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
Commit History
Add evaluation results on the default config and test split of billsum (#1) 703cc84
Update README.md 2b0a387
ArtifactAI commited on
Update README.md 0ad71dc
ArtifactAI commited on
Update README.md 4c22648
ArtifactAI commited on
Delete READEME.md c6ab3f2
ArtifactAI commited on
Create README.md 73471bc
ArtifactAI commited on
Create READEME.md 9f692cf
ArtifactAI commited on
first commit 4fff009
Artifact-AI commited on
initial commit bd979bd
ArtifactAI commited on