Instructions to use EMBEDDIA/litlat-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBEDDIA/litlat-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EMBEDDIA/litlat-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/litlat-bert") model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/litlat-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b18ab5b60f7bfd60f3d3e563629a87169ef859c339dd21cb4d0862a7788cbe65
- Size of remote file:
- 606 MB
- SHA256:
- 9190e707b724c8e1dc50135b0f7f884ef53c0adb11a821443d499f7a287513a4
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