Instructions to use deepset/gbert-base-germandpr-question_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gbert-base-germandpr-question_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="deepset/gbert-base-germandpr-question_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deepset/gbert-base-germandpr-question_encoder") model = AutoModel.from_pretrained("deepset/gbert-base-germandpr-question_encoder") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1651465ddd56e02eaea3639a8ff8c335726d524606ac9120539dbaa8c3741500
- Size of remote file:
- 440 MB
- SHA256:
- 1d51a6346f9d437f9618a3181eab7731539743327c8838e130cee06dfe2fa270
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.