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1_Pooling/config.json

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+{
+    "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

@@ -1,3 +1,590 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- sentence-transformers
+- modchembert
+- cheminformatics
+- smiles
+- molecular-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:19381001
+- loss:Matryoshka2dLoss
+- loss:MatryoshkaLoss
+- loss:TanimotoSentLoss
+base_model: Derify/ModChemBERT-IR-BASE
+widget:
+- source_sentence: COC(=O)c1sc(-c2ccc(C)cc2)c2c1NC(=O)C2(c1ccccc1)c1ccccc1
+  sentences:
+  - COC(=O)c1sc(Nc2ccc(Br)cn2)c2c1NC(=O)C2(c1ccccc1)c1ccccc1
+  - CC[NH+]1CCOC(C(NN)c2ccccc2Br)C1
+  - CC([NH2+]C(C)c1ccccc1)C(=O)P(C)C(C)(C)C
+- source_sentence: O=C(C=Cc1ccccc1)CC(=O)c1ccccc1O
+  sentences:
+  - COCCN(NCc1c(C)n(C(C)=O)c2ccc(OC)cc12)c1nccs1
+  - CCN(CCC(N)=O)C(=O)c1ccc(=O)[nH]n1
+  - N=CCC(=Cc1ccccc1)C(=O)COc1ccccc1O
+- source_sentence: COc1cccc(-c2sc3ccccc3c2C#N)c1
+  sentences:
+  - COCC(C)(C)c1cnnn1CCCI
+  - N#Cc1c(-c2cccc(CN)c2)sc2ccccc12
+  - COc1ccccc1NC(=O)c1cc(NCc2ccco2)cc[nH+]1
+- source_sentence: Nc1nc(-c2ccccc2)c2nc(N)c(N)nc2n1
+  sentences:
+  - CC(C)CC1NC(=O)C(Cc2ccccc2)NC(=O)c2ccc(cc2)CN(C(=O)CC2CCOCC2)CCCCNC(=O)C(C)NC1=O
+  - O=Nc1cccc(OCCC(F)F)c1
+  - CCCCNCc1nc(N)nc2nc(N)c(N)nc12
+- source_sentence: OCCCc1cc(F)cc(F)c1
+  sentences:
+  - CCC(C)C(=O)C1(C(NN)C(C)C)CCCC1
+  - Cc1[nH]c2c(C(N)=O)ccc(C(=O)N3CCCCC3)c2c1C
+  - Fc1cc(F)cc(-n2cc[o+]n2)c1
+datasets:
+- Derify/pubchem_10m_genmol_similarity
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- spearman
+co2_eq_emissions:
+  emissions: 4039.5232961852894
+  energy_consumed: 19.679154905865374
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: AMD Ryzen 7 3700X 8-Core Processor
+  ram_total_size: 62.69887161254883
+  hours_used: 74.966
+  hardware_used: 2 x NVIDIA GeForce RTX 3090
+model-index:
+- name: 'ChemMRL: SMILES Matryoshka Representation Learning Embedding Transformer'
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: pubchem 10m genmol similarity (validation)
+      type: pubchem_10m_genmol_similarity_validation
+    metrics:
+    - type: spearman
+      value: 0.9881056976837288
+      name: Spearman
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: pubchem 10m genmol similarity (test)
+      type: pubchem_10m_genmol_similarity_test
+    metrics:
+    - type: spearman
+      value: 0.988127555600757
+      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/ModChemBERT-IR-BASE](https://huggingface.co/Derify/ModChemBERT-IR-BASE) 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/ModChemBERT-IR-BASE](https://huggingface.co/Derify/ModChemBERT-IR-BASE) <!-- at revision fde8c1ed2606783be3ff621be0a4fde825f12169 -->
+- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False, 'architecture': 'ModChemBertModel'})
+  (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",
+    trust_remote_code=True,
+    model_kwargs={"torch_dtype": "bfloat16"},
+)
+# Run inference
+sentences = [
+    'OCCCc1cc(F)cc(F)c1',
+    'Fc1cc(F)cc(-n2cc[o+]n2)c1',
+    'CCC(C)C(=O)C1(C(NN)C(C)C)CCCC1',
+]
+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, 0.4184, 0.0166],
+#         [0.4158, 1.0000, 0.0136],
+#         [0.0167, 0.0137, 1.0000]])
+```
+
+### Direct Usage (Sentence Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer(
+    "Derify/ChemMRL",
+    # SentenceTransformer doesn't support tanimoto similarity natively so we set a different similarity function here
+    similarity_fn_name="cosine",
+    trust_remote_code=True,
+    model_kwargs={"torch_dtype": "bfloat16"},
+)
+# Run inference
+sentences = [
+    'OCCCc1cc(F)cc(F)c1',
+    'Fc1cc(F)cc(-n2cc[o+]n2)c1',
+    'CCC(C)C(=O)C1(C(NN)C(C)C)CCCC1',
+]
+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, 0.5887, 0.0327],
+#         [0.5887, 1.0000, 0.0269],
+#         [0.0327, 0.0269, 1.0000]])
+```
+
+</details>
+
+## 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** | **0.98811** |
+| **test**       | **spearman** | **0.98813** |
+
+## Training Details
+
+### Training Dataset
+
+#### pubchem_10m_genmol_similarity
+
+* Dataset: [pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity) at [9aec8fd](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity/tree/9aec8fd3ed70c21a0e39a3164830879a9929b052)
+* Size: 19,381,001 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: 42.36 tokens</li><li>max: 122 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 40.93 tokens</li><li>max: 122 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.56</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": 11,
+      "last_layer_weight": 1.0,
+      "prior_layers_weight": 1.5,
+      "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": 4
+  }
+  ```
+
+### Evaluation Dataset
+
+#### pubchem_10m_genmol_similarity
+
+* Dataset: [pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity) at [9aec8fd](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity/tree/9aec8fd3ed70c21a0e39a3164830879a9929b052)
+* Size: 1,080,394 evaluation 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: 16 tokens</li><li>mean: 42.05 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 40.23 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.57</li><li>max: 1.0</li></ul> |
