Update README.md

README.md

@@ -104,6 +104,10 @@ model-index:
 
 This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
 
+See [training_gooaq_bce.py](https://github.com/UKPLab/sentence-transformers/blob/feat/cross_encoder_trainer/examples/cross_encoder/training/rerankers/training_gooaq_bce.py) for the training script - only the base model was updated from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) to [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large). This script is also described in the [Cross Encoder > Training Overview](https://sbert.net/docs/cross_encoder/training_overview.html) documentation and the [Training and Finetuning Reranker Models with Sentence Transformers v4](https://huggingface.co/blog/train-reranker) blogpost.
+
+![Model size vs NDCG for Rerankers on GooAQ](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/train-reranker/reranker_gooaq_model_size_ndcg.png)
+
 ## Model Details
 
 ### Model Description
@@ -211,6 +215,23 @@ You can finetune this model on your own dataset.
 
 #### Cross Encoder Reranking
 
+* Dataset: `gooaq-dev`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.8176 (+0.2865)     |
+| mrr@10      | 0.8166 (+0.2926)     |
+| **ndcg@10** | **0.8581 (+0.2669)** |
+
+#### Cross Encoder Reranking
+
 * Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
 * Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
   ```json