Instructions to use shirzady1934/trainer_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shirzady1934/trainer_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="shirzady1934/trainer_output")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("shirzady1934/trainer_output") model = AutoModelForMaskedLM.from_pretrained("shirzady1934/trainer_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df2893520396589aaa9b179c238aad172452288d6ca57df4d541ec9f71469fed
- Size of remote file:
- 5.3 kB
- SHA256:
- b27d360890bd8c8bf4efa3d179a153bd1991d7a05d50cdc33dfdf9d379a3e82b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.