Instructions to use vespa-engine/colbert-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vespa-engine/colbert-medium with Transformers:
# Load model directly from transformers import AutoTokenizer, ColBERT tokenizer = AutoTokenizer.from_pretrained("vespa-engine/colbert-medium") model = ColBERT.from_pretrained("vespa-engine/colbert-medium") - Notebooks
- Google Colab
- Kaggle
Jo Kristian Bergum commited on
Commit ·
ec9f05f
1
Parent(s): fa29120
Add initial Readme
Browse files
README.md
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# MS Marco Ranking with ColBERT on Vespa.ai This is work in progress.
|
| 2 |
+
|
| 3 |
+
Model is based on [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://arxiv.org/abs/2004.12832). This bert model is based on BERT Medium uncased [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8)To use this model with vespa.ai for MS Marco Passage Ranking, see [MS Marco Ranking using Vespa.ai sample app](https://github.com/vespa-engine/sample-apps/tree/master/msmarco-ranking)
|