Summarization
Transformers
PyTorch
Safetensors
English
led
text2text-generation
Eval Results (legacy)
Instructions to use AlgorithmicResearchGroup/led_large_16384_billsum_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlgorithmicResearchGroup/led_large_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_large_16384_billsum_summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AlgorithmicResearchGroup/led_large_16384_billsum_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("AlgorithmicResearchGroup/led_large_16384_billsum_summarization") - Notebooks
- Google Colab
- Kaggle
ArtifactAI commited on
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Parent(s): 5e23461
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README.md
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# Longformer Encoder-Decoder (LED) fine-tuned on Billsum
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This model is a fine-tuned version of led-large-16384 on the billsum dataset.
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As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, led-
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# Use In Transformers
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# Longformer Encoder-Decoder (LED) fine-tuned on Billsum
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This model is a fine-tuned version of led-large-16384 on the billsum dataset.
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As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, led-large-16384 was initialized from bart-base since both models share the exact same architecture. To be able to process 16K tokens, bart-base's position embedding matrix was simply copied 16 times.
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# Use In Transformers
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