Instructions to use UdS-LSV/smole-bert-mtr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UdS-LSV/smole-bert-mtr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="UdS-LSV/smole-bert-mtr")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UdS-LSV/smole-bert-mtr") model = AutoModelForMaskedLM.from_pretrained("UdS-LSV/smole-bert-mtr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 030e4d7fff1c9b4fbad74643d900666a36ab4dd834220e21758af2cea3bead3f
- Size of remote file:
- 88.6 MB
- SHA256:
- b2114facbeca1a625cd172fde8cafcdccd1a44e6417a89473d556ef695d43b99
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