Instructions to use sshleifer/distilbart-xsum-12-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-xsum-12-3 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-12-3")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-12-3") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-12-3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 17a97ebbf86b64ad96b34a91ee1c4db0bc36611aaa9f7c04ad8b07ca0b343eb8
- Size of remote file:
- 716 MB
- SHA256:
- a3966b04914a8401eec02f674062c15fe8619c110816a271f178d52483bf804b
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