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
Add evaluation results on the default config and test split of billsum
#2
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator π!
Your model has been evaluated on the default config and test split of the billsum dataset by @Artifact-AI, using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.
matthewkenney changed pull request status to merged