Instructions to use grenmon/bart-base-finetuned-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grenmon/bart-base-finetuned-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="grenmon/bart-base-finetuned-summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("grenmon/bart-base-finetuned-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("grenmon/bart-base-finetuned-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from grenmon/bart-base-finetuned-summarization: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/grenmon/bart-base-finetuned-summarization/resolve/main/training_args.bin
- Command line
-
hf download hf://grenmon/bart-base-finetuned-summarization/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/grenmon/bart-base-finetuned-summarization/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- 23b2fdbae8973493e64f7f6f07c229aaca0843a4682960050f60166b934d0e02
- Size of remote file:
- 3.77 kB
- SHA256:
- dc4aeb6850085664f4f6e48d051040bd9614002f88ea5b20525160ea24d89daf
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