README.md

metadata

title: Job Description Parser
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.48.0
app_file: app.py
pinned: false

Job Parser Model (Qwen Fine-Tuned)

This repository contains a fine-tuned version of the Qwen model, specifically adapted to parse job descriptions into structured JSON format.

---

💼 Use Case

The model takes raw job descriptions (JD) as input and outputs structured JSON data containing:

• Job Titles
• Company Name & Website
• Skills
• Compensation
• Location
• Work Mode
• Experience
• Qualification
• Industry
• Posted Date
• Notice Period
• Job Type

Perfect for building:

• Resume & JD analyzers
• Job boards with smart filtering
• HR automation tools
• Job matching engines

---

🧠 Model Details

• Base Model: Qwen (qwen/Qwen3-0.6B)
• Fine-tuned on: 80+ custom-labeled job descriptions
• Trained using: Hugging Face Transformers & TRL's SFTTrainer
• Dataset Format: Few-shot prompting with Qwen’s <|im_start|> / <|im_end|> chat template
• Output: Structured JSON response

---

🚀 How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Rithankoushik/job-parser-model-qwen"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)

prompt = """<|im_start|>system
You are a helpful assistant that extracts structured information from job descriptions.
<|im_end|>
<|im_start|>user
[Paste job description here]
<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))