Summarization
Transformers
Safetensors
mbart
text2text-generation
mbart-50
multilingual
news-summarization
xlsum
Instructions to use mskayacioglu/mbart50-xlsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mskayacioglu/mbart50-xlsum 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="mskayacioglu/mbart50-xlsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mskayacioglu/mbart50-xlsum") model = AutoModelForSeq2SeqLM.from_pretrained("mskayacioglu/mbart50-xlsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b1fd72ef1c3625ced7e41ab06de1a8331bde5e25f6a43e574d7c42e95cf318b
- Size of remote file:
- 5.78 kB
- SHA256:
- 865c6b689bd419500c29a34544dfd901341928ef3e104ce32af7c44520caa03d
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