Summarization
Transformers
PyTorch
Safetensors
Enawené-Nawé
bart
text2text-generation
Trained with AutoTrain
Instructions to use Cohee/bart-factbook-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cohee/bart-factbook-summarization 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="Cohee/bart-factbook-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Cohee/bart-factbook-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Cohee/bart-factbook-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 060cc616c2589ce5ff27f029a070a3f947f03b1826688592fbb8402e334828cf
- Size of remote file:
- 1.63 GB
- SHA256:
- 9475e8877d8eeac728be0fe0f8e2b11246cf11e8a6a74e1e709354b014c859e0
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