model_type

Choose models based on input data type to be processed and Generative AI models

use_cases

Choose models by AI Use Cases for your product, customer engagement, productivity, etc.

industry

functions

Choose models based on Organizational Functions

LLaMA-3 8B is a cutting-edge language model that has been trained on a massive dataset of text from the internet, books, and other sources. With its 8 billion parameters, this model is capable of understanding and generating human-like text with unprecedented accuracy and nuance. LLaMA-3 8B is particularly well-suited for applications that require advanced language understanding and generation capabilities, such as chatbots, virtual assistants, and natural language processing pipelines. Some potential use cases for LLaMA-3 8B include:&nbsp;* Building conversational AI systems that can engage in natural-sounding conversations with humans&nbsp;* Generating high-quality content, such as articles, blog posts, and social media updates * Improving language translation and localization capabilities for global businesses&nbsp;* Enhancing customer service and support systems with more accurate and empathetic responses&nbsp;* Developing personalized recommendation systems that can understand and respond to user preferencesOverall, LLaMA-3 8B is an incredibly powerful tool that has the potential to transform the way we interact with language and technology. Its advanced capabilities make it an ideal choice for a wide range of applications, from customer service and content generation to language translation and more.

LLama3 - 8B

input_text

system_prompt

response

Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-Beta is the first model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO).The model was initially fine-tuned on a variant of the UltraChat dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with HuggingFace TRL's DPOTrainer on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our demo to test its capabilities.Model description<ul><li>Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.</li><li>Language(s) (NLP): Primarily English</li><li>License: MIT</li><li>Finetuned from model: mistralai/Mistral-7B-v0.1</li></ul>Reference: HuggingFaceH4/zephyr-7b-beta

Zephyr-7B

Zephyr is a series of language models that are trained to act as helpful assistants.

effi-13B parameters is a causal decoder-only model built by AI Planet based on Llama-2-13b-chat-hf and fine tuned using the 1.8 Million coversations from CoT dataset available in huggingface datasets. The model is made available under the Apache 2.0 license.&nbsp;Why use effi-13B-Instruct?<ul><li>This is a ready to use chat/instruct model based on Llama-2-13b-chat-hf, which provides a rationale for the context provided.</li><li>Llama-2 is the best open-source model available. This is an instruct model, which may not be ideal for further finetuning. If you are interested in building your own instruct/chat model, we recommend starting from Llama-2-13b-chat-hf</li></ul><blockquote>You will need at least 85-100GB of memory to swiftly run inference with effi-13b.</blockquote>DescriptionThis model has been fine-tuned on Chain of Thought datasets, which has context from mixed sources with corresponding rationale. The final finetuned Large Language Model(LLM) have shown enhanced capabilities of solving novel tasks by providing a reasoning.Developed by: AI Planet Model type: Casual Decoder only Finetuned from model: Llama-2-13b-chat-hf&nbsp;Training Detailseffi-13b has been finetuned on https://huggingface.co/datasets/kaist-ai/CoT-Collection The data was tokenized with the meta-llama/Llama-2-13b-chat-hf tokenizer.Fine-tuning approach using PefT and Qlora(<a href="https://github.com/aiplanethub/genai-stack/pull/60">https://huggingface.co/blog/4bit-transformers-bitsandbytes</a>)<ul><li>lora_alpha=32,</li><li>lora_dropout=0.05,</li><li>r=8,</li><li>bias="none",</li><li>task_type="CAUSAL_LM"</li><li>load_in_4bit=True,</li><li>bnb_4bit_quant_type = "nf4",</li><li>bnb_4bit_use_double_quant=True,</li><li>bnb_4bit_compute_dtype=torch.bfloat16</li><li>num_train_epochs = 1</li><li>fp16 = False</li><li>bf16 = False</li><li>per_device_train_batch_size = 1</li><li>per_device_eval_batch_size = 1</li><li>gradient_accumulation_steps = 4</li><li>gradient_checkpointing = True</li><li>max_grad_norm = 0.3</li><li>learning_rate = 2e-4</li><li>weight_decay = 0.001</li><li>optim = "paged_adamw_32bit"</li><li>lr_scheduler_type = "constant"</li><li>max_steps = 500</li><li>warmup_ratio = 0.03</li><li>group_by_length = True</li><li>save_steps = 25</li><li>logging_steps = 5</li><li>max_seq_length = 2048</li><li>packing = False</li><li>device_map = {"": 0}</li></ul>&nbsp;

effi-13b

instruction

This model is an instruction finetuned model. It provides reasoning behind the response generated against an instruction specified.

