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
Auto fine-tune: 23090 examples (LoRA adapter)
Browse files- adapter_model.safetensors +1 -1
- checkpoint-10104/adapter_config.json +2 -2
- checkpoint-10104/adapter_model.safetensors +1 -1
- checkpoint-10104/optimizer.pt +1 -1
- checkpoint-10104/scaler.pt +1 -1
- checkpoint-10104/trainer_state.json +0 -0
- checkpoint-10104/training_args.bin +1 -1
- checkpoint-12630/adapter_config.json +2 -2
- checkpoint-12630/adapter_model.safetensors +1 -1
- checkpoint-12630/optimizer.pt +1 -1
- checkpoint-12630/scaler.pt +1 -1
- checkpoint-12630/trainer_state.json +0 -0
- checkpoint-12630/training_args.bin +1 -1
- checkpoint-7578/adapter_config.json +2 -2
- checkpoint-7578/adapter_model.safetensors +1 -1
- checkpoint-7578/optimizer.pt +1 -1
- checkpoint-7578/scaler.pt +1 -1
- checkpoint-7578/trainer_state.json +0 -0
- checkpoint-7578/training_args.bin +1 -1
adapter_model.safetensors
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