Instructions to use sshleifer/distilbart-xsum-12-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-xsum-12-6 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-12-6")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-12-6") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-12-6", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e79a1ff174422d78baf094f7229e5426db293c91a46e35cbd8aab8389cd913fc
- Size of remote file:
- 611 MB
- SHA256:
- 6e9ebfc94e474225457570ad33c225cf66ca26279c5cd1cbfb67e089a03a791b
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