_stderr\": 0.027724989449509317\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.37353324641460234,\n\
\ \"acc_stderr\": 0.012354994823515267,\n \"acc_norm\": 0.37353324641460234,\n\
\ \"acc_norm_stderr\": 0.012354994823515267\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.3639705882352941,\n \"acc_stderr\": 0.029227192460032025,\n\
\ \"acc_norm\": 0.3639705882352941,\n \"acc_norm_stderr\": 0.029227192460032025\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4444444444444444,\n \"acc_stderr\": 0.02010258389588718,\n \
\ \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.02010258389588718\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5272727272727272,\n\
\ \"acc_stderr\": 0.04782001791380061,\n \"acc_norm\": 0.5272727272727272,\n\
\ \"acc_norm_stderr\": 0.04782001791380061\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5673469387755102,\n \"acc_stderr\": 0.03171752824062664,\n\
\ \"acc_norm\": 0.5673469387755102,\n \"acc_norm_stderr\": 0.03171752824062664\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6218905472636815,\n\
\ \"acc_stderr\": 0.03428867848778658,\n \"acc_norm\": 0.6218905472636815,\n\
\ \"acc_norm_stderr\": 0.03428867848778658\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.41566265060240964,\n\
\ \"acc_stderr\": 0.038367221765980515,\n \"acc_norm\": 0.41566265060240964,\n\
\ \"acc_norm_stderr\": 0.038367221765980515\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.03811079669833531,\n\
\ \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03811079669833531\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32313341493268055,\n\
\ \"mc1_stderr\": 0.0163718362864546,\n \"mc2\": 0.5073211090135596,\n\
\ \"mc2_stderr\": 0.015470937650792245\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6535122336227308,\n \"acc_stderr\": 0.013373773411685642\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.24639878695981804,\n \
\ \"acc_stderr\": 0.011869498557755346\n }\n}\n```"
repo_url: https://huggingface.co/rishiraj/smol-3b
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_13T10_26_39.414520
path:
- '**/details_harness|arc:challenge|25_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|gsm8k|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hellaswag|10_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-26-39.414520.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T10-26-39.414520.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- '**/details_harness|winogrande|5_2023-12-13T10-26-39.414520.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-13T10-26-39.414520.parquet'
- config_name: results
data_files:
- split: 2023_12_13T10_26_39.414520
path:
- results_2023-12-13T10-26-39.414520.parquet
- split: latest
path:
- results_2023-12-13T10-26-39.414520.parquet
---
# Dataset Card for Evaluation run of rishiraj/smol-3b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [rishiraj/smol-3b](https://huggingface.co/rishiraj/smol-3b) 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 1 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_rishiraj__smol-3b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-13T10:26:39.414520](https://huggingface.co/datasets/open-llm-leaderboard/details_rishiraj__smol-3b/blob/main/results_2023-12-13T10-26-39.414520.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": 0.4630403817848583,
"acc_stderr": 0.03471311735087895,
"acc_norm": 0.467024043140488,
"acc_norm_stderr": 0.035458188509216476,
"mc1": 0.32313341493268055,
"mc1_stderr": 0.0163718362864546,
"mc2": 0.5073211090135596,
"mc2_stderr": 0.015470937650792245
},
"harness|arc:challenge|25": {
"acc": 0.42662116040955633,
"acc_stderr": 0.014453185592920293,
"acc_norm": 0.46331058020477817,
"acc_norm_stderr": 0.014572000527756998
},
"harness|hellaswag|10": {
"acc": 0.510157339175463,
"acc_stderr": 0.004988751698341138,
"acc_norm": 0.6823341963752241,
"acc_norm_stderr": 0.004646172373100999
},
"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": 0.4444444444444444,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.04063302731486671,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.04063302731486671
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5245283018867924,
"acc_stderr": 0.030735822206205608,
"acc_norm": 0.5245283018867924,
"acc_norm_stderr": 0.030735822206205608
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5347222222222222,
"acc_stderr": 0.04171115858181618,
"acc_norm": 0.5347222222222222,
"acc_norm_stderr": 0.04171115858181618
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4161849710982659,
"acc_stderr": 0.03758517775404948,
"acc_norm": 0.4161849710982659,
"acc_norm_stderr": 0.03758517775404948
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.28431372549019607,
"acc_stderr": 0.04488482852329017,
"acc_norm": 0.28431372549019607,
"acc_norm_stderr": 0.04488482852329017
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3446808510638298,
"acc_stderr": 0.031068985963122145,
"acc_norm": 0.3446808510638298,
"acc_norm_stderr": 0.031068985963122145
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.30701754385964913,
"acc_stderr": 0.04339138322579861,
"acc_norm": 0.30701754385964913,
"acc_norm_stderr": 0.04339138322579861
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.46206896551724136,
"acc_stderr": 0.041546596717075474,
"acc_norm": 0.46206896551724136,
"acc_norm_stderr": 0.041546596717075474
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2804232804232804,
"acc_stderr": 0.023135287974325625,
"acc_norm": 0.2804232804232804,
"acc_norm_stderr": 0.023135287974325625
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3253968253968254,
"acc_stderr": 0.04190596438871136,
"acc_norm": 0.3253968253968254,
"acc_norm_stderr": 0.04190596438871136
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.532258064516129,
"acc_stderr": 0.02838474778881333,
"acc_norm": 0.532258064516129,
"acc_norm_stderr": 0.02838474778881333
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.37438423645320196,
"acc_stderr": 0.03405155380561952,
"acc_norm": 0.37438423645320196,
"acc_norm_stderr": 0.03405155380561952
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6060606060606061,
"acc_stderr": 0.03815494308688931,
"acc_norm": 0.6060606060606061,
"acc_norm_stderr": 0.03815494308688931
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.5303030303030303,
"acc_stderr": 0.03555804051763929,
"acc_norm": 0.5303030303030303,
"acc_norm_stderr": 0.03555804051763929
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6373056994818653,
"acc_stderr": 0.03469713791704371,
"acc_norm": 0.6373056994818653,
"acc_norm_stderr": 0.03469713791704371
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4282051282051282,
"acc_stderr": 0.025088301454694827,
"acc_norm": 0.4282051282051282,
"acc_norm_stderr": 0.025088301454694827
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.025348097468097845,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.025348097468097845
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```
## Dataset Details
### Dataset Description
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[More Information Needed] | [
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open-llm-leaderboard/details_rwitz2__grindin | open-llm-leaderboard | 2023-12-13T10:35:46Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T10:35:05Z | ---
pretty_name: Evaluation run of rwitz2/grindin
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rwitz2/grindin](https://huggingface.co/rwitz2/grindin) 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 1 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_rwitz2__grindin\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-13T10:32:16.157494](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz2__grindin/blob/main/results_2023-12-13T10-32-16.157494.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\": 0.6543257120477071,\n\
\ \"acc_stderr\": 0.032083375252123514,\n \"acc_norm\": 0.6543717910762578,\n\
\ \"acc_norm_stderr\": 0.03274372504664638,\n \"mc1\": 0.4394124847001224,\n\
