| --- |
| 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](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 |
| pip install -U "transformers>=4.56.1,<5.0.0" |
| ``` |
|
|
| 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, 0.3876, 0.0078], |
| # [0.3876, 1.0000, 0.0028], |
| # [0.0078, 0.0028, 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, 0.5587, 0.0155], |
| # [0.5587, 1.0000, 0.0055], |
| # [0.0155, 0.0055, 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** | **0.98914** | |
| | **test** | **spearman** | **0.98916** | |
| |
| ## 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": -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](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": -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 |
| <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`: 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`: {} |
|
|
| </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 | - | 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 | |
|
|
| </details> |
|
|
| ### Environmental Impact |
| Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/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 |
| ```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) |
|
|
