Instructions to use floflodebilbao/LED_sum_challenge2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use floflodebilbao/LED_sum_challenge2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("floflodebilbao/LED_sum_challenge2") model = AutoModelForSeq2SeqLM.from_pretrained("floflodebilbao/LED_sum_challenge2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f06420bfe377cb792b1e871318b1866edecd7c91c10912d63644efd8270199a
- Size of remote file:
- 648 MB
- SHA256:
- a4e468c3454e274b1b40f9e53663dd1a3b4afebc3d2cee64b529f84147323b33
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