Instructions to use Winmodel/Mlukebasewithpaperparameter_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Winmodel/Mlukebasewithpaperparameter_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Winmodel/Mlukebasewithpaperparameter_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Winmodel/Mlukebasewithpaperparameter_v2") model = AutoModelForSequenceClassification.from_pretrained("Winmodel/Mlukebasewithpaperparameter_v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e15c56b6e56d6743aca3c98e6933de1ef33a3297d72ec1be07fe2597205fc32e
- Size of remote file:
- 2.34 GB
- SHA256:
- 4d2a39a7630354c86cab341fa46bcee7414251375b0d1cc103ecf7207254cbd6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.