This application allows for fast and scalable searching across collections of documents, making it suitable for real-time applications.Vector embeddings, especially those obtained through deep learning models, often have reduced dimensionality compared to the original documents. This reduction helps speed up search operations and reduces storage requirements.By using embeddings that capture semantic information, vector stores can perform semantic search. This means that even for queries with different phrasing or wording, the search can find similar documents based on their semantic similarity, making the search results more relevant.Overall, using vector stores for document search enables efficient and powerful similarity-based querying, making it a valuable tool in various applications, including information retrieval, recommender systems, content-based search, and more.

Analyze Your Document (OCR enabled)

pdf1

query

This application helps to chat and analyze your documents for the context enquired and present the context related to the query along with the page number for reference.

&nbsp;Leveraging the power of advanced language processing and a vast database of medical knowledge, our agent is capable of addressing a wide range of healthcare inquiries. Whether you&#39;re seeking information on symptoms, treatments, medications, or general health concerns, our medical agent is here to assist you. Some of the benefits are mentioned below :

<ul>
	<li>Access to reliable medical information: The medical agent taps into a vast database of medical knowledge, providing users with accurate and trustworthy information. This can help individuals make informed decisions about their health, understand their symptoms, and gain insights into various medical conditions and treatments.</li>
	<li>Immediate availability: The medical agent is accessible 24/7, allowing users to obtain answers to their medical questions at any time. This can be particularly useful for individuals who may have concerns outside of regular office hours or those in different time zones.</li>
	<li>Convenience and efficiency: With the medical agent, users can quickly obtain information without at given point of time prior to scheduling appointments or wait for a healthcare provider&#39;s availability. This can save time and streamline the process of seeking medical advice or clarifying doubts.</li>
	<li>Privacy and confidentiality: Using the medical agent provides a secure and private platform to ask medical questions. Users can feel confident in discussing their health concerns without worrying about the potential breach of personal information.</li>
	<li>Education and empowerment: The medical agent serves as a valuable educational resource, enabling users to learn more about various medical topics. By gaining a better understanding of their health conditions, users can take a more active role in managing their well-being and engaging in productive discussions with healthcare professionals.</li>
	<li>Scalability: As an AI-powered solution, the medical agent can handle a large volume of queries simultaneously, making it scalable and capable of serving a wide user base. This scalability ensures that more people can benefit from access to reliable medical information.</li>
</ul>

Medical Agent

This model helps to answer basic medical queries.

Introducing the Advanced Text to Image Diffusion AI Model &ndash; a ground-breaking development in the field of artificial intelligence. This powerful model leverages the potential of AI to convert textual descriptions into high-definition, realistic images.

The applications are endless - from content creators needing illustrative images for their work, educators explaining complex concepts through visuals, to businesses developing advertising materials based on innovative ideas. The model also proves to be a boon for those with visual impairments, translating written information into visuals.

The model is powered by the open-source version of Stable Diffusion 2.1.

Text to Image Diffusion AI Model

guidance_scale

Height of the image. Defaults to 512. Defaults to 512. width * height should not be greater than 786432.

height

inference_steps

negative_prompt

prompt

Width of the image. Defaults to 512. width * height should not be greater than 786432.

width

image_0

Generate high quality images with simple using state of the art AI model

Multilingual Text-to-Speech (TTS) systems have become increasingly popular in various applications such as voice assistants, audiobooks, and language learning platforms.

Best Practices:

<ul>
	<li>Text Preprocessing:</li>
</ul>

&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Ensure that the input text is clean and properly formatted. 
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; If necessary, perform text normalization or tokenization to enhance the quality of the generated speech.

<ul>
	<li>Language and Pronunciation:</li>
</ul>

&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Be mindful of language-specific nuances and pronunciation rules when providing input text. 
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Use language-appropriate punctuation and symbols to achieve the desired speech output.

