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 training_args.bin from cs608/multi-news-diff-weight: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/cs608/multi-news-diff-weight/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://cs608/multi-news-diff-weight@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cs608/multi-news-diff-weight/resolve/refs%2Fpr%2F1/training_args.bin
4.03 kB
- Xet hash:
- 1724c67ec43a3e8e3f795a5aaa0995b74960f22a66e9cef0dd92b00629aa9b35
- Size of remote file:
- 4.03 kB
- SHA256:
- c725d740039ba9f688c5a2cf170cc1af079c65c2527c30bfd95a7dfb35a8e035
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