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aimhkimi74/
Bert-Model-living-room

Name: Bert-Model-living-room
Availability: InStock

google-bert/bert-base-uncased

finetune

mit

endpoints_compatible

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🏡 Fine-Tuned BERT for Interior Design (Living Room)

This is a fine-tuned BERT model for interior design prompt validation and style classification. It was developed as part of my Final Year Project: Text-to-Image Interior Design Generator with Generative AI Assistance.

The model supports:

✅ Prompt Validation – distinguish valid vs. invalid design prompts
🏷️ Style Classification – classify valid prompts into 7 living room design styles:
- Modern
- Scandinavian
- Rustic
- Industrial
- Traditional
- Mid-Century Modern
- Coastal

📊 Results

Prompt Validation (binary classification) → F1-score: 1.00
Style Classification (7 classes) → F1-score: 0.99

📂 Model Details

Base model: bert-base-uncased
Library: Transformers (PyTorch)
Trained with: custom dataset of interior design prompts
Labels mapping:

json
1{ 2 "id2label": { 3 "0": "Modern", 4 "1": "Scandinavian", 5 "2": "Rustic", 6 "3": "Industrial", 7 "4": "Traditional", 8 "5": "Mid-Century Modern", 9 "6": "Coastal" 10 }, 11 "label2id": { 12 "Modern": 0, 13 "Scandinavian": 1, 14 "Rustic": 2, 15 "Industrial": 3, 16 "Traditional": 4, 17 "Mid-Century Modern": 5, 18 "Coastal": 6 19 } 20}

⚙️ How to Use

python

from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load model from Hugging Face Hub
tokenizer = BertTokenizer.from_pretrained("aimhkimi74/Bert-Model-living-room")
model = BertForSequenceClassification.from_pretrained("aimhkimi74/Bert-Model-living-room")

# Example prompt
text = "A modern living room with a gray sofa and wooden floor."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
pred = torch.argmax(outputs.logits, dim=1)

# Map prediction to style
label_map = {
    0: "Modern",
    1: "Scandinavian",
    2: "Rustic",
    3: "Industrial",
    4: "Traditional",
    5: "Mid-Century Modern",
    6: "Coastal"
}
print("Predicted Style:", label_map[pred.item()])

📖 Training Procedure

Optimizer: AdamW
Learning Rate: 5e-5
Epochs: 4
Batch size: 32
Evaluation metrics: Accuracy, F1-score

⚠️ Intended Use & Limitations

Intended use: Assist a text-to-image system by validating prompts and tagging living-room styles.
Not intended for: Architectural safety decisions, non-living-room styles, or multilingual inputs (English only).
Known limits: Performance may drop outside the 7 styles or with very short/ambiguous prompts.

📜 License

This model is released under the MIT License.

📚 References

Devlin, J. et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Hugging Face Transformers library
Rombach, R. et al. High-Resolution Image Synthesis with Latent Diffusion Models