<ul>
	<li>Performance Considerations:</li>
</ul>

&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Large input texts or complex linguistic structures may result in longer processing times. 
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Split long texts into smaller segments and make multiple API requests if necessary.

Conclusion: 
Multilingual Text-to-Speech provided by Elevenlab.ai offers a powerful and user-friendly solution for generating high-quality speech in multiple languages. By following the guidelines outlined in this documentation, you can seamlessly integrate this functionality into your applications, enhancing the user experience and enabling multilingual speech synthesis.

Multilingual-Text-To-Speech

The text file contains data which has to be converted to specch

Thee output file contains the audio of the provided text

audio

Convert text in multiple languages, including English, German, Polish, Spanish, Italian, French, Portuguese, and Hindi to respective audio files

This application evaluates essays written by the students based on set criteria like content, style, and structure and provide students with feedback to help improve their writing skills.

&nbsp;

Attributes that are required to use API: Expectations from the Essay, Grading Criteria, Topic, Essay written by the student or learner, Grading Scale. Read documentation for detailed guidelines to use this.

&nbsp;

 
&nbsp;

Essay Evaluator with Specific Grading Criteria

Specify the weightage associated with each parameter

attribute

content

Specify the parameters based on which the essay has to be evaluated

requirement

topic

This application evaluates essays written by the students based on set criteria like content, style, and structure and provide students with feedback.

Provide evaluation and feedback on a given essay.

The objective of an AI Medical Report Analyzer can be summarized as follows:

<ul>
	<li>Accurately interpret and extract relevant information from the medical report provided, such as patient history, symptoms, test results, and other clinical data.</li>
	<li>Analyze this information using advanced algorithms and machine learning techniques to generate a comprehensive diagnosis, prognosis, and treatment plan tailored to the patient&#39;s individual needs.</li>
	<li>Provide actionable recommendations for medication and follow-up care, including dosage, frequency, and potential side effects.</li>
	<li>Continuously learn and adapt to new data and information, improving accuracy and precision over time.</li>
	<li>Ensure patient privacy and confidentiality by adhering to strict data security protocols and compliance with regulatory requirements.</li>
	<li>Integrate seamlessly with existing healthcare systems and workflows, enabling healthcare professionals to access and act upon the insights provided by the AI medical report analyzer in real-time.</li>
</ul>

Overall, the objective of an AI medical report analyzer to provide Diagnosis, prognosis, medication, and follow-up information from the medical report provided is to help healthcare professionals make more informed decisions about patient care, leading to better outcomes and improved quality of life for patients.

&nbsp;

 
&nbsp;

Medical Report Analyzer

The response from the LLM is captured here

output

This model analyzes the medical report to retrieve the diagnosis, prognosis and medications specified in the medical report provided.

Indian language to Indian language translation using AI has several potential benefits and applications. Here are a few examples:<ul><li>Communication: India is a diverse country with multiple languages spoken across various regions. Language barriers can pose a significant challenge for communication, especially in a business or educational setting. AI-based language translation can help bridge this gap, enabling people from different regions to communicate with each other seamlessly.</li><li>Education: AI-based language translation can be used to translate educational content from one Indian language to another, making it more accessible to a wider audience. This can be especially useful in areas where students may not have access to education in their native language.</li><li>Government: In a country like India, where there are multiple official languages, AI-based language translation can help government agencies and officials to communicate effectively with citizens who may not speak the same language as them. This can improve government services and make them more accessible to citizens.</li><li>Business: India is a rapidly growing market with many businesses looking to expand into different regions. AI-based language translation can help businesses communicate effectively with potential customers and partners, allowing them to better understand the needs of the market and develop more effective strategies.</li></ul>Overall, Indian language to Indian language translation using AI has the potential to break down language barriers and enable greater communication, understanding, and collaboration across the country.This model supports 11 prominent Indian languages: Assamese (as),Hindi (hi),Marathi (mr),Tamil (ta),Bengali (bn),Kannada (kn),Odia (or),Telugu (te),Gujarati (gu),Malayalam (ml),Punjabi (pa)

Indian Language Translation

This model translates text from one Indian to another Indian language. This model supports 11 prominent Indian languages: Assamese ,Hindi ,Marathi ,Tamil ,Bengali ,Kannada Odia, Telugu, Gujarati, Malayalam, Punjabi.