+* Samples:
+  | smiles_a                               | smiles_b                                  | label                           |
+  | :------------------------------------- | :---------------------------------------- | :------------------------------ |
+  | <code>N#CCCN(Cc1cnc(N)cn1)C1CC1</code> | <code>N#CCCN(Cc1cnc(N)cn1)C1CCCC1</code>  | <code>0.8600000143051147</code> |
+  | <code>N#CCCN(Cc1cnc(N)cn1)C1CC1</code> | <code>N#CCCN(Cc1cnc(N)cn1)C1CCOCC1</code> | <code>0.7962962985038757</code> |
+  | <code>N#CCCN(Cc1cnc(N)cn1)C1CC1</code> | <code>N#CCCN(Cc1cnc(N)cn1)CC(F)F</code>   | <code>0.5517241358757019</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": 11,
+      "last_layer_weight": 1.0,
+      "prior_layers_weight": 1.5,
+      "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": 4
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 192
+- `per_device_eval_batch_size`: 512
+- `learning_rate`: 8e-06
+- `weight_decay`: 1e-05
+- `max_grad_norm`: None
+- `lr_scheduler_type`: warmup_stable_decay
+- `lr_scheduler_kwargs`: {'num_decay_steps': 100943, 'warmup_type': 'linear', 'decay_type': '1-sqrt'}
+- `warmup_steps`: 100943
+- `data_seed`: 42
+- `bf16`: True
+- `bf16_full_eval`: True
+- `tf32`: True
+- `optim`: stable_adamw
+- `optim_args`: decouple_lr=True,max_lr=8.0e-6
+- `dataloader_pin_memory`: False
+- `eval_on_start`: True
+
+#### 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`: 192
+- `per_device_eval_batch_size`: 512
+- `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`: 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': 100943, 'warmup_type': 'linear', 'decay_type': '1-sqrt'}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 100943
+- `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
+- `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`: 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`: False
+- `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=8.0e-6
+- `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`: False
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `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`: 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`: 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 loss | pubchem_10m_genmol_similarity_spearman |
+| :----: | :----: | :-----------: | :--------------------------------: | :------------------------------------: |
+|   0    |   0    |       -       |              85.7997               |                 0.7261                 |
+| 0.0000 |   1    |    69.0605    |                 -                  |                   -                    |
+| 0.2477 | 25000  |    47.1696    |                 -                  |                   -                    |
+| 0.2500 | 25235  |       -       |              56.9634               |                 0.8997                 |
+| 0.4978 | 50250  |    45.6212    |                 -                  |                   -                    |
+| 0.5000 | 50470  |       -       |              55.4366               |                 0.9599                 |
+| 0.7479 | 75500  |    45.1404    |                 -                  |                   -                    |
+| 0.7500 | 75705  |       -       |              54.5667               |                 0.9755                 |
+| 0.9981 | 100750 |    44.5023    |                 -                  |                   -                    |
+| 1.0000 | 100940 |       -       |              54.1244               |                 0.9810                 |
+| 1.2482 | 126000 |    43.7545    |                 -                  |                   -                    |
+| 1.2500 | 126175 |       -       |              53.6974               |                 0.9838                 |
+| 1.4984 | 151250 |    43.7865    |                 -                  |                   -                    |
+| 1.5000 | 151410 |       -       |              53.4775               |                 0.9855                 |
+| 1.7485 | 176500 |    43.3512    |                 -                  |                   -                    |
+| 1.7499 | 176645 |       -       |              53.3775               |                 0.9866                 |
+| 1.9987 | 201750 |    43.5808    |                 -                  |                   -                    |
+| 1.9999 | 201880 |       -       |              53.3119               |                 0.9874                 |
+| 2.2488 | 227000 |    43.281     |                 -                  |                   -                    |
+| 2.2499 | 227115 |       -       |              53.1854               |                 0.9879                 |
+| 2.4989 | 252250 |    43.3097    |                 -                  |                   -                    |
+| 2.4999 | 252350 |       -       |              53.1972               |                 0.9880                 |
+| 2.7491 | 277500 |    43.2376    |                 -                  |                   -                    |
+| 2.7499 | 277585 |       -       |              53.1833               |                 0.9881                 |
+| 2.9992 | 302750 |    43.2006    |                 -                  |                   -                    |