\ \"mc1_stderr\": 0.017374520482513707,\n \"mc2\": 0.5933596198853605,\n\
\ \"mc2_stderr\": 0.015591069923791908\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6629692832764505,\n \"acc_stderr\": 0.013813476652902274,\n\
\ \"acc_norm\": 0.6988054607508533,\n \"acc_norm_stderr\": 0.01340674176784764\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6933877713602868,\n\
\ \"acc_stderr\": 0.004601446124041573,\n \"acc_norm\": 0.8702449711212906,\n\
\ \"acc_norm_stderr\": 0.003353469625027664\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\
\ \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n\
\ \"acc_norm_stderr\": 0.04094376269996792\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\": 0.67,\n\
\ \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\": 0.67,\n \
\ \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n\
\ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\
\ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\
\ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n\
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\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\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03202563076101735,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03202563076101735\n },\n\
\ \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4417989417989418,\n \"acc_stderr\": 0.025576257061253833,\n \"\
acc_norm\": 0.4417989417989418,\n \"acc_norm_stderr\": 0.025576257061253833\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\
\ \"acc_stderr\": 0.023904914311782648,\n \"acc_norm\": 0.7709677419354839,\n\
\ \"acc_norm_stderr\": 0.023904914311782648\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\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\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\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\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033477,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033477\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \
\ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083008,\n \
\ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083008\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121622,\n\
\ \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121622\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8275229357798165,\n \"acc_stderr\": 0.016197807956848043,\n \"\
acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.016197807956848043\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8137254901960784,\n\
\ \"acc_stderr\": 0.027325470966716312,\n \"acc_norm\": 0.8137254901960784,\n\
\ \"acc_norm_stderr\": 0.027325470966716312\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621133,\n\
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621133\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\
\ \"acc_stderr\": 0.030636591348699803,\n \"acc_norm\": 0.7040358744394619,\n\
\ \"acc_norm_stderr\": 0.030636591348699803\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.036412970813137296,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.036412970813137296\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\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\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\
\ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\
\ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.02335736578587403,\n\
\ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.02335736578587403\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39553072625698327,\n\
\ \"acc_stderr\": 0.016353415410075775,\n \"acc_norm\": 0.39553072625698327,\n\
\ \"acc_norm_stderr\": 0.016353415410075775\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.02582916327275748,\n\
\ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.02582916327275748\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\
: 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \"\
acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4784876140808344,\n\
\ \"acc_stderr\": 0.012758410941038915,\n \"acc_norm\": 0.4784876140808344,\n\
\ \"acc_norm_stderr\": 0.012758410941038915\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.0286619962023353,\n\
\ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.0286619962023353\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069446,\n \
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069446\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128445,\n\
\ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128445\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.02519692987482706,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.02519692987482706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\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\": 0.4394124847001224,\n\
\ \"mc1_stderr\": 0.017374520482513707,\n \"mc2\": 0.5933596198853605,\n\
\ \"mc2_stderr\": 0.015591069923791908\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8089976322020521,\n \"acc_stderr\": 0.011047808761510429\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.709628506444276,\n \
\ \"acc_stderr\": 0.012503592481818957\n }\n}\n```"
repo_url: https://huggingface.co/rwitz2/grindin
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_13T10_32_16.157494
path:
- '**/details_harness|arc:challenge|25_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|gsm8k|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hellaswag|10_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-32-16.157494.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T10-32-16.157494.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- '**/details_harness|winogrande|5_2023-12-13T10-32-16.157494.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-13T10-32-16.157494.parquet'
- config_name: results
data_files:
- split: 2023_12_13T10_32_16.157494
path:
- results_2023-12-13T10-32-16.157494.parquet
- split: latest
path:
- results_2023-12-13T10-32-16.157494.parquet
---
# Dataset Card for Evaluation run of rwitz2/grindin
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [rwitz2/grindin](https://huggingface.co/rwitz2/grindin) 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 1 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_rwitz2__grindin",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-13T10:32:16.157494](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz2__grindin/blob/main/results_2023-12-13T10-32-16.157494.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": 0.6543257120477071,
"acc_stderr": 0.032083375252123514,
"acc_norm": 0.6543717910762578,
"acc_norm_stderr": 0.03274372504664638,
"mc1": 0.4394124847001224,
"mc1_stderr": 0.017374520482513707,
"mc2": 0.5933596198853605,
"mc2_stderr": 0.015591069923791908
},
"harness|arc:challenge|25": {
"acc": 0.6629692832764505,
"acc_stderr": 0.013813476652902274,
"acc_norm": 0.6988054607508533,
"acc_norm_stderr": 0.01340674176784764
},
"harness|hellaswag|10": {
"acc": 0.6933877713602868,
"acc_stderr": 0.004601446124041573,
"acc_norm": 0.8702449711212906,
"acc_norm_stderr": 0.003353469625027664
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.04094376269996792,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.04094376269996792
},
"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": 0.67,
"acc_stderr": 0.04725815626252607,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252607
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7169811320754716,
"acc_stderr": 0.027724236492700918,
"acc_norm": 0.7169811320754716,
"acc_norm_stderr": 0.027724236492700918
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7847222222222222,
"acc_stderr": 0.03437079344106135,
"acc_norm": 0.7847222222222222,
"acc_norm_stderr": 0.03437079344106135
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6820809248554913,
"acc_stderr": 0.0355068398916558,
"acc_norm": 0.6820809248554913,
"acc_norm_stderr": 0.0355068398916558
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107224,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107224
