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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- de |
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base_model: |
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- EuroBERT/EuroBERT-210m |
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pipeline_tag: token-classification |
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--- |
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# C-EBERT |
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C-EBERT is a multi-task fine-tuned German EuroBERT to extract causal attribution. |
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## Model details |
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- **Model architecture**: EuroBERT-210m + token & relation heads |
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- **Fine-tuned on**: environmental causal attribution corpus (German) |
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- **Tasks**: |
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1. Token classification (BIO tags for INDICATOR / ENTITY) |
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2. Relation classification (CAUSE, EFFECT, INTERDEPENDENCY) |
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## Usage |
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Find the custom [library](https://github.com/padjohn/causalbert). Once installed, run inference like so: |
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```python |
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from transformers import AutoTokenizer |
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from causalbert.infer import load_model, analyze_sentence_with_confidence |
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model, tokenizer, config, device = load_model("pdjohn/C-EBERT") |
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result = analyze_sentence_with_confidence( |
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model, tokenizer, config, "Autoverkehr verursacht Bienensterben.", [] |
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) |
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``` |
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## Training |
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- **Base model**: `EuroBERT/EuroBERT-210m` |
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- **Epochs**: 3, **LR**: 2e-5, **Batch size**: 8 |
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- See [train.py](https://github.com/padjohn/causalbert/blob/main/causalbert/train.py) for details. |
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## Limitations |
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- German only. |
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- Sentence-level; doesn’t handle cross-sentence causality. |
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- Relation classification depends on detected spans — errors in token tagging propagate. |