+| 2.9999 | 302820 |       -       |              53.1241               |                 0.9881                 |
+| 3.0000 | 302829 |       -       |                 -                  |                0.98811                 |
+
+</details>
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 19.679 kWh
+- **Carbon Emitted**: 4.040 kg of CO2
+- **Hours Used**: 74.966 hours
+
+### 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: 3.13.7
+- Sentence Transformers: 5.1.1
+- Transformers: 4.57.1
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 3.6.0
+- 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",
+}
+```
+
+#### 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)

config.json

@@ -0,0 +1,53 @@
+{
+  "architectures": [
+    "ModChemBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.1,
+  "auto_map": {
+    "AutoConfig": "configuration_modchembert.ModChemBertConfig",
+    "AutoModel": "modeling_modchembert.ModChemBertModel",
+    "AutoModelForMaskedLM": "modeling_modchembert.ModChemBertForMaskedLM",
+    "AutoModelForSequenceClassification": "modeling_modchembert.ModChemBertForSequenceClassification"
+  },
+  "bos_token_id": 0,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "max_seq_mha",
+  "classifier_pooling_attention_dropout": 0.1,
+  "classifier_pooling_last_k": 5,
+  "classifier_pooling_num_attention_heads": 4,
+  "cls_token_id": 0,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "bfloat16",
+  "embedding_dropout": 0.1,
+  "eos_token_id": 1,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "hidden_activation": "gelu",
+  "hidden_size": 1024,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 8,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 512,
+  "mlp_bias": false,
+  "mlp_dropout": 0.1,
+  "model_type": "modchembert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 22,
+  "pad_token_id": 2,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 1,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.57.1",
+  "vocab_size": 2362
+}

config_chem_mrl.json

@@ -0,0 +1,37 @@
+{
+    "__version__": "0.7.4",
+    "embedding_pooling": "mean",
+    "eval_metric": "spearman",
+    "eval_similarity_fct": "tanimoto",
+    "kl_div_weight": 0.5,
+    "kl_temperature": 0.3,
+    "last_layer_weight": 1.0,
+    "loss_func": "tanimotosentloss",
+    "model_name": "Derify/ModChemBERT-IR-BASE",
+    "mrl_dimension_weights": [
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1
+    ],
+    "mrl_dimensions": [
+        1024,
+        512,
+        256,
+        128,
+        64,
+        32,
+        16,
+        8
+    ],
+    "n_dims_per_step": 4,
+    "n_layers_per_step": 11,
+    "prior_layers_weight": 1.5,
+    "tanimoto_similarity_loss_func": null,
+    "use_2d_matryoshka": true,
+    "use_query_tokenizer": false
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.57.1",
+    "pytorch": "2.8.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "tanimoto"
+}

configuration_modchembert.py

@@ -0,0 +1,90 @@
+# Copyright 2025 Emmanuel Cortes, All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Literal
+
+from transformers.models.modernbert.configuration_modernbert import ModernBertConfig
+
+
+class ModChemBertConfig(ModernBertConfig):
+    """
+    Configuration class for ModChemBert models.
+
+    This configuration class extends ModernBertConfig with additional parameters specific to
+    chemical molecule modeling and custom pooling strategies for classification/regression tasks.
+    It accepts all arguments and keyword arguments from ModernBertConfig.
+
+    Args:
+        classifier_pooling (str, optional): Pooling strategy for sequence classification.
+            Available options:
+            - "cls": Use CLS token representation
+            - "mean": Attention-weighted average pooling
+            - "sum_mean": Sum all hidden states across layers, then mean pool over sequence (ChemLM approach)
+            - "sum_sum": Sum all hidden states across layers, then sum pool over sequence
+            - "mean_mean": Mean all hidden states across layers, then mean pool over sequence
+            - "mean_sum": Mean all hidden states across layers, then sum pool over sequence
+            - "max_cls": Element-wise max pooling over last k hidden states, then take CLS token
+            - "cls_mha": Multi-head attention with CLS token as query and full sequence as keys/values
+            - "max_seq_mha": Max pooling over last k states + multi-head attention with CLS as query
+            - "max_seq_mean": Max pooling over last k hidden states, then mean pooling over sequence
+            Defaults to "sum_mean".
+        classifier_pooling_num_attention_heads (int, optional): Number of attention heads for multi-head attention
+            pooling strategies (cls_mha, max_seq_mha). Defaults to 4.
+        classifier_pooling_attention_dropout (float, optional): Dropout probability for multi-head attention
+            pooling strategies (cls_mha, max_seq_mha). Defaults to 0.0.
+        classifier_pooling_last_k (int, optional): Number of last hidden layers to use for max pooling
+            strategies (max_cls, max_seq_mha, max_seq_mean). Defaults to 8.
+        *args: Variable length argument list passed to ModernBertConfig.
+        **kwargs: Arbitrary keyword arguments passed to ModernBertConfig.