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6,
"acc_stderr": 0.03202563076101735,
"acc_norm": 0.6,
"acc_norm_stderr": 0.03202563076101735
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4417989417989418,
"acc_stderr": 0.025576257061253833,
"acc_norm": 0.4417989417989418,
"acc_norm_stderr": 0.025576257061253833
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7709677419354839,
"acc_stderr": 0.023904914311782648,
"acc_norm": 0.7709677419354839,
"acc_norm_stderr": 0.023904914311782648
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5024630541871922,
"acc_stderr": 0.035179450386910616,
"acc_norm": 0.5024630541871922,
"acc_norm_stderr": 0.035179450386910616
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.031922715695483,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.031922715695483
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.029620227874790482,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.029620227874790482
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.021500249576033477,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.021500249576033477
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6743589743589744,
"acc_stderr": 0.02375966576741229,
"acc_norm": 0.6743589743589744,
"acc_norm_stderr": 0.02375966576741229
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.029116617606083008,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.029116617606083008
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6554621848739496,
"acc_stderr": 0.030868682604121622,
"acc_norm": 0.6554621848739496,
"acc_norm_stderr": 0.030868682604121622
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33112582781456956,
"acc_stderr": 0.038425817186598696,
"acc_norm": 0.33112582781456956,
"acc_norm_stderr": 0.038425817186598696
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8275229357798165,
"acc_stderr": 0.016197807956848043,
"acc_norm": 0.8275229357798165,
"acc_norm_stderr": 0.016197807956848043
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5277777777777778,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.5277777777777778,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8137254901960784,
"acc_stderr": 0.027325470966716312,
"acc_norm": 0.8137254901960784,
"acc_norm_stderr": 0.027325470966716312
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
"acc_stderr": 0.025955020841621133,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.025955020841621133
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7040358744394619,
"acc_stderr": 0.030636591348699803,
"acc_norm": 0.7040358744394619,
"acc_norm_stderr": 0.030636591348699803
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7786259541984732,
"acc_stderr": 0.036412970813137296,
"acc_norm": 0.7786259541984732,
"acc_norm_stderr": 0.036412970813137296
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228732,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228732
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.754601226993865,
"acc_stderr": 0.03380939813943354,
"acc_norm": 0.754601226993865,
"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.48214285714285715,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.48214285714285715,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406957,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406957
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8263090676883781,
"acc_stderr": 0.01354741565866226,
"acc_norm": 0.8263090676883781,
"acc_norm_stderr": 0.01354741565866226
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7485549132947977,
"acc_stderr": 0.02335736578587403,
"acc_norm": 0.7485549132947977,
"acc_norm_stderr": 0.02335736578587403
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.39553072625698327,
"acc_stderr": 0.016353415410075775,
"acc_norm": 0.39553072625698327,
"acc_norm_stderr": 0.016353415410075775
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7156862745098039,
"acc_stderr": 0.02582916327275748,
"acc_norm": 0.7156862745098039,
"acc_norm_stderr": 0.02582916327275748
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
"acc_stderr": 0.025922371788818763,
"acc_norm": 0.7041800643086816,
"acc_norm_stderr": 0.025922371788818763
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.75,
"acc_stderr": 0.02409347123262133,
"acc_norm": 0.75,
"acc_norm_stderr": 0.02409347123262133
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
"acc_stderr": 0.029752389657427047,
"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.029752389657427047
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4784876140808344,
"acc_stderr": 0.012758410941038915,
"acc_norm": 0.4784876140808344,
"acc_norm_stderr": 0.012758410941038915
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6654411764705882,
"acc_stderr": 0.0286619962023353,
"acc_norm": 0.6654411764705882,
"acc_norm_stderr": 0.0286619962023353
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6797385620915033,
"acc_stderr": 0.018875682938069446,
"acc_norm": 0.6797385620915033,
"acc_norm_stderr": 0.018875682938069446
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6454545454545455,
"acc_stderr": 0.045820048415054174,
"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.726530612244898,
"acc_stderr": 0.028535560337128445,
"acc_norm": 0.726530612244898,
"acc_norm_stderr": 0.028535560337128445
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.02519692987482706,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.02519692987482706
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5602409638554217,
"acc_stderr": 0.03864139923699122,
"acc_norm": 0.5602409638554217,
"acc_norm_stderr": 0.03864139923699122
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4394124847001224,
"mc1_stderr": 0.017374520482513707,
"mc2": 0.5933596198853605,
"mc2_stderr": 0.015591069923791908
},
"harness|winogrande|5": {
"acc": 0.8089976322020521,
"acc_stderr": 0.011047808761510429
},
"harness|gsm8k|5": {
"acc": 0.709628506444276,
"acc_stderr": 0.012503592481818957
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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[More Information Needed] | [
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open-llm-leaderboard/details_KnutJaegersberg__Walter-Llama-1B | open-llm-leaderboard | 2023-12-13T10:36:54Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T10:36:10Z | ---
pretty_name: Evaluation run of KnutJaegersberg/Walter-Llama-1B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KnutJaegersberg/Walter-Llama-1B](https://huggingface.co/KnutJaegersberg/Walter-Llama-1B)\
\ 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 1 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_KnutJaegersberg__Walter-Llama-1B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-13T10:33:54.615691](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Llama-1B/blob/main/results_2023-12-13T10-33-54.615691.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\": 0.27851461271728306,\n\
\ \"acc_stderr\": 0.031543900001055364,\n \"acc_norm\": 0.2811714189383077,\n\
\ \"acc_norm_stderr\": 0.032378381556401825,\n \"mc1\": 0.1909424724602203,\n\
\ \"mc1_stderr\": 0.013759285842685718,\n \"mc2\": 0.33931994336755883,\n\
\ \"mc2_stderr\": 0.014516204773412781\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.31143344709897613,\n \"acc_stderr\": 0.013532472099850945,\n\
\ \"acc_norm\": 0.32849829351535836,\n \"acc_norm_stderr\": 0.013724978465537371\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.46355307707627963,\n\
\ \"acc_stderr\": 0.004976507121076265,\n \"acc_norm\": 0.6105357498506274,\n\
\ \"acc_norm_stderr\": 0.004866322258335982\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2222222222222222,\n\
\ \"acc_stderr\": 0.03591444084196969,\n \"acc_norm\": 0.2222222222222222,\n\
\ \"acc_norm_stderr\": 0.03591444084196969\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.03690677986137283,\n\
\ \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.03690677986137283\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.23,\n\
