Token Classification
Transformers
Safetensors
English
modernbert
semantic-highlighting
extractive-qa
evidence-selection
acl-anthology
custom_code
Instructions to use KRLabsOrg/acl-verbatim-modernbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KRLabsOrg/acl-verbatim-modernbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KRLabsOrg/acl-verbatim-modernbert", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KRLabsOrg/acl-verbatim-modernbert", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("KRLabsOrg/acl-verbatim-modernbert", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from KRLabsOrg/acl-verbatim-modernbert: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/KRLabsOrg/acl-verbatim-modernbert/resolve/main/tokenizer.json
- Command line
-
hf download hf://KRLabsOrg/acl-verbatim-modernbert/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/KRLabsOrg/acl-verbatim-modernbert/resolve/main/tokenizer.json
3.58 MB
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