Upload README.md

README.md

@@ -52,7 +52,7 @@ model-index:
 
 This [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) is finetuned from [Derify/ModChemBERT-IR-BASE](https://huggingface.co/Derify/ModChemBERT-IR-BASE) using hard-negative triplets derived from [Derify/pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity). Positive SMILES pairs are first filtered by quality and similarity constraints, then reduced to one strongest positive target per anchor molecule to create a high-signal training set for reranking. The model computes relevance scores for pairs of SMILES strings, enabling SMILES reranking and molecular semantic search.
 
-For this variant, positives are selected with a composite ranking criterion that combines high QED and similarity, where the similarity contribution is explicitly capped at `0.75` to prevent similarity from dominating the ranking score. The quality stage uses strict inequality filtering (`QED > 0.85`, `similarity > 0.5`, with similarity also bounded below 1.0).
+For this variant, the positives are selected with a composite ranking criterion that combines high QED and similarity, where the similarity contribution is explicitly capped at `0.75` to prevent similarity from dominating the ranking score. The quality stage uses strict inequality filtering (`QED > 0.85`, `similarity > 0.5`, with similarity also bounded below 1.0), and then keeps the top-scoring pair per anchor molecule.
 
 Hard negatives are mined with [Sentence Transformers](https://www.sbert.net/) using [Derify/ChemMRL-beta](https://huggingface.co/Derify/ChemMRL-beta) as the teacher model and a TopK-PercPos-style margin setting based on [NV-Retriever](https://arxiv.org/abs/2407.15831), with `relative_margin=0.05` and `max_negative_score_threshold = pos_score * percentage_margin`. Training uses triplet-format samples with 5 mined negatives per anchor-positive pair and optimizes a multiple-negatives ranking objective, while reranking evaluation uses n-tuple samples with 30 mined negatives per query.
 
@@ -65,7 +65,6 @@ Hard negatives are mined with [Sentence Transformers](https://www.sbert.net/) us
 - **Number of Output Labels:** 1 label
 - **Training Dataset:**
   - [Derify/pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity) Mined Hard Negatives
-<!-- - **Language:** Unknown -->
 - **License:** apache-2.0
 
 ### Model Sources