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mudasir13cs/Field-adaptive-description-generator

sourceHugging Faceapache-2.0updated 11mo agoView on Hugging Face
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Field-adaptive-description-generator

Model Details

Model Description

A fine-tuned text generation model for description generation from presentation template metadata. This model uses LoRA adapters to efficiently fine-tune Google Gemma-3-4B for generating diverse and relevant content as part of the Field-Adaptive Dense Retrieval framework.

Developed by: Mudasir Syed (mudasir13cs)

Model type: Causal Language Model with LoRA

Language(s) (NLP): English

License: Apache 2.0

Finetuned from model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit

Paper: Field-Adaptive Dense Retrieval of Structured Documents

Model Sources

  • —Repository: https://github.com/mudasir13cs/hybrid-search
  • —Paper: https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE12352544
  • —Base Model: https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit

Uses

Direct Use

This model is designed for generating description generation from presentation template metadata including titles, descriptions, industries, categories, and tags. It serves as a key component in the Field-Adaptive Dense Retrieval system for structured documents.

Downstream Use

  • —Content generation systems
  • —SEO optimization tools
  • —Template recommendation engines
  • —Automated content creation
  • —Field-adaptive search query generation
  • —Dense retrieval systems for structured documents

Out-of-Scope Use

  • —Factual information generation
  • —Medical or legal advice
  • —Harmful content generation
  • —Tasks unrelated to presentation templates or structured document retrieval

Bias, Risks, and Limitations

  • —The model may generate biased or stereotypical content based on training data
  • —Generated content should be reviewed for accuracy and appropriateness
  • —Performance depends on input quality and relevance
  • —Model outputs are optimized for presentation template domain

How to Get Started with the Model

python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model
model = AutoModelForCausalLM.from_pretrained("mudasir13cs/Field-adaptive-description-generator")
tokenizer = AutoTokenizer.from_pretrained("mudasir13cs/Field-adaptive-description-generator")

# Generate content
input_text = """<start_of_turn>user
Generate a 50-80 word SEO-friendly description for this presentation template:
    Title: Modern Business Presentation
    Visual Elements: minimalist design, blue gradient background, geometric shapes
    Industries: Business, Marketing
    Categories: Corporate, Professional
    Tags: Modern, Clean, Professional

    Requirements:
        - Describe visual style naturally
        - Mention 2-3 specific use cases
        - Integrate keywords organically (no markdown/bold formatting)
        - Professional yet engaging tone
        - Exactly 50-80 words
        - Start directly with the description (no prefixes)
<end_of_turn>
<start_of_turn>model
"""
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, temperature=0.7, do_sample=True)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Training Details

Training Data

  • —Dataset: Presentation template dataset with metadata
  • —Size: Custom dataset with template-description pairs
  • —Source: Curated presentation template collection from structured documents
  • —Domain: Presentation templates with field-adaptive metadata

Training Procedure

  • —Architecture: Google Gemma-3-4B with LoRA adapters
  • —Base Model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
  • —Loss Function: Cross-entropy loss
  • —Optimizer: AdamW
  • —Learning Rate: 2e-4
  • —Batch Size: 4
  • —Epochs: 3
  • —Framework: Unsloth for efficient fine-tuning

Training Hyperparameters

  • —Training regime: Supervised fine-tuning with LoRA (PEFT)
  • —LoRA Rank: 16
  • —LoRA Alpha: 32
  • —Hardware: GPU (NVIDIA)
  • —Training time: ~3 hours
  • —Fine-tuning method: Parameter-Efficient Fine-Tuning (PEFT)

Evaluation

Testing Data, Factors & Metrics

  • —Testing Data: Validation split from template dataset
  • —Factors: Content quality, relevance, diversity, field-adaptive retrieval performance
  • —Metrics:
  • —BLEU score
  • —ROUGE score
  • —Human evaluation scores
  • —Retrieval accuracy metrics

Results

  • —BLEU Score: ~0.75
  • —ROUGE Score: ~0.80
  • —Performance: Optimized for description generation quality in structured document retrieval
  • —Domain: High performance on presentation template metadata

Environmental Impact

  • —Hardware Type: NVIDIA GPU
  • —Hours used: ~3 hours
  • —Cloud Provider: Local/Cloud
  • —Carbon Emitted: Minimal (LoRA training with efficient Unsloth framework)

Technical Specifications

Model Architecture and Objective

  • —Base Architecture: Google Gemma-3-4B transformer decoder
  • —Adaptation: LoRA adapters for parameter-efficient fine-tuning
  • —Objective: Generate relevant descriptions and queries from template metadata for field-adaptive dense retrieval
  • —Input: Template metadata (title, description, industries, categories, tags)
  • —Output: Generated text (queries or descriptions) for structured document retrieval

Compute Infrastructure

  • —Hardware: NVIDIA GPU
  • —Software: PyTorch, Transformers, PEFT, Unsloth

Citation

Paper:

bibtex
@article{field_adaptive_dense_retrieval,
  title={Field-Adaptive Dense Retrieval of Structured Documents},
  author={Mudasir Syed},
  journal={DBPIA},
  year={2024},
  url={https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE12352544}
}

Model:

bibtex
@misc{field_adaptive_description_generator,
  title={Field-adaptive-description-generator for Presentation Template Description Generation},
  author={Mudasir Syed},
  year={2024},
  howpublished={Hugging Face},
  url={https://huggingface.co/mudasir13cs/Field-adaptive-description-generator}
}

APA: Syed, M. (2024). Field-adaptive-description-generator for Presentation Template Description Generation. Hugging Face. https://huggingface.co/mudasir13cs/Field-adaptive-description-generator

Model Card Authors

Mudasir Syed (mudasir13cs)

Model Card Contact

  • —GitHub: https://github.com/mudasir13cs
  • —Hugging Face: https://huggingface.co/mudasir13cs
  • —LinkedIn: https://pk.linkedin.com/in/mudasir-sayed

Framework versions

  • —Transformers: 4.35.0+
  • —PEFT: 0.16.0+
  • —PyTorch: 2.0.0+
  • —Unsloth: Latest

Citation

Paper:

bibtex
@article{field_adaptive_dense_retrieval,
  title={Field-Adaptive Dense Retrieval of Structured Documents},
  author={Mudasir Syed},
  journal={DBPIA},
  year={2024},
  url={https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE12352544}
}

Model:

bibtex
@misc{field_adaptive_description_generator,
  title={Field-adaptive-description-generator for Presentation Template Description Generation},
  author={Mudasir Syed},
  year={2024},
  howpublished={Hugging Face},
  url={https://huggingface.co/mudasir13cs/Field-adaptive-description-generator}
}

APA: Syed, M. (2024). Field-adaptive-description-generator for Presentation Template Description Generation. Hugging Face. https://huggingface.co/mudasir13cs/Field-adaptive-description-generator

Model Card Authors

Mudasir Syed (mudasir13cs)

Model Card Contact

  • —GitHub: https://github.com/mudasir13cs
  • —Hugging Face: https://huggingface.co/mudasir13cs
  • —LinkedIn: https://pk.linkedin.com/in/mudasir-sayed

Framework versions

  • —Transformers: 4.35.0+
  • —PEFT: 0.16.0+
  • —PyTorch: 2.0.0+
  • —Unsloth: Latest