Summarization
Transformers
PyTorch
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use cs608/multi-news-diff-weight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs608/multi-news-diff-weight 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="cs608/multi-news-diff-weight")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cs608/multi-news-diff-weight") model = AutoModelForSeq2SeqLM.from_pretrained("cs608/multi-news-diff-weight", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cs608/multi-news-diff-weight: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/cs608/multi-news-diff-weight/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cs608/multi-news-diff-weight/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cs608/multi-news-diff-weight/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- f56e082a0b45af209dd6459801bff99fd216fab3890959025ace463e83f3b6f2
- Size of remote file:
- 558 MB
- SHA256:
- 742bfeedff4e832f8ba8c8a10ebc59286db358b8a3e8a42575a733e72911fae4
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