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---
license: apache-2.0
tags:
- sentence-transformers
- molecular-similarity
- feature-extraction
- dense
- generated_from_trainer
- loss:Matryoshka2dLoss
- loss:MatryoshkaLoss
- loss:TanimotoSentLoss
base_model: Derify/ChemBERTa-druglike
widget:
- source_sentence: CC1CCc2c(N)nc(C3CCCC3)n2C1
sentences:
- CC1CCc2c(N)nc(OC3CC3)n2C1
- CN1CC[NH+](C[C@H](O)C2CC2)C2(CCCCC2)C1
- Cc1c(F)cc(CNCC2CCC(C3CCC(C)CO3)CO2)cc1F
- source_sentence: CC(CCCO)NC(=O)CNc1ccccc1
sentences:
- CC(CCCO)N[C@H]1CCCN(Nc2ccccc2)[C@H]1C
- Cc1ccc(OC2=NCCO2)nc1
- Cc1ccccc1C#Cc1ccccc1N(O)c1ccccc1
- source_sentence: CCCCCCCc1ccc(CC=N[NH+]=C(N)N)cc1
sentences:
- COCC1(N2CCN(C)CC2)CCC[NH+]1Cc1cnc(N(C)C)nc1
- Cc1ccc(N=C(c2ccccc2)c2ccc(-n3ccnn3)cc2)cc1
- CCCCCCCc1cncc(CC=N[NH+]=C(N)N)c1
- source_sentence: CC(=CCCS(=O)(=O)[O-])C(=O)OCCCS(=O)(=O)[O-]
sentences:
- CC(=CCCS(=O)(=O)[O-])C(=O)OCCCS(=O)(=O)[O-]
- CCCCCOc1ccc(NC(=S)NC=O)cc1
- CCC(=O)N1CCCC(NC(=O)c2ccc(S(=O)(=O)N(C)C)cc2)C1
- source_sentence: Clc1nccc(C#CCCc2nc3ccccc3o2)n1
sentences:
- O=Cc1nc2ccccc2o1
- >-
O=C([O-])COc1ccc(CCCS(=O)(=O)c2ccc(Cl)cc2)cc1NC(=O)c1cccc(C=Cc2nc3ccccc3s2)c1
- O[C@H]1CN(C(Cc2ccccc2)c2ccccc2)C[C@@H]1Cc1cnc[nH]1
datasets:
- Derify/pubchem_10m_genmol_similarity
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- spearman
model-index:
- name: 'ChemMRL: SMILES Matryoshka Representation Learning Embedding Transformer'
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: pubchem 10m genmol similarity
type: pubchem_10m_genmol_similarity
metrics:
- type: spearman
value: 0.9932120589500998
name: Spearman
new_version: Derify/ChemMRL
---
# ChemMRL: SMILES Matryoshka Representation Learning Embedding Transformer
This is a [Chem-MRL](https://github.com/emapco/chem-mrl) ([sentence-transformers](https://www.SBERT.net)) model finetuned from [Derify/ChemBERTa-druglike](https://huggingface.co/Derify/ChemBERTa-druglike) on the [pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity) dataset. It maps SMILES to a 1024-dimensional dense vector space and can be used for molecular similarity, semantic search, database indexing, molecular classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** ChemMRL (Sentence Transformer)
- **Base model:** [Derify/ChemBERTa-druglike](https://huggingface.co/Derify/ChemBERTa-druglike) <!-- at revision 5e76559157fde4f1aead643d9e1d402289f522af -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Tanimoto
- **Training Dataset:**
- [pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity)
- **License:** apache-2.0
### Model Sources
- **Repository:** [Chem-MRL on GitHub](https://github.com/emapco/chem-mrl)
- **Demo App Repository:** [Chem-MRL-demo on GitHub](https://github.com/emapco/chem-mrl-demo)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'RobertaModel'})
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Chem-MRL)
First install the Chem-MRL library:
```bash
pip install -U chem-mrl>=0.7.3
```
Then you can load this model and run inference.
