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
Download training_args.bin from MbahLaba/Sergia_Summarization: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/MbahLaba/Sergia_Summarization/resolve/main/training_args.bin
- Command line
-
hf download hf://MbahLaba/Sergia_Summarization/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MbahLaba/Sergia_Summarization/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- d24110d8e233188ba0ce7d0dc6ee82c878dbe926d7565c135c70c59f28c4e5c6
- Size of remote file:
- 5.18 kB
- SHA256:
- 494b5ab19cf821c4aa365f8a828231ed88da956e8d8b844487f1ad6c2280461b
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