Sachin21112004/carrerflow-ai

Model Card for CareerFlow-AI

Model Summary

CareerFlow-AI is a career-focused NLP model designed to understand, summarize, and reason over career guidance content, job descriptions, resumes, and skill-oriented text.
It is optimized for educational and professional career guidance use cases, covering school-level guidance (Class 1–12), higher education paths, and job-market intelligence.

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Model Details

Model Description

CareerFlow-AI is a PEFT (LoRA)-based fine-tuned model built on DistilBERT, created to provide structured career intelligence.
The model understands career-related language such as roles, skills, qualifications, career paths, and job descriptions, and can be used in career advisory systems, dashboards, and AI assistants.

It is lightweight, fast, and suitable for real-world deployment where efficiency and interpretability are important.

• Developed by: Sachin Rao
• Funded by: Sachin Rao
• Shared by: Sachin Rao
• Model type: DistilBERT-based NLP model (PEFT / LoRA)
• Language(s) (NLP): English
• License: Other
• Finetuned from model: DistilBERT

Model Sources

• Repository: https://huggingface.co/Sachin21112004/carrerflow-ai
• Paper: Not available
• Demo: Not available

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Uses

Direct Use

CareerFlow-AI can be directly used for:

• Career guidance summarization
• Understanding job descriptions and career text
• Educational career advisory chatbots
• Resume and skill-related content understanding
• Career dashboards and analytics platforms

Downstream Use

The model can be integrated into:

• Career recommendation engines
• Student guidance portals
• Job–skill matching systems
• Resume analysis pipelines
• Educational AI assistants

Out-of-Scope Use

• Medical, legal, or financial advice
• Autonomous hiring or rejection decisions
• Real-time labor market prediction
• High-stakes decision-making without human review

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Bias, Risks, and Limitations

• The model may reflect biases present in job market and career datasets
• Certain domains (e.g., technology careers) may be over-represented
• Career suggestions should not be treated as absolute recommendations
• Performance may degrade for non-English or highly informal text

Recommendations

Users should apply human oversight when using the model in decision-support systems.
It is recommended to combine CareerFlow-AI outputs with domain expertise and fairness checks.

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How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "Sachin21112004/carrerflow-ai"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "I want to become a software engineer and learn Python and DSA."
inputs = tokenizer(text, return_tensors="pt", truncation=True)
outputs = model(**inputs)