\ \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \
\ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.2943396226415094,\n \"acc_stderr\": 0.028049186315695248,\n\
\ \"acc_norm\": 0.2943396226415094,\n \"acc_norm_stderr\": 0.028049186315695248\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2708333333333333,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.2708333333333333,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.22,\n\
\ \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \
\ \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2832369942196532,\n\
\ \"acc_stderr\": 0.034355680560478746,\n \"acc_norm\": 0.2832369942196532,\n\
\ \"acc_norm_stderr\": 0.034355680560478746\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.04158307533083286,\n\
\ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.04158307533083286\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.24,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.31063829787234043,\n \"acc_stderr\": 0.03025123757921317,\n\
\ \"acc_norm\": 0.31063829787234043,\n \"acc_norm_stderr\": 0.03025123757921317\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n\
\ \"acc_stderr\": 0.038924311065187546,\n \"acc_norm\": 0.21929824561403508,\n\
\ \"acc_norm_stderr\": 0.038924311065187546\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.03695183311650232,\n\
\ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.03695183311650232\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.25925925925925924,\n \"acc_stderr\": 0.022569897074918407,\n \"\
acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.022569897074918407\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.25396825396825395,\n\
\ \"acc_stderr\": 0.038932596106046734,\n \"acc_norm\": 0.25396825396825395,\n\
\ \"acc_norm_stderr\": 0.038932596106046734\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.267741935483871,\n\
\ \"acc_stderr\": 0.025189006660212378,\n \"acc_norm\": 0.267741935483871,\n\
\ \"acc_norm_stderr\": 0.025189006660212378\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.29064039408866993,\n \"acc_stderr\": 0.03194740072265541,\n\
\ \"acc_norm\": 0.29064039408866993,\n \"acc_norm_stderr\": 0.03194740072265541\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.2606060606060606,\n \"acc_stderr\": 0.034277431758165236,\n\
\ \"acc_norm\": 0.2606060606060606,\n \"acc_norm_stderr\": 0.034277431758165236\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.32323232323232326,\n \"acc_stderr\": 0.03332299921070644,\n \"\
acc_norm\": 0.32323232323232326,\n \"acc_norm_stderr\": 0.03332299921070644\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.35233160621761656,\n \"acc_stderr\": 0.03447478286414358,\n\
\ \"acc_norm\": 0.35233160621761656,\n \"acc_norm_stderr\": 0.03447478286414358\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.33589743589743587,\n \"acc_stderr\": 0.023946724741563976,\n\
\ \"acc_norm\": 0.33589743589743587,\n \"acc_norm_stderr\": 0.023946724741563976\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712173,\n \
\ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712173\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.3067226890756303,\n \"acc_stderr\": 0.029953823891887048,\n\
\ \"acc_norm\": 0.3067226890756303,\n \"acc_norm_stderr\": 0.029953823891887048\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.30458715596330277,\n \"acc_stderr\": 0.01973229942035405,\n \"\
acc_norm\": 0.30458715596330277,\n \"acc_norm_stderr\": 0.01973229942035405\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.38425925925925924,\n \"acc_stderr\": 0.03317354514310742,\n \"\
acc_norm\": 0.38425925925925924,\n \"acc_norm_stderr\": 0.03317354514310742\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.30392156862745096,\n \"acc_stderr\": 0.03228210387037894,\n \"\
acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.03228210387037894\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.2616033755274262,\n \"acc_stderr\": 0.028609516716994934,\n \
\ \"acc_norm\": 0.2616033755274262,\n \"acc_norm_stderr\": 0.028609516716994934\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.23766816143497757,\n\
\ \"acc_stderr\": 0.028568079464714274,\n \"acc_norm\": 0.23766816143497757,\n\
\ \"acc_norm_stderr\": 0.028568079464714274\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.03915345408847834,\n\
\ \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.03915345408847834\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2892561983471074,\n \"acc_stderr\": 0.041391127276354626,\n \"\
acc_norm\": 0.2892561983471074,\n \"acc_norm_stderr\": 0.041391127276354626\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.23148148148148148,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.23148148148148148,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3128834355828221,\n \"acc_stderr\": 0.03642914578292404,\n\
\ \"acc_norm\": 0.3128834355828221,\n \"acc_norm_stderr\": 0.03642914578292404\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.16071428571428573,\n\
\ \"acc_stderr\": 0.03485946096475741,\n \"acc_norm\": 0.16071428571428573,\n\
\ \"acc_norm_stderr\": 0.03485946096475741\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.33980582524271846,\n \"acc_stderr\": 0.046897659372781335,\n\
\ \"acc_norm\": 0.33980582524271846,\n \"acc_norm_stderr\": 0.046897659372781335\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2863247863247863,\n\
\ \"acc_stderr\": 0.029614323690456648,\n \"acc_norm\": 0.2863247863247863,\n\
\ \"acc_norm_stderr\": 0.029614323690456648\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2388250319284802,\n\
\ \"acc_stderr\": 0.015246803197398698,\n \"acc_norm\": 0.2388250319284802,\n\
\ \"acc_norm_stderr\": 0.015246803197398698\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.26011560693641617,\n \"acc_stderr\": 0.023618678310069363,\n\
\ \"acc_norm\": 0.26011560693641617,\n \"acc_norm_stderr\": 0.023618678310069363\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24134078212290502,\n\
\ \"acc_stderr\": 0.014310999547961459,\n \"acc_norm\": 0.24134078212290502,\n\
\ \"acc_norm_stderr\": 0.014310999547961459\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.02633661346904663,\n\
\ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.02633661346904663\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2379421221864952,\n\
\ \"acc_stderr\": 0.024185150647818707,\n \"acc_norm\": 0.2379421221864952,\n\
\ \"acc_norm_stderr\": 0.024185150647818707\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.25617283950617287,\n \"acc_stderr\": 0.0242885336377261,\n\
\ \"acc_norm\": 0.25617283950617287,\n \"acc_norm_stderr\": 0.0242885336377261\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.24822695035460993,\n \"acc_stderr\": 0.02577001564429039,\n \
\ \"acc_norm\": 0.24822695035460993,\n \"acc_norm_stderr\": 0.02577001564429039\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2588005215123859,\n\
\ \"acc_stderr\": 0.011186109046564613,\n \"acc_norm\": 0.2588005215123859,\n\
\ \"acc_norm_stderr\": 0.011186109046564613\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.41544117647058826,\n \"acc_stderr\": 0.029935342707877753,\n\
\ \"acc_norm\": 0.41544117647058826,\n \"acc_norm_stderr\": 0.029935342707877753\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.238562091503268,\n \"acc_stderr\": 0.01724238582877961,\n \
\ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.01724238582877961\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.3181818181818182,\n\
\ \"acc_stderr\": 0.04461272175910507,\n \"acc_norm\": 0.3181818181818182,\n\
\ \"acc_norm_stderr\": 0.04461272175910507\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.19591836734693877,\n \"acc_stderr\": 0.025409301953225678,\n\
\ \"acc_norm\": 0.19591836734693877,\n \"acc_norm_stderr\": 0.025409301953225678\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.263681592039801,\n\
\ \"acc_stderr\": 0.03115715086935554,\n \"acc_norm\": 0.263681592039801,\n\
