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--- |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- modchembert |
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- cheminformatics |
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- smiles |
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- molecular-similarity |
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- feature-extraction |
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- dense |
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- generated_from_trainer |
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- dataset_size:19381001 |
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- loss:Matryoshka2dLoss |
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- loss:MatryoshkaLoss |
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- loss:TanimotoSentLoss |
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base_model: Derify/ModChemBERT-IR-BASE |
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widget: |
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- source_sentence: COC(=O)c1sc(-c2ccc(C)cc2)c2c1NC(=O)C2(c1ccccc1)c1ccccc1 |
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sentences: |
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- COC(=O)c1sc(Nc2ccc(Br)cn2)c2c1NC(=O)C2(c1ccccc1)c1ccccc1 |
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- CC[NH+]1CCOC(C(NN)c2ccccc2Br)C1 |
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- CC([NH2+]C(C)c1ccccc1)C(=O)P(C)C(C)(C)C |
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- source_sentence: O=C(C=Cc1ccccc1)CC(=O)c1ccccc1O |
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sentences: |
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- COCCN(NCc1c(C)n(C(C)=O)c2ccc(OC)cc12)c1nccs1 |
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- CCN(CCC(N)=O)C(=O)c1ccc(=O)[nH]n1 |
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- N=CCC(=Cc1ccccc1)C(=O)COc1ccccc1O |
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- source_sentence: COc1cccc(-c2sc3ccccc3c2C#N)c1 |
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sentences: |
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- COCC(C)(C)c1cnnn1CCCI |
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- N#Cc1c(-c2cccc(CN)c2)sc2ccccc12 |
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- COc1ccccc1NC(=O)c1cc(NCc2ccco2)cc[nH+]1 |
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- source_sentence: Nc1nc(-c2ccccc2)c2nc(N)c(N)nc2n1 |
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sentences: |
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- CC(C)CC1NC(=O)C(Cc2ccccc2)NC(=O)c2ccc(cc2)CN(C(=O)CC2CCOCC2)CCCCNC(=O)C(C)NC1=O |
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- O=Nc1cccc(OCCC(F)F)c1 |
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- CCCCNCc1nc(N)nc2nc(N)c(N)nc12 |
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- source_sentence: OCCCc1cc(F)cc(F)c1 |
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sentences: |
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- CCC(C)C(=O)C1(C(NN)C(C)C)CCCC1 |
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- Cc1[nH]c2c(C(N)=O)ccc(C(=O)N3CCCCC3)c2c1C |
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- Fc1cc(F)cc(-n2cc[o+]n2)c1 |
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datasets: |
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- Derify/pubchem_10m_genmol_similarity |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- spearman |
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co2_eq_emissions: |
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emissions: 6350.153020081601 |
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energy_consumed: 30.935740629629628 |
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source: codecarbon |
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training_type: fine-tuning |
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on_cloud: false |
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cpu_model: AMD Ryzen 7 3700X 8-Core Processor |
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ram_total_size: 62.69887161254883 |
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hours_used: 116.388 |
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hardware_used: 2 x NVIDIA GeForce RTX 3090 |
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model-index: |
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- name: 'ChemMRL: SMILES Matryoshka Representation Learning Embedding Transformer' |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: pubchem 10m genmol similarity (validation) |
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type: pubchem_10m_genmol_similarity_validation |
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metrics: |
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- type: spearman |
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value: 0.989142152637452 |
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name: Spearman |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: pubchem 10m genmol similarity (test) |
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type: pubchem_10m_genmol_similarity_test |
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metrics: |
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- type: spearman |
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value: 0.9891625268496924 |
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name: Spearman |
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--- |
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# ChemMRL: SMILES Matryoshka Representation Learning Embedding Transformer |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** ChemMRL (Sentence Transformer) |
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- **Base model:** [Derify/ModChemBERT-IR-BASE](https://huggingface.co/Derify/ModChemBERT-IR-BASE) <!-- at revision fde8c1ed2606783be3ff621be0a4fde825f12169 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Tanimoto |
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- **Training Dataset:** |
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- [pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity) |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Repository:** [Chem-MRL on GitHub](https://github.com/emapco/chem-mrl) |
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- **Demo App Repository:** [Chem-MRL-demo on GitHub](https://github.com/emapco/chem-mrl-demo) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'ModChemBertModel'}) |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Chem-MRL) |
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First install the Chem-MRL library: |
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```bash |
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pip install -U chem-mrl>=0.7.3 |
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pip install -U "transformers>=4.56.1,<5.0.0" |
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``` |
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Then you can load this model and run inference. |
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```python |
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from chem_mrl import ChemMRL |
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# Download from the 🤗 Hub |
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model = ChemMRL( |
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"Derify/ChemMRL", |
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trust_remote_code=True, |
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model_kwargs={"torch_dtype": "bfloat16"}, |
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) |
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# Run inference |
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sentences = [ |
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'OCCCc1cc(F)cc(F)c1', |
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'Fc1cc(F)cc(-n2cc[o+]n2)c1', |
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'CCC(C)C(=O)C1(C(NN)C(C)C)CCCC1', |
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] |
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embeddings = model.backbone.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.backbone.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[1.0000, 0.3876, 0.0078], |
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# [0.3876, 1.0000, 0.0028], |
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# [0.0078, 0.0028, 1.0000]]) |
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``` |
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### Direct Usage (Sentence Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer( |
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"Derify/ChemMRL", |
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# SentenceTransformer doesn't support tanimoto similarity natively so we set a different similarity function here |
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similarity_fn_name="cosine", |
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trust_remote_code=True, |
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model_kwargs={"torch_dtype": "bfloat16"}, |
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) |
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# Run inference |
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sentences = [ |
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'OCCCc1cc(F)cc(F)c1', |
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'Fc1cc(F)cc(-n2cc[o+]n2)c1', |
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'CCC(C)C(=O)C1(C(NN)C(C)C)CCCC1', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[1.0000, 0.5587, 0.0155], |
