Summarization
Transformers
PyTorch
Safetensors
led
text2text-generation
summary
longformer
booksum
long-document
long-form
Eval Results (legacy)
Instructions to use pszemraj/led-base-book-summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/led-base-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-base-book-summary")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-base-book-summary") model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-base-book-summary", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81f15fe4da4ef092fb54df559ff1be274d754f4ace43c907d10bd32e7d005124
- Size of remote file:
- 648 MB
- SHA256:
- a8c74d469611ef8013b779aefc5cab0cf0cd16bd65dfe5c90d4aeb4dd20e57ec
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