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")
https://huggingface.co/MbahLaba/Sergia_Summarization/resolve/main/README.md
hf download hf://MbahLaba/Sergia_Summarization/README.md
curl -L -o README.md https://huggingface.co/MbahLaba/Sergia_Summarization/resolve/main/README.md
language: - id base_model: - facebook/bart-large-cnn pipeline_tag: summarization library_name: transformers tags: - medical
