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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4e7566de3c318facc00e9aca9d9a6906ef373a54b175f7bbb29e5712acde49a
- Size of remote file:
- 14.5 kB
- SHA256:
- 012037003124996636148a24a33514dd83df0cce94d80bcb476325f3a4fd9b3c
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