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# [0.5587, 1.0000, 0.0055], |
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# [0.0155, 0.0055, 1.0000]]) |
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``` |
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</details> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `pubchem_10m_genmol_similarity` |
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* Evaluated with <code>chem_mrl.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator</code> with these parameters: |
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```json |
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{ |
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"precision": "float32" |
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} |
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``` |
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| Split | Metric | Value | |
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| :------------- | :----------- | :---------- | |
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| **validation** | **spearman** | **0.98914** | |
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| **test** | **spearman** | **0.98916** | |
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## Training Details |
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### Training Dataset |
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#### pubchem_10m_genmol_similarity |
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* 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) |
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* Size: 19,381,001 training samples |
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* Columns: <code>smiles_a</code>, <code>smiles_b</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | smiles_a | smiles_b | label | |
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| :------ | :---------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------- | :-------------------------------------------------------------- | |
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| type | string | string | float | |
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| 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> | |
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* Samples: |
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| smiles_a | smiles_b | label | |
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| :----------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------- | :------------------------------ | |
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| <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> | |
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| <code>OCCN1CC\[NH+\](Cc2ccccc2OC2CC2)CC1</code> | <code>OCCN1CC\[NH+\](Cc2ccccc2On2cccn2)CC1</code> | <code>0.6615384817123413</code> | |
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| <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> | |
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* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshka2dloss) with these parameters: |
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```json |
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{ |
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"loss": "TanimotoSentLoss", |
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"n_layers_per_step": -1, |
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"last_layer_weight": 2.0, |
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"prior_layers_weight": 1.0, |
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"kl_div_weight": 0.0, |
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"kl_temperature": 0.0, |
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"matryoshka_dims": [ |
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1024, |
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512, |
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256, |
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128, |
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64, |
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32, |
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16, |
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8 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Evaluation Dataset |
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#### pubchem_10m_genmol_similarity |
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* 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) |
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* Size: 1,080,394 evaluation samples |
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* Columns: <code>smiles_a</code>, <code>smiles_b</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | smiles_a | smiles_b | label | |
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| :------ | :---------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------- | :------------------------------------------------------------- | |
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| type | string | string | float | |
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| 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> | |
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* Samples: |
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| smiles_a | smiles_b | label | |
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| :------------------------------------- | :---------------------------------------- | :------------------------------ | |
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| <code>N#CCCN(Cc1cnc(N)cn1)C1CC1</code> | <code>N#CCCN(Cc1cnc(N)cn1)C1CCCC1</code> | <code>0.8600000143051147</code> | |
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| <code>N#CCCN(Cc1cnc(N)cn1)C1CC1</code> | <code>N#CCCN(Cc1cnc(N)cn1)C1CCOCC1</code> | <code>0.7962962985038757</code> | |
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| <code>N#CCCN(Cc1cnc(N)cn1)C1CC1</code> | <code>N#CCCN(Cc1cnc(N)cn1)CC(F)F</code> | <code>0.5517241358757019</code> | |
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* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshka2dloss) with these parameters: |
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```json |
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{ |
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"loss": "TanimotoSentLoss", |
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"n_layers_per_step": -1, |
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"last_layer_weight": 2.0, |
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"prior_layers_weight": 1.0, |
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"kl_div_weight": 0.0, |
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"kl_temperature": 0.0, |
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"matryoshka_dims": [ |
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1024, |
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512, |
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256, |
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128, |
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64, |
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32, |
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16, |
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8 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 192 |
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- `per_device_eval_batch_size`: 512 |
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- `learning_rate`: 8e-06 |
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- `weight_decay`: 1e-05 |
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- `max_grad_norm`: None |
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- `lr_scheduler_type`: warmup_stable_decay |
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- `lr_scheduler_kwargs`: {'num_decay_steps': 100943, 'warmup_type': 'linear', 'decay_type': '1-sqrt'} |
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- `warmup_steps`: 100943 |
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- `data_seed`: 42 |
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- `bf16`: True |
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- `bf16_full_eval`: True |
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- `tf32`: True |
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- `optim`: stable_adamw |
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- `optim_args`: decouple_lr=True,max_lr=8.0e-6 |
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- `gradient_checkpointing`: True |
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- `eval_on_start`: True |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 192 |
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- `per_device_eval_batch_size`: 512 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 8e-06 |
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- `weight_decay`: 1e-05 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: None |
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- `num_train_epochs`: 3 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: warmup_stable_decay |
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- `lr_scheduler_kwargs`: {'num_decay_steps': 100943, 'warmup_type': 'linear', 'decay_type': '1-sqrt'} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 100943 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: 42 |
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- `jit_mode_eval`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: True |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `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) |
|
|
|
