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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ed0c021845e223734c30aeea279336ccfa1ce380a4bd424b1b01a5c780f91b7
- Size of remote file:
- 4.4 kB
- SHA256:
- 4b45e019c7ec05993cda59a7c7816ca58ae002e1bb6428693619fe1f07541b7e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.