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1_Pooling/config.json +10 -0
README.md +515 -3
config.json +26 -0
config_chem_mrl.json +37 -0
config_sentence_transformers.json +14 -0
merges.txt +1 -0
model.safetensors +3 -0
modules.json +20 -0
sentence_bert_config.json +4 -0
similarity_evaluation_pubchem_10m_genmol_similarity_float32_results.csv +38 -0
special_tokens_map.json +51 -0
tokenizer.json +684 -0
tokenizer_config.json +59 -0
vocab.json +1 -0

1_Pooling/config.json ADDED Viewed

	@@ -0,0 +1,10 @@

+{
+    "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
+}

README.md CHANGED Viewed

@@ -1,3 +1,515 @@
----
-license: apache-2.0
----

+---
+language:
+- en
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:19692766
+- 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: SentenceTransformer based on Derify/ChemBERTa-druglike
+  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
+---
+# SentenceTransformer based on Derify/ChemBERTa-druglike
+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 semantic textual 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)
+- **Language:** en
+- **License:** [Apache-2.0](https://huggingface.co/Derify/ChemBERTa-druglike/blob/main/LICENSE)
+### 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 (Sentence Transformers)
+First install the Sentence Transformers library:
+```bash
+pip install -U sentence-transformers
+```
+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.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.4848, 0.2158],
+#         [0.4848, 1.0000, 0.1735],
+#         [0.2158, 0.1735, 1.0000]])
+```
+## Evaluation
+### Metrics
+#### Semantic Similarity
+* Dataset: `pubchem_10m_genmol_similarity`
+* Evaluated with <code>chem_mrl.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator</code> with these parameters:
+  ```json
+  {
+      "precision": "float32"
+  }
+  ```
+| Metric       | Value      |
+| :----------- | :--------- |
+| **spearman** | **0.9932** |
+<!--
+## 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
+#### 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> |
+* Samples:
+  | smiles_a                                                                                             | smiles_b                                                                                           | label                           |
+  | :--------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------- | :------------------------------ |
+  | <code>COc1ccc(NC(=O)C2CC[NH+](C(C)C(=O)Nc3ccc(C(=O)Nc4ccc(F)c(F)c4)cc3C)CC2)cc1NC(=O)C1CCCCC1</code> | <code>Cc1cc(C(=O)Nc2ccc(F)c(F)c2)ccc1NC(=O)C(C)[NH+]1CCC(C(=O)Nc2cccc(NC(=O)C3CCCCC3)c2)CC1</code> | <code>0.8495575189590454</code> |
+  | <code>OCCN1CC[NH+](Cc2ccccc2OC2CC2)CC1</code>                                                        | <code>OCCN1CC[NH+](Cc2ccccc2On2cccn2)CC1</code>                                                    | <code>0.6615384817123413</code> |
+  | <code>CC1CN(C(=O)C2CC[NH+](Cc3cccc(C(N)=O)c3)CC2)CC(C)O1</code>                                      | <code>CC1CN(C(=O)C2CC[NH+](Cc3ccccc3)CC2)CC(C)O1</code>                                            | <code>0.7123287916183472</code> |
+* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshka2dloss) with these parameters:
+  ```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
+  }
+  ```
+### 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{emapco-chem-mrl-tanimotosentloss,
+    title={TanimotoSentLoss: Tanimoto Loss for SMILES Embeddings},
+    author={Emmanuel Cortes},
+    year={2025},
+    month={Jan},
+    url={https://github.com/emapco/chem-mrl/blob/main/chem_mrl/losses/TanimotoLoss.py},
+}
+```
+## Model Card Authors
+[@eacortes](https://huggingface.co/eacortes)
+## Model Card Contact
+Manny Cortes (manny@derifyai.com)

config.json ADDED Viewed

	@@ -0,0 +1,26 @@

+{
+  "architectures": [
+    "RobertaModel"
+  ],
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