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
Add evaluation results on the 3.0.0 config and test split of cnn_dailymail
#17
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!
Your model has been evaluated on the 3.0.0 config and test split of the cnn_dailymail dataset by @kaprerna135 , 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.
pszemraj changed pull request status to merged