Instructions to use MbahLaba/Sergia_Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MbahLaba/Sergia_Summarization 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="MbahLaba/Sergia_Summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MbahLaba/Sergia_Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("MbahLaba/Sergia_Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from MbahLaba/Sergia_Summarization: direct link, hf CLI and curl.
- Browser
- Download file 131 Bytes
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https://huggingface.co/MbahLaba/Sergia_Summarization/resolve/main/README.md
- Command line
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hf download hf://MbahLaba/Sergia_Summarization/README.md
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curl -L -o README.md https://huggingface.co/MbahLaba/Sergia_Summarization/resolve/main/README.md
131 Bytes
| language: | |
| - id | |
| base_model: | |
| - facebook/bart-large-cnn | |
| pipeline_tag: summarization | |
| library_name: transformers | |
| tags: | |
| - medical | |