Instructions to use income/jpq-gpl-arguana-question_encoder-base-msmarco-distilbert-tas-b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use income/jpq-gpl-arguana-question_encoder-base-msmarco-distilbert-tas-b with Transformers:
# Load model directly from transformers import AutoTokenizer, JPQTowerTASB tokenizer = AutoTokenizer.from_pretrained("income/jpq-gpl-arguana-question_encoder-base-msmarco-distilbert-tas-b") model = JPQTowerTASB.from_pretrained("income/jpq-gpl-arguana-question_encoder-base-msmarco-distilbert-tas-b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2aa731a9e8e207bf375d25533382ca97840c9cc9ee084dc0ad59468a0254ccb1
- Size of remote file:
- 269 MB
- SHA256:
- b24b58e29dc93a01df0a28b5aec1650d7d32283b8cc402989f484cb8e89bfd1a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.