Text Classification
PEFT
Safetensors
Transformers
lora
torchao
emotion-classification
int8
Eval Results (legacy)
Instructions to use Wb-az/peft-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Wb-az/peft-roberta-base with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("roberta-base") model = PeftModel.from_pretrained(base_model, "Wb-az/peft-roberta-base") - Transformers
How to use Wb-az/peft-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wb-az/peft-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Wb-az/peft-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Wb-az/peft-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/Wb-az/peft-roberta-base/resolve/main/training_args.bin
- Command line
-
hf download hf://Wb-az/peft-roberta-base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Wb-az/peft-roberta-base/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 57108a65668f06567d4d96c60210b6f97da9cdfb204676a7c8e047abbec49d05
- Size of remote file:
- 5.27 kB
- SHA256:
- 08747cf29321824e71aa993ca57b70a3e903a69c34a3e7cefaf3115f8b5d97db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.