The use of medical diagnosis using AI based on the symptoms provided by the patient is to provide a more accurate and timely diagnosis of a patient&#39;s medical condition. AI-based diagnostic tools can analyze vast amounts of patient data, including symptoms, medical history etc, to identify patterns and make predictions about potential diagnoses.

This technology can help healthcare providers make more informed decisions about patient care, potentially leading to earlier detection and treatment of diseases. Additionally, AI-based diagnosis can assist in identifying rare and complex conditions that may be difficult for human clinicians to diagnose.

However, it is important to note that AI-based diagnosis should be used as a tool to augment clinical decision-making, not replace it. Human clinicians are still essential in interpreting and analyzing patient data and making treatment decisions based on the patient&#39;s individual circumstances.

AI  Medical Diagnosis

For how long has the patient been experiencing these symptoms

Duration_of_Symptom

Medication_if_any

Patient_Age

Mention any previously diagnosed medical condition

Previous_Medical_Condition_if_any

Mention the Symptoms experienced by the patient

Symptoms

This model will suggest on  the medical condition of the patient based on the information below and advise on the disease the symptoms might be indicating .It as well will recommend the next steps of action.

The objective of tutorial generation is to automatically create instructional materials, such as tutorials, guides, or how-to manuals, using computational methods. The main goal of tutorial generation is to provide users with clear and concise information on how to perform a particular task or acquire a specific skill, using natural language. For any support-related queries, you can reach out to us at <a href="mailto:support@aimarketplace.co">support@aimarketplace.co</a>.

Some of the specific objectives of tutorial generation include:

<ol>
	<li>Improving Accessibility: Tutorial generation aims to make educational resources more accessible to a wider audience, including people with disabilities or those with limited access to traditional educational resources.</li>
	<li>Enhancing Learning Outcomes: Tutorial generation aims to improve the quality of instructional materials, providing users with clear and concise explanations that are more effective in helping them acquire knowledge and develop new skills.</li>
	<li>Saving Time and Resources: Tutorial generation can automate the creation of instructional materials, reducing the need for manual content creation and thereby saving time and resources.</li>
	<li>Personalization: Tutorial generation can be used to create personalized learning materials that are tailored to a user&#39;s learning preferences, abilities, and prior knowledge, improving the efficiency and effectiveness of the learning process.</li>
	<li>Improving User Experience: Tutorial generation aims to improve the user experience of online learning platforms, technical documentation, and customer support services by providing clear and concise instructional materials that are easy to follow.</li>
</ol>

Tutorial generation has applications in a variety of domains, such as education, training, e-learning, and technical writing. It has the potential to save time and resources, by automating the creation of high-quality instructional materials, and improving the accessibility and usability of online learning resources.

Tutorial generation has many potential applications in various domains, including:

<ol>
	<li>Education and E-Learning: Tutorial generation can be used to create high-quality educational materials such as tutorials, guides, and lesson plans for online learning platforms, educational software, and e-learning courses. It can also be used to generate personalized learning materials based on the user&#39;s learning preferences and level of knowledge.</li>
	<li>Technical Writing and Documentation: Tutorial generation can be used to create technical manuals and guides for complex software and hardware systems, making it easier for users to understand and operate them. It can also be used to automatically generate API documentation and user guides.</li>
	<li>Customer Service and Support: Tutorial generation can be used to create help documentation and tutorials for customer support services and chatbots, helping customers to solve problems and learn about products or services.</li>
	<li>Gaming and Simulation: Tutorial generation can be used to create interactive tutorials and walkthroughs for video games and simulations, allowing users to learn about the game mechanics and master new skills quickly.</li>
	<li>Healthcare and Medicine: Tutorial generation can be used to create training materials for healthcare professionals, such as procedural guidelines, surgical training, and patient education materials.</li>
</ol>

 
&nbsp;

Generate Tutorial

Tutorial generation is a type of natural language generation (NLG) that focuses on creating step-by-step instructional materials or tutorials for a particular subject or task.

