Summarization
Transformers
PyTorch
English
led
text2text-generation
summary
longformer
booksum
long-document
long-form
Eval Results (legacy)
Instructions to use andreaparker/long-summ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andreaparker/long-summ 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="andreaparker/long-summ")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("andreaparker/long-summ") model = AutoModelForSeq2SeqLM.from_pretrained("andreaparker/long-summ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fef33e10f586afff50b4730c7f822e395b314180a6407d8284d5f0286d74742b
- Size of remote file:
- 130 Bytes
- SHA256:
- 58f013dc79528514b3c1cce2c180b789f59730e3d4dce985927ddda73bae54d2
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