Instructions to use Mia2024/CS5100TextSummarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mia2024/CS5100TextSummarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Mia2024/CS5100TextSummarization") model = AutoModelForSeq2SeqLM.from_pretrained("Mia2024/CS5100TextSummarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53c5c68872ebaeba7dd7bee2b6a56059e8bc995862ef686b42be05d4545df240
- Size of remote file:
- 1.63 GB
- SHA256:
- b082b243fab6e5e898b5b1d64300109cd1070904d2de8bc206733b58751e4f45
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