Add README and evaluation results

CrossEncoderRerankingEvaluator_results_@10.csv

@@ -0,0 +1,9 @@
+epoch,steps,MAP,MRR@10,NDCG@10
+0,0,0.201234224737145,0.24622625665838818,0.3333765906191056
+0,0,0.201234224737145,0.24622625665838818,0.3333765906191056
+1.0,6238,0.42031973501159536,0.661484421265373,0.681130899332091
+1.0,6238,0.4175184482992122,0.6579728914084031,0.677183299653141
+2.0,12476,0.42596499853521474,0.6698020841462649,0.6890968672904094
+2.0,12476,0.42618104932280626,0.6712443276500806,0.6884164601804822
+3.0,18714,0.4266379953496367,0.6710111071325281,0.6901091880496036
+3.0,18714,0.4264841421286288,0.6705833257778512,0.6898266949491281

README.md

@@ -1,3 +1,483 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- modchembert
+- cheminformatics
+- smiles
+- generated_from_trainer
+- dataset_size:3193917
+- loss:MultipleNegativesRankingLoss
+base_model: Derify/ModChemBERT-IR-BASE
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+co2_eq_emissions:
+  emissions: 3666.7922463213226
+  energy_consumed: 17.863338649668595
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: AMD Ryzen 7 3700X 8-Core Processor
+  ram_total_size: 62.69877243041992
+  hours_used: 29.477
+  hardware_used: 2 x NVIDIA GeForce RTX 3090
+model-index:
+- name: 'Derify/ChemRanker-alpha-qed-sim'
+  results:
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: map
+      value: 0.4266379953496367
+      name: Map
+    - type: mrr@10
+      value: 0.6710111071325281
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.6901091880496036
+      name: Ndcg@10
+---
+
+# Derify/ChemRanker-alpha-qed-sim
+
+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 without an additional similarity-contribution cutoff. 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [Derify/ModChemBERT-IR-BASE](https://huggingface.co/Derify/ModChemBERT-IR-BASE) <!-- at revision 1d8fd449edb3eadeaa5ebdd1c891e3ce95aebc3d -->
+- **Maximum Sequence Length:** 512 tokens
+- **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
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Transformers and Sentence Transformers libraries:
+
+```bash
+pip install -U "transformers>=4.57.1,<5.0.0"
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("Derify/ChemRanker-alpha-qed-sim")
+# Get scores for pairs of texts
+pairs = [
+    ['c1snnc1C[NH2+]Cc1cc2c(s1)CCC2', 'c1snnc1CCC[NH2+]Cc1cc2c(s1)CCC2'],
+    ['c1sc2c(c1-c1nc(C3CCOC3)no1)CCCC2', 'O=C([O-])Cc1noc(-c2csc3c2CCCC3)n1'],
+    ['c1sc(C[NH2+]C2CC2)nc1C[NH+]1CCN2CCCC2C1', 'c1sc(C[NH2+]C2CC2)nc1C1CC([NH+]2CCN3CCCC3C2)C1'],
+    ['c1sc(CC[NH+]2CCOCC2)nc1C[NH2+]C1CC1', 'CCc1nc(C[NH2+]C2CC2)cs1'],
+    ['c1sc(CC2CCC[NH2+]2)nc1C1CCCO1', 'c1sc(CC2CCC[NH2+]2)nc1C1CCCC1'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'c1snnc1C[NH2+]Cc1cc2c(s1)CCC2',
+    [
+        'c1snnc1CCC[NH2+]Cc1cc2c(s1)CCC2',
+        'O=C([O-])Cc1noc(-c2csc3c2CCCC3)n1',
+        'c1sc(C[NH2+]C2CC2)nc1C1CC([NH+]2CCN3CCCC3C2)C1',
+        'CCc1nc(C[NH2+]C2CC2)cs1',
+        'c1sc(CC2CCC[NH2+]2)nc1C1CCCC1',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Cross Encoder Reranking
+
+* 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
+  }
+  ```
+
+| Metric      | Value      |
+| :---------- | :--------- |
+| map         | 0.4266     |
+| mrr@10      | 0.671      |
+| **ndcg@10** | **0.6901** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### GenMol Similarity Hard Negatives
+
+* Dataset: GenMol Similarity Hard Negatives
+* Size: 3,193,917 training samples
+* Columns: <code>smiles_a</code>, <code>smiles_b</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | smiles_a                                                                                       | smiles_b                                                                                       | negative                                                                                       |
+  | :------ | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- |
+  | type    | string                                                                                         | string                                                                                         | string                                                                                         |