\ \"acc_norm_stderr\": 0.03115715086935554\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.25903614457831325,\n\
\ \"acc_stderr\": 0.03410646614071856,\n \"acc_norm\": 0.25903614457831325,\n\
\ \"acc_norm_stderr\": 0.03410646614071856\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.22807017543859648,\n \"acc_stderr\": 0.032180937956023566,\n\
\ \"acc_norm\": 0.22807017543859648,\n \"acc_norm_stderr\": 0.032180937956023566\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.1909424724602203,\n\
\ \"mc1_stderr\": 0.013759285842685718,\n \"mc2\": 0.33931994336755883,\n\
\ \"mc2_stderr\": 0.014516204773412781\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5643251775848461,\n \"acc_stderr\": 0.013935709739615712\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/KnutJaegersberg/Walter-Llama-1B
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_13T10_33_54.615691
path:
- '**/details_harness|arc:challenge|25_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|gsm8k|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hellaswag|10_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-33-54.615691.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T10-33-54.615691.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- '**/details_harness|winogrande|5_2023-12-13T10-33-54.615691.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-13T10-33-54.615691.parquet'
- config_name: results
data_files:
- split: 2023_12_13T10_33_54.615691
path:
- results_2023-12-13T10-33-54.615691.parquet
- split: latest
path:
- results_2023-12-13T10-33-54.615691.parquet
---
# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Llama-1B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [KnutJaegersberg/Walter-Llama-1B](https://huggingface.co/KnutJaegersberg/Walter-Llama-1B) 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 1 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_KnutJaegersberg__Walter-Llama-1B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-13T10:33:54.615691](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Llama-1B/blob/main/results_2023-12-13T10-33-54.615691.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": 0.27851461271728306,
"acc_stderr": 0.031543900001055364,
"acc_norm": 0.2811714189383077,
"acc_norm_stderr": 0.032378381556401825,
"mc1": 0.1909424724602203,
"mc1_stderr": 0.013759285842685718,
"mc2": 0.33931994336755883,
"mc2_stderr": 0.014516204773412781
},
"harness|arc:challenge|25": {
"acc": 0.31143344709897613,
"acc_stderr": 0.013532472099850945,
"acc_norm": 0.32849829351535836,
"acc_norm_stderr": 0.013724978465537371
},
"harness|hellaswag|10": {
"acc": 0.46355307707627963,
"acc_stderr": 0.004976507121076265,
"acc_norm": 0.6105357498506274,
"acc_norm_stderr": 0.004866322258335982
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720683,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720683
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.03591444084196969,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.03591444084196969
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.2894736842105263,
"acc_stderr": 0.03690677986137283,
"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.03690677986137283
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2943396226415094,
"acc_stderr": 0.028049186315695248,
"acc_norm": 0.2943396226415094,
"acc_norm_stderr": 0.028049186315695248
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2708333333333333,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.2708333333333333,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932269,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932269
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2832369942196532,
"acc_stderr": 0.034355680560478746,
"acc_norm": 0.2832369942196532,
"acc_norm_stderr": 0.034355680560478746
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.22549019607843138,
"acc_stderr": 0.04158307533083286,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.04158307533083286
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.24,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.24,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.31063829787234043,
"acc_stderr": 0.03025123757921317,
"acc_norm": 0.31063829787234043,
"acc_norm_stderr": 0.03025123757921317
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.21929824561403508,
"acc_stderr": 0.038924311065187546,
"acc_norm": 0.21929824561403508,
"acc_norm_stderr": 0.038924311065187546
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2689655172413793,
"acc_stderr": 0.03695183311650232,
"acc_norm": 0.2689655172413793,
"acc_norm_stderr": 0.03695183311650232
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.022569897074918407,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.022569897074918407
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.25396825396825395,
"acc_stderr": 0.038932596106046734,
"acc_norm": 0.25396825396825395,
"acc_norm_stderr": 0.038932596106046734
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
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```
## Dataset Details
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[More Information Needed] | [
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Seongill/Trivia_5_only_adversary_1159_original | Seongill | 2023-12-13T10:38:41Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T10:38:38Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ent_type
dtype: string
splits:
- name: train
num_bytes: 5372371
num_examples: 1159
download_size: 3091601
dataset_size: 5372371
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] |
Seongill/Trivia_5_only_adversary_1159_masked | Seongill | 2023-12-13T10:46:29Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T10:46:25Z | ---
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: num_advs
dtype: int64
- name: masked_query
dtype: string
- name: query_embedding
sequence: float32
splits:
- name: train
num_bytes: 11052514
num_examples: 1159
download_size: 7672855
dataset_size: 11052514
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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nguyenthanhdo/safety_en_14k | nguyenthanhdo | 2023-12-13T10:49:37Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T10:49:32Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: type
dtype: string
- name: split
dtype: string
- name: translated
dtype: bool
splits:
- name: train
num_bytes: 10930380
num_examples: 14187
download_size: 5673826
dataset_size: 10930380
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "safety_en_14k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Rooni/TestDS | Rooni | 2023-12-13T12:06:09Z | 0 | 0 | [
"language:ru",
"region:us"
] | null | 2023-12-13T10:50:14Z | ---
language:
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pretty_name: data.json
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ferreiralima1/audio1_ | ferreiralima1 | 2023-12-13T10:57:15Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-13T10:55:11Z | ---
license: openrail
---
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open-llm-leaderboard/details_Sao10K__Ana-v1-m7 | open-llm-leaderboard | 2023-12-13T11:04:14Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T11:03:33Z | ---
pretty_name: Evaluation run of Sao10K/Ana-v1-m7
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Sao10K/Ana-v1-m7](https://huggingface.co/Sao10K/Ana-v1-m7) 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 1 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_Sao10K__Ana-v1-m7\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-13T11:00:42.385261](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Ana-v1-m7/blob/main/results_2023-12-13T11-00-42.385261.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\": 0.6450876976103553,\n\
\ \"acc_stderr\": 0.032232788125298194,\n \"acc_norm\": 0.6484944592637535,\n\
\ \"acc_norm_stderr\": 0.032869268033759086,\n \"mc1\": 0.39657282741738065,\n\
\ \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.5503162010975955,\n\
