Update README.md

README.md

@@ -9,19 +9,19 @@ base_model:
 
 # Model Card for norygano/C-BERT
 
-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.
+CausalBERT (C-BERT) is a multi-task fine-tuned German BERT that extracts causal attributions.
 
 ## Model details
-
 - **Model architecture**: BERT-base-German-cased + token & relation heads  
-- **Fine-tuned on**: custom causal attribution corpus (German)  
+- **Fine-tuned on**: environmental causal attribution corpus (German)  
 - **Tasks**:  
   1. Token classification (BIO tags for INDICATOR / ENTITY)  
   2. Relation classification (CAUSE, EFFECT, INTERDEPENDENCY)
 
 ## Usage
+Find the custom library [here](https://github.com/norygano/causalbert)
 
+## Inference
 ```python
 from transformers import AutoTokenizer
 from causalbert.infer import load_model, analyze_sentence_with_confidence
@@ -42,17 +42,4 @@ result = analyze_sentence_with_confidence(
 
 - 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
----
+- Relation classification depends on detected spans — errors in token tagging propagate.