transformers safetensors mistral text-generation en dataset:quac arxiv:1910.09700 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- quac
language:
- en
library_name: transformers
pipeline_tag: text-generation
---

# 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]

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## Uses

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### Direct Use

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## How to Get Started with the Model

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## Training Details

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### Training Procedure 

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#### Training Hyperparameters

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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).

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## Technical Specifications [optional]

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