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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kurtpayne/skillscan-deberta-adapter") model = AutoModelForSequenceClassification.from_pretrained("kurtpayne/skillscan-deberta-adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Auto fine-tune: 23090 examples (LoRA adapter)
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Auto fine-tune: 21855 examples (ONNX FP16)
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Auto fine-tune: 21249 examples (ONNX FP16)
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Auto fine-tune: 16589 examples (ONNX FP16)
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Auto fine-tune: 11674 examples (LoRA adapter)
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Auto fine-tune: 1509 examples (ONNX INT8)
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Auto fine-tune: 1090 examples (ONNX INT8)
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Auto fine-tune: 113 examples (ONNX INT8)
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