| --- |
| tags: |
| - reranker |
| - qwen3 |
| - information-retrieval |
| - product-search |
| base_model: Qwen/Qwen3-Reranker-0.6B |
| license: mit |
| language: |
| - en |
| pipeline_tag: text-classification |
| --- |
| |
| # Qwen3-Reranker-HomeDepot |
|
|
| Fine-tuned [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) on the Home Depot product search dataset for e-commerce search ranking. |
|
|
| ## Model Description |
|
|
| This model is a cross-encoder reranker trained to score query-product pairs for relevance. It takes a search query and product description as input and outputs a relevance score between 0 and 1. |
|
|
| **Base Model**: Qwen/Qwen3-Reranker-0.6B |
| **Training Dataset**: Home Depot Product Search |
| **Training Samples**: 51,911 |
| **Task**: Binary relevance classification (relevant/irrelevant) |
|
|
| ## Training Details |
|
|
| ### Dataset |
|
|
| - **Total samples**: 51,911 |
| - **Splits**: 70% train / 15% validation / 15% test |
| - **Splitting strategy**: Query-stratified (prevents data leakage) |
| - **Label threshold**: Relevance ≥ 2.33 → relevant (1), else irrelevant (0) |
| - **Label distribution**: ~68% relevant, ~32% irrelevant |
|
|
| ### Training Configuration |
|
|
| ``` |
| Learning rate: 5e-6 |
| Batch size: 8 × 2 = 16 |
| Epochs: 3 |
| Optimizer: AdamW (weight_decay=0.01) |
| Scheduler: Linear warmup + decay |
| Mixed precision: BF16 |
| ``` |
|
|
| ### Hardware |
|
|
| - **GPU**: NVIDIA A100 80GB |
| - **Training time**: ~2-4 hours |
|
|
| ## Usage |
|
|
| ### Installation |
|
|
| ```bash |
| pip install transformers torch |
| ``` |
|
|
| ### Basic Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| |
| # Load model and tokenizer |
| model = AutoModelForCausalLM.from_pretrained( |
| "codefactory4791/Qwen3-Reranker-HomeDepot", |
| torch_dtype=torch.bfloat16, |
| device_map="auto", |
| trust_remote_code=True |
| ) |
| tokenizer = AutoTokenizer.from_pretrained( |
| "codefactory4791/Qwen3-Reranker-HomeDepot", |
| trust_remote_code=True |
| ) |
| |
| # Prepare input |
| query = "cordless drill" |
| document = "DEWALT 20V MAX Cordless Drill Kit with battery and charger" |
| |
| # Format prompt |
| prompt = f'''<|im_start|>system |
| Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|> |
| <|im_start|>user |
| <Instruct>: Given a web search query, retrieve relevant passages that answer the query |
| <Query>: {query} |
| <Document>: {document}<|im_end|> |
| <|im_start|>assistant |
| <think> |
| |
| </think> |
| |
| ''' |
| |
| # Tokenize and get score |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model(**inputs) |
| logits = outputs.logits[0, -1, :] |
| |
| # Get yes/no token probabilities |
| token_yes = tokenizer.convert_tokens_to_ids('yes') |
| token_no = tokenizer.convert_tokens_to_ids('no') |
| score = torch.sigmoid(logits[token_yes] - logits[token_no]).item() |
| |
| print(f"Relevance score: {score:.4f}") |
| ``` |
|
|
| ### Using with Ranking-Qwen Library |
|
|
| ```python |
| from ranking_qwen.models import QwenReranker |
| |
| # Load fine-tuned model |
| reranker = QwenReranker(model_name="codefactory4791/Qwen3-Reranker-HomeDepot") |
| |
| # Score multiple candidates |
| scores = reranker.compute_scores( |
| queries=["drill bits", "drill bits"], |
| documents=[ |
| "DEWALT 14-Piece Titanium Drill Bit Set", |
| "Black+Decker Screwdriver Set" |
| ] |
| ) |
| # Returns: [0.92, 0.31] |
| ``` |
|
|
| ## Performance |
|
|
| Expected metrics on Home Depot test set: |
|
|
| - **NDCG@10**: ≥ 0.80 |
| - **MAP**: ≥ 0.75 |
| - **MRR**: ≥ 0.85 |
| - **AUC**: ≥ 0.90 |
|
|
| ## Limitations |
|
|
| - Trained specifically for Home Depot product search |
| - May not generalize well to other domains without fine-tuning |
| - Maximum sequence length: 8192 tokens (though 2048 is recommended for speed) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{qwen3-reranker-homedepot, |
| author = {Your Name}, |
| title = {Qwen3-Reranker Fine-tuned on Home Depot Dataset}, |
| year = {2026}, |
| publisher = {HuggingFace}, |
| howpublished = {\url{https://huggingface.co/codefactory4791/Qwen3-Reranker-HomeDepot}} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT License - See base model license for additional details. |
|
|
| ## Acknowledgments |
|
|
| - Base model: [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) |
| - Dataset: Home Depot Product Search Relevance |
| - Training framework: HuggingFace Transformers |
|
|
