Instructions to use huberm/ModernBERT-base-Croft-Tagger-root-labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huberm/ModernBERT-base-Croft-Tagger-root-labels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="huberm/ModernBERT-base-Croft-Tagger-root-labels")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("huberm/ModernBERT-base-Croft-Tagger-root-labels") model = AutoModelForTokenClassification.from_pretrained("huberm/ModernBERT-base-Croft-Tagger-root-labels") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
A classifier built on top of ModernBERT-base that classifies words in English text into its root according to Croftian tags. Croftian tags are per-word semantic labels, each consisting of a (root, function)-pair. An example of this is given in the following table, where roots and functions are given as rows and columns, respectively.
| reference | modification | predication | |
|---|---|---|---|
| object | water | water(y) color | she is watering the plants |
| property | wetness | wet water | water is wet |
| action | the freezing | frozen water | water freezes easily |
See also this model's counterpart that classifies words in English text into its function according to Croftian tags.
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Base model
answerdotai/ModernBERT-base