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:
- 1134e22547b60bd4345159d59dabcab843aaff6396eeaf291b22bd6be06a83ca
- Size of remote file:
- 1.11 GB
- SHA256:
- 7b8389ae5d293c787c9b102fb886c5869bed91913296250f5e2e44fe5b1c158c
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