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metadata

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: 6350.153020081601
  energy_consumed: 30.935740629629628
  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: 116.388
  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: 0.989142152637452
            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: 0.9891625268496924
            name: Spearman

ChemMRL: SMILES Matryoshka Representation Learning Embedding Transformer

This is a Chem-MRL (sentence-transformers) model finetuned from Derify/ModChemBERT-IR-BASE on the 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Tanimoto
Training Dataset:
- pubchem_10m_genmol_similarity
License: apache-2.0

Model Sources

Repository: Chem-MRL on GitHub
Demo App Repository: Chem-MRL-demo on GitHub

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:

pip install -U chem-mrl>=0.7.3
pip install -U "transformers>=4.56.1,<5.0.0"

Then you can load this model and run inference.

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, 0.3876, 0.0078],
#         [0.3876, 1.0000, 0.0028],
#         [0.0078, 0.0028, 1.0000]])

Direct Usage (Sentence Transformers)

Click to see the direct usage in Transformers

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

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, 0.5587, 0.0155],
#         [0.5587, 1.0000, 0.0055],
#         [0.0155, 0.0055, 1.0000]])

Evaluation

Metrics

Semantic Similarity

Dataset: pubchem_10m_genmol_similarity
Evaluated with chem_mrl.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator with these parameters:
```
{
    "precision": "float32"
}
```

Split	Metric	Value
validation	spearman	0.98914
test	spearman	0.98916

Training Details

Training Dataset

pubchem_10m_genmol_similarity

Dataset: pubchem_10m_genmol_similarity at 9aec8fd
Size: 19,381,001 training samples
Columns: smiles_a, smiles_b, and label
Approximate statistics based on the first 1000 samples:
smiles_a smiles_b label
type string string float
details
min: 17 tokens
mean: 42.36 tokens
max: 122 tokens

min: 11 tokens
mean: 40.93 tokens
max: 122 tokens

min: 0.02
mean: 0.56
max: 1.0

	smiles_a	smiles_b	label
type	string	string	float
details	min: 17 tokens mean: 42.36 tokens max: 122 tokens	min: 11 tokens mean: 40.93 tokens max: 122 tokens	min: 0.02 mean: 0.56 max: 1.0

Samples:

smiles_a	smiles_b	label
`COc1ccc(NC(=O)C2CC[NH+](C(C)C(=O)Nc3ccc(C(=O)Nc4ccc(F)c(F)c4)cc3C)CC2)cc1NC(=O)C1CCCCC1`	`Cc1cc(C(=O)Nc2ccc(F)c(F)c2)ccc1NC(=O)C(C)[NH+]1CCC(C(=O)Nc2cccc(NC(=O)C3CCCCC3)c2)CC1`	`0.8495575189590454`
`OCCN1CC[NH+](Cc2ccccc2OC2CC2)CC1`	`OCCN1CC[NH+](Cc2ccccc2On2cccn2)CC1`	`0.6615384817123413`
`CC1CN(C(=O)C2CC[NH+](Cc3cccc(C(N)=O)c3)CC2)CC(C)O1`	`CC1CN(C(=O)C2CC[NH+](Cc3ccccc3)CC2)CC(C)O1`	`0.7123287916183472`

Loss: Matryoshka2dLoss with these parameters:

{
    "loss": "TanimotoSentLoss",
    "n_layers_per_step": -1,
    "last_layer_weight": 2.0,
    "prior_layers_weight": 1.0,
    "kl_div_weight": 0.0,
    "kl_temperature": 0.0,
    "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
}

Evaluation Dataset

pubchem_10m_genmol_similarity

Dataset: pubchem_10m_genmol_similarity at 9aec8fd
Size: 1,080,394 evaluation samples
Columns: smiles_a, smiles_b, and label
Approximate statistics based on the first 1000 samples:
smiles_a smiles_b label
type string string float
details
min: 16 tokens
mean: 42.05 tokens
max: 101 tokens

min: 11 tokens
mean: 40.23 tokens
max: 104 tokens

min: 0.0
mean: 0.57
max: 1.0

	smiles_a	smiles_b	label
type	string	string	float
details	min: 16 tokens mean: 42.05 tokens max: 101 tokens	min: 11 tokens mean: 40.23 tokens max: 104 tokens	min: 0.0 mean: 0.57 max: 1.0

Samples:

smiles_a	smiles_b	label
`N#CCCN(Cc1cnc(N)cn1)C1CC1`	`N#CCCN(Cc1cnc(N)cn1)C1CCCC1`	`0.8600000143051147`
`N#CCCN(Cc1cnc(N)cn1)C1CC1`	`N#CCCN(Cc1cnc(N)cn1)C1CCOCC1`	`0.7962962985038757`
`N#CCCN(Cc1cnc(N)cn1)C1CC1`	`N#CCCN(Cc1cnc(N)cn1)CC(F)F`	`0.5517241358757019`

Loss: Matryoshka2dLoss with these parameters:

{
    "loss": "TanimotoSentLoss",
    "n_layers_per_step": -1,
    "last_layer_weight": 2.0,
    "prior_layers_weight": 1.0,
    "kl_div_weight": 0.0,
    "kl_temperature": 0.0,
    "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: 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
gradient_checkpointing: True
eval_on_start: True

All Hyperparameters

Click to expand

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: True
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: True
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: 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: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	pubchem 10m genmol similarity loss	pubchem_10m_genmol_similarity_spearman
0	0	-	297.6136	0.7261
0.0000	1	244.6862	-	-
0.2477	25000	161.5037	-	-
0.2500	25235	-	195.4624	0.9067
0.4978	50250	155.7822	-	-
0.5000	50470	-	189.4068	0.9655
0.7479	75500	152.7915	-	-
0.7500	75705	-	186.3661	0.9780
0.9981	100750	151.0411	-	-
1.0000	100940	-	184.6362	0.9829
1.2482	126000	149.8544	-	-
1.2500	126175	-	183.5648	0.9855
1.4984	151250	149.2916	-	-
1.5000	151410	-	182.8947	0.9868
1.7485	176500	148.7942	-	-
1.7499	176645	-	182.3662	0.9879
1.9987	201750	148.3459	-	-
1.9999	201880	-	181.9855	0.9885
2.2488	227000	148.0316	-	-
2.2499	227115	-	181.7683	0.9889
2.4989	252250	147.8658	-	-
2.4999	252350	-	181.6711	0.9890
2.7491	277500	147.9642	-	-
2.7499	277585	-	181.6077	0.9891
2.9992	302750	147.8874	-	-
2.9999	302820	-	181.6066	0.9891
3.0000	302829	-	-	0.98914

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 30.936 kWh
Carbon Emitted: 6.350 kg of CO2
Hours Used: 116.388 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: 5.1.2
Transformers: 4.57.1
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.3.0
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@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

@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

@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

@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

@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

Model Card Contact

Manny Cortes (manny@derifyai.com)