```python
from chem_mrl import ChemMRL
# Download from the 🤗 Hub
model = ChemMRL("Derify/ChemMRL-beta")
# Run inference
sentences = [
"Clc1nccc(C#CCCc2nc3ccccc3o2)n1",
"O=Cc1nc2ccccc2o1",
"O[C@H]1CN(C(Cc2ccccc2)c2ccccc2)C[C@@H]1Cc1cnc[nH]1",
]
embeddings = model.backbone.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.backbone.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3200, 0.1209],
# [0.3200, 1.0000, 0.0950],
# [0.1209, 0.0950, 1.0000]])
# Load the model with half precision
model = ChemMRL("Derify/ChemMRL-beta", use_half_precision=True)
sentences = [
"Clc1nccc(C#CCCc2nc3ccccc3o2)n1",
"O=Cc1nc2ccccc2o1",
"O[C@H]1CN(C(Cc2ccccc2)c2ccccc2)C[C@@H]1Cc1cnc[nH]1",
]
embeddings = model.embed(sentences) # Use the embed method for half precision
print(embeddings.shape)
# [3, 1024]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `pubchem_10m_genmol_similarity`
* Evaluated with <code>chem_mrl.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator</code> with these parameters:
```json
{
"precision": "float32"
}
```
| Split | Metric | Value |
| :------------- | :----------- | :----------- |
| **validation** | **spearman** | **0.993212** |
| **test** | **spearman** | **0.993243** |
## Training Details
### Training Dataset
#### pubchem_10m_genmol_similarity
* Dataset: [pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity) at [f68d779](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity/tree/f68d779a6284578132a3922655f6b1f74c576642)
* Size: 19,692,766 training samples
* Columns: <code>smiles_a</code>, <code>smiles_b</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | smiles_a | smiles_b | label |
| :------ | :---------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------- | :-------------------------------------------------------------- |
| type | string | string | float |
| details | <ul><li>min: 17 tokens</li><li>mean: 39.66 tokens</li><li>max: 119 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 38.29 tokens</li><li>max: 115 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.57</li><li>max: 1.0</li></ul> | | <code>0.7123287916183472</code> |
* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshka2dloss) with these parameters:
<details><summary>Click to expand</summary>
```json
{
"loss": "TanimotoSentLoss",
"n_layers_per_step": -1,
"last_layer_weight": 2.0,
"prior_layers_weight": 1.0,
"kl_div_weight": 0.5,
"kl_temperature": 0.3,
"matryoshka_dims": [
1024,
512,
256,
128,
64,
32,
16,
8
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 128
- `learning_rate`: 8e-06
- `weight_decay`: 6.505130550397454e-06
- `warmup_ratio`: 0.2
- `data_seed`: 42
- `fp16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_apex_fused
- `dataloader_pin_memory`: False
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 8e-06
- `weight_decay`: 6.505130550397454e-06
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `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`: 42
- `data_seed`: 42
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `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}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_apex_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: False
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `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`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `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`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | pubchem_10m_genmol_similarity_spearman |
