Instructions to use manu02/distilbert-base-uncased-lora-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use manu02/distilbert-base-uncased-lora-text-classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased") model = PeftModel.from_pretrained(base_model, "manu02/distilbert-base-uncased-lora-text-classification") - Transformers
How to use manu02/distilbert-base-uncased-lora-text-classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("manu02/distilbert-base-uncased-lora-text-classification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 366863525abd6342f48ebb457e6331c157e02d34ec8c01cbd58599bcbec4749e
- Size of remote file:
- 5.2 kB
- SHA256:
- 58841370b63f49684dcd30facc01c8e0e84bb2d00743c81c78db1709db886c0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.