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:
- eedb3714a5d33323822f1b0cb3ba6742f4649f8336be088fc37d4fd04a05f42d
- Size of remote file:
- 587 MB
- SHA256:
- 11a8532f2b4f8339c4480abe6b004762df230cee56e8cd72712373f7c52a0eae
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