Added model card informatio and citation

README.md

@@ -8,4 +8,89 @@ base_model:
 - FacebookAI/xlm-roberta-base
 pipeline_tag: token-classification
 library_name: transformers
----
+tags:
+- arxiv:2509.07459
+---
+
+# Model Card: AIxcellent Vibes' Model for Candy Speech Detection
+
+## Model Details
+
+- **Model Type:** Transformer-based encoder (XLM-RoBERTa-Large)
+- **Developed by:** Christian Rene Thelen, Patrick Gustav Blaneck, Tobias Bornheim, Niklas Grieger, Stephan Bialonski (FH Aachen, RWTH Aachen, ORDIX AG, Utrecht University)
+- **Paper:** [AIxcellent Vibes at GermEval 2025 Shared Task on Candy Speech Detection: Improving Model Performance by Span-Level Training](https://arxiv.org/abs/2509.07459v2)
+- **Base Model:** [XLM-RoBERTa-Large](https://huggingface.co/FacebookAI/xlm-roberta-large) (Conneau et al., 2020)
+- **Fine-tuning Objective:** Detection of *candy speech* (positive/supportive language) in German YouTube comments.
+
+## Model Description
+
+This model is a fine-tuned **XLM-RoBERTa-Large** adapted for the **GermEval 2025 Shared Task on Candy Speech Detection**.
+It was trained to identify *candy speech* at both:
+
+- **Binary level:** Classify whether a comment contains candy speech.
+- **Span level:** Detect the exact spans and categories of candy speech within comments, using a BIO tagging scheme across **10 categories** (positive feedback, compliment, affection declaration, encouragement, gratitude, agreement, ambiguous, implicit, group membership, sympathy).
+
+The span-level model also proved effective for binary detection by classifying a comment as candy speech if at least one positive span was detected.
+
+## Intended Uses
+
+- **Research:** Analysis of positive/supportive communication in German social media.
+- **Applications:** Social media analytics, conversational AI safety (mitigating sycophancy), computational social science.
+- **Not for:** Deployments without fairness/robustness testing on out-of-domain data.
+
+## Performance
+
+- **Dataset:** 46k German YouTube comments, annotated with candy speech spans.
+- **Training Data Split:** 37,057 comments (train), 9,229 (test).
+- **Shared Task Results:**
+
+  - **Subtask 1 (binary detection):** Positive F1 = **0.891** (ranked 1st)
+  - **Subtask 2 (span detection):** Strict F1 = **0.631** (ranked 1st)
+
+## Training Procedure
+
+- **Architecture:** XLM-RoBERTa-Large + linear classification layer (BIO tagging, 21 labels including “O”).
+- **Optimizer:** AdamW
+- **Learning Rate:** Peak 2e-5 with linear decay and warmup (500 steps).
+- **Epochs:** 20 (with early stopping).
+- **Batch Size:** 32
+- **Regularization:** Dropout (0.1), weight decay (0.01), gradient clipping (L2 norm 1.0).
+- **Postprocessing:** BIO tag correction and subword alignment.
+
+## Limitations
+
+- **Domain Specificity:** Trained only on German YouTube comments; performance may degrade on other platforms, genres, or languages.
+- **Overlapping Spans:** Cannot handle overlapping spans, as they were rare (<2%) in the training data.
+- **Biases:** May reflect biases present in the dataset (e.g., demographic skews in YouTube communities).
+- **Generalization:** Needs evaluation before deployment in real-world moderation systems.
+
+## Ethical Considerations
+
+- **Positive speech detection** is less studied than toxic speech, but automatic labeling of “supportiveness” may reinforce cultural biases about what counts as “positive.”
+- Must be complemented with **human-in-the-loop moderation** to avoid misuse.
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@inproceedings{thelen-etal-2025-aixcellent,
+    title = "{AI}xcellent Vibes at {G}erm{E}val 2025 Shared Task on Candy Speech Detection: Improving Model Performance by Span-Level Training",
+    author = "Thelen, Christian Rene  and
+      Blaneck, Patrick Gustav  and
+      Bornheim, Tobias  and
+      Grieger, Niklas  and
+      Bialonski, Stephan",
+    editor = "Wartena, Christian  and
+      Heid, Ulrich",
+    booktitle = "Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025): Workshops",
+    month = sep,
+    year = "2025",
+    address = "Hannover, Germany",
+    publisher = "HsH Applied Academics",
+    url = "https://aclanthology.org/2025.konvens-2.33/",
+    pages = "398--403"
+}
+```
+
+[AIxcellent Vibes at GermEval 2025 Shared Task on Candy Speech Detection: Improving Model Performance by Span-Level Training](https://aclanthology.org/2025.konvens-2.33/) (Thelen et al., KONVENS 2025)