Instructions to use we1kkk/Randeng-MLT-PromptCBLUE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use we1kkk/Randeng-MLT-PromptCBLUE with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("we1kkk/Randeng-MLT-PromptCBLUE") model = AutoModelForSeq2SeqLM.from_pretrained("we1kkk/Randeng-MLT-PromptCBLUE") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
This repo for weight of Randeng-MLT Model finetune on PromptCBLUE dataset.
Dataset credits to : CCKS2023-PromptCBLUE\
On based of Chinese MLT Pretrained model Randeng-MLT We finetune it on a Chinese Multitask medical Seq2Seq dataset, PromptCBLUE. Also added verbaliser for better and faster model convergence.
Code implementation:
More details please refers to:Randeng-MLT-PromptCBLUE[https://github.com/we1k/Randeng-MLT-PromptCBLUE]
Pretrained model: IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese [https://huggingface.co/IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese]
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