Blog post generation using AI is a process that involves using machine learning algorithms, NLP, state of the art Transformers to generate written content for blogs. The process typically begins by providing a topic or keyword, and then letting the AI generate the content, which can include introductions, body paragraphs, and even full articles. AI-powered blog post generation can save content creators a significant amount of time and effort, and can be used to create high-quality, engaging content that resonates with their audience. However, it&#39;s important to note that the quality of the content generated by AI is not always perfect, and often requires human oversight and editing. Overall, blog post generation using AI can be a powerful tool for content marketers, bloggers, and other content creators, helping them to scale their content creation efforts and reach a wider audience. For any support-related queries, you can reach out to us at support@aimarketplace.co.

&nbsp;

AI blog post generation can be used in a variety of applications, including:

<ol>
	<li>Content Marketing: AI-generated blog posts can be used by content marketers to create high-quality content quickly and at scale.</li>
	<li>News Aggregation: AI can be used to automatically generate news articles, summarizations or write-ups for a wide range of topics.</li>
	<li>SEO and SEM: AI-generated blog posts can be used to improve search engine optimization (SEO) and search engine marketing (SEM) efforts by creating content that is keyword-rich and relevant to the topic.</li>
	<li>Chatbots and Virtual Assistants: AI-generated blog posts can be used as a knowledge base for chatbots and virtual assistants, allowing them to provide accurate and helpful responses to user queries.</li>
	<li>E-commerce: AI-generated product descriptions can be used to describe and promote products on e-commerce websites, helping to increase sales and conversion rates.</li>
	<li>Education and Training: AI-generated blog posts can be used to create learning materials for educational and training purposes, including online courses, tutorials, and other instructional content.</li>
</ol>

Overall, AI blog post generation can be used in any application where high-quality written content is required, and where the speed and scalability of AI can be leveraged to improve efficiency and productivity.

&nbsp;

&nbsp;

 
&nbsp;

Generate Blogpost

Provide the details  for the blogpost creation

Blog post generation using AI is a process that involves using machine learning algorithms,NLP,State of the art Transformers, to generate written content for blogs

An AI teaching assistant is an artificial intelligence software application that is designed to support teaching and learning in educational settings. It uses natural language processing (NLP) and machine learning algorithms to interact with students and provide them with personalized learning experiences.

The AI teaching assistant can perform various tasks such as answering student questions, providing feedback on assignments, monitoring student progress, and suggesting additional resources for learning. It can also provide real-time assistance to students, helping them to solve problems, clarify concepts, and understand complex topics.

AI teaching assistants can be deployed in a variety of educational settings, including K-12 schools, higher education institutions, and corporate training programs. They are typically accessed through web applications, chatbots, or learning management systems (LMS), and can be integrated with other educational technologies such as virtual learning environments (VLE) and educational content management systems (CMS).

The use of AI teaching assistants is growing rapidly, as they offer a scalable and cost-effective solution to many of the challenges faced by educators, including large class sizes, individualized learning needs, and the need for real-time feedback and support. By leveraging the power of machine learning and natural language processing, AI teaching assistants can enhance the learning experience for students and improve learning outcomes.

&nbsp;For any support-related queries, you can reach out to us at <a href="mailto:support@aimarketplace.co">support@aimarketplace.co</a>.

There are many possible use cases for AI teaching assistants in different educational settings. Here are some examples:

<ol>
	<li>Answering student questions: An AI teaching assistant can provide real-time answers to student questions about course content, assignments, or other educational material. This can save time for instructors, help students get answers quickly, and promote student engagement.</li>
	<li>Providing feedback on assignments: AI teaching assistants can analyze student work and provide automated feedback on assignments. This can help students improve their performance, identify areas for improvement, and provide insights to instructors on common student misconceptions.</li>
	<li>Monitoring student progress: AI teaching assistants can track student performance and identify areas where students are struggling. This can enable instructors to intervene early and provide targeted support to students who need it.</li>
	<li>Personalizing learning experiences: AI teaching assistants can use machine learning algorithms to analyze student data and provide personalized learning experiences based on individual needs and preferences. This can help students learn more effectively and efficiently.</li>
	<li>Supporting language learning: AI teaching assistants can help students learn foreign languages by providing real-time feedback on pronunciation, grammar, and vocabulary. They can also provide practice exercises and quizzes to help students improve their language skills.</li>
	<li>Creating chatbots for student support: AI teaching assistants can be integrated into chatbots, which can be used to provide instant support to students on various educational issues such as course registration, library services, and career counseling.</li>
	<li>Improving accessibility: AI teaching assistants can provide accessibility features such as automated closed captioning, text-to-speech, and speech-to-text to help students with disabilities learn more effectively.</li>
</ol>

 
&nbsp;

Teaching Assistant

Provide how AI needs to explain the topic asked in the question

explain_mode

Provide the question you have related to a topic.

question

The purpose of an AI teaching assistant is to provide personalized and adaptive support to students in their learning process.

