Summarization
Transformers
PyTorch
Safetensors
English
led
text2text-generation
summary
longformer
booksum
long-document
long-form
Eval Results (legacy)
Instructions to use pszemraj/led-large-book-summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/led-large-book-summary 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="pszemraj/led-large-book-summary")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-large-book-summary") model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-large-book-summary", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- baf0a95555e80efecd741517602eb93744be5d5c794be50263bb60170cb007c9
- Size of remote file:
- 1.84 GB
- SHA256:
- 0603a70f15308ebccb9b66369464a83efb920ed266f736bdca0279bc066eecf7
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