Instructions to use ainize/bart-base-cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ainize/bart-base-cnn 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="ainize/bart-base-cnn")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ainize/bart-base-cnn") model = AutoModel.from_pretrained("ainize/bart-base-cnn") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on the samsum config and test split of samsum
#5
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator π!
Your model has been evaluated on the samsum config and test split of the samsum dataset by @sasha , 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.