Instructions to use Samuela39/summarization_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Samuela39/summarization_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Samuela39/summarization_model") model = AutoModelForSeq2SeqLM.from_pretrained("Samuela39/summarization_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Samuela39/summarization_model: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/Samuela39/summarization_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Samuela39/summarization_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Samuela39/summarization_model/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- acd302ef0228157db358593f4d8e17c116a8ad686180bc16c788eeb8eb8e3a82
- Size of remote file:
- 5.05 kB
- SHA256:
- fef26ecc4ff5bf82919651bb23a74df197df8feba6f6a30c4dacdf78636716f8
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