Instructions to use DeepNeural/Pubmed_Summarizer_Model_Trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepNeural/Pubmed_Summarizer_Model_Trained with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("DeepNeural/Pubmed_Summarizer_Model_Trained") model = AutoModelForSeq2SeqLM.from_pretrained("DeepNeural/Pubmed_Summarizer_Model_Trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b67f2032e2c549c7edfdfcf89a639a734af76d8133512d614e2aad69b95bfd5
- Size of remote file:
- 5.91 kB
- SHA256:
- bbb8487e5ffab37fd05a2217b9c70ce54f8680bb1fa634fbf28edc17296304f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.