Zero-Shot Classification
Transformers
PyTorch
JAX
Safetensors
Norwegian
bert
text-classification
nb-bert
tensorflow
norwegian
Instructions to use NbAiLab/nb-bert-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-bert-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="NbAiLab/nb-bert-base-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-bert-base-mnli") model = AutoModelForSequenceClassification.from_pretrained("NbAiLab/nb-bert-base-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/nb-bert-base-mnli: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/NbAiLab/nb-bert-base-mnli/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/nb-bert-base-mnli/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/nb-bert-base-mnli/resolve/main/flax_model.msgpack
711 MB
- Xet hash:
- 2bf7b462ab1fb05aff690e833192d8776b3f29c42730323a44370b3c62955ae8
- Size of remote file:
- 711 MB
- SHA256:
- 726c9fb6856f1def9a3895303fa6cc3df53c43d4d15976961fc585c122816f9c
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