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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1f7b716ede7ea568718a4874615975ae036d9c86b9cc4b9570ff0bb3b18e823
- Size of remote file:
- 2.44 GB
- SHA256:
- 4830ec499ecc3dabbd9508d48c24656afb155f19d7e864505bd5e7614cb79f41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.