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 pytorch_model.bin from grenmon/bart-large-finetuned-summarization: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/grenmon/bart-large-finetuned-summarization/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://grenmon/bart-large-finetuned-summarization/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/grenmon/bart-large-finetuned-summarization/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 6e6b4ebd4f0ccea9cdbf28e064dd73265152b592f6edd861bfff8f6f85a0271f
- Size of remote file:
- 1.63 GB
- SHA256:
- 756708c734fcb41b3d4e2ee05f518a6ecdd5952bdb9b4ed3185036f3ab52ac57
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