Update README.md

README.md

@@ -3,197 +3,89 @@ library_name: transformers
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-# Model Card for Model ID
+# Rephraser-0.5B-v0.1-SFT
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+Rephraser-0.5B-v0.1-SFT is a specialized query analysis and rephrasing model fine-tuned from [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) for Retrieval-Augmented Generation (RAG) systems. The model has been trained to analyze user queries and produce structured XML outputs that include domain classification, intent detection, and optimized query reformulations designed to improve document retrieval precision.
 
+This model addresses a critical challenge in RAG systems: converting ambiguous, vague, or suboptimally-phrased user queries into retrieval-friendly formats. Rather than directly answering questions, the model performs query understanding and transformation, making it an ideal preprocessing component for information retrieval pipelines.
 
-## Model Details
+## Intended Use
 
-### Model Description
+The model is designed to serve as a query analysis layer in RAG systems, where it processes user inputs before they are sent to retrieval components. It excels at handling queries with varying degrees of ambiguity and can identify when queries lack sufficient context for effective retrieval. The structured XML output enables downstream systems to make informed decisions about how to handle each query based on confidence scores, ambiguity flags, and the rephrased version.
 
-<!-- Provide a longer summary of what this model is. -->
+Typical deployment scenarios include conversational search systems, question-answering platforms, document retrieval services, and any application where understanding user intent and optimizing queries for semantic search is critical. The model's compact 0.5B parameter size makes it suitable for deployment in resource-constrained environments or as part of larger multi-component systems where latency is a concern.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Training Data
 
-- **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 model was trained on the [krogoldAI/Query_rephraser_SFT](https://huggingface.co/datasets/krogoldAI/Query_rephraser_SFT) dataset, which contains 7,305 high-quality query-analysis pairs. This dataset was carefully curated from three established sources: [rag-datasets/rag-mini-wikipedia](https://huggingface.co/datasets/rag-datasets/rag-mini-wikipedia), [razbit96/Ambiguity-Handling-in-User-Queries](https://huggingface.co/datasets/razbit96/Ambiguity-Handling-in-User-Queries), and [glaiveai/RAG-v1](https://huggingface.co/datasets/glaiveai/RAG-v1). Approximately 20% of the training examples include queries with systematically introduced ambiguity at varying levels (low, medium, and high) to ensure the model can handle realistic user inputs across the ambiguity spectrum.
 
-### Model Sources [optional]
+The training data underwent rigorous quality assurance through a dual evaluation framework. Each example was validated for strict XML schema conformance and semantically evaluated using an LLM-as-a-judge protocol with six quality dimensions. Only examples achieving both perfect structural validity and high semantic quality scores were included in the final dataset, ensuring the model was trained exclusively on gold-standard examples.
 
-<!-- Provide the basic links for the model. -->
+## Training Procedure
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+The model underwent full fine-tuning (not parameter-efficient methods like LoRA) of all parameters in Qwen2.5-0.5B-Instruct. Training was conducted over three epochs with a per-device batch size of 4 and gradient accumulation over 4 steps, yielding an effective batch size of 16. The learning rate was set to 2e-5 with a weight decay of 0.01 to prevent overfitting. A warmup period of 100 steps allowed the model to gradually adapt to the task-specific distribution before reaching the full learning rate. Training was performed on an NVIDIA A100 SXM GPU.
 
-## Uses
+This training configuration was chosen to balance thorough adaptation to the structured output task while preserving the base model's language understanding capabilities. The full fine-tuning approach, rather than parameter-efficient alternatives, ensures the model fully internalizes the strict XML formatting requirements and the nuanced query analysis patterns present in the training data.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Model Capabilities
 
-### Direct Use
+The model generates structured XML analyses following a specific schema that includes both mandatory and optional fields. For every query, it provides domain classification and intent detection with confidence scores that sum to 1.0, properly handling both unambiguous cases (single candidate with confidence 1.0) and ambiguous cases (multiple candidates with distributed confidence). The model can optionally extract explicit and implicit concepts, identify relations between entities using subject-predicate-object triples, and normalize ambiguous terms when disambiguation would improve retrieval.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+The rephrasing capability focuses on retrieval optimization rather than query answering. The model transforms queries by using specific terminology likely to appear in relevant documents, expanding acronyms when contextually appropriate, adding disambiguating context, and making implicit references explicit through placeholder notation such as [PERSON] or [COMPANY]. Importantly, the model has learned to preserve already-optimal queries unchanged, recognizing when rephrasing would not improve retrieval effectiveness.
 
