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slauw87
/
bart_summarisation

Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Model card Files Files and versions
xet
Community
6

Instructions to use slauw87/bart_summarisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use slauw87/bart_summarisation with Transformers:

    # Use a pipeline as a high-level helper
    # Warning: Pipeline type "summarization" is no longer supported in transformers v5.
    # You must load the model directly (see below) or downgrade to v4.x with:
    # 'pip install "transformers<5.0.0'
    from transformers import pipeline
    
    pipe = pipeline("summarization", model="slauw87/bart_summarisation")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("slauw87/bart_summarisation")
    model = AutoModelForSeq2SeqLM.from_pretrained("slauw87/bart_summarisation")
  • Notebooks
  • Google Colab
  • Kaggle
bart_summarisation / coreml /text2text-generation
2.45 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
blackhole1123's picture
blackhole1123
Add Core ML conversion
f1cb58f almost 3 years ago
  • decoder_float32_model.mlpackage
    Add Core ML conversion almost 3 years ago
  • encoder_float16_model.mlpackage
    Add Core ML conversion almost 3 years ago
  • encoder_float32_model.mlpackage
    Add Core ML conversion almost 3 years ago