+
+    Note:
+        This class inherits all configuration parameters from ModernBertConfig including
+        hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, etc.
+    """
+
+    model_type = "modchembert"
+
+    def __init__(
+        self,
+        *args,
+        classifier_pooling: Literal[
+            "cls",
+            "mean",
+            "sum_mean",
+            "sum_sum",
+            "mean_mean",
+            "mean_sum",
+            "max_cls",
+            "cls_mha",
+            "max_seq_mha",
+            "max_seq_mean",
+        ] = "max_seq_mha",
+        classifier_pooling_num_attention_heads: int = 4,
+        classifier_pooling_attention_dropout: float = 0.0,
+        classifier_pooling_last_k: int = 8,
+        **kwargs,
+    ):
+        # Pass classifier_pooling="cls" to circumvent ValueError in ModernBertConfig init
+        super().__init__(*args, classifier_pooling="cls", **kwargs)
+        # Override with custom value
+        self.classifier_pooling = classifier_pooling
+        self.classifier_pooling_num_attention_heads = classifier_pooling_num_attention_heads
+        self.classifier_pooling_attention_dropout = classifier_pooling_attention_dropout
+        self.classifier_pooling_last_k = classifier_pooling_last_k
+        self.auto_map = {
+            "AutoConfig": "configuration_modchembert.ModChemBertConfig",
+            "AutoModel": "modeling_modchembert.ModChemBertModel",
+            "AutoModelForMaskedLM": "modeling_modchembert.ModChemBertForMaskedLM",
+            "AutoModelForSequenceClassification": "modeling_modchembert.ModChemBertForSequenceClassification",
+        }

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4bdfc920fcc3c65314ef0cf0f5129884443d23748f09e23467015d54d5338ce4
+size 397110232

modeling_modchembert.py

@@ -0,0 +1,780 @@
+# Copyright 2025 Emmanuel Cortes, All Rights Reserved.
+#
+# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is adapted from the transformers library.
+# Modifications include:
+# - Additional classifier_pooling options for ModChemBertForSequenceClassification
+#   - sum_mean, sum_sum, mean_sum, mean_mean: from ChemLM (utilizes all hidden states)
+#   - max_cls, cls_mha, max_seq_mha: from MaxPoolBERT (utilizes last k hidden states)
+#   - max_seq_mean: a merge between sum_mean and max_cls (utilizes last k hidden states)
+# - Addition of ModChemBertPoolingAttention for cls_mha and max_seq_mha pooling options
+
+import copy
+import math
+import typing
+from contextlib import nullcontext
+
+import torch
+import torch.nn as nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
+from transformers.modeling_outputs import BaseModelOutput, MaskedLMOutput, SequenceClassifierOutput
+from transformers.models.modernbert.modeling_modernbert import (
+    MODERNBERT_ATTENTION_FUNCTION,
+    ModernBertEmbeddings,
+    ModernBertEncoderLayer,
+    ModernBertModel,
+    ModernBertPredictionHead,
+    ModernBertPreTrainedModel,
+    ModernBertRotaryEmbedding,
+    _pad_modernbert_output,
+    _unpad_modernbert_input,
+)
+from transformers.utils import logging
+
+from .configuration_modchembert import ModChemBertConfig
+
+logger = logging.get_logger(__name__)
+
+
+class InitWeightsMixin:
+    def _init_weights(self, module: nn.Module):
+        super()._init_weights(module)  # type: ignore
+
+        cutoff_factor = self.config.initializer_cutoff_factor  # type: ignore
+        if cutoff_factor is None:
+            cutoff_factor = 3
+
+        def init_weight(module: nn.Module, std: float):
+            if isinstance(module, nn.Linear):
+                nn.init.trunc_normal_(
+                    module.weight,
+                    mean=0.0,
+                    std=std,
+                    a=-cutoff_factor * std,
+                    b=cutoff_factor * std,
+                )
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+
+        stds = {
+            "in": self.config.initializer_range,  # type: ignore
+            "out": self.config.initializer_range / math.sqrt(2.0 * self.config.num_hidden_layers),  # type: ignore
+            "final_out": self.config.hidden_size**-0.5,  # type: ignore
+        }
+
+        if isinstance(module, ModChemBertForMaskedLM):
+            init_weight(module.decoder, stds["out"])
+        elif isinstance(module, ModChemBertForSequenceClassification):
+            init_weight(module.classifier, stds["final_out"])
+        elif isinstance(module, ModChemBertPoolingAttention):
+            init_weight(module.Wq, stds["in"])
+            init_weight(module.Wk, stds["in"])
+            init_weight(module.Wv, stds["in"])
+            init_weight(module.Wo, stds["out"])
+
+
+class ModChemBertPoolingAttention(nn.Module):
+    """Performs multi-headed self attention on a batch of sequences."""
