multi-vector-encoder
/

ModernBERT-base-en-v1

Feature Extraction

sentence-transformers

late-interaction

text-embeddings-inference

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tomaarsen HF Staff commited on 3 days ago

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@@ -20,7 +20,7 @@ This is a [Multi-Vector Encoder](https://www.sbert.net/docs/multi_vector_encoder
 The merged model scores above the average of its two parents on **9 of the 13 NanoBEIR datasets**, including **5 where it beats both**. It achieves a mean nDCG@10 of **0.7061**, compared with **0.6996** for GLInt and **0.6979** for LateOn-regularized, using the same inference settings for all three models.
-It took a whopping 6 lines of code to merge the models and set the document length, and it took about 12 seconds to execute. See [Model Creation](#model-creation) for the full generation script.
 ## Model Details

 The merged model scores above the average of its two parents on **9 of the 13 NanoBEIR datasets**, including **5 where it beats both**. It achieves a mean nDCG@10 of **0.7061**, compared with **0.6996** for GLInt and **0.6979** for LateOn-regularized, using the same inference settings for all three models.
+It took a whopping 6 lines of code to merge the models and set the document length, and it took about 12 seconds to execute. See [Model Creation](#model-creation) for the full generation script. The merging settings were not based on any evaluation results.
 ## Model Details