Instructions to use KPF/KPF-bert-cls2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KPF/KPF-bert-cls2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KPF/KPF-bert-cls2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KPF/KPF-bert-cls2") model = AutoModelForSequenceClassification.from_pretrained("KPF/KPF-bert-cls2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7e59a2395e18cc0673882c0605a3b53fe86cf6dabb1723747650789e724e77d
- Size of remote file:
- 456 MB
- SHA256:
- f9a2a90ee44e5062cc3f5392602d07cb904790e33c01ffa5a0b5c60cb72ff8eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.