Update README.md

README.md

@@ -1,77 +1,42 @@
 ---
 base_model: google/gemma-2-2b-it
 library_name: peft
+datasets:
+- andreapdr/LID-M4ABS-gemma
+language:
+- en
 ---
 
-# Model Card for Model ID
+# LID-gemma-2-2B-M4ABS
 
-<!-- Provide a quick summary of what the model is/does. -->
+<div align="center">
 
+<img src="https://raw.githubusercontent.com/gpucce/control_mgt/refs/heads/main/assets/Stress-testingMachineGeneratedTextDetection_graphical.png"  height="300" width="auto" style="border-radius:3%" />
 
+</div>
 
-## Model Details
+The LoRa adapters for the **LID-gemma-2-2B** LLM. This model has been fine-tuned using DPO to align its writing style with the distribution of linguistic features profiled in human-written texts (HWT) sampled from the Abstract subset of the M4 dataset, a corpus of scientific abstracts.
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
+- **Developed by:** [AI4Text](https://hlt-isti.github.io/) @[CNR-ISTI](https://www.isti.cnr.it/en/), [ItaliaNLP](http://www.italianlp.it/) @[CNR-ILC](https://www.ilc.cnr.it/)
+- **Model type:** LoRA adapters (different iterations are stored in branches)
+- **Finetuned from model:** `google/gemma-2-2b-it`
 
 ## 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.
+This model is intended to be used as a adversarial samples generator. The model can be used to either generate sampels to benchmark current Machine-Generated-Text Detectors, or to augment the training set of novel approaches to syntethic text detection.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+
+base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it")
+model = PeftModel.from_pretrained(base_model, "andreapdr/LID-gemma-2-2b-M4ABS-ling", revision="main")     # switch to other branches by changing the revision argument
+```
 
 ## Training Details
 
@@ -79,124 +44,60 @@ Use the code below to get started with the model.
 
 <!-- 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]
+The model has been fine-tuned on the [LID-M4ABS dataset](https://huggingface.co/datasets/andreapdr/LID-M4ABS-llama), based on the ArXiv subset of the [M4 dataset](https://github.com/mbzuai-nlp/M4?tab=readme-ov-file#data). We provide pre-trained LoRA adapters for two iterations, stored in different branches.
 
 ### 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]
-
+DPO fine-tuning with LoRA Adapters
+
+```python
+LoraConfig(
+    r=32 ,
+    lora_alpha=16 ,
+    target_modules=[
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj",
+    ],
+    bias="none" ,
+    lora_dropout=0.05,
+    task_type="CAUSAL_LM"
+    )
+```
+
+Model prompt:
+
+- **System Prompt:**: "You are a journalist from the United Kingdom writing for a national newspaper on a broad range of topics."
+- **User Prompt:**: "Write a piece of news, that will appear in a national news-papers in the UK and that has the following title: `title`. In writing avoid any kind of formatting, do not repeat the title and keep the text informative and not vague. You don’t have to add the date of the event but you can, use at most 500 words"
 
 #### 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. -->
+- **Learning Rate:** {5e−7, 5e−6}
+- **Beta:**: {0.1, 0.5, 1.0}
 
-[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]
+### Framework versions
 
-[More Information Needed]
+- PEFT 0.14.0
+- TRL 0.12.2
 
-## Model Card Contact
+## Citation
 
-[More Information Needed]
-### Framework versions
+if you use part of this work, please consider citing the paper as follows:
 
-- PEFT 0.14.0
+```bibtex
+@misc{pedrotti2025stresstestingMGT,
+      title={Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors}, 
+      author={Andrea Pedrotti and Michele Papucci and Cristiano Ciaccio and Alessio Miaschi and Giovanni Puccetti and Felice Dell'Orletta and Andrea Esuli},
+      year={2025},
+      eprint={2505.24523},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.24523}, 
+}
+```