+  | details | <ul><li>min: 19 characters</li><li>mean: 33.64 characters</li><li>max: 65 characters</li></ul> | <ul><li>min: 20 characters</li><li>mean: 34.24 characters</li><li>max: 48 characters</li></ul> | <ul><li>min: 19 characters</li><li>mean: 33.27 characters</li><li>max: 57 characters</li></ul> |
+* Samples:
+  | smiles_a                                        | smiles_b                                           | negative                                           |
+  | :---------------------------------------------- | :------------------------------------------------- | :------------------------------------------------- |
+  | <code>c1sc2cc3c(cc2c1CC[NH2+]C1CC1)OCCO3</code> | <code>FC(F)(F)[NH2+]CCc1csc2cc3c(cc12)OCCO3</code> | <code>[NH3+]CCCc1cc2c(cc1C1CC1)OCO2</code>         |
+  | <code>c1sc2cc3c(cc2c1CC[NH2+]C1CC1)OCCO3</code> | <code>FC(F)(F)[NH2+]CCc1csc2cc3c(cc12)OCCO3</code> | <code>COc1cc2c(cc1C[NH2+]C1CCC1)OCO2</code>        |
+  | <code>c1sc2cc3c(cc2c1CC[NH2+]C1CC1)OCCO3</code> | <code>FC(F)(F)[NH2+]CCc1csc2cc3c(cc12)OCCO3</code> | <code>O=c1[nH]c2cc3c(cc2cc1CNC1CCCCC1)OCCO3</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 10.0,
+      "num_negatives": 4,
+      "activation_fn": "torch.nn.modules.activation.Sigmoid"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### GenMol Similarity Hard Negatives
+
+* Dataset: GenMol Similarity Hard Negatives
+* Size: 165,968 evaluation samples
+* Columns: <code>smiles_a</code>, <code>smiles_b</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, <code>negative_5</code>, <code>negative_6</code>, <code>negative_7</code>, <code>negative_8</code>, <code>negative_9</code>, <code>negative_10</code>, <code>negative_11</code>, <code>negative_12</code>, <code>negative_13</code>, <code>negative_14</code>, <code>negative_15</code>, <code>negative_16</code>, <code>negative_17</code>, <code>negative_18</code>, <code>negative_19</code>, <code>negative_20</code>, <code>negative_21</code>, <code>negative_22</code>, <code>negative_23</code>, <code>negative_24</code>, <code>negative_25</code>, <code>negative_26</code>, <code>negative_27</code>, <code>negative_28</code>, <code>negative_29</code>, and <code>negative_30</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | smiles_a                                                                                       | smiles_b                                                                                       | negative_1                                                                                     | negative_2                                                                                     | negative_3                                                                                     | negative_4                                                                                     | negative_5                                                                                     | negative_6                                                                                     | negative_7                                                                                     | negative_8                                                                                     | negative_9                                                                                     | negative_10                                                                                    | negative_11                                                                                    | negative_12                                                                                    | negative_13                                                                                    | negative_14                                                                                    | negative_15                                                                                    | negative_16                                                                                   | negative_17                                                                                    | negative_18                                                                                    | negative_19                                                                                    | negative_20                                                                                    | negative_21                                                                                    | negative_22                                                                                    | negative_23                                                                                    | negative_24                                                                                    | negative_25                                                                                    | negative_26                                                                                    | negative_27                                                                                    | negative_28                                                                                    | negative_29                                                                                    | negative_30                                                                                    |
+  | :------ | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------- |
+  | type    | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                        | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         | string                                                                                         |
+  | details | <ul><li>min: 17 characters</li><li>mean: 37.57 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 34.45 characters</li><li>max: 70 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 35.67 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 35.13 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.28 characters</li><li>max: 81 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 35.36 characters</li><li>max: 73 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.12 characters</li><li>max: 70 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.09 characters</li><li>max: 84 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 35.16 characters</li><li>max: 64 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 35.26 characters</li><li>max: 90 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 35.16 characters</li><li>max: 90 characters</li></ul> | <ul><li>min: 15 characters</li><li>mean: 35.36 characters</li><li>max: 74 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 35.16 characters</li><li>max: 63 characters</li></ul> | <ul><li>min: 15 characters</li><li>mean: 35.51 characters</li><li>max: 73 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 35.21 characters</li><li>max: 69 characters</li></ul> | <ul><li>min: 10 characters</li><li>mean: 34.93 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 35.41 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 35.1 characters</li><li>max: 72 characters</li></ul> | <ul><li>min: 15 characters</li><li>mean: 35.43 characters</li><li>max: 62 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.36 characters</li><li>max: 65 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.48 characters</li><li>max: 65 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 35.25 characters</li><li>max: 65 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.48 characters</li><li>max: 81 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.38 characters</li><li>max: 68 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 35.67 characters</li><li>max: 68 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 35.53 characters</li><li>max: 67 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.39 characters</li><li>max: 83 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 35.74 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 35.56 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 35.37 characters</li><li>max: 64 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 35.51 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 35.35 characters</li><li>max: 72 characters</li></ul> |
+* Samples:
