Instructions to use maveriq/lingbert-mini-60k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maveriq/lingbert-mini-60k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="maveriq/lingbert-mini-60k")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("maveriq/lingbert-mini-60k") model = AutoModelForMaskedLM.from_pretrained("maveriq/lingbert-mini-60k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7526adc0dc0b929b10d76b13dc8c5810b9abb43cde2d869b3270fec310d313d
- Size of remote file:
- 44.9 MB
- SHA256:
- 87a5fb02bf52fa6d184415a56725389fd794777232b1c58ebcb907ccfc791626
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.