Instructions to use helliun/roberta-query2target with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helliun/roberta-query2target with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="helliun/roberta-query2target")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("helliun/roberta-query2target") model = AutoModel.from_pretrained("helliun/roberta-query2target", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 291444d77ff9b7e56d1de246affd556787bde75c10ba56f335384eac83667eec
- Size of remote file:
- 499 MB
- SHA256:
- d70ac2bdd6553532c2a9e80933dda0c2b3e98b00afb2d2f3f38e6219929e4559
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.