+  | smiles_a                                             | smiles_b                                                    | negative_1                                                  | negative_2                                            | negative_3                                         | negative_4                                  | negative_5                                          | negative_6                                 | negative_7                                         | negative_8                                           | negative_9                                            | negative_10                                        | negative_11                                              | negative_12                                      | negative_13                                             | negative_14                                       | negative_15                                             | negative_16                                          | negative_17                                       | negative_18                                      | negative_19                                           | negative_20                                      | negative_21                                            | negative_22                                         | negative_23                                            | negative_24                                        | negative_25                                    | negative_26                                                | negative_27                                       | negative_28                                          | negative_29                                              | negative_30                                      |
+  | :--------------------------------------------------- | :---------------------------------------------------------- | :---------------------------------------------------------- | :---------------------------------------------------- | :------------------------------------------------- | :------------------------------------------ | :-------------------------------------------------- | :----------------------------------------- | :------------------------------------------------- | :--------------------------------------------------- | :---------------------------------------------------- | :------------------------------------------------- | :------------------------------------------------------- | :----------------------------------------------- | :------------------------------------------------------ | :------------------------------------------------ | :------------------------------------------------------ | :--------------------------------------------------- | :------------------------------------------------ | :----------------------------------------------- | :---------------------------------------------------- | :----------------------------------------------- | :----------------------------------------------------- | :-------------------------------------------------- | :----------------------------------------------------- | :------------------------------------------------- | :--------------------------------------------- | :--------------------------------------------------------- | :------------------------------------------------ | :--------------------------------------------------- | :------------------------------------------------------- | :----------------------------------------------- |
+  | <code>c1snnc1C[NH2+]Cc1cc2c(s1)CCC2</code>           | <code>c1snnc1CCC[NH2+]Cc1cc2c(s1)CCC2</code>                | <code>c1snnc1CCC[NH2+]Cc1cc2c(s1)CCC2</code>                | <code>Cn1cc(C[NH2+]Cc2cc3c(s2)CCC3)nn1</code>         | <code>Cn1cc(CC[NH2+]Cc2cc3c(s2)CCC3)nn1</code>     | <code>Cc1cc(C[NH2+]Cc2csnn2)sc1C</code>     | <code>NC(=O)c1csc(C[NH2+]Cc2cc3c(s2)CCC3)c1</code>  | <code>Cc1cc(CC[NH2+]Cc2csnn2)sc1C</code>   | <code>N#CCc1csc(C[NH2+]Cc2cc3c(s2)CCC3)c1</code>   | <code>Ic1ccc(C[NH2+]Cc2cc3c(s2)CCC3)o1</code>        | <code>c1ncc(C[NH2+]Cc2csnn2)s1</code>                 | <code>c1c(C[NH2+]CC2CC2)sc2c1CSCC2</code>          | <code>N#Cc1cc(F)cc(C[NH2+]Cc2cc3c(s2)CCC3)c1</code>      | <code>c1cc(C[NH2+]Cc2nc3c(s2)CCC3)no1</code>     | <code>CCc1ccc(C[NH2+]Cc2csnn2)s1</code>                 | <code>NCc1csc(NCc2cc3c(s2)CCC3)n1</code>          | <code>C[NH+](Cc1cscn1)Cc1nnc(-c2cc3c(s2)CCCC3)o1</code> | <code>Fc1cc(C[NH2+]Cc2cc3c(s2)CCC3)ccc1Br</code>     | <code>FC(F)(F)C[NH2+]Cc1cc2c(s1)CCSC2</code>      | <code>c1cc(C[NH2+]Cc2cc3c(s2)CCC3)c[nH]1</code>  | <code>Cc1cc(C)c(CC[NH2+]Cc2cc3c(s2)CCC3)c(C)c1</code> | <code>Oc1ccc(C[NH2+]Cc2cc3c(s2)CCC3)cc1Br</code> | <code>O=C([O-])c1ccc(CC[NH2+]Cc2cc3c(s2)CCC3)s1</code> | <code>c1c(C[NH2+]CC2CCCC2)sc2c1CCC2</code>          | <code>O=C([O-])c1ccc(C[NH2+]Cc2cc3c(s2)CCC3)s1</code>  | <code>COc1cc(C)cc(C[NH2+]Cc2cc3c(s2)CCC3)c1</code> | <code>CCc1cnc(C[NH2+]Cc2csnn2)s1</code>        | <code>Clc1cc(C[NH2+]Cc2cc3c(s2)CCC3)ccc1Br</code>          | <code>c1c(C[NH2+]CC2CC2)sc2c1CCCCC2</code>        | <code>Cc1ccccc1C[NH2+]Cc1cc2c(s1)CCC2</code>         | <code>c1cc(C[NH+]2CCCC2)sc1C[NH2+]Cc1cc2c(s1)CCC2</code> | <code>Cc1cc(C[NH2+]Cc2cc3c(s2)CCC3)ccc1F</code>  |
