Instructions to use kmok1/cs_mT5-large2_2e-5_50_v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmok1/cs_mT5-large2_2e-5_50_v0.3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("kmok1/cs_mT5-large2_2e-5_50_v0.3") model = AutoModelForSeq2SeqLM.from_pretrained("kmok1/cs_mT5-large2_2e-5_50_v0.3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a0f248046ec5ff5abd648cc81c68292fc9190eeb50273d5be41915e8383a7dd
- Size of remote file:
- 16.3 MB
- SHA256:
- a0910f648726ceada34086ae80066cd253863e183cbb52ae566659a2d37716f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.