Text Classification
PEFT
Safetensors
Transformers
lora
torchao
emotion-classification
int8
Eval Results (legacy)
Instructions to use Wb-az/peft-modernbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Wb-az/peft-modernbert-base with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base") model = PeftModel.from_pretrained(base_model, "Wb-az/peft-modernbert-base") - Transformers
How to use Wb-az/peft-modernbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wb-az/peft-modernbert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Wb-az/peft-modernbert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Wb-az/peft-modernbert-base: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/Wb-az/peft-modernbert-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://Wb-az/peft-modernbert-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Wb-az/peft-modernbert-base/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.