Update README.md

README.md

@@ -9,7 +9,158 @@ base_model:
 - answerdotai/ModernBERT-base
 pipeline_tag: token-classification
 ---
-The model is trained in the DAMASHA-MAS dataset. The paper link: https://arxiv.org/abs/2512.04838. The dataset link: https://huggingface.co/datasets/saiteja33/DAMASHA. Finally the main github link for running the model in the local with this in download is: https://github.com/saitejalekkala33/DAMASHA.
+
+# DAMASHA-MAS: Mixed-Authorship Adversarial Segmentation (Token Classification)
+
+This repository contains a **token-classification model** trained on the **DAMASHA-MAS** benchmark, introduced in:
+
+> **DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution** :contentReference[oaicite:0]{index=0}  
+
+The model aims to **segment mixed human–AI text** at *token level* – i.e., decide for each token whether it was written by a *human* or an *LLM*, even under **syntactic adversarial attacks**.
+
+- **Base encoders:**  
+  - [`FacebookAI/roberta-base`](https://huggingface.co/FacebookAI/roberta-base) :contentReference[oaicite:1]{index=1}  
+  - [`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/ModernBERT-base) :contentReference[oaicite:2]{index=2}  
+- **Architecture (high level):** RoBERTa + ModernBERT feature fusion → BiGRU + CRF with the **Info-Mask** gating mechanism from the paper. :contentReference[oaicite:3]{index=3}  
+- **Task:** Token classification (binary authorship: human vs AI).  
+- **Language:** English  
+- **License (this model):** MIT  
+- **Training data license:** CC-BY-4.0 via the DAMASHA dataset. :contentReference[oaicite:4]{index=4}  
+
+If you use this model, **please also cite the DAMASHA paper and dataset** (see Citation section).
+
+---
+
+## 1. Model Highlights
+
+- **Fine-grained mixed-authorship detection**  
+  Predicts authorship **per token**, allowing reconstruction of human vs AI **spans** in long documents. :contentReference[oaicite:5]{index=5}  
+
+- **Adversarially robust**  
+  Trained and evaluated on **syntactically attacked texts** (misspelling, Unicode substitutions, invisible characters, punctuation swaps, case perturbations, and “all-mixed” attacks). :contentReference[oaicite:6]{index=6}  
+
+- **Human-interpretable Info-Mask**  
+  The architecture incorporates **stylometric features** (perplexity, POS density, punctuation density, lexical diversity, readability) via an **Info-Mask** module that gates token representations in an interpretable way. :contentReference[oaicite:7]{index=7}  
+
+- **Strong reported performance (from the paper)**  
+  On DAMASHA-MAS, the **RMC\*** model (RoBERTa + ModernBERT + CRF + Info-Mask) achieves: :contentReference[oaicite:8]{index=8}  
+  - **Token-level**: Accuracy / Precision / Recall / F1 ≈ **0.98**  
+  - **Span-level (strict)**: SBDA ≈ **0.45**, SegPre ≈ **0.41**  
+  - **Span-level (relaxed IoU ≥ 0.5)**: ≈ **0.82**  
+
+> ⚠️ The exact numbers for *this* specific checkpoint may differ depending on training run and configuration. The values above are from the paper’s best configuration (RMC\*).
+
+---
+
+## 2. Intended Use
+
+### What this model is for
+
+- **Research on human–AI co-authorship**  
+  - Studying where LLMs “take over” in mixed texts.  
+  - Analysing robustness of detectors under adversarial perturbations.
+
+- **Tooling / applications (with human oversight)**  
+  - Assisting editors, educators, or moderators to **highlight suspicious spans** rather than making final decisions.  
+  - Exploring **interpretability overlays** (e.g., heatmaps over tokens) when combined with Info-Mask outputs.
+
+### What this model is *not* for
+
+- Automated “cheating detector” / plagiarism court.  
+- High-stakes decisions affecting people’s livelihood, grades, or reputation **without human review**.  
+- Non-English or heavily code-mixed text (training data is English-centric). :contentReference[oaicite:9]{index=9}  
+
