Sachin21112004
/

carrerflow-ai

@@ -11,200 +11,93 @@ language:
 pipeline_tag: summarization
 ---
-# Model Card for Model ID
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 ## Model Details
 ### Model Description
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 ## How to Get Started with the Model
-Use the code below to get started with the model.
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-### Framework versions
-- PEFT 0.18.0

 pipeline_tag: summarization
 ---
+# 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.
+---
 ## 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
+---
 ## 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
+---
 ## 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.
+---
 ## How to Get Started with the Model
+```python
+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)