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README.md

@@ -1,199 +1,164 @@
 ---
-library_name: transformers
-tags: []
+tags:
+- reranker
+- qwen3
+- information-retrieval
+- product-search
+base_model: Qwen/Qwen3-Reranker-0.6B
+license: mit
+language:
+- en
+pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
+# Qwen3-Reranker-HomeDepot
 
-<!-- Provide a quick summary of what the model is/does. -->
+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.
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+**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
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
+### Dataset
 
-[More Information Needed]
+- **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
 
-### Compute Infrastructure
+```
+Learning rate: 5e-6
+Batch size: 8 × 2 = 16
+Epochs: 3
+Optimizer: AdamW (weight_decay=0.01)
+Scheduler: Linear warmup + decay
+Mixed precision: BF16
+```
 
-[More Information Needed]
+### Hardware
 
-#### Hardware
+- **GPU**: NVIDIA A100 80GB
+- **Training time**: ~2-4 hours
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
 
-[More Information Needed]
+### Basic Usage
 
-#### Software
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-[More Information Needed]
+# 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
+)
 
-## Citation [optional]
+# Prepare input
+query = "cordless drill"
+document = "DEWALT 20V MAX Cordless Drill Kit with battery and charger"
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+# 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>
 
-**BibTeX:**
+</think>
 
-[More Information Needed]
+'''
 
-**APA:**
+# Tokenize and get score
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model(**inputs)
+logits = outputs.logits[0, -1, :]
 
-[More Information Needed]
+# 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()
 
-## Glossary [optional]
+print(f"Relevance score: {score:.4f}")
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Using with Ranking-Qwen Library
 
-[More Information Needed]
+```python
+from ranking_qwen.models import QwenReranker
 
-## More Information [optional]
+# Load fine-tuned model
+reranker = QwenReranker(model_name="codefactory4791/Qwen3-Reranker-HomeDepot")
 
-[More Information Needed]
+# 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]
+```
 
-## Model Card Authors [optional]
+## Performance
 
-[More Information Needed]
+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)
 
-## Model Card Contact
+## Citation
 
-[More Information Needed]
+```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