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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ff52951bda9a86b9f1f9b1fbb3b1d7491b08484f5ab33503ca6b425f0f64dc7
- Size of remote file:
- 5.43 kB
- SHA256:
- 4dc87b5bbf11ee08afd292f0a445f22d7e84dfafffcb162f0243f54062b83824
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.