-[More Information Needed]
+## Limitations and Considerations
 
-### Downstream Use [optional]
+As a 0.5B parameter model, Rephraser-0.5B-v0.1-SFT prioritizes efficiency and deployability over the capabilities of larger language models. While it performs well on the types of queries represented in its training distribution, performance may degrade on highly specialized domains, multilingual queries, or query types significantly different from the training examples. The model focuses exclusively on English-language queries and has been optimized for the specific XML output format defined in its training.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+The model's ambiguity detection and confidence scoring reflect patterns learned from the training data, which includes both natural and synthetically augmented ambiguous queries. While the training process incorporated diverse ambiguity levels, edge cases or novel forms of ambiguity may not be handled with the same reliability as more common patterns. Users should consider the model's confidence scores as informative signals rather than calibrated probabilities.
 
-[More Information Needed]
+Since this is version 0.1, it represents an initial release focused on establishing baseline capabilities. Future iterations may address current limitations, expand domain coverage, or incorporate additional output features based on real-world deployment feedback.
 
-### Out-of-Scope Use
+## Usage Example
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-[More Information Needed]
+model_name = "krogoldAI/Rephraser-0.5B-v0.1-SFT"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-## Bias, Risks, and Limitations
+system_prompt = """You are a query analysis and rephraser for a Retrieval-Augmented Generation (RAG) system.
+Your sole task is to analyze user queries and output a structured XML document.
+You must not answer the query itself, only analyze and rephrase it."""
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+user_query = "How do I reset my password?"
 
-[More Information Needed]
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_query}
+]
 
-### Recommendations
+input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(input_text, return_tensors="pt")
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+outputs = model.generate(**inputs, max_new_tokens=512)
+analysis = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+print(analysis)
+```
 
-## How to Get Started with the Model
+## Evaluation and Performance
 
-Use the code below to get started with the model.
+The model was trained on data that passed stringent quality thresholds, with all training examples achieving perfect structural conformance and semantic quality scores of 4 or higher across six evaluation dimensions: domain accuracy, intent accuracy, ambiguity assessment, rephrasing quality, intent preservation, and guideline adherence. This ensures the model learned from consistently high-quality demonstrations of the desired behavior.
 
-[More Information Needed]
+Performance characteristics will vary based on query type, domain, and ambiguity level. The model is expected to perform strongest on queries similar to those in the training distribution and may require additional fine-tuning or prompt engineering for specialized applications or domains underrepresented in the training data.
 
-## Training Details
+## Citation
 
-### Training Data
+If you use this model in your research or applications, please cite the model and acknowledge the training dataset [krogoldAI/Query_rephraser_SFT](https://huggingface.co/datasets/krogoldAI/Query_rephraser_SFT).
 
-<!-- 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. -->
+## License
 
-[More Information Needed]
+[Specify license - typically inherits from Qwen2.5 license]
 
-### Training Procedure
+## Acknowledgments
 
-<!-- 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]
+This model builds upon [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) by the Qwen team at Alibaba Cloud. The training data incorporates queries from [rag-datasets/rag-mini-wikipedia](https://huggingface.co/datasets/rag-datasets/rag-mini-wikipedia), [razbit96/Ambiguity-Handling-in-User-Queries](https://huggingface.co/datasets/razbit96/Ambiguity-Handling-in-User-Queries), and [glaiveai/RAG-v1](https://huggingface.co/datasets/glaiveai/RAG-v1).