The Boolean Question Generation model is designed to generate questions that can be answered with a simple &quot;yes&quot; or &quot;no&quot; response, also known as a Boolean response. The primary goal of a Boolean question generator is to make it easier and more efficient to gather data on a wide range of topics, including knowledge, skills, preferences, and experiences. Get started now by reading the documentation. For any support-related queries, you can reach out to us at <a href="mailto:support@aimarketplace.co">support@aimarketplace.co</a>.

Some of the everyday use cases for this model in various industries include:

<ul>
	<li>Education: Boolean question generators can be used to create quizzes, tests, and exams, allowing educators to quickly and easily generate questions based on specific topics and educational goals.</li>
	<li>Recruitment and HR: Boolean question generators can be used to create pre-employment assessments, helping HR departments quickly and easily screen potential candidates for specific skills and qualifications.</li>
	<li>Market research: Boolean question generators can be used to create survey questions, allowing market researchers to gather data from large numbers of people in a quick and efficient manner.</li>
	<li>Healthcare: Boolean question generators can be used to create medical quizzes, tests, and assessments, helping healthcare providers diagnose and treat patients more effectively.</li>
	<li>Customer service: Boolean question generators can be used to create customer satisfaction surveys, allowing companies to gather data on customer experiences and identify areas for improvement.</li>
	<li>Gaming: Boolean question generators can be used to create trivia games and quizzes, providing players with a fun and engaging way to test their knowledge on a variety of topics.</li>
</ul>

&nbsp;

Boolean Question Generator

This model generates yes or no questions along with relevant answers

MCQ generation refers to the process of automatically generating multiple-choice questions (MCQs) using natural language processing (NLP) and machine learning techniques. MCQs are a widely used form of assessment in education and can be used to evaluate a student&#39;s knowledge, understanding, and critical thinking skills.

The process of MCQ generation involves analyzing a text or a set of texts and identifying the key concepts and information that should be tested. This information is then used to generate a set of questions and answer options that assess the student&#39;s knowledge and understanding of the material.

MCQ generation can be used to create large sets of questions quickly and efficiently, which can be especially useful in situations where a large number of assessments are required, such as in online courses or standardized tests. However, it can be challenging to ensure that the generated questions are valid, reliable, and appropriately aligned with the learning objectives. Therefore, human oversight and quality control are necessary to ensure the quality of the generated questions. For any support-related queries, you can reach out to us at <a href="mailto:support@aimarketplace.co">support@aimarketplace.co</a>.

&nbsp;

MCQ generation has a variety of applications in education, assessment, and research. Some of the most common applications include:

<ol>
	<li>Education: MCQ generation can be used to create quizzes, practice exams, and other forms of self-assessment for students. This can help students prepare for exams, identify areas where they need to improve, and reinforce their understanding of key concepts.</li>
	<li>Assessment: MCQ generation can be used to create standardized tests for large-scale assessment purposes, such as college admissions exams or professional certification exams.</li>
	<li>Language Learning: MCQ generation can be used to create language proficiency tests or vocabulary quizzes that assess a student&#39;s ability to understand and use specific words, phrases, or grammatical structures.</li>
	<li>Research: MCQ generation can be used in scientific research to develop tests or surveys that measure specific constructs, such as attitudes, beliefs, or behaviors.</li>
	<li>Hiring and Recruitment: MCQ generation can be used to create pre-employment tests or assessments that evaluate job candidates&#39; knowledge, skills, and abilities.</li>
</ol>

Generate MCQ

provide the context here for which MCQ has to be generated

context

This model uses state of the art Transformer models to generate Questions along with Answers with multiple choice  options