Update README.md

README.md

@@ -6,10 +6,53 @@ language:
 base_model:
 - google-bert/bert-base-german-cased
 ---
-Model Card for Model ID
 
-This model aims to extract attributions of causality and yield its constituents (currently CAUSE, INDICATOR and EFFECT).
-Model Details
-Model Description
+# Model Card for norygano/C-BERT
 
-    Model type: Semantic Relation
+CausalBERT (C-BERT) is a multi-task fine-tuned German BERT that extracts causal attributions  
+— identifying INDICATORs and ENTITY spans, then classifying CAUSE/EFFECT relationships.
+
+## Model details
+
+- **Model architecture**: BERT-base-German-cased + token & relation heads  
+- **Fine-tuned on**: custom causal attribution corpus (German)  
+- **Tasks**:  
+  1. Token classification (BIO tags for INDICATOR / ENTITY)  
+  2. Relation classification (CAUSE, EFFECT, INTERDEPENDENCY)
+
+## Usage
+
+```python
+from transformers import AutoTokenizer
+from causalbert.infer import load_model, analyze_sentence_with_confidence
+
+model, tokenizer, config, device = load_model("norygano/C-BERT")
+result = analyze_sentence_with_confidence(
+    model, tokenizer, config, "Autoverkehr verursacht Bienensterben.", []
+)
+```
+
+## Training
+
+- **Base model**: `google-bert/bert-base-german-cased`  
+- **Epochs**: 3, **LR**: 2e-5, **Batch size**: 8  
+- See [train.py](https://github.com/norygano/causalbert/blob/main/causalbert/train.py) for details.
+
+## Limitations
+
+- Only German.  
+- Sentence-level; doesn’t handle cross-sentence causality.  
+- Relation classification depends on detected spans—errors in token tagging propagate.
+
+## References & Source
+
+- 🔗 GitHub: https://github.com/norygano/causalbert  
+- 🤗 Hub: https://huggingface.co/norygano/C-BERT// filepath: /mnt/work/Projects/BERTopic_AiO/data/model/C-BERT/README.md
+---
+library_name: transformers
+license: apache-2.0
+language:
+- de
+base_model:
+- google-bert/bert-base-german-cased
+---