Instructions to use philschmid/bart-large-cnn-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/bart-large-cnn-samsum 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="philschmid/bart-large-cnn-samsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("philschmid/bart-large-cnn-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("philschmid/bart-large-cnn-samsum") - Inference
- Notebooks
- Google Colab
- Kaggle
Align task name and type with Hub taxonomy
This PR proposes to align task name and type for the self-reported evaluation with the Hub taxonomy (i.e. the high-level tasks defined in hf.co/models)
The self-reported results will then become visible on this PwC leaderboard: https://paperswithcode.com/sota/summarization-on-samsum
cc @julien-c
why don't you just group all the metrics into the same (task, dataset) tuple, then? would be cleaner, no?
Yes it would be cleaner that way, but self-reported evaluations rarely specify the dataset config / split that was used. This means you can't group the verified and self-reported metrics under a single dataset field.
A unique grouping would be something like (task, dataset_id, dataset_config, dataset_split) - I'll double check if the metadata_update() function from huggingface_hub that we use automatically groups along those fields