+  | <code>c1sc2c(c1-c1nc(C3CCOC3)no1)CCCC2</code>        | <code>O=C([O-])Cc1noc(-c2csc3c2CCCC3)n1</code>              | <code>Nc1sc2c(c1-c1nc(C3CCOC3)no1)CCCC2</code>              | <code>Nc1sc2c(c1-c1nc(C3CCC3)no1)CCCC2</code>         | <code>c1c(-c2nc(C3CCCNC3)no2)sc2c1CCCCCC2</code>   | <code>Nc1sccc1-c1nc(C2CCCOC2)no1</code>     | <code>Nc1sc2c(c1-c1nc(C3CCCO3)no1)CCCC2</code>      | <code>Cc1csc(-c2nc(C3CCOCC3)no2)c1N</code> | <code>Cc1oc2c(c1-c1nc(C3CCOC3)no1)C(=O)CCC2</code> | <code>c1c(-c2nc(C3C[NH2+]CCO3)no2)sc2c1CCCCC2</code> | <code>O=C([O-])Nc1sc2c(c1-c1nc(C3CC3)no1)CCCC2</code> | <code>c1cc2c(s1)CCCC2c1nc(C2CC2)no1</code>         | <code>CC(=O)N1CCCC(c2noc(-c3cc4c(s3)CCCCCC4)n2)C1</code> | <code>Cc1cc(-c2nc([C@@H]3CCOC3)no2)c(N)s1</code> | <code>c1cc2c(nc1-c1noc(C3CCCOC3)n1)CCCC2</code>         | <code>Nc1sccc1-c1nc(C2CCCC2)no1</code>            | <code>c1cc2c(nc1-c1noc(C3CCOCC3)n1)CCCC2</code>         | <code>[NH3+]C(c1noc(-c2cc3c(s2)CCCC3)n1)C1CC1</code> | <code>c1cc2c(c(-c3nc(C4CCOCC4)no3)c1)CCCN2</code> | <code>c1c(-c2nc(C3CC3)no2)nn2c1CCCC2</code>      | <code>CN1CC(c2noc(-c3cc4c(s3)CCCC4)n2)CC1=O</code>    | <code>O=C([O-])Cc1noc(-c2csc3c2CCCC3)n1</code>   | <code>Oc1c(-c2nc(C3CCC(F)(F)C3)no2)ccc2c1CCCC2</code>  | <code>Cc1cc(=O)c(-c2noc(C3CCCOC3)n2)c2n1CCC2</code> | <code>O=C([O-])CNc1sc2c(c1-c1nc(C3CC3)no1)CCCC2</code> | <code>CC1CCc2c(sc(N)c2-c2nc(C3CC3)no2)C1</code>    | <code>Cn1nc(-c2nc(C3CCCO3)no2)c2c1CCCC2</code> | <code>O=C(Nc1sc2c(c1-c1nc(C3CC3)no1)COCC2)C1=CCCCC1</code> | <code>Cc1cscc1-c1noc(C2CCOCC2)n1</code>           | <code>CC1(C)CCCc2sc(N)c(-c3nc(C4CC4)no3)c21</code>   | <code>Clc1cc2c(c(-c3nc(C4CCOC4)no3)c1)OCC2</code>        | <code>Nc1sc2c(c1-c1nnc(C3CC3)o1)CCCC2</code>     |
+  | <code>c1sc(C[NH2+]C2CC2)nc1C[NH+]1CCN2CCCC2C1</code> | <code>c1sc(C[NH2+]C2CC2)nc1C1CC([NH+]2CCN3CCCC3C2)C1</code> | <code>c1sc(C[NH2+]C2CC2)nc1C1CC([NH+]2CCN3CCCC3C2)C1</code> | <code>CC(C)[NH2+]Cc1nc(C[NH+]2CCC3CCCCC3C2)cs1</code> | <code>CN1C2CCC1C[NH+](Cc1csc(C[NH3+])n1)CC2</code> | <code>Nc1nc(CC[NH+]2CCCN3CCCC3C2)cs1</code> | <code>CC1C[NH+](Cc2csc(C[NH2+]C3CC3)n2)CCN1C</code> | <code>Oc1csc(CN2CCCC3C[NH2+]CC32)n1</code> | <code>CCc1nc(C[NH+]2CCCC3CCCCC32)cs1</code>        | <code>C[NH2+]Cc1csc(N2CC[NH+]3CCCC3C2)n1</code>      | <code>[NH3+]Cc1nc(C[NH+]2CCC3CCCCC32)cs1</code>       | <code>CC1CN2CCCCC2C[NH+]1Cc1csc(CC[NH3+])n1</code> | <code>CCCc1nc(CN2CCCC2C2CCC[NH2+]2)cs1</code>            | <code>ClCCc1nc(CN2CCCC2C2CCC[NH2+]2)cs1</code>   | <code>c1cc(C[NH2+]C2CC2)c(C[NH+]2CCN3CCCCC3C2)o1</code> | <code>O=C(Cc1nc(CCl)cs1)N1CCC[NH+]2CCCC2C1</code> | <code>N#CCc1nc(C[NH+]2CCCC3CCCCC32)cs1</code>           | <code>CC[NH2+]Cc1csc(N2CCC3C(CCC[NH+]3C)C2)n1</code> | <code>c1sc(C[NH2+]C2CC2)nc1C[NH+]1CCCCC1</code>   | <code>[NH3+]Cc1nc(C[NH+]2CCCC2C2CCCC2)cs1</code> | <code>Cc1csc(C[NH+]2CCC3C[NH2+]CC3C2)n1</code>        | <code>ClOCc1csc(C[NH+]2CC3C[NH2+]CC3C2)n1</code> | <code>c1cc(C[NH+]2CCCN3CCCC3C2)nc(C2CC2)n1</code>      | <code>Cc1ccsc1C[NH2+]CCN1CCN2CCCC2C1</code>         | <code>c1sc(C[NH2+]C2CCCC2)nc1C[NH+]1CCCCC1</code>      | <code>Brc1csc(C[NH2+]CCN2CCN3CCCCC3C2)c1</code>    | <code>Cc1nc(CCC[NH2+]C2CCN3CCCCC23)cs1</code>  | <code>CCOC(=O)c1nc(CN2CC3CCC[NH2+]C3C2)cs1</code>          | <code>CCCC(=O)c1nc(CN2CC3CCC[NH2+]C3C2)cs1</code> | <code>CC(C)(C)c1csc(CN2CCC[NH2+]C(C3CC3)C2)n1</code> | <code>COCc1nc(CN2CCC([NH3+])C2)cs1</code>                | <code>CCC[NH2+]Cc1nc(C[NH+]2CC3CCC2C3)cs1</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 10.0,
+      "num_negatives": 4,
+      "activation_fn": "torch.nn.modules.activation.Sigmoid"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `torch_empty_cache_steps`: 1000
+- `learning_rate`: 3e-05
+- `weight_decay`: 1e-05
+- `max_grad_norm`: None
+- `lr_scheduler_type`: warmup_stable_decay
+- `lr_scheduler_kwargs`: {'num_decay_steps': 6238, 'warmup_type': 'linear', 'decay_type': '1-sqrt'}
+- `warmup_steps`: 6238
+- `seed`: 12
+- `data_seed`: 24681357
