Summarization
Transformers
PyTorch
Safetensors
English
led
text2text-generation
Eval Results (legacy)
Instructions to use AlgorithmicResearchGroup/led_base_16384_billsum_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlgorithmicResearchGroup/led_base_16384_billsum_summarization 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="AlgorithmicResearchGroup/led_base_16384_billsum_summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AlgorithmicResearchGroup/led_base_16384_billsum_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("AlgorithmicResearchGroup/led_base_16384_billsum_summarization") - Notebooks
- Google Colab
- Kaggle
ArtifactAI commited on
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Parent(s): ac3ed5d
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README.md
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@@ -191,8 +191,10 @@ Please find a notebook to test the model below:
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## Citing & Authors
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@misc{led_base_16384_billsum_summarization,
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title={led_base_16384_billsum_summarization},
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author={Matthew Kenney},
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year={2023}
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}
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## Citing & Authors
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```
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@misc{led_base_16384_billsum_summarization,
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title={led_base_16384_billsum_summarization},
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author={Matthew Kenney},
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year={2023}
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}
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```
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