Summarization
Transformers
TensorBoard
Safetensors
English
bart
text2text-generation
summarizer
text summarization
abstractive summarization
Instructions to use KipperDev/bart_summarizer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KipperDev/bart_summarizer_model 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="KipperDev/bart_summarizer_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KipperDev/bart_summarizer_model") model = AutoModelForSeq2SeqLM.from_pretrained("KipperDev/bart_summarizer_model") - Notebooks
- Google Colab
- Kaggle
bart_summarizer_model / runs /Jan26_02-08-37_2d658fece0d8 /events.out.tfevents.1706234923.2d658fece0d8.303.0
- Xet hash:
- 564b46ae6f9704a78ca519f08c60e1e06640e7ddde58a37be9c04663cb7a8406
- Size of remote file:
- 16 kB
- SHA256:
- 2ea68bd09cc05066d153fe53eea65539f09ff427a3d05c0df055097e133af59e
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