Instructions to use omar47/t5-base-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omar47/t5-base-summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("omar47/t5-base-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("omar47/t5-base-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from omar47/t5-base-summarization: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/omar47/t5-base-summarization/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://omar47/t5-base-summarization/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/omar47/t5-base-summarization/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- cbc94ccf3673251b8ed2113523464d6d1b2f7ca0a8e00685624184c1ae7b0368
- Size of remote file:
- 892 MB
- SHA256:
- fcee9686c2b3e3c00400aac791eb11d27a5a11d27c3733595ff607631746167f
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