Instructions to use Tommert25/multibert_dataaugmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tommert25/multibert_dataaugmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Tommert25/multibert_dataaugmentation")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Tommert25/multibert_dataaugmentation") model = AutoModelForTokenClassification.from_pretrained("Tommert25/multibert_dataaugmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3e56247e9e3afb2843e460ddd51a46a80dacce3d793557ccdc2042f43f97935
- Size of remote file:
- 667 MB
- SHA256:
- 61e0070a550c825cd960de7d12418ff6de29818c0dba62743265c945ab7a677d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.