| :----: | :----: | :-----------: | :------------------------------------: |
| 0.0796 | 24500 | 121.4633 | - |
| 0.08 | 24616 | - | 0.9739 |
| 0.1592 | 49000 | 118.6111 | - |
| 0.16 | 49232 | - | 0.9817 |
| 0.2389 | 73500 | 117.491 | - |
| 0.24 | 73848 | - | 0.9848 |
| 0.3185 | 98000 | 116.3786 | - |
| 0.32 | 98464 | - | 0.9865 |
| 0.3997 | 123000 | 115.9773 | - |
| 0.4 | 123080 | - | 0.9873 |
| 0.4794 | 147500 | 115.2441 | - |
| 0.48 | 147696 | - | 0.9885 |
| 0.5590 | 172000 | 114.8674 | - |
| 0.56 | 172312 | - | 0.9887 |
| 0.6386 | 196500 | 114.6483 | - |
| 0.64 | 196928 | - | 0.9892 |
| 0.7199 | 221500 | 114.0507 | - |
| 0.72 | 221544 | - | 0.9898 |
| 0.7995 | 246000 | 113.5606 | - |
| 0.8 | 246160 | - | 0.9902 |
| 0.8791 | 270500 | 113.2762 | - |
| 0.88 | 270776 | - | 0.9907 |
| 0.9587 | 295000 | 113.3295 | - |
| 0.96 | 295392 | - | 0.9908 |
| 1.0400 | 320000 | 112.9253 | - |
| 1.04 | 320008 | - | 0.9909 |
| 1.1196 | 344500 | 112.584 | - |
| 1.12 | 344624 | - | 0.9910 |
| 1.1992 | 369000 | 112.616 | - |
| 1.2 | 369240 | - | 0.9916 |
| 1.2788 | 393500 | 112.4692 | - |
| 1.28 | 393856 | - | 0.9914 |
| 1.3585 | 418000 | 112.2679 | - |
| 1.3600 | 418472 | - | 0.9917 |
| 1.4397 | 443000 | 112.1639 | - |
| 1.44 | 443088 | - | 0.9919 |
| 1.5193 | 467500 | 112.1139 | - |
| 1.52 | 467704 | - | 0.9921 |
| 1.5990 | 492000 | 111.8096 | - |
| 1.6 | 492320 | - | 0.9923 |
| 1.6786 | 516500 | 111.8252 | - |
| 1.6800 | 516936 | - | 0.9922 |
| 1.7598 | 541500 | 111.836 | - |
| 1.76 | 541552 | - | 0.9924 |
| 1.8395 | 566000 | 111.8471 | - |
| 1.8400 | 566168 | - | 0.9924 |
| 1.9191 | 590500 | 111.7778 | - |
| 1.92 | 590784 | - | 0.9925 |
| 1.9987 | 615000 | 111.4892 | - |
| 2.0 | 615400 | - | 0.9927 |
| 2.0799 | 640000 | 111.2659 | - |
| 2.08 | 640016 | - | 0.9928 |
| 2.1596 | 664500 | 111.3635 | - |
| 2.16 | 664632 | - | 0.9927 |
| 2.2392 | 689000 | 111.0114 | - |
| 2.24 | 689248 | - | 0.9928 |
| 2.3188 | 713500 | 111.0559 | - |
| 2.32 | 713864 | - | 0.9929 |
| 2.3984 | 738000 | 110.5276 | - |
| 2.4 | 738480 | - | 0.9929 |
| 2.4797 | 763000 | 110.9828 | - |
| 2.48 | 763096 | - | 0.9930 |
| 2.5593 | 787500 | 110.8404 | - |
| 2.56 | 787712 | - | 0.9930 |
| 2.6389 | 812000 | 111.1937 | - |
| 2.64 | 812328 | - | 0.9931 |
| 2.7186 | 836500 | 110.6662 | - |
| 2.7200 | 836944 | - | 0.9931 |
| 2.7998 | 861500 | 110.7714 | - |
| 2.8 | 861560 | - | 0.9932 |
| 2.8794 | 886000 | 110.7638 | - |
| 2.88 | 886176 | - | 0.9932 |
| 2.9591 | 910500 | 110.7021 | - |
| 2.96 | 910792 | - | 0.9932 |
| 2.9997 | 923000 | 110.6097 | - |
</details>
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: AMD Ryzen 7 3700X 8-Core Processor
- **RAM Size**: 62.70 GB
### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.0.0
- Transformers: 4.53.3
- PyTorch: 2.7.1+cu126
- Accelerate: 1.9.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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",
}
```
#### Matryoshka2dLoss
```bibtex
@misc{li20242d,
title={2D Matryoshka Sentence Embeddings},
author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
year={2024},
eprint={2402.14776},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
#### TanimotoSentLoss
```bibtex
@online{cortes-2025-tanimotosentloss,
title={TanimotoSentLoss: Tanimoto Loss for SMILES Embeddings},
author={Emmanuel Cortes},
year={2025},
month={Jan},
url={https://github.com/emapco/chem-mrl},
}
```
## Model Card Authors
[@eacortes](https://huggingface.co/eacortes)
## Model Card Contact
Manny Cortes (manny@derifyai.com)
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