Summarization
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
booksum
summary
book
Instructions to use KamilAin/bart-base-booksum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KamilAin/bart-base-booksum 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="KamilAin/bart-base-booksum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KamilAin/bart-base-booksum") model = AutoModelForSeq2SeqLM.from_pretrained("KamilAin/bart-base-booksum") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff7efc92f6bad5d7d3a7f58bc77d01b871343211b21cdb3f4efe323984e83cea
- Size of remote file:
- 558 MB
- SHA256:
- 3815f2c4f12defa0a5978de011704b5789be8f3b44b11e838bc0a11023a2e7f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.