Update README.md

README.md

@@ -30,4 +30,113 @@ language:
 pretty_name: MMLU auxiliary trained set labelled by e5 mistral 7b instruct
 size_categories:
 - 10K<n<100K
+---
+
+# Dataset Card for MMLU Auxiliary Trained Set Labelled by e5-mistral-7b-instruct
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contact Information](#contact-information)
+  - [Visualizations](#visualizations)
+
+## Dataset Description
+
+### Dataset Summary
+This dataset, named "MMLU Auxiliary Trained Set Labelled by e5-mistral-7b-instruct," consists of 99,842 examples spanning various subjects. Each instance includes a question, multiple choice options, a subject category, and an answer. The unique aspect of this dataset is the task label for each question, generated by a zero-shot classifier constructed from the `intfloat/e5-mistral-7b-instruct` model and trained on the auxiliary set of the Massive Multitask Language Understanding (MMLU).
+
+### Supported Tasks and Leaderboards
+This dataset supports text-generation tasks. It is particularly useful for training and evaluating models on a wide range of subjects using the task labels generated by the zero-shot classifier.
+
+### Languages
+The dataset is predominantly in English.
+
+## Dataset Structure
+
+### Data Instances
+A typical data instance in this dataset comprises:
+- `question`: A textual question or prompt.
+- `subject`: The subject category of the question.
+- `choices`: A list of possible answers.
+- `answer`: The correct answer's index from the choices.
+- `task`: The task label assigned by the zero-shot classifier.
+
+### Data Fields
+- `question`: string
+- `subject`: string
+- `choices`: sequence of strings
+- `answer`: int64
+- `task`: string
+
+### Data Splits
+- Train Split: 99,842 examples
+
+## Dataset Creation
+
+### Curation Rationale
+The dataset was curated to enhance the diversity and scope of language models in understanding and generating responses across a wide range of subjects. The use of a zero-shot classifier for task labelling introduces a novel approach to categorizing and understanding textual data.
+
+### Source Data
+The data was sourced from the auxiliary-train set of MMLU and processed to include task labels generated by the `intfloat/e5-mistral-7b-instruct` model.
+
+### Annotations
+#### Annotation process
+The task labels were generated automatically by a zero-shot classifier model, specifically `intfloat/e5-mistral-7b-instruct`.
+#### Who are the annotators?
+There were no human annotators; the process was entirely automated using the zero-shot classifier.
+
+### Personal and Sensitive Information
+The dataset does not contain personal or sensitive information as it is focused on general knowledge questions and subjects.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+This dataset can aid in developing more versatile and knowledgeable language models, potentially impacting various domains like education, research, and AI development.
+
+### Discussion of Biases
+Given the automated nature of task label generation and diverse subject matter, biases may be minimal but could still exist based on the underlying training data of the zero-shot classifier.
+
+### Other Known Limitations
+The primary limitation is the reliance on the zero-shot classifier's accuracy for task labeling, which may not always align with human judgment.
+
+## Additional Information
+
+### Dataset Curators
+The dataset was curated by the team involved in the development of `mmlu`.
+
+### Licensing Information
+The dataset is available under the Apache-2.0 License.
+
+### Citation Information
+[To be added based on dataset publication or release details.]
+
+### Contact Information
+[To be provided by dataset curators or maintainers.]
+
+### Visualizations
+- **Counts by Category**
+  ![https://huggingface.co/datasets/kz919/mmlu-auxiliary-train-e5-mistral-7b-instruct/blob/main/per_category_distribution.png]
+
+- **Counts by Super Category**
+  ![https://huggingface.co/datasets/kz919/mmlu-auxiliary-train-e5-mistral-7b-instruct/blob/main/super_category_distribution.png]
+
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