README.md

---
library_name: transformers
pipeline_tag: text-generation
datasets:
- quac
language:
- en
---

# Model Card for Model ID

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## Model Details

### Model Description

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**Model Nam**e: Mistral_7b_question_generation_v1

**Base Model**: mistral_7b_v0.1

Version: 1

**Description**: Mistral_7b_question_generation_v1 is a fine-tuned variant of the mistral_7b_v0.1 model specifically tailored for question generation tasks. The base model, mistral_7b_v0.1, is a large-scale language model trained by Mistral AI, featuring 7 billion parameters. This finetuned version has undergone additional training on a dataset specifically curated for question-generation tasks. The model is adept at understanding context and generating meaningful questions based on given text inputs.

**Key Features**:

**Question Generation**: The primary function of this model is to generate questions based on input text passages. It can analyze the content of the text and formulate questions that probe various aspects of the information presented.

**Natural Language Understanding**: Leveraging its pre-trained knowledge and fine-tuning question generation data, the model demonstrates a strong ability to understand nuances in language and context, enabling it to generate relevant and coherent questions.

**Large Scale**: With 7 billion parameters, Mistral_7b_question_generation_v1 encompasses a vast amount of linguistic knowledge, allowing it to generate questions across a wide range of topics and domains.

**Contextual Awareness**: The model takes into account the context provided in the input text to generate questions that are contextually relevant and meaningful, ensuring that the generated questions accurately reflect the content of the passage.

**Adaptability**: Mistral_7b_question_generation_v1 can be fine-tuned further on specific question generation tasks or domains to enhance its performance and tailor it to specific application scenarios.

**Potential Use Cases**:

**Educational Content Creation**: The model can be used to automatically generate questions for educational materials, textbooks, or study guides based on given passages.
Content Summarization and Assessment: Mistral_7b_question_generation_v1 can aid in summarizing content by generating questions that cover key points, and it can also be used for assessing comprehension and understanding through question-based evaluations.
QA Systems Enhancement: Integrating the model into question-answering systems can improve their capabilities by enabling them to generate relevant questions to further explore topics or clarify information.

- **Developed by:** [Hyun Lee]
- **Model type:** [LLM]
- **Language(s) (NLP):** [EN-NLP]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [Mistral_7b_v0.1]

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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