+
+    def __init__(self, config: ModChemBertConfig):
+        super().__init__()
+        self.config = copy.deepcopy(config)
+        # Override num_attention_heads to use classifier_pooling_num_attention_heads
+        self.config.num_attention_heads = config.classifier_pooling_num_attention_heads
+        # Override attention_dropout to use classifier_pooling_attention_dropout
+        self.config.attention_dropout = config.classifier_pooling_attention_dropout
+
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads "
+                f"({config.num_attention_heads})"
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.all_head_size = self.head_dim * self.num_heads
+        self.Wq = nn.Linear(config.hidden_size, self.all_head_size, bias=config.attention_bias)
+        self.Wk = nn.Linear(config.hidden_size, self.all_head_size, bias=config.attention_bias)
+        self.Wv = nn.Linear(config.hidden_size, self.all_head_size, bias=config.attention_bias)
+
+        # Use global attention
+        self.local_attention = (-1, -1)
+        rope_theta = config.global_rope_theta
+        # sdpa path from original ModernBert implementation
+        config_copy = copy.deepcopy(config)
+        config_copy.rope_theta = rope_theta
+        self.rotary_emb = ModernBertRotaryEmbedding(config=config_copy)
+
+        self.Wo = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
+        self.out_drop = (
+            nn.Dropout(config.attention_dropout)
+            if config.attention_dropout > 0.0
+            else nn.Identity()
+        )
+        self.pruned_heads = set()
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        kv: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        bs, seq_len = kv.shape[:2]
+        q_proj: torch.Tensor = self.Wq(q)
+        k_proj: torch.Tensor = self.Wk(kv)
+        v_proj: torch.Tensor = self.Wv(kv)
+        qkv = torch.stack(
+            (
+                q_proj.reshape(bs, seq_len, self.num_heads, self.head_dim),
+                k_proj.reshape(bs, seq_len, self.num_heads, self.head_dim),
+                v_proj.reshape(bs, seq_len, self.num_heads, self.head_dim),
+            ),
+            dim=2,
+        )  # (bs, seq_len, 3, num_heads, head_dim)
+
+        device = kv.device
+        if attention_mask is None:
+            attention_mask = torch.ones((bs, seq_len), device=device, dtype=torch.bool)
+        position_ids = torch.arange(seq_len, device=device).unsqueeze(0).long()
+
+        attn_outputs = MODERNBERT_ATTENTION_FUNCTION["sdpa"](
+            self,
+            qkv=qkv,
+            attention_mask=_prepare_4d_attention_mask(attention_mask, kv.dtype),
+            sliding_window_mask=None,  # not needed when using global attention
+            position_ids=position_ids,
+            local_attention=self.local_attention,
+            bs=bs,
+            dim=self.all_head_size,
+            **kwargs,
+        )
+        hidden_states = attn_outputs[0]
+        hidden_states = self.out_drop(self.Wo(hidden_states))
+
+        return hidden_states
+
+
+class ModChemBertModel(ModernBertPreTrainedModel):
+    config_class = ModChemBertConfig
+
+    def __init__(self, config: ModChemBertConfig):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = ModernBertEmbeddings(config)
+        self.layers = nn.ModuleList(
+            [
+                ModernBertEncoderLayer(config, layer_id)
+                for layer_id in range(config.num_hidden_layers)
+            ]
+        )
+        self.final_norm = nn.LayerNorm(
+            config.hidden_size, eps=config.norm_eps, bias=config.norm_bias
+        )
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.tok_embeddings = value  # type: ignore
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        sliding_window_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        indices: torch.Tensor | None = None,
+        cu_seqlens: torch.Tensor | None = None,
+        max_seqlen: int | None = None,
+        batch_size: int | None = None,
+        seq_len: int | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple[torch.Tensor, ...] | BaseModelOutput:
+        r"""
+        sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+            perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+            far-away tokens in the local attention layers when not using Flash Attention.
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        """  # noqa: E501
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        self._maybe_set_compile()
+
+        if input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+
+        if batch_size is None and seq_len is None:
+            if inputs_embeds is not None:
+                batch_size, seq_len = inputs_embeds.shape[:2]
+            else:
+                batch_size, seq_len = input_ids.shape[:2]  # type: ignore
+        device = input_ids.device if input_ids is not None else inputs_embeds.device  # type: ignore
+
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)  # type: ignore
+
+        repad = False
+        if self.config._attn_implementation == "flash_attention_2":
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                repad = True
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+                            inputs=input_ids,  # type: ignore
+                            attention_mask=attention_mask,  # type: ignore
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+                        inputs=inputs_embeds,
+                        attention_mask=attention_mask,  # type: ignore
+                    )
+        else:
+            if position_ids is None:
+                position_ids = torch.arange(seq_len, device=device).unsqueeze(0)  # type: ignore
+
+            attention_mask, sliding_window_mask = self._update_attention_mask(
+                attention_mask,  # type: ignore
+                output_attentions=output_attentions,  # type: ignore
+            )
+
+        hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
+
+        for encoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)  # type: ignore
+
+            layer_outputs = encoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                sliding_window_mask=sliding_window_mask,
+                position_ids=position_ids,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+                output_attentions=output_attentions,
+            )
+            hidden_states = layer_outputs[0]
+            if output_attentions and len(layer_outputs) > 1:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)  # type: ignore
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)  # type: ignore
+
+        hidden_states = self.final_norm(hidden_states)
+
+        if repad:
+            hidden_states = _pad_modernbert_output(
+                inputs=hidden_states,
+                indices=indices,  # type: ignore
+                batch=batch_size,  # type: ignore
+                seqlen=seq_len,  # type: ignore
+            )
+            if all_hidden_states is not None:
+                all_hidden_states = tuple(
+                    _pad_modernbert_output(
+                        inputs=hs, indices=indices, batch=batch_size, seqlen=seq_len
+                    )  # type: ignore
+                    for hs in all_hidden_states
+                )
+
+        if not return_dict:
+            return tuple(
+                v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None
+            )
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,  # type: ignore
+            hidden_states=all_hidden_states,  # type: ignore
+            attentions=all_self_attentions,
+        )
+
+    def _update_attention_mask(
+        self, attention_mask: torch.Tensor, output_attentions: bool
+    ) -> torch.Tensor:
+        if output_attentions:
+            if self.config._attn_implementation == "sdpa":
+                logger.warning_once(  # type: ignore
+                    "Outputting attentions is only supported with the 'eager' attention implementation, "
+                    'not with "sdpa". Falling back to `attn_implementation="eager"`.'
