You can deploy this model and start earning money today!

pdjohn/
C-BERT

Name: C-BERT
Rating: 2.5 (5 reviews)

transformers

safetensors

token-classification

google-bert/bert-base-german-cased

finetune

apache-2.0

endpoints_compatible

Views

No views yet

C-BERT

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: 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. Once installed, run inference like so:

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 for details.

Limitations

Only German.
Sentence-level; doesn’t handle cross-sentence causality.
Relation classification depends on detected spans — errors in token tagging propagate.

pdjohn/C-BERT