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
File size: 286 Bytes
e034d93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"eos_token": "[SEP]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": {
"content": "[UNK]",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
}
}
|