\ \"mc2_stderr\": 0.01574450133768535\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6407849829351536,\n \"acc_stderr\": 0.014020224155839152,\n\
\ \"acc_norm\": 0.674061433447099,\n \"acc_norm_stderr\": 0.013697432466693247\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6762597092212707,\n\
\ \"acc_stderr\": 0.0046694598919176915,\n \"acc_norm\": 0.859788886675961,\n\
\ \"acc_norm_stderr\": 0.0034649633793799287\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\
\ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\
\ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.035868792800803406,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.035868792800803406\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\
\ \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n\
\ \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.049598599663841815,\n\
\ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.049598599663841815\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\
\ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5175438596491229,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41534391534391535,\n \"acc_stderr\": 0.0253795249107784,\n \"\
acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.0253795249107784\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7935483870967742,\n \"acc_stderr\": 0.023025899617188712,\n \"\
acc_norm\": 0.7935483870967742,\n \"acc_norm_stderr\": 0.023025899617188712\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"\
acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.02833560973246336,\n \"acc_norm\"\
: 0.803030303030303,\n \"acc_norm_stderr\": 0.02833560973246336\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6230769230769231,\n \"acc_stderr\": 0.024570975364225995,\n\
\ \"acc_norm\": 0.6230769230769231,\n \"acc_norm_stderr\": 0.024570975364225995\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524565,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524565\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8330275229357799,\n \"acc_stderr\": 0.01599015488507338,\n \"\
acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.01599015488507338\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.47685185185185186,\n \"acc_stderr\": 0.034063153607115065,\n \"\
acc_norm\": 0.47685185185185186,\n \"acc_norm_stderr\": 0.034063153607115065\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"\
acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728744,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728744\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917671,\n \"\
acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917671\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.03408997886857529,\n\
\ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.03408997886857529\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\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\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.020588491316092368,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.020588491316092368\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8288633461047255,\n\
\ \"acc_stderr\": 0.013468201614066302,\n \"acc_norm\": 0.8288633461047255,\n\
\ \"acc_norm_stderr\": 0.013468201614066302\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\
\ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.35977653631284917,\n\
\ \"acc_stderr\": 0.016051419760310263,\n \"acc_norm\": 0.35977653631284917,\n\
\ \"acc_norm_stderr\": 0.016051419760310263\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292452,\n\
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292452\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.025670259242188933,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.025670259242188933\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\
: 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \"\
acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4634941329856584,\n\
\ \"acc_stderr\": 0.012736153390214961,\n \"acc_norm\": 0.4634941329856584,\n\
\ \"acc_norm_stderr\": 0.012736153390214961\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.028661996202335303,\n\
\ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.028661996202335303\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6748366013071896,\n \"acc_stderr\": 0.01895088677080631,\n \
\ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.01895088677080631\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291286,\n\
\ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291286\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\
\ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\
\ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.03061111655743253,\n\
\ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.03061111655743253\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.39657282741738065,\n\
\ \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.5503162010975955,\n\
\ \"mc2_stderr\": 0.01574450133768535\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7805840568271507,\n \"acc_stderr\": 0.011631268360607778\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5253980288097043,\n \
\ \"acc_stderr\": 0.01375470508911231\n }\n}\n```"
repo_url: https://huggingface.co/Sao10K/Ana-v1-m7
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_13T11_00_42.385261
path:
- '**/details_harness|arc:challenge|25_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|gsm8k|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hellaswag|10_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-00-42.385261.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T11-00-42.385261.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- '**/details_harness|winogrande|5_2023-12-13T11-00-42.385261.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-13T11-00-42.385261.parquet'
- config_name: results
data_files:
- split: 2023_12_13T11_00_42.385261
path:
- results_2023-12-13T11-00-42.385261.parquet
- split: latest
path:
- results_2023-12-13T11-00-42.385261.parquet
---
# Dataset Card for Evaluation run of Sao10K/Ana-v1-m7
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Sao10K/Ana-v1-m7](https://huggingface.co/Sao10K/Ana-v1-m7) 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 1 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_Sao10K__Ana-v1-m7",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-13T11:00:42.385261](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Ana-v1-m7/blob/main/results_2023-12-13T11-00-42.385261.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": 0.6450876976103553,
"acc_stderr": 0.032232788125298194,
"acc_norm": 0.6484944592637535,
"acc_norm_stderr": 0.032869268033759086,
"mc1": 0.39657282741738065,
"mc1_stderr": 0.017124930942023515,
"mc2": 0.5503162010975955,
"mc2_stderr": 0.01574450133768535
},
"harness|arc:challenge|25": {
"acc": 0.6407849829351536,
"acc_stderr": 0.014020224155839152,
"acc_norm": 0.674061433447099,
"acc_norm_stderr": 0.013697432466693247
},
"harness|hellaswag|10": {
"acc": 0.6762597092212707,
"acc_stderr": 0.0046694598919176915,
"acc_norm": 0.859788886675961,
"acc_norm_stderr": 0.0034649633793799287
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.03803510248351585,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.03803510248351585
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.035868792800803406,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.035868792800803406
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6358381502890174,
"acc_stderr": 0.03669072477416907,
"acc_norm": 0.6358381502890174,
"acc_norm_stderr": 0.03669072477416907
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.46078431372549017,
"acc_stderr": 0.049598599663841815,
"acc_norm": 0.46078431372549017,
"acc_norm_stderr": 0.049598599663841815
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5702127659574469,
"acc_stderr": 0.03236214467715564,
"acc_norm": 0.5702127659574469,
"acc_norm_stderr": 0.03236214467715564
