Update README.md

README.md

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----
-license: gemma
-tags:
-- llama-factory
----
+---
+license: gemma
+base_model: google/gemma-2-9b-it
+datasets:
+  - gcelikmasat-work/BPMN-IT-Dataset
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - bpmn
+  - business-process-modeling
+  - process-modeling
+  - instruction-tuning
+  - lora
+  - peft
+  - dot
+  - graphviz
+  - llama-factory
+  - gemma2
+model-index:
+  - name: Gemma2-9B-BPMG-IT
+    results:
+      - task:
+          type: text-generation
+          name: BPMN Model Generation from Text
+        dataset:
+          type: gcelikmasat-work/BPMN-IT-Dataset
+          name: BPMN-IT (stratified 180-instance benchmark across 15 business domains, seed split)
+        metrics:
+          - type: bleu
+            value: 82.98
+            name: BLEU
+          - type: rouge
+            value: 94.61
+            name: ROUGE-L
+          - type: meteor
+            value: 92.67
+            name: METEOR
+          - type: relative-graph-edit-distance
+            value: 97.78
+            name: R-GED Accuracy (%)
+---
+
+# Gemma2-9B-BPMG-IT
+
+Gemma2-9B-BPMG-IT is an instruction-tuned language model that converts natural-language business process descriptions into BPMN models rendered in [Graphviz DOT](https://graphviz.org/doc/info/lang.html). It is a LoRA adaptation of [`google/gemma-2-9b-it`](https://huggingface.co/google/gemma-2-9b-it), trained on a cleaned subset of the [MaD dataset](https://ieeexplore.ieee.org/abstract/document/10191898) for the paper:
+
+> **Generating Business Process Models with Open Source Large Language Models using Instruction Tuning.**
+> Gökberk Çelikmasat, Atay Özgövde, Fatma Başak Aydemir.
+> *International Conference on Product-Focused Software Process Improvement (PROFES 2025)*, Springer LNCS, pp. 269–284.
+> DOI: [10.1007/978-3-032-12089-2_17](https://doi.org/10.1007/978-3-032-12089-2_17)
+
+This model is the subject of our PROFES 2025 conference paper, in which we introduced the instruction-tuning approach and evaluated it against open-weight and proprietary baselines on textual and structural metrics. It is also the **BPMG-IT** baseline in our subsequent journal paper:
+
+> **InstruBPM: Instruction-Tuning Open-Weight Language Models for BPMN Model Generation.**
+> Çelikmasat, Özgövde, Aydemir. *Software and Systems Modeling*, under review, 2026.
+> arXiv: [2512.12063](https://arxiv.org/abs/2512.12063)
+
+For new projects we recommend the successor model, [`gcelikmasat-work/Qwen3_4B_BPMN_IT`](https://huggingface.co/gcelikmasat-work/Qwen3_4B_BPMN_IT), which matches this model's accuracy with roughly **half** the parameter count (4B vs. 9B) and ships with quantized and merge-scale variants for deployment trade-offs.
+
+## Results
+
+Evaluated on the 180-instance stratified benchmark used in the InstruBPM journal paper (Table 2), this model attains:
+
+| Metric        | Score  |
+| ------------- | -----: |
+| BLEU          |  82.98 |
+| ROUGE-L       |  94.61 |
+| METEOR        |  92.67 |
+| R-GED Acc.    |  97.78 |
+
+These scores are very close to those of the newer 4B Qwen3 successor (which reaches 83.06 / 94.43 / 92.82 / 99.44 on the same benchmark), while requiring more than twice the memory and compute at inference time.
+
+## Intended use
+
+Generate first-draft BPMN models from textual process descriptions to accelerate early-stage modeling. Intended as an assistant for business process modelers and analysts; human review remains recommended, particularly for gateway logic and activity labels.
+
+## Supported BPMN subset
+
+The model generates BPMN process fragments in DOT notation covering: start events, end events, tasks (activities), sequence flows, and AND/XOR gateways (splits and joins). It does **not** generate pools, lanes, message flows, data objects, intermediate/boundary events, sub-processes, or annotations.
