Token Classification
Transformers
ONNX
Safetensors
multilingual
deberta-v2
causal-extraction
causality
cause-effect
reasongraph
Instructions to use Berk/causal-span-mdeberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Berk/causal-span-mdeberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Berk/causal-span-mdeberta")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Berk/causal-span-mdeberta") model = AutoModelForTokenClassification.from_pretrained("Berk/causal-span-mdeberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e168dd99d578aee7594d4d5c98bdcd4491d3846f22f66179d4adaaaea4c4eeb
- Size of remote file:
- 16.4 MB
- SHA256:
- ec980f042a21f3b85585684593685bf02a10fdf6d6e4b032da29418edaf23090
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