Transformers
Safetensors
English
t5
text2text-generation
interview-questions
question-generation
hr-tech
text-generation-inference
Instructions to use sudaisdev/Question_Generation_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sudaisdev/Question_Generation_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sudaisdev/Question_Generation_model") model = AutoModelForSeq2SeqLM.from_pretrained("sudaisdev/Question_Generation_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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language:
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- en
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tags:
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- text2text-generation
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- t5
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- interview-questions
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- question-generation
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- hr-tech
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text2text-generation
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base_model: t5-base
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---
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# Interview Question Generation Model (T5-base)
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This is a fine-tuned T5-base model that generates interview questions
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from job details such as job title, description, required experience,
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and skills.
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It is designed for HR platforms, hiring tools, and interview preparation
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apps.
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---
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## Model Details
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- **Base model:** T5-base
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- **Architecture:** T5ForConditionalGeneration (encoder-decoder)
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- **Parameters:** ~220M
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- **Language:** English
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- **Fine-tuned on:** custom interview question dataset
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- **Task:** text-to-text generation
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---
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## Intended Use
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Use this model when you want to automatically generate a single
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interview question for a given job role.
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**Good for:**
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- HR screening tools
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- Interview preparation apps
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- Recruitment automation
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**Not good for:**
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- Generating full interview scripts
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- Non-English languages
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- Legal or medical advice
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---
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## Input Format
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The model expects **one plain text prompt** in this exact format:
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```text
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Generate an interview question.
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Job Title: <job title>
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Job Description: <job description>
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Required Experience: <experience>
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Skills: <comma-separated skills>
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Question Type: <question type>
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