Instructions to use sshleifer/distilbart-xsum-6-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-xsum-6-6 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="sshleifer/distilbart-xsum-6-6")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-6-6") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-6-6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a48ac99283e18eb1a17ea8e8b1b056cbb25ae591f90e0a9344cf4adadc6c3529
- Size of remote file:
- 460 MB
- SHA256:
- 55a0d11015c122fa43ecddaab6d863bf5ec329d5e76253bf8356d26024c42884
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