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
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#pipeline_tag: zero-shot-classification
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# candidate_labels: "politikk, helse, sport, religion"
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# hypothesis_template: "Dette eksempelet er {}."
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# multi_class: true
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- xnli
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#pipeline_tag: zero-shot-classification
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- text: "Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september. </s></s> Dette eksempelet er om helse."
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# candidate_labels: "politikk, helse, sport, religion"
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# hypothesis_template: "Dette eksempelet er {}."
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# multi_class: true
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