Instructions to use kurtpayne/skillscan-deberta-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kurtpayne/skillscan-deberta-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base") model = PeftModel.from_pretrained(base_model, "kurtpayne/skillscan-deberta-adapter") - Transformers
How to use kurtpayne/skillscan-deberta-adapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kurtpayne/skillscan-deberta-adapter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kurtpayne/skillscan-deberta-adapter") model = AutoModelForSequenceClassification.from_pretrained("kurtpayne/skillscan-deberta-adapter") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 464d4e1ac7f123cbc278385afb085c3b27e5b764b5cd8141284d850d4936cdf4
- Size of remote file:
- 82.7 MB
- SHA256:
- bda7d82e3984369bac068f08d4eb3e14c693361456eeb9749a000ec6383a22d2
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