Update model card

README.md

@@ -1,199 +1,149 @@
 ---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+tags:
+- information-retrieval
+- boolean-search
+- NL2BM25
+- GRPO
+- RLVR
+- tantivy
+- BEIR
+- searchlm
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
+# SearchLM NL2BM25 — GRPO v2 Shaped Reward (Qwen2.5-3B-Instruct)
 
-<!-- Provide a quick summary of what the model is/does. -->
+The best-performing SearchLM checkpoint. Trained via GRPO with a shaped reward
+that eliminates the keyword-bag gaming observed in [GRPO v1](Supreeth/searchlm-nl2bm25-grpo).
 
+See the [SearchLM collection](https://huggingface.co/collections/Supreeth/searchlm) for all checkpoints
+and the [v2 analysis report](https://github.com/SupreethRao99/searchLM/blob/main/REWARD_HACKING_REPORT_V2.md)
+for full benchmark results and behavioral analysis.
 
+## What it does
 
-## Model Details
+Given a natural language query, the model reasons step-by-step about key concepts,
+synonyms, and boolean structure, then emits a Tantivy boolean search query:
 
-### Model Description
+```
+Input: Do Cholesterol Statin Drugs Cause Breast Cancer?
 
-<!-- Provide a longer summary of what this model is. -->
+Output:
+<reasoning>
+Key concepts: (1) cholesterol-lowering statin drugs — synonyms: statin, HMG-CoA reductase
+inhibitor, simvastatin, atorvastatin; (2) causal relationship — cause, risk, association,
+induce; (3) breast cancer — "breast cancer", "breast carcinoma", "breast neoplasm".
+Connect with AND; expand synonyms with OR.
+</reasoning>
+<query>(statin OR "HMG-CoA reductase inhibitor" OR simvastatin OR atorvastatin) AND
+(cause OR risk OR association OR induce) AND ("breast cancer" OR "breast carcinoma")</query>
+```
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## How v2 eliminated reward hacking
 
-- **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]
+The v1 reward (`0.6 × NDCG@10 + 0.4 × MRR`) was gameable with keyword bags on small corpora.
+v2 uses a shaped reward with three anti-gaming mechanisms:
 
-### Model Sources [optional]
+```
+base   = 0.6 × max(0, NDCG@10 − keyword_baseline) + 0.4 × MRR
+shaped = complexity_mult × base + 0.15 × min(reasoning_tokens / 100, 1.0)
+reward = 0.0  if  len(query.split()) < 3  else  shaped
 
-<!-- Provide the basic links for the model. -->
+where:
+  keyword_baseline = NDCG@10 of naive noun-extraction (precomputed per query)
+  complexity_mult  = 1.0 with boolean operators, 0.5 without
+```
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+1. **Keyword baseline delta** — must beat noun-extraction to earn NDCG credit
+2. **Hard length gate** — queries < 3 tokens → reward = 0.0
+3. **Reasoning depth bonus** — up to +0.15 for substantive reasoning blocks
 
-## Uses
+## Benchmark (test split, NDCG@10)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+| Dataset | Base | SFT v1 | GRPO v1 | SFT v2 | **GRPO v2** |
+|---------|------|--------|--------|--------|------------|
+| NFCorpus | 0.455 | 0.441 | 0.556 | 0.466 | **0.577** |
+| SciFact  | 0.386 | 0.273 | 0.608 | 0.358 | **0.657** |
 
-### Direct Use
+## Behavioral comparison vs GRPO v1
 
-<!-- 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]
+| Dimension | GRPO v1 | GRPO v2 |
+|-----------|---------|---------|
+| Mean completion length | 6 tokens | **147 tokens** |
+| Boolean operator usage | 0% | ~35% |
+| `frac_reward_zero_std` (early training) | 90–96% | **0%** |
+| Reasoning block | empty | substantive |
 
 ## 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
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+| Setting | Value |
+|---------|-------|
+| Base model | [searchlm-nl2bm25-sft-v2](Supreeth/searchlm-nl2bm25-sft-v2) |
+| Method | GRPO (TRL + vLLM colocate) |
+| Reward | Shaped (baseline delta + complexity mult + reasoning bonus) |
+| Training datasets | NFCorpus + SciFact + FiQA (3K queries, 57K docs) |
+| Epochs | 1 |
+| `num_generations` | 8 |
+| Steps | 2,879 |
+| Hardware | NVIDIA H100 80 GB (~3h 3m) |
+| Final mean reward | ~0.29 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Supreeth/searchlm-nl2bm25-grpo-v2",
+    torch_dtype="auto",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("Supreeth/searchlm-nl2bm25-grpo-v2")
+
+SYSTEM_PROMPT = """You are an expert information retrieval specialist. Convert the natural language query into a Tantivy boolean search query.
+
+Output format (strictly follow this):
+<reasoning>
+Step-by-step concept extraction and synonym expansion.
+</reasoning>
+<query>your boolean query here</query>"""
+
+nl_query = "effects of climate change on coral reef ecosystems"
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {"role": "user", "content": f"Convert to a Tantivy boolean search query:\n\n{nl_query}"},
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+## Tantivy Boolean Syntax
+
+| Construct | Syntax |
+|-----------|--------|
+| Single term | `cancer` |
+| Exact phrase | `"bone density"` |
+| AND | `vitamin AND calcium` |
+| OR | `cancer OR tumor OR malignancy` |
+| NOT | `NOT review` |
+| Grouping | `(cat OR feline) AND behavior` |
+| Field scope | `title:"machine learning"` |
+| Boost | `cancer^2 OR tumor` |
+
+## Citation
+
+```bibtex
+@misc{searchlm2026,
+  title  = {SearchLM: Training Small Language Models for Boolean Query Generation via RLVR},
+  author = {Rao, Supreeth},
+  year   = {2026},
+  url    = {https://github.com/SupreethRao99/searchLM},
+}
+```