readme: ---
  pipeline_tag: zero-shot-classification
  tags:
    - zero-shot-classification
    - swedish
    - megatron-bert
  language:
    - sv
  datasets:
    - KBLab/overlim
  widget:
    - example_title: Zero-shot
      text: Många skjuter upp sina tandläkarbesök
      candidate_labels: hälsa, politik, sport, religion
  inference:
    parameters:
      hypothesis_template: Detta exempel handlar om {}.
  ---
  
  
  # Megatron-BERT-large Swedish 165k for zero-shot classification
  
  
  This model is based on Megatron-BERT-large-165k (https://huggingface.co/KBLab/megatron-bert-large-swedish-cased-165k). It was fine-tuned on the QNLI task and further fine-tuned on the MNLI task.
  The model can be used with the Hugging Face zero-shot classification pipeline.
  
  
  
  ## Usage
  
  ```python
  >>> from transformers import pipeline
  >>> classifier = pipeline(
  ...     "zero-shot-classification",
  ...     model="KBlab/megatron-bert-large-swedish-cased-165k-zero-shot"
  ... )
  >>> classifier(
  ...     "Ruben Östlunds ”Triangle of sadness” nomineras till en Golden Globe i kategorin bästa musikal eller komedi.",
  ...     candidate_labels=["hälsa", "politik", "sport", "religion", "nöje"],
  ...     hypothesis_template="Detta exempel handlar om {}.",
  ... )
  {'sequence': 'Ruben Östlunds ”Triangle of sadness” nomineras till en Golden Globe i kategorin bästa musikal eller komedi.',
   'labels': ['nöje', 'sport', 'religion', 'hälsa', 'politik'],
   'scores': [0.9274595379829407,
    0.025105971843004227,
    0.018440095707774162,
    0.017049923539161682,
    0.011944468133151531]}
  ```
  