Instructions to use grenmon/bart-large-finetuned-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grenmon/bart-large-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-large-finetuned-summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("grenmon/bart-large-finetuned-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("grenmon/bart-large-finetuned-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from grenmon/bart-large-finetuned-summarization: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/grenmon/bart-large-finetuned-summarization/resolve/main/training_args.bin
- Command line
-
hf download hf://grenmon/bart-large-finetuned-summarization/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/grenmon/bart-large-finetuned-summarization/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- b1b06ccacd9e7d1ea31470a88fff355a6c49e6b4bee5de9389122c9495abab66
- Size of remote file:
- 3.77 kB
- SHA256:
- ae7d7d11a8c16bd5c9f8b5352f8d1a693da667fdd33886a1479bf67f73698651
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.