Text Classification
Transformers
PyTorch
Safetensors
Northern Sami
Norwegian
English
bert
sami relevant
Instructions to use NbAiLab/nb-bert-base-sami-relevant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-bert-base-sami-relevant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NbAiLab/nb-bert-base-sami-relevant")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-bert-base-sami-relevant") model = AutoModelForSequenceClassification.from_pretrained("NbAiLab/nb-bert-base-sami-relevant") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d75f570eecdf1e52d5fc382b5949c275f286f4e3a72f14c0452aa2a3c01c4e1e
- Size of remote file:
- 3.44 kB
- SHA256:
- 31c91c4225b7cde5415204dfa61aaa00c321582cccd83920acc6a5c22c146756
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.