+- `bf16`: True
+- `bf16_full_eval`: True
+- `tf32`: True
+- `dataloader_num_workers`: 8
+- `dataloader_prefetch_factor`: 2
+- `load_best_model_at_end`: True
+- `optim`: stable_adamw
+- `optim_args`: decouple_lr=True,max_lr=3e-05
+- `dataloader_persistent_workers`: True
+- `resume_from_checkpoint`: False
+- `gradient_checkpointing`: True
+- `torch_compile`: True
+- `torch_compile_backend`: inductor
+- `torch_compile_mode`: max-autotune
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: 1000
+- `learning_rate`: 3e-05
+- `weight_decay`: 1e-05
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: None
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: warmup_stable_decay
+- `lr_scheduler_kwargs`: {'num_decay_steps': 6238, 'warmup_type': 'linear', 'decay_type': '1-sqrt'}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 6238
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 12
+- `data_seed`: 24681357
+- `jit_mode_eval`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: True
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 8
+- `dataloader_prefetch_factor`: 2
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: stable_adamw
+- `optim_args`: decouple_lr=True,max_lr=3e-05
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: True
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: False
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: True
+- `torch_compile_backend`: inductor
+- `torch_compile_mode`: max-autotune
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+|  Epoch  |   Step    | Training Loss | Validation Loss |  ndcg@10   |
+| :-----: | :-------: | :-----------: | :-------------: | :--------: |
+| 0.0002  |     1     |    1.2724     |        -        |     -      |
+| 0.1603  |   1000    |    0.1583     |        -        |     -      |
+| 0.3206  |   2000    |    0.0196     |        -        |     -      |
+| 0.4809  |   3000    |    0.0112     |        -        |     -      |
+| 0.6412  |   4000    |    0.0079     |        -        |     -      |
+| 0.8015  |   5000    |    0.0063     |        -        |     -      |
+| 0.9618  |   6000    |    0.0053     |        -        |     -      |
+|   1.0   |   6238    |       -       |     1.6835      |   0.6811   |
+| 1.1222  |   7000    |    0.0045     |        -        |     -      |
+| 1.2825  |   8000    |    0.0041     |        -        |     -      |
+| 1.4428  |   9000    |    0.0037     |        -        |     -      |
+| 1.6031  |   10000   |    0.0034     |        -        |     -      |
+| 1.7634  |   11000   |    0.0032     |        -        |     -      |
+| 1.9237  |   12000   |     0.003     |        -        |     -      |
+|   2.0   |   12476   |       -       |     1.6853      |   0.6891   |
+| 2.0840  |   13000   |    0.0028     |        -        |     -      |
+| 2.2443  |   14000   |    0.0026     |        -        |     -      |
+| 2.4046  |   15000   |    0.0026     |        -        |     -      |
+| 2.5649  |   16000   |    0.0025     |        -        |     -      |
+| 2.7252  |   17000   |    0.0024     |        -        |     -      |
+| 2.8855  |   18000   |    0.0023     |        -        |     -      |
+| **3.0** | **18714** |     **-**     |   **1.6982**    | **0.6901** |
+
+* The bold row denotes the saved checkpoint.
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 17.863 kWh
+- **Carbon Emitted**: 3.667 kg of CO2
+- **Hours Used**: 29.477 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 2 x NVIDIA GeForce RTX 3090
+- **CPU Model**: AMD Ryzen 7 3700X 8-Core Processor
+- **RAM Size**: 62.70 GB
+
+### Framework Versions
+- Python: 3.13.7
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.1
+- PyTorch: 2.9.0+cu128
+- Accelerate: 1.11.0
+- Datasets: 4.4.1
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### NV-Retriever
+```bibtex
+@misc{moreira2025nvretrieverimprovingtextembedding,
+      title={NV-Retriever: Improving text embedding models with effective hard-negative mining}, 
+      author={Gabriel de Souza P. Moreira and Radek Osmulski and Mengyao Xu and Ronay Ak and Benedikt Schifferer and Even Oldridge},
+      year={2025},
+      eprint={2407.15831},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2407.15831}, 
+}
+```
+
+<!--
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+
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