| --- |
| 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) |
|
|
