Instructions to use musabg/mt5-large-tr-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use musabg/mt5-large-tr-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("musabg/mt5-large-tr-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("musabg/mt5-large-tr-summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1fb68fd8cadb87966d0cf7e7442c76765fa1d7d0912e901d2c14f7d76358ecb
- Size of remote file:
- 4.92 GB
- SHA256:
- aa5290f504b7174fdcbb9a3b04abf8e73b458ae66879fbbdd2d8b9d6c46bc27e
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