Instructions to use vsearch/svdr-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vsearch/svdr-bert with Transformers:
# Load model directly from transformers import Retriever model = Retriever.from_pretrained("vsearch/svdr-bert", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "Retriever" | |
| ], | |
| "device": null, | |
| "encoder_p": { | |
| "max_len": 256, | |
| "model_id": "bert-base-uncased", | |
| "norm": false, | |
| "pooling": "max", | |
| "pooling_topk": null, | |
| "shift_vocab_num": 999, | |
| "topk": 768, | |
| "type": "vdr" | |
| }, | |
| "encoder_q": { | |
| "max_len": 128, | |
| "model_id": "bert-base-uncased", | |
| "norm": false, | |
| "pooling": "max", | |
| "pooling_topk": null, | |
| "shift_vocab_num": 999, | |
| "topk": 768, | |
| "type": "vdr" | |
| }, | |
| "shared_encoder": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.40.2" | |
| } | |