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:
- 01ddbfd3dff620550e7b3673055eac08766d7896c9635d756259cc1e2728245e
- Size of remote file:
- 558 MB
- SHA256:
- 76e796105a21c8575f6fc195b1ad70c315d9c822b07aa2475d77a79b908b87cd
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