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--- |
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library_name: transformers |
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pipeline_tag: text-generation |
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datasets: |
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- quac |
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language: |
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- en |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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**Model Nam**e: Mistral_7b_question_generation_v1 |
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**Base Model**: mistral_7b_v0.1 |
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Version: 1 |
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**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. |
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**Key Features**: |
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**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. |
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**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. |
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**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. |
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**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. |
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**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. |
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**Potential Use Cases**: |
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**Educational Content Creation**: The model can be used to automatically generate questions for educational materials, textbooks, or study guides based on given passages. |
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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. |
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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. |
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- **Developed by:** [Hyun Lee] |
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- **Model type:** [LLM] |
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- **Language(s) (NLP):** [EN-NLP] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [Mistral_7b_v0.1] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
