Instructions to use savasy/mt5-mlsum-turkish-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use savasy/mt5-mlsum-turkish-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("savasy/mt5-mlsum-turkish-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("savasy/mt5-mlsum-turkish-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da94f66e3c597fc17d4481d28056d8e47dfb265a99b457d841bc6cf09839703c
- Size of remote file:
- 1.2 GB
- SHA256:
- 9b3056994479fda746ecbd4c43c94c67d0df35318c96edf3eec6373f3fd05e33
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