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
- Xet hash:
- 7586d4362f2d40d93b0d657294918476d2e46694a7ce4fc04e2a6e49bc397c1d
- Size of remote file:
- 558 MB
- SHA256:
- 82f96d19587e45e5f6d765de4820b8d458899ad3288e02fa5c6e8363dcc0f396
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.