Instructions to use BridgingVarieties/DialectBench-Reproduce-DEP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BridgingVarieties/DialectBench-Reproduce-DEP with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BridgingVarieties/DialectBench-Reproduce-DEP") model = AutoModel.from_pretrained("BridgingVarieties/DialectBench-Reproduce-DEP") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c23950f9c1b433a34b9b724a1a8226088bc6c1968dbf98353c783412011678d0
- Size of remote file:
- 14.2 kB
- SHA256:
- db6581720d0a0b4074469313b6fb630a7941095ba466754e83b3f609a1a81e3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.