+
+## How to use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "gcelikmasat-work/gemma-2-9b-it-BPMN"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+instruction = (
+    "You are an expert in BPMN modeling and DOT language. Your task is to "
+    "convert detailed textual descriptions of business processes into accurate "
+    "BPMN model codes written in DOT language. Label all nodes with their "
+    "activity names. Represent all connections between nodes without labeling "
+    "the connections. Represent each node and its connections accurately, "
+    "ensuring all decision points and flows are included and connected. "
+    "Now, generate BPMN business process model code in DOT language for the "
+    "following textual description of a business process: "
+)
+
+description = (
+    "The process begins when the customer submits an application. After submission, "
+    "the application is reviewed by the credit officer. If the application is approved, "
+    "the loan is disbursed. Otherwise, a rejection letter is sent. The process ends."
+)
+
+# Gemma 2 uses a single user turn without a separate system role
+messages = [{"role": "user", "content": instruction + description}]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    out = model.generate(**inputs, max_new_tokens=2048, temperature=0.1, top_p=1.0, do_sample=True)
+
+dot_code = tokenizer.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+print(dot_code)
+```
+
+## Training
+
+Trained with LLaMA-Factory using LoRA on Gemma-2 9B Instruct. Detailed hyperparameters are reported in the PROFES 2025 paper. Training data: 21.5k cleaned instruction–input–output triples from MaD, split 80/10/10 for train/validation/test. The full splits are available at [`gcelikmasat-work/BPMN-IT-Dataset`](https://huggingface.co/datasets/gcelikmasat-work/BPMN-IT-Dataset).
+
+## Limitations
+
+- **Scope.** Control-flow slice of BPMN only (tasks, events, sequence flows, AND/XOR gateways). No pools, lanes, message flows, data objects, or sub-processes.
+- **Language.** English only.
+- **Parameter efficiency.** At 9B parameters, this model is roughly twice the size of the Qwen3-4B successor for comparable accuracy. For deployment-constrained settings, the 4B successor is preferred.
+- **Semantic equivalence.** Structural similarity does not imply semantic equivalence, especially when input descriptions are ambiguous.
+
+## Citation
+
+If you use this model, please cite the PROFES 2025 paper:
+
+```bibtex
+@inproceedings{celikmasat2025bpmg,
+  title     = {Generating Business Process Models with Open Source Large Language Models using Instruction Tuning},
+  author    = {{\c{C}}elikmasat, G{\"o}kberk and {\"O}zg{\"o}vde, Atay and Aydemir, Fatma Ba{\c{s}}ak},
+  booktitle = {Product-Focused Software Process Improvement (PROFES 2025)},
+  series    = {Lecture Notes in Computer Science},
+  pages     = {269--284},
+  year      = {2025},
+  publisher = {Springer},
+  doi       = {10.1007/978-3-032-12089-2_17},
+  url       = {https://doi.org/10.1007/978-3-032-12089-2_17}
+}
+```
+
+If you are comparing against this model as a baseline in a follow-up study, please also cite the journal extension:
+
+```bibtex
+@article{celikmasat2026instrubpm,
+  title   = {InstruBPM: Instruction-Tuning Open-Weight Language Models for BPMN Model Generation},
+  author  = {{\c{C}}elikmasat, G{\"o}kberk and {\"O}zg{\"o}vde, Atay and Aydemir, Fatma Ba{\c{s}}ak},
+  journal = {Software and Systems Modeling},
+  year    = {2026},
+  note    = {Under review. arXiv:2512.12063},
+  url     = {https://arxiv.org/abs/2512.12063}
+}
+```
+
+Please also cite the source dataset:
+
+```bibtex
+@inproceedings{li2023mad,
+  title     = {{MaD}: A Dataset for Interview-based {BPM} in Business Process Management},
+  author    = {Li, Xiang and Ni, Lijuan and Li, Ran and Liu, Jiafei and Zhang, Ming},
+  booktitle = {2023 International Joint Conference on Neural Networks (IJCNN)},
+  pages     = {1--8},
+  year      = {2023},
+  publisher = {IEEE}
+}
+```
+
+## License
+
+Released under the [Gemma license](https://ai.google.dev/gemma/terms), inherited from the base model ([`google/gemma-2-9b-it`](https://huggingface.co/google/gemma-2-9b-it)). Use is subject to Google's Gemma Prohibited Use Policy. The training data is distributed separately under the terms of the MaD dataset.