Instructions to use tcapelle/random-tiny-modernbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tcapelle/random-tiny-modernbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tcapelle/random-tiny-modernbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tcapelle/random-tiny-modernbert") model = AutoModelForSequenceClassification.from_pretrained("tcapelle/random-tiny-modernbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06f1accf404d22e839839739ed33824fc3fb385693a913b83a748446c29c2b02
- Size of remote file:
- 6.54 MB
- SHA256:
- 2865505afcd2087e3ca1a578af0291f447de5cad1af6d628bc1098caa64262c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.