---
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
tags:
- text-generation
- gemma
- lora
- peft
- presentation-templates
- information-retrieval
- crello
datasets:
- cyberagent/crello
language:
- en
base_model:
- unsloth/gemma-3-4b-it-unsloth-bnb-4bit
---

# 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](https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE12352544)

### 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