Instructions to use damlab/GO-language with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use damlab/GO-language with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="damlab/GO-language")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("damlab/GO-language") model = AutoModelForMaskedLM.from_pretrained("damlab/GO-language", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fd1f85f933aac73244dc81032925f870a6bcd89522487bdeaeb5464188fdefe
- Size of remote file:
- 2.99 kB
- SHA256:
- 4b93dc9b41e3f64226cd7d96a64142035edcb5e48077686ada7a7f294f6174e3
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