Instructions to use midas/gupshup_h2e_t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use midas/gupshup_h2e_t5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("midas/gupshup_h2e_t5") model = AutoModelForSeq2SeqLM.from_pretrained("midas/gupshup_h2e_t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba9fe911f90e8edfbe78505a52d4c6dd61d597df23d0c2ab05edfae1603174cf
- Size of remote file:
- 892 MB
- SHA256:
- f504a53c8ea2837691454a4bedc04be2c915d23306a2e2704b63672ccf6d3161
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