+                )
+                self.config._attn_implementation = "eager"
+            elif self.config._attn_implementation != "eager":
+                logger.warning_once(  # type: ignore
+                    "Outputting attentions is only supported with the eager attention implementation, "
+                    f'not with {self.config._attn_implementation}. Consider setting `attn_implementation="eager"`.'
+                    " Setting `output_attentions=False`."
+                )
+
+        global_attention_mask = _prepare_4d_attention_mask(attention_mask, self.dtype)
+
+        # Create position indices
+        rows = torch.arange(global_attention_mask.shape[2]).unsqueeze(0)
+        # Calculate distance between positions
+        distance = torch.abs(rows - rows.T)
+
+        # Create sliding window mask (1 for positions within window, 0 outside)
+        window_mask = (
+            (distance <= self.config.local_attention // 2)
+            .unsqueeze(0)
+            .unsqueeze(0)
+            .to(attention_mask.device)
+        )
+        # Combine with existing mask
+        sliding_window_mask = global_attention_mask.masked_fill(
+            window_mask.logical_not(), torch.finfo(self.dtype).min
+        )
+
+        return global_attention_mask, sliding_window_mask  # type: ignore
+
+
+class ModChemBertForMaskedLM(InitWeightsMixin, ModernBertPreTrainedModel):
+    config_class = ModChemBertConfig
+    _tied_weights_keys = ["decoder.weight"]
+
+    def __init__(self, config: ModChemBertConfig):
+        super().__init__(config)
+        self.config = config
+        self.model = ModChemBertModel(config)
+        self.head = ModernBertPredictionHead(config)
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)
+
+        self.sparse_prediction = self.config.sparse_prediction
+        self.sparse_pred_ignore_index = self.config.sparse_pred_ignore_index
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.decoder
+
+    def set_output_embeddings(self, new_embeddings: nn.Linear):
+        self.decoder = new_embeddings
+
+    @torch.compile(dynamic=True)
+    def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
+        return self.decoder(self.head(output))
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        sliding_window_mask: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        indices: torch.Tensor | None = None,
+        cu_seqlens: torch.Tensor | None = None,
+        max_seqlen: int | None = None,
+        batch_size: int | None = None,
+        seq_len: int | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor] | tuple[torch.Tensor, typing.Any] | MaskedLMOutput:
+        r"""
+        sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+            perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+            far-away tokens in the local attention layers when not using Flash Attention.
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids & pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        self._maybe_set_compile()
+
+        if self.config._attn_implementation == "flash_attention_2":  # noqa: SIM102
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                if batch_size is None and seq_len is None:
+                    if inputs_embeds is not None:
+                        batch_size, seq_len = inputs_embeds.shape[:2]
+                    else:
+                        batch_size, seq_len = input_ids.shape[:2]  # type: ignore
+                device = input_ids.device if input_ids is not None else inputs_embeds.device  # type: ignore
+
+                if attention_mask is None:
+                    attention_mask = torch.ones(
+                        (batch_size, seq_len), device=device, dtype=torch.bool
+                    )  # type: ignore
+
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = (
+                            _unpad_modernbert_input(
+                                inputs=input_ids,  # type: ignore
+                                attention_mask=attention_mask,  # type: ignore
+                                position_ids=position_ids,
+                                labels=labels,
+                            )
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = (
+                        _unpad_modernbert_input(
+                            inputs=inputs_embeds,
+                            attention_mask=attention_mask,  # type: ignore
+                            position_ids=position_ids,
+                            labels=labels,
+                        )
+                    )
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = outputs[0]
+
+        if self.sparse_prediction and labels is not None:
+            # flatten labels and output first
+            labels = labels.view(-1)
+            last_hidden_state = last_hidden_state.view(labels.shape[0], -1)
+
+            # then filter out the non-masked tokens
+            mask_tokens = labels != self.sparse_pred_ignore_index
+            last_hidden_state = last_hidden_state[mask_tokens]
+            labels = labels[mask_tokens]
+
+        logits = (
+            self.compiled_head(last_hidden_state)
+            if self.config.reference_compile
+            else self.decoder(self.head(last_hidden_state))
+        )
