--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10000 - loss:ContrastiveLoss base_model: DeepChem/ChemBERTa-77M-MLM widget: - source_sentence: CC(C)N=c1cc2n(-c3ccc(Cl)cc3)c3ccccc3nc-2cc1Nc1ccc(Cl)cc1 sentences: - C[NH+]1CCC(=C2c3ccccc3CCn3c(C=O)c[nH+]c32)CC1 - COc1ccc(C(=O)CC(=O)c2ccc(C(C)(C)C)cc2)cc1 - CC1CNc2c(cccc2S(=O)(=O)NC(CCC[NH+]=C(N)N)C(=O)N2CCC(C)CC2C(=O)[O-])C1 - source_sentence: CC(C)c1ccc2oc3nc(N)c(C(=O)[O-])cc3c(=O)c2c1 sentences: - COC1=CC(=O)CC(C)C12Oc1c(Cl)c(OC)cc(OC)c1C2=O - CON=C(C(=O)NC1C(=O)N2C(C(=O)[O-])=C(C[N+]3(C)CCCC3)CSC12)c1csc(N)n1 - CC1C=CC=CC=CC=CC=CC=CC=CC(OC2OC(C)C(O)C([NH3+])C2O)CC2OC(O)(CC(O)CC(O)C(O)CCC(O)CC(O)CC(=O)OC(C)C(C)C1O)CC(O)C2C(=O)[O-] - source_sentence: C[NH2+]C1CCc2[nH]c3ccc(C(N)=O)cc3c2C1 sentences: - CC(OC(=O)c1ccccc1)C1=CCC23OCC[NH+](C)CC12CC(O)C12OC4(O)CCC1(C)C(CC=C32)C4 - CC(=O)NC(Cc1ccc2ccccc2c1)C(=O)NC(Cc1ccc(Cl)cc1)C(=O)NC(Cc1cccnc1)C(=O)NC(CO)C(=O)NC(Cc1ccc(NC(=O)C2CC(=O)NC(=O)N2)cc1)C(=O)NC(Cc1ccc(NC(N)=O)cc1)C(=O)NC(CC(C)C)C(=O)NC(CCCC[NH2+]C(C)C)C(=O)N1CCCC1C(=O)NC(C)C(N)=O - C[NH+](C)CCOC(=O)C(c1ccccc1)C1(O)CCCC1 - source_sentence: CC(C)n1c(C=CC(O)CC(O)CC(=O)[O-])c(-c2ccc(F)cc2)c2ccccc21 sentences: - C#CC1(O)CCC2C3CCC4=C(CCC(=O)C4)C3CCC21C - CC(C=CC(C)C(C)(C)O)C1CCC2C(=CC=C3CC(O)CC(O)C3)CCCC21C - CC(C)CNCc1ccc(-c2ccccc2S(=O)(=O)N2CCCC2)cc1 - source_sentence: CC#CCn1c(N2CCCC([NH3+])C2)nc2c1c(=O)n(Cc1nc(C)c3ccccc3n1)c(=O)n2C sentences: - C[N+]1(C)CCCC(OC(=O)C(O)(c2ccccc2)c2ccccc2)C1 - CC(Cc1ccc(O)c(O)c1)C(C)Cc1ccc(O)c(O)c1 - CC12CCC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)COC(=O)CCC1CCCC1 pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap model-index: - name: SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM results: - task: type: binary-classification name: Binary Classification dataset: name: all dev type: all-dev metrics: - type: cosine_accuracy value: 0.9066 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.5664876699447632 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9510122731564041 name: Cosine F1 - type: cosine_f1_threshold value: 0.5664876699447632 name: Cosine F1 Threshold - type: cosine_precision value: 0.9067813562712542 name: Cosine Precision - type: cosine_recall value: 0.9997794441993825 name: Cosine Recall - type: cosine_ap value: 0.9523113003188102 name: Cosine Ap --- # SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [DeepChem/ChemBERTa-77M-MLM](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [DeepChem/ChemBERTa-77M-MLM](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 384, '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}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("HassanCS/chemBERTa-tuned-on-ClinTox-3") # Run inference sentences = [ 'CC#CCn1c(N2CCCC([NH3+])C2)nc2c1c(=O)n(Cc1nc(C)c3ccccc3n1)c(=O)n2C', 'CC12CCC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)COC(=O)CCC1CCCC1', 'CC(Cc1ccc(O)c(O)c1)C(C)Cc1ccc(O)c(O)c1', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Binary Classification * Dataset: `all-dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:--------------------------|:-----------| | cosine_accuracy | 0.9066 | | cosine_accuracy_threshold | 0.5665 | | cosine_f1 | 0.951 | | cosine_f1_threshold | 0.5665 | | cosine_precision | 0.9068 | | cosine_recall | 0.9998 | | **cosine_ap** | **0.9523** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,000 training samples * Columns: smiles1, smiles2, and label * Approximate statistics based on the first 1000 samples: | | smiles1 | smiles2 | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | smiles1 | smiles2 | label | |:----------------------------------------|:-------------------------------------------------------------|:---------------| | Cn1c(=O)c2c(ncn2C)n(C)c1=O | Cc1cc2c(s1)=Nc1ccccc1NC=2N1CC[NH+](C)CC1 | 1 | | Oc1ccc(OCc2ccccc2)cc1 | Oc1ccc(CCCC[NH2+]CC(O)c2ccc(O)c(O)c2)cc1 | 1 | | OCC(S)CS | CC12CCC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)CO | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 5,000 evaluation samples * Columns: smiles1, smiles2, and label * Approximate statistics based on the first 1000 samples: | | smiles1 | smiles2 | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | smiles1 | smiles2 | label | |:---------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:---------------| | CC(=CC(=O)OCCCCCCCCC(=O)[O-])CC1OCC(CC2OC2C(C)C(C)O)C(O)C1O | CC(C=CC(C)C(C)(C)O)C1CCC2C(=CC=C3CC(O)CC(O)C3)CCCC21C | 1 | | C=C1c2cccc([O-])c2C(=O)C2=C([O-])C3(O)C(=O)C(C(N)=O)=C([O-])C([NH+](C)C)C3C(O)C12 | CC(c1ncncc1F)C(O)(Cn1cncn1)c1ccc(F)cc1F | 1 | | CC(C)CC1C(=O)N2CCCC2C2(O)OC(NC(=O)C3C=C4c5cccc6[nH]c(Br)c(c56)CC4[NH+](C)C3)(C(C)C)C(=O)N12 | C[NH+](C)CCC=C1c2ccccc2Sc2ccc(Cl)cc21 | 1 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `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} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: 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 - `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 - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | all-dev_cosine_ap | |:-----:|:----:|:-------------:|:---------------:|:-----------------:| | 0.8 | 500 | 0.0264 | 0.0112 | 0.9213 | | 1.6 | 1000 | 0.0152 | 0.0122 | 0.9362 | | 2.4 | 1500 | 0.0134 | 0.0128 | 0.9463 | | 3.2 | 2000 | 0.0112 | 0.0134 | 0.9502 | | 4.0 | 2500 | 0.01 | 0.0125 | 0.9513 | | 4.8 | 3000 | 0.0097 | 0.0132 | 0.9523 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ```