Instructions to use ARTeLab/mbart-summarization-mlsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARTeLab/mbart-summarization-mlsum 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="ARTeLab/mbart-summarization-mlsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/mbart-summarization-mlsum") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/mbart-summarization-mlsum") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93f8667742f9d3eae0f4e885b80093ab8dd494160551dc7f25e6bb174b83f773
- Size of remote file:
- 3.12 kB
- SHA256:
- 220f886d0999bdd69b763fad992695f6444333bfadff0c614901535ccdc10495
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