+Use this model as a **signal**, not a judge.
+
+---
+
+## 3. Data: DAMASHA-MAS
+
+The model is trained on the **MAS** benchmark released with the DAMASHA paper and hosted as the Hugging Face dataset:
+
+- **Dataset:** [`saiteja33/DAMASHA`](https://huggingface.co/datasets/saiteja33/DAMASHA) :contentReference[oaicite:10]{index=10}  
+
+### 3.1 What’s in MAS?
+
+MAS consists of **mixed human–AI texts with explicit span tags**: :contentReference[oaicite:11]{index=11}  
+
+- Human text comes from several corpora for **domain diversity**, including:  
+  - Reddit (M4-Reddit)  
+  - Yelp & /r/ChangeMyView (MAGE-YELP, MAGE-CMV)  
+  - News summaries (XSUM)  
+  - Wikipedia (M4-Wiki, MAGE-SQuAD)  
+  - ArXiv abstracts (MAGE-SciGen)  
+  - QA texts (MAGE-ELI5)
+
+- AI text is generated by multiple modern LLMs:  
+  - **DeepSeek-V3-671B** (open-source)  
+  - **GPT-4o, GPT-4.1, GPT-4.1-mini** (closed-source) :contentReference[oaicite:12]{index=12}  
+
+### 3.2 Span tagging
+
+Authorship is marked using **explicit tags** around AI spans: :contentReference[oaicite:13]{index=13}  
+
+- `<AI_Start>` … `</AI_End>` denote AI-generated segments within otherwise human text.  
+- The dataset stores text in a `hybrid_text` column, plus metadata such as `has_pair`, and adversarial variants include `attack_name`, `tag_count`, and `attacked_text`.  
+- Tags are sentence-level in annotation, but the model is trained to output **token-level** predictions for finer segmentation.
+
+> During training, these tags are converted into **token labels** (2 labels total; see `config.id2label` in the model files).
+
+### 3.3 Adversarial attacks
+
+MAS includes multiple **syntactic attacks** applied to the mixed text: :contentReference[oaicite:14]{index=14}  
+
+- Misspelling  
+- Unicode character substitution  
+- Invisible characters  
+- Punctuation substitution  
+- Upper/lower case swapping  
+- All-mixed combinations of the above  
+
+These perturbations make tokenization brittle and test robustness of detectors in realistic settings.
+
+---
+
+## 4. Model Architecture & Training
+
+### 4.1 Architecture (conceptual)
+
+The model follows the **Info-Mask RMC\*** architecture described in the DAMASHA paper: :contentReference[oaicite:15]{index=15}  
+
+1. **Dual encoders**  
+   - RoBERTa-base and ModernBERT-base encode the same input sequence.  
+2. **Feature fusion**  
+   - Hidden states from both encoders are fused into a shared representation.  
+3. **Stylometric Info-Mask**  
+   - Hand-crafted style features (perplexity, POS density, punctuation density, lexical diversity, readability) are projected, passed through multi-head attention, and turned into a **scalar mask per token**.  
+   - This mask gates the fused encoder states, down-weighting style-irrelevant tokens and emphasizing style-diagnostic ones. :contentReference[oaicite:16]{index=16}  
+4. **Sequence model + CRF**  
+   - A BiGRU layer captures sequential dependencies, followed by a **CRF** layer for structured token labeling with a sequence-level loss. :contentReference[oaicite:17]{index=17}  
+
+### 4.2 Training setup (from the paper)
+
+Key hyperparameters used for the Info-Mask models on MAS: :contentReference[oaicite:18]{index=18}  
+
+- **Number of labels:** 2  
+- **Max sequence length:** 512  
+- **Batch size:** 64  
+- **Epochs:** 5  
+- **Optimizer:** AdamW (with cosine annealing LR schedule)  
+- **Weight decay:** 0.01  
+- **Gradient clipping:** 1.0  
+- **Dropout:** Dynamic 0.1–0.3 (initial 0.1)  
+- **Warmup ratio:** 0.1  
+- **Early stopping patience:** 2  
+
+**Hardware & compute** (as reported): :contentReference[oaicite:19]{index=19}  
+
+- AWS EC2 g6e.xlarge, NVIDIA L40S (48GB) GPU, Ubuntu 24.04  
+- ≈ 400 GPU hours (~USD $720) for experiments.
+
+> The exact training script used for this checkpoint is available in the project GitHub:  
+> <https://github.com/saitejalekkala33/DAMASHA>
+
+---
 
 ---
 license: mit