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license: apache-2.0
tags:
- sentence-transformers
- cross-encoder
- reranker
- modchembert
- cheminformatics
- smiles
- generated_from_trainer
- dataset_size:3269544
- 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: 2511.7170953063833
energy_consumed: 12.236213576221271
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: 19.958
hardware_used: 2 x NVIDIA GeForce RTX 3090
model-index:
- name: 'Derify/ChemRanker-alpha-sim'
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: Unknown
type: unknown
metrics:
- type: map
value: 0.4322642780868116
name: Map
- type: mrr@10
value: 0.6974610113213638
name: Mrr@10
- type: ndcg@10
value: 0.7033573942360365
name: Ndcg@10
---
# Derify/ChemRanker-alpha-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, the positive selection objective is pure similarity ranking where each anchor keeps the highest-similarity candidate after filtering, rather than using a QED+similarity composite 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.
## 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
- **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-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=CCc1noc(-c2csc3c2CCCC3)n1'],
['c1sc(C[NH2+]C2CC2)nc1C[NH+]1CCN2CCCC2C1', 'FC(F)[NH2+]Cc1nc(C[NH+]2CCN3CCCC3C2)cs1'],
['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=CCc1noc(-c2csc3c2CCCC3)n1',
'FC(F)[NH2+]Cc1nc(C[NH+]2CCN3CCCC3C2)cs1',
'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
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## 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 | 0.4323 |
| mrr@10 | 0.6975 |
| **ndcg@10** | **0.7034** |
<!--
## 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.*
-->
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### 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: 3,269,544 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: 34.16 characters</li><li>max: 54 characters</li></ul> | <ul><li>min: 19 characters</li><li>mean: 33.28 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: 34.46 characters</li><li>max: 70 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 35.94 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 35.1 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.09 characters</li><li>max: 81 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 35.38 characters</li><li>max: 74 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 35.17 characters</li><li>max: 70 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.25 characters</li><li>max: 84 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 35.2 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 35.05 characters</li><li>max: 80 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 35.25 characters</li><li>max: 90 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 35.23 characters</li><li>max: 74 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 34.88 characters</li><li>max: 60 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.42 characters</li><li>max: 66 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 35.36 characters</li><li>max: 69 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 34.81 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 10 characters</li><li>mean: 35.12 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 35.05 characters</li><li>max: 69 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.47 characters</li><li>max: 72 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.12 characters</li><li>max: 65 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 35.44 characters</li><li>max: 72 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.0 characters</li><li>max: 64 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 35.79 characters</li><li>max: 81 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 35.43 characters</li><li>max: 67 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.76 characters</li><li>max: 68 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 35.29 characters</li><li>max: 62 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 35.42 characters</li><li>max: 66 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 35.31 characters</li><li>max: 83 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 35.64 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 35.47 characters</li><li>max: 77 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 35.23 characters</li><li>max: 65 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 35.26 characters</li><li>max: 77 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>Ic1ccc(C[NH2+]Cc2cc3c(s2)CCC3)o1</code> | <code>Cc1cc(C[NH2+]CCCCc2cc3c(s2)CCC3)c(C)s1</code> | <code>c1ccc(C[NH2+]Cc2cc3c(s2)CCC3)cc1</code> | <code>c1ncc(C[NH2+]Cc2csnn2)s1</code> | <code>FC(F)c1csc(C[NH2+]Cc2cc3c(s2)CCC3)c1</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>PSc1ccc(C[NH2+]Cc2csnn2)s1</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>c1sc2c(c1-c1nc(C3CCOC3)no1)CCCC2</code> | <code>O=CCc1noc(-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>Oc1c(-c2nc(C3CCC(F)(F)C3)no2)ccc2c1CCCC2</code> | <code>O=CCc1noc(-c2csc3c2CCCC3)n1</code> | <code>Cc1cc(=O)c(-c2noc(C3CCCOC3)n2)c2n1CCC2</code> | <code>O=C([O-])CNc1sc2c(c1-c1nc(C3CC3)no1)CCCC2</code> | <code>c1cc(-c2noc(C3CCCOC3)n2)cs1</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>FC(F)[NH2+]Cc1nc(C[NH+]2CCN3CCCC3C2)cs1</code> | <code>FC(F)[NH2+]Cc1nc(C[NH+]2CCN3CCCC3C2)cs1</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>NCc1nc(C[NH+]2CCCC3CCCCC32)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>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>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> | <code>CCC1CN2CCCC2C[NH+]1CCc1csc(C)n1</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': 6385, 'warmup_type': 'linear', 'decay_type': '1-sqrt'}
- `warmup_steps`: 6385
- `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
- `eval_on_start`: True
- `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': 6385, 'warmup_type': 'linear', 'decay_type': '1-sqrt'}
- `warmup_ratio`: 0.0
- `warmup_steps`: 6385
- `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`: True
- `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 |
| :-----: | :-------: | :-----------: | :-------------: | :--------: |
| 1.0963 | 7000 | 0.0046 | - | - |
| 1.2529 | 8000 | 0.0043 | - | - |
| 1.4096 | 9000 | 0.0038 | - | - |
| 1.5662 | 10000 | 0.0035 | - | - |
| 1.7228 | 11000 | 0.0033 | - | - |
| 1.8794 | 12000 | 0.0031 | - | - |
| 2.0 | 12770 | - | 1.5814 | 0.6986 |
| 2.0360 | 13000 | 0.003 | - | - |
| 2.1926 | 14000 | 0.0027 | - | - |
| 2.3493 | 15000 | 0.0025 | - | - |
| 2.5059 | 16000 | 0.0025 | - | - |
| 2.6625 | 17000 | 0.0024 | - | - |
| 2.8191 | 18000 | 0.0024 | - | - |
| 2.9757 | 19000 | 0.0024 | - | - |
| **3.0** | **19155** | **-** | **1.5688** | **0.7034** |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 12.236 kWh
- **Carbon Emitted**: 2.512 kg of CO2
- **Hours Used**: 19.958 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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