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        if self.config._attn_implementation == "flash_attention_2":
+            with (
+                nullcontext()
+                if self.config.repad_logits_with_grad or labels is None
+                else torch.no_grad()
+            ):
+                logits = _pad_modernbert_output(
+                    inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len
+                )  # type: ignore
+
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=typing.cast(torch.FloatTensor, logits),
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class ModChemBertForSequenceClassification(InitWeightsMixin, ModernBertPreTrainedModel):
+    config_class = ModChemBertConfig
+
+    def __init__(self, config: ModChemBertConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.model = ModernBertModel(config)
+        if self.config.classifier_pooling in {"cls_mha", "max_seq_mha"}:
+            self.pooling_attn = ModChemBertPoolingAttention(config=self.config)
+        else:
+            self.pooling_attn = None
+        self.head = ModernBertPredictionHead(config)
+        self.drop = torch.nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        sliding_window_mask: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        indices: torch.Tensor | None = None,
+        cu_seqlens: torch.Tensor | None = None,
+        max_seqlen: int | None = None,
+        batch_size: int | None = None,
+        seq_len: int | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor] | tuple[torch.Tensor, typing.Any] | SequenceClassifierOutput:
+        r"""
+        sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+            perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+            far-away tokens in the local attention layers when not using Flash Attention.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids & pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        self._maybe_set_compile()
+
+        if input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+
+        if batch_size is None and seq_len is None:
+            if inputs_embeds is not None:
+                batch_size, seq_len = inputs_embeds.shape[:2]
+            else:
+                batch_size, seq_len = input_ids.shape[:2]  # type: ignore
+        device = input_ids.device if input_ids is not None else inputs_embeds.device  # type: ignore
+
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)  # type: ignore
+
+        # Ensure output_hidden_states is True in case pooling mode requires all hidden states
+        output_hidden_states = True
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = outputs[0]
+        hidden_states = outputs[1]
+
+        last_hidden_state = _pool_modchembert_output(
+            self,
+            last_hidden_state,
+            hidden_states,
+            typing.cast(torch.Tensor, attention_mask),
+        )
+        pooled_output = self.head(last_hidden_state)
+        pooled_output = self.drop(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (
+                    labels.dtype == torch.long or labels.dtype == torch.int
+                ):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+def _pool_modchembert_output(
+    module: ModChemBertForSequenceClassification,
+    last_hidden_state: torch.Tensor,
+    hidden_states: list[torch.Tensor],
+    attention_mask: torch.Tensor,
+):
+    """
+    Apply pooling strategy to hidden states for sequence-level classification/regression tasks.
+
+    This function implements various pooling strategies to aggregate sequence representations
+    into a single vector for downstream classification or regression tasks. The pooling method
+    is determined by the `classifier_pooling` configuration parameter.
+
+    Available pooling strategies:
+    - cls: Use the CLS token ([CLS]) representation from the last hidden state
+    - mean: Average pooling over all tokens in the sequence (attention-weighted)
+    - max_cls: Element-wise max pooling over the last k hidden states, then take CLS token
+    - cls_mha: Multi-head attention with CLS token as query and full sequence as keys/values
+    - max_seq_mha: Max pooling over last k states + multi-head attention with CLS as query
+    - max_seq_mean: Max pooling over last k hidden states, then mean pooling over sequence
+    - sum_mean: Sum all hidden states across layers, then mean pool over sequence
+    - sum_sum: Sum all hidden states across layers, then sum pool over sequence
+    - mean_sum: Mean all hidden states across layers, then sum pool over sequence
+    - mean_mean: Mean all hidden states across layers, then mean pool over sequence
+
+    Args:
+        module: The model instance containing configuration and pooling attention if needed
+        last_hidden_state: Final layer hidden states of shape (batch_size, seq_len, hidden_size)
+        hidden_states: List of hidden states from all layers, each of shape (batch_size, seq_len, hidden_size)
+        attention_mask: Attention mask of shape (batch_size, seq_len) indicating valid tokens
+
+    Returns:
+        torch.Tensor: Pooled representation of shape (batch_size, hidden_size)
+
+    Note:
+        Some pooling strategies (cls_mha, max_seq_mha) require the module to have a pooling_attn
+        attribute containing a ModChemBertPoolingAttention instance.
