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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- summarization
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license: apache-2.0
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datasets:
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- xsum
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thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
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---
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| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 |
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| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 |
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| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 |
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| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 |
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| distilbart-6-6-cnn | 230 | 182 |
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- summarization
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license: apache-2.0
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datasets:
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- cnn_dailymail
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- xsum
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thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
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---
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| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 |
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| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 |
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| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 |
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| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 |
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| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 |
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| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 |
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