README.md

---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- embeddings
- semantic-search
- sentence-transformers
- presentation-templates
- information-retrieval
---

# Field-adaptive-bi-encoder

## Model Details

### Model Description
A fine-tuned SentenceTransformers bi-encoder model for semantic similarity and information retrieval. This model is specifically trained for finding relevant presentation templates based on user queries, descriptions, and metadata (industries, categories, tags).

**Developed by:** Mudasir Syed (mudasir13cs)

**Model type:** SentenceTransformer (Bi-encoder)

**Language(s) (NLP):** English

**License:** Apache 2.0

**Finetuned from model:** Microsoft/MiniLM-L12-H384-uncased

### Model Sources
**Repository:** https://github.com/mudasir13cs/hybrid-search

## Uses

### Direct Use
This model is designed for semantic search and information retrieval tasks, specifically for finding relevant presentation templates based on natural language queries.

### Downstream Use
- Presentation template recommendation systems
- Content discovery platforms
- Semantic search engines
- Information retrieval systems

### Out-of-Scope Use
- Text generation
- Question answering
- Machine translation
- Any task not related to semantic similarity

## Bias, Risks, and Limitations
- The model is trained on presentation template data and may not generalize well to other domains
- Performance may vary based on the quality and diversity of training data
- The model inherits biases present in the base model and training data

## How to Get Started with the Model

```python
from sentence_transformers import SentenceTransformer
import torch

# Load the model
model = SentenceTransformer("mudasir13cs/Field-adaptive-bi-encoder")

# Encode text for similarity search
queries = ["business presentation template", "marketing slides for startups"]
embeddings = model.encode(queries)

# Compute similarity
from sentence_transformers import util
cosine_scores = util.cos_sim(embeddings[0], embeddings[1])
print(f"Similarity: {cosine_scores.item():.4f}")
```

## Training Details

### Training Data
- **Dataset:** Presentation template dataset with descriptions and queries
- **Size:** Custom dataset of presentation templates with metadata
- **Source:** Curated presentation template collection

### Training Procedure
- **Architecture:** SentenceTransformer with triplet loss
- **Loss Function:** Triplet loss with hard negative mining
- **Optimizer:** AdamW
- **Learning Rate:** 2e-5
- **Batch Size:** 16
- **Epochs:** 3

### Training Hyperparameters
- **Training regime:** Supervised learning with triplet loss
- **Hardware:** GPU (NVIDIA)
- **Training time:** ~2 hours

## Evaluation

### Testing Data, Factors & Metrics
- **Testing Data:** Validation split from presentation template dataset
- **Factors:** Query-description similarity, template relevance
- **Metrics:** 
  - MAP@K (Mean Average Precision at K)
  - MRR@K (Mean Reciprocal Rank at K)
  - Cosine similarity scores

### Results
- **MAP@10:** ~0.85
- **MRR@10:** ~0.90
- **Performance:** Optimized for presentation template retrieval

## Environmental Impact
- **Hardware Type:** NVIDIA GPU
- **Hours used:** ~2 hours
- **Cloud Provider:** Local/Cloud
- **Carbon Emitted:** Minimal (local training)

## Technical Specifications

### Model Architecture and Objective
- **Architecture:** Transformer-based bi-encoder
- **Objective:** Learn semantic representations for similarity search
- **Input:** Text sequences (queries and descriptions)
- **Output:** 384-dimensional embeddings

### Compute Infrastructure
- **Hardware:** NVIDIA GPU
- **Software:** PyTorch, SentenceTransformers, Transformers

## Citation

**BibTeX:**
```bibtex
@misc{field-adaptive-bi-encoder,
  title={Field-adaptive Bi-encoder for Presentation Template Search},
  author={Mudasir Syed},
  year={2024},
  url={https://huggingface.co/mudasir13cs/Field-adaptive-bi-encoder}
}
```

**APA:**
Syed, M. (2024). Field-adaptive Bi-encoder for Presentation Template Search. Hugging Face. https://huggingface.co/mudasir13cs/Field-adaptive-bi-encoder

## Model Card Authors
Mudasir Syed (mudasir13cs)

## Model Card Contact
- **GitHub:** https://github.com/mudasir13cs
- **Hugging Face:** https://huggingface.co/mudasir13cs

## Framework versions
- SentenceTransformers: 2.2.2
- Transformers: 4.35.0
- PyTorch: 2.0.0