+    """
+    config = typing.cast(ModChemBertConfig, module.config)
+    if config.classifier_pooling == "cls":
+        last_hidden_state = last_hidden_state[:, 0]
+    elif config.classifier_pooling == "mean":
+        last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(
+            dim=1
+        ) / attention_mask.sum(dim=1, keepdim=True)
+    elif config.classifier_pooling == "max_cls":
+        k_hidden_states = hidden_states[-config.classifier_pooling_last_k :]
+        theta = torch.stack(k_hidden_states, dim=1)  # (batch, k, seq_len, hidden)
+        pooled_seq = torch.max(
+            theta, dim=1
+        ).values  # Element-wise max over k -> (batch, seq_len, hidden)
+        last_hidden_state = pooled_seq[:, 0, :]  # (batch, hidden)
+    elif config.classifier_pooling == "cls_mha":
+        # Similar to max_seq_mha but without the max pooling step
+        # Query is CLS token (position 0); Keys/Values are full sequence
+        q = last_hidden_state[:, 0, :].unsqueeze(1)  # (batch, 1, hidden)
+        q = q.expand(-1, last_hidden_state.shape[1], -1)  # (batch, seq_len, hidden)
+        attn_out: torch.Tensor = module.pooling_attn(  # type: ignore
+            q=q, kv=last_hidden_state, attention_mask=attention_mask
+        )  # (batch, seq_len, hidden)
+        last_hidden_state = torch.mean(attn_out, dim=1)
+    elif config.classifier_pooling == "max_seq_mha":
+        k_hidden_states = hidden_states[-config.classifier_pooling_last_k :]
+        theta = torch.stack(k_hidden_states, dim=1)  # (batch, k, seq_len, hidden)
+        pooled_seq = torch.max(
+            theta, dim=1
+        ).values  # Element-wise max over k -> (batch, seq_len, hidden)
+        # Query is pooled CLS token (position 0); Keys/Values are pooled sequence
+        q = pooled_seq[:, 0, :].unsqueeze(1)  # (batch, 1, hidden)
+        q = q.expand(-1, pooled_seq.shape[1], -1)  # (batch, seq_len, hidden)
+        attn_out: torch.Tensor = module.pooling_attn(  # type: ignore
+            q=q, kv=pooled_seq, attention_mask=attention_mask
+        )  # (batch, seq_len, hidden)
+        last_hidden_state = torch.mean(attn_out, dim=1)
+    elif config.classifier_pooling == "max_seq_mean":
+        k_hidden_states = hidden_states[-config.classifier_pooling_last_k :]
+        theta = torch.stack(k_hidden_states, dim=1)  # (batch, k, seq_len, hidden)
+        pooled_seq = torch.max(
+            theta, dim=1
+        ).values  # Element-wise max over k -> (batch, seq_len, hidden)
+        last_hidden_state = torch.mean(pooled_seq, dim=1)  # Mean over sequence length
+    elif config.classifier_pooling == "sum_mean":
+        # ChemLM uses the mean of all hidden states
+        # which outperforms using just the last layer mean or the cls embedding
+        # https://doi.org/10.1038/s42004-025-01484-4
+        # https://static-content.springer.com/esm/art%3A10.1038%2Fs42004-025-01484-4/MediaObjects/42004_2025_1484_MOESM2_ESM.pdf
+        all_hidden_states = torch.stack(hidden_states)
+        w = torch.sum(all_hidden_states, dim=0)
+        last_hidden_state = torch.mean(w, dim=1)
+    elif config.classifier_pooling == "sum_sum":
+        all_hidden_states = torch.stack(hidden_states)
+        w = torch.sum(all_hidden_states, dim=0)
+        last_hidden_state = torch.sum(w, dim=1)
+    elif config.classifier_pooling == "mean_sum":
+        all_hidden_states = torch.stack(hidden_states)
+        w = torch.mean(all_hidden_states, dim=0)
+        last_hidden_state = torch.sum(w, dim=1)
+    elif config.classifier_pooling == "mean_mean":
+        all_hidden_states = torch.stack(hidden_states)
+        w = torch.mean(all_hidden_states, dim=0)
+        last_hidden_state = torch.mean(w, dim=1)
+    return last_hidden_state
+
+
+__all__ = [
+    "ModChemBertForMaskedLM",
+    "ModChemBertForSequenceClassification",
+]

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

similarity_evaluation_pubchem_10m_genmol_similarity_float32_results.csv

@@ -0,0 +1,14 @@
+epoch,steps,spearman
+0,0,0.7261446896400275
+0.2499925700642937,25235,0.899727524994741
+0.4999851401285874,50470,0.9599428082697957
+0.7499777101928812,75705,0.9755030703217896
+0.9999702802571748,100940,0.9809624466313892
+1.2499628503214686,126175,0.9838128954121899
+1.4999554203857621,151410,0.9854756886661312
+1.749947990450056,176645,0.9865980464822579
+1.9999405605143497,201880,0.9873943693937194
+2.2499331305786434,227115,0.9878659546563734
+2.499925700642937,252350,0.9879865870047979
+2.749918270707231,277585,0.9881075350289332
+2.9999108407715243,302820,0.9881056976837288

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,2554 @@
+{
+  "version": "1.0",
+  "truncation": {
+    "direction": "Right",
+    "max_length": 512,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
+  "padding": {
+    "strategy": "BatchLongest",
+    "direction": "Right",
+    "pad_to_multiple_of": null,
+    "pad_id": 2,
+    "pad_type_id": 0,
+    "pad_token": "[PAD]"
+  },
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "[CLS]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1,
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+    },
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+  }
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
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+    "2361": {
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+  "cls_token": "[CLS]",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_input_names": [
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "[UNK]"
+}