Instructions to use fcfrank10/dbert_model_05 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fcfrank10/dbert_model_05 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fcfrank10/dbert_model_05")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fcfrank10/dbert_model_05") model = AutoModelForTokenClassification.from_pretrained("fcfrank10/dbert_model_05", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87c5d16d656df90b3c66de8ad09b3ddfbf3839b540bb258a1d3596d8cdc81f3c
- Size of remote file:
- 539 MB
- SHA256:
- 08e8c83a8d0b3b997606ba1e1497e328de1d77d58b6e5605ffd38d38575eec31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.