Instructions to use tringuyen1032/summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tringuyen1032/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="tringuyen1032/summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tringuyen1032/summarization") model = AutoModelForSeq2SeqLM.from_pretrained("tringuyen1032/summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tringuyen1032/summarization: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/tringuyen1032/summarization/resolve/main/training_args.bin
- Command line
-
hf download hf://tringuyen1032/summarization/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tringuyen1032/summarization/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- 016f64cef7c960c6830cc699c8d4ad057c96e5a11f5596b1b5ae6e81fe619d50
- Size of remote file:
- 4.22 kB
- SHA256:
- 6d7f2847c32dc249be1dbbd06c7de065a30587aa1649e1c1a1a0079b582d2455
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