Instructions to use xummer/adversarial_qa_dbert_based_on with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xummer/adversarial_qa_dbert_based_on with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("xummer/adversarial_qa_dbert_based_on") model = AutoModelForSeq2SeqLM.from_pretrained("xummer/adversarial_qa_dbert_based_on") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94e036dbbcca3f6932f77b9b7fc58fd07bdf2ad2335e99b42dfddae7f41e9f74
- Size of remote file:
- 5.37 kB
- SHA256:
- e0f04ace49acf5efe02ee208349a509f54f132580f56de191de683868dd8c281
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.