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5175438596491229,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.5175438596491229,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555497,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41534391534391535,
"acc_stderr": 0.0253795249107784,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.0253795249107784
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42063492063492064,
"acc_stderr": 0.04415438226743744,
"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.04415438226743744
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7935483870967742,
"acc_stderr": 0.023025899617188712,
"acc_norm": 0.7935483870967742,
"acc_norm_stderr": 0.023025899617188712
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5123152709359606,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909282,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909282
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.032876667586034906,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.032876667586034906
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.02833560973246336,
"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.02833560973246336
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.02381447708659355,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.02381447708659355
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6230769230769231,
"acc_stderr": 0.024570975364225995,
"acc_norm": 0.6230769230769231,
"acc_norm_stderr": 0.024570975364225995
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.028661201116524565,
"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.028661201116524565
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.0395802723112157,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.0395802723112157
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8330275229357799,
"acc_stderr": 0.01599015488507338,
"acc_norm": 0.8330275229357799,
"acc_norm_stderr": 0.01599015488507338
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.47685185185185186,
"acc_stderr": 0.034063153607115065,
"acc_norm": 0.47685185185185186,
"acc_norm_stderr": 0.034063153607115065
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8088235294117647,
"acc_stderr": 0.027599174300640766,
"acc_norm": 0.8088235294117647,
"acc_norm_stderr": 0.027599174300640766
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
"acc_stderr": 0.02595502084162113,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.02595502084162113
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.695067264573991,
"acc_stderr": 0.030898610882477515,
"acc_norm": 0.695067264573991,
"acc_norm_stderr": 0.030898610882477515
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7557251908396947,
"acc_stderr": 0.03768335959728744,
"acc_norm": 0.7557251908396947,
"acc_norm_stderr": 0.03768335959728744
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8264462809917356,
"acc_stderr": 0.03457272836917671,
"acc_norm": 0.8264462809917356,
"acc_norm_stderr": 0.03457272836917671
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7484662576687117,
"acc_stderr": 0.03408997886857529,
"acc_norm": 0.7484662576687117,
"acc_norm_stderr": 0.03408997886857529
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5357142857142857,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.5357142857142857,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.020588491316092368,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.020588491316092368
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8288633461047255,
"acc_stderr": 0.013468201614066302,
"acc_norm": 0.8288633461047255,
"acc_norm_stderr": 0.013468201614066302
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7052023121387283,
"acc_stderr": 0.024547617794803828,
"acc_norm": 0.7052023121387283,
"acc_norm_stderr": 0.024547617794803828
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.35977653631284917,
"acc_stderr": 0.016051419760310263,
"acc_norm": 0.35977653631284917,
"acc_norm_stderr": 0.016051419760310263
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.738562091503268,
"acc_stderr": 0.025160998214292452,
"acc_norm": 0.738562091503268,
"acc_norm_stderr": 0.025160998214292452
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7138263665594855,
"acc_stderr": 0.025670259242188933,
"acc_norm": 0.7138263665594855,
"acc_norm_stderr": 0.025670259242188933
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.75,
"acc_stderr": 0.02409347123262133,
"acc_norm": 0.75,
"acc_norm_stderr": 0.02409347123262133
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4858156028368794,
"acc_stderr": 0.02981549448368206,
"acc_norm": 0.4858156028368794,
"acc_norm_stderr": 0.02981549448368206
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4634941329856584,
"acc_stderr": 0.012736153390214961,
"acc_norm": 0.4634941329856584,
"acc_norm_stderr": 0.012736153390214961
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6654411764705882,
"acc_stderr": 0.028661996202335303,
"acc_norm": 0.6654411764705882,
"acc_norm_stderr": 0.028661996202335303
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6748366013071896,
"acc_stderr": 0.01895088677080631,
"acc_norm": 0.6748366013071896,
"acc_norm_stderr": 0.01895088677080631
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7183673469387755,
"acc_stderr": 0.028795185574291286,
"acc_norm": 0.7183673469387755,
"acc_norm_stderr": 0.028795185574291286
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8606965174129353,
"acc_stderr": 0.024484487162913973,
"acc_norm": 0.8606965174129353,
"acc_norm_stderr": 0.024484487162913973
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.03379976689896309,
"acc_norm": 0.87,
"acc_norm_stderr": 0.03379976689896309
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
"acc_stderr": 0.03061111655743253,
"acc_norm": 0.8011695906432749,
"acc_norm_stderr": 0.03061111655743253
},
"harness|truthfulqa:mc|0": {
"mc1": 0.39657282741738065,
"mc1_stderr": 0.017124930942023515,
"mc2": 0.5503162010975955,
"mc2_stderr": 0.01574450133768535
},
"harness|winogrande|5": {
"acc": 0.7805840568271507,
"acc_stderr": 0.011631268360607778
},
"harness|gsm8k|5": {
"acc": 0.5253980288097043,
"acc_stderr": 0.01375470508911231
}
}
```
## Dataset Details
### Dataset Description
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[More Information Needed] | [
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open-llm-leaderboard/details_PistachioAlt__Synatra-MCS-7B-v0.3-RP-Slerp | open-llm-leaderboard | 2023-12-13T11:06:34Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T11:05:51Z | ---
pretty_name: Evaluation run of PistachioAlt/Synatra-MCS-7B-v0.3-RP-Slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PistachioAlt/Synatra-MCS-7B-v0.3-RP-Slerp](https://huggingface.co/PistachioAlt/Synatra-MCS-7B-v0.3-RP-Slerp)\
\ 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 1 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_PistachioAlt__Synatra-MCS-7B-v0.3-RP-Slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-13T11:03:01.883125](https://huggingface.co/datasets/open-llm-leaderboard/details_PistachioAlt__Synatra-MCS-7B-v0.3-RP-Slerp/blob/main/results_2023-12-13T11-03-01.883125.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\": 0.6393052547076683,\n\
\ \"acc_stderr\": 0.03226129096267341,\n \"acc_norm\": 0.640236665344228,\n\
\ \"acc_norm_stderr\": 0.032916099826004463,\n \"mc1\": 0.37209302325581395,\n\
\ \"mc1_stderr\": 0.016921090118814038,\n \"mc2\": 0.5393109292355184,\n\
\ \"mc2_stderr\": 0.015020765741482643\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.014169664520303098,\n\
\ \"acc_norm\": 0.6663822525597269,\n \"acc_norm_stderr\": 0.013778687054176538\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6587333200557658,\n\
\ \"acc_stderr\": 0.004731657228906991,\n \"acc_norm\": 0.8497311292571201,\n\
\ \"acc_norm_stderr\": 0.0035660447773274194\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.042446332383532265,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.042446332383532265\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\
\ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\
\ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\
\ \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n\
\ \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n\
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4312169312169312,\n \"acc_stderr\": 0.025506481698138204,\n \"\
acc_norm\": 0.4312169312169312,\n \"acc_norm_stderr\": 0.025506481698138204\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7645161290322581,\n\
\ \"acc_stderr\": 0.02413763242933771,\n \"acc_norm\": 0.7645161290322581,\n\
\ \"acc_norm_stderr\": 0.02413763242933771\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\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047709,\n\
\ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047709\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218967,\n \"\
acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218967\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121437,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121437\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6461538461538462,\n \"acc_stderr\": 0.02424378399406216,\n \
\ \"acc_norm\": 0.6461538461538462,\n \"acc_norm_stderr\": 0.02424378399406216\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815635,\n \
\ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815635\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n\
\ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8348623853211009,\n \"acc_stderr\": 0.015919557829976037,\n \"\
acc_norm\": 0.8348623853211009,\n \"acc_norm_stderr\": 0.015919557829976037\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.48148148148148145,\n \"acc_stderr\": 0.03407632093854052,\n \"\
acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.03407632093854052\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"\
acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7932489451476793,\n \"acc_stderr\": 0.0263616516683891,\n \
\ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.0263616516683891\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728744,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728744\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.036401182719909456,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.036401182719909456\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.041858325989283136,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.041858325989283136\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8365261813537676,\n\
\ \"acc_stderr\": 0.013223928616741612,\n \"acc_norm\": 0.8365261813537676,\n\
\ \"acc_norm_stderr\": 0.013223928616741612\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n\
\ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3418994413407821,\n\
\ \"acc_stderr\": 0.015864506461604644,\n \"acc_norm\": 0.3418994413407821,\n\
\ \"acc_norm_stderr\": 0.015864506461604644\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n\
\ \"acc_stderr\": 0.025311765975426122,\n \"acc_norm\": 0.7266881028938906,\n\
\ \"acc_norm_stderr\": 0.025311765975426122\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\
\ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \
\ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45371577574967403,\n\
\ \"acc_stderr\": 0.012715404841277738,\n \"acc_norm\": 0.45371577574967403,\n\
\ \"acc_norm_stderr\": 0.012715404841277738\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\
\ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6617647058823529,\n \"acc_stderr\": 0.019139943748487036,\n \
\ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.019139943748487036\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644286,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644286\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675606,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675606\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454125\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\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.37209302325581395,\n\
\ \"mc1_stderr\": 0.016921090118814038,\n \"mc2\": 0.5393109292355184,\n\
\ \"mc2_stderr\": 0.015020765741482643\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7971586424625099,\n \"acc_stderr\": 0.011301439925936648\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6618650492797574,\n \
\ \"acc_stderr\": 0.013030829145172226\n }\n}\n```"
repo_url: https://huggingface.co/PistachioAlt/Synatra-MCS-7B-v0.3-RP-Slerp
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_13T11_03_01.883125
path:
- '**/details_harness|arc:challenge|25_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|gsm8k|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hellaswag|10_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-03-01.883125.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T11-03-01.883125.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- '**/details_harness|winogrande|5_2023-12-13T11-03-01.883125.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-13T11-03-01.883125.parquet'
- config_name: results
data_files:
- split: 2023_12_13T11_03_01.883125
path:
- results_2023-12-13T11-03-01.883125.parquet
- split: latest
path:
- results_2023-12-13T11-03-01.883125.parquet
---
# Dataset Card for Evaluation run of PistachioAlt/Synatra-MCS-7B-v0.3-RP-Slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [PistachioAlt/Synatra-MCS-7B-v0.3-RP-Slerp](https://huggingface.co/PistachioAlt/Synatra-MCS-7B-v0.3-RP-Slerp) 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 1 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_PistachioAlt__Synatra-MCS-7B-v0.3-RP-Slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-13T11:03:01.883125](https://huggingface.co/datasets/open-llm-leaderboard/details_PistachioAlt__Synatra-MCS-7B-v0.3-RP-Slerp/blob/main/results_2023-12-13T11-03-01.883125.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": 0.6393052547076683,
"acc_stderr": 0.03226129096267341,
"acc_norm": 0.640236665344228,
"acc_norm_stderr": 0.032916099826004463,
"mc1": 0.37209302325581395,
"mc1_stderr": 0.016921090118814038,
"mc2": 0.5393109292355184,
"mc2_stderr": 0.015020765741482643
},
"harness|arc:challenge|25": {
"acc": 0.6220136518771331,
"acc_stderr": 0.014169664520303098,
"acc_norm": 0.6663822525597269,
"acc_norm_stderr": 0.013778687054176538
},
"harness|hellaswag|10": {
"acc": 0.6587333200557658,
"acc_stderr": 0.004731657228906991,
"acc_norm": 0.8497311292571201,
"acc_norm_stderr": 0.0035660447773274194
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.042446332383532265,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.042446332383532265
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.03803510248351585,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.03803510248351585
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
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```
## Dataset Details
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[More Information Needed] | [
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Maxime62/JuniaLLM | Maxime62 | 2023-12-13T11:14:44Z | 0 | 0 | [
"language:fr",
"region:us"
] | null | 2023-12-13T11:12:33Z | ---
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ammarnasr/secure_1_with_code_dataset | ammarnasr | 2023-12-13T11:27:05Z | 0 | 0 | [
"region:us"
] | null | 2023-12-13T11:27:04Z | ---
dataset_info:
features:
- name: repo_name
dtype: string
- name: repo_url
dtype: string
- name: repo_description
dtype: string
- name: repo_stars
dtype: int64
- name: repo_forks
dtype: int64
- name: repo_last_updated
dtype: string
- name: repo_created_at
dtype: string
- name: repo_size
dtype: int64
- name: repo_license
dtype: string
- name: language
dtype: string
- name: text
dtype: string
- name: avg_line_length
dtype: float64
- name: max_line_length
dtype: int64
- name: alphnanum_fraction
dtype: float64
splits:
- name: train
num_bytes: 90026
num_examples: 23
download_size: 38373
dataset_size: 90026
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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