--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:118400 - loss:TripletLoss base_model: DeepChem/ChemBERTa-77M-MLM widget: - source_sentence: CC(C)C1CCC(C(=O)NC(Cc2ccccc2)C(=O)[O-])CC1 sentences: - C[NH2+]CCCC1c2ccccc2C=Cc2ccccc21 - COC(=O)NC(C(=O)NC(Cc1ccccc1)C(O)CN(Cc1ccc(-c2ccccn2)cc1)NC(=O)C(NC(=O)OC)C(C)(C)C)C(C)(C)C - CC1C=CC=CCCC=CC=CC=CC=CC(OC2OC(C)C(O)C([NH3+])C2O)CC(O)C(C(=O)[O-])C(O)CC(=O)CC(O)C(O)CCC(O)CC(O)CC(O)CC(=O)OC(C)C(C)C1O - source_sentence: C[NH+]1CCCC1Cc1c[nH]c2ccc(CCS(=O)(=O)c3ccccc3)cc12 sentences: - CC(C)CC([NH+](C)C)C1(c2ccc(Cl)cc2)CCC1 - CC(C)CNCc1ccc(-c2ccccc2S(=O)(=O)N2CCCC2)cc1 - CC(Oc1cc(-c2cnn(C3CC[NH2+]CC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl - source_sentence: C[NH+]1C2CCC1CC(OC(c1ccccc1)c1ccccc1)C2 sentences: - C[NH2+]C1C(O)C([NH2+]C)C2OC3(O)C(=O)CC(C)OC3OC2C1O - C=CC1(C)CC(OC(=O)CSC2CC3CCC(C2)[NH+]3C)C2(C)C(C)CCC3(CCC(=O)C32)C(C)C1O - CC(C)CC(NC(=O)C(CCc1ccccc1)NC(=O)CN1CCOCC1)C(=O)NC(Cc1ccccc1)C(=O)NC(CC(C)C)C(=O)C1(C)CO1 - source_sentence: CC(C)CC(NC(=O)C(Cc1ccc2ccccc2c1)NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(CO)NC(=O)C(Cc1c[nH]c2ccccc12)NC(=O)C(Cc1c[nH]cn1)NC(=O)C1CCC(=O)N1)C(=O)NC(CCCNC(N)=[NH2+])C(=O)N1CCCC1C(=O)NCC(N)=O sentences: - C[NH2+]C1CCC(c2ccc(Cl)c(Cl)c2)c2ccccc21 - C=C1CC2CCC34CC5OC6C(OC7CCC(CC(=O)CC8C(CC9OC(CCC1O2)CC(C)C9=C)OC(CC(O)CN)C8OC)OC7C6O3)C5O4 - C[N+]1(C)CCC(=C(c2ccccc2)c2ccccc2)CC1 - source_sentence: CON=C(C(=O)NC1C(=O)N2C(C(=O)[O-])=C(C[N+]3(C)CCCC3)CSC12)c1csc(N)n1 sentences: - CC1CNc2c(cccc2S(=O)(=O)NC(CCC[NH+]=C(N)N)C(=O)N2CCC(C)CC2C(=O)[O-])C1 - CC1C=CC=CCCC=CC=CC=CC=CC(OC2OC(C)C(O)C([NH3+])C2O)CC(O)C(C(=O)[O-])C(O)CC(=O)CC(O)C(O)CCC(O)CC(O)CC(O)CC(=O)OC(C)C(C)C1O - CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1 pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM results: - task: type: triplet name: Triplet dataset: name: all dev type: all-dev metrics: - type: cosine_accuracy value: 0.7135134935379028 name: Cosine Accuracy --- # 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-using-MultipleNegativesRankingLoss") # Run inference sentences = [ 'CON=C(C(=O)NC1C(=O)N2C(C(=O)[O-])=C(C[N+]3(C)CCCC3)CSC12)c1csc(N)n1', 'CC1CNc2c(cccc2S(=O)(=O)NC(CCC[NH+]=C(N)N)C(=O)N2CCC(C)CC2C(=O)[O-])C1', 'CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1', ] 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 #### Triplet * Dataset: `all-dev` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.7135** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 118,400 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------| | CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O | CC(=O)OC1CCC2(C)C(=CCC3C2CCC2(C)C(c4cccnc4)=CCC32)C1 | CCOC(=O)c1ncn2c1CN(C)C(=O)c1cc(F)ccc1-2 | | CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O | COc1ccc(C(CN(C)C)C2(O)CCCCC2)cc1 | C[NH2+]C1(C)C2CCC(C2)C1(C)C | | CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O | CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1.Cc1ccc(S(=O)(=O)O)cc1 | Nc1ncnc2c1ncn2C1OC(CO)C(O)C1O | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 1,480 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:------------------------------------------------------------------------|:------------------------------------------------------------|:-------------------------------------------------------------------------| | CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1 | CC12CCCCCC(Cc3ccc(O)cc31)C2[NH3+] | CC(C)C(CN1CCC(C)(c2cccc(O)c2)C(C)C1)NC(=O)C1Cc2ccc(O)cc2CN1 | | CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1 | COc1cc2c(cc1OC)C1CC(=O)C(CC(C)C)C[NH+]1CC2 | CC(C)C(CN1CCC(C)(c2cccc(O)c2)C(C)C1)NC(=O)C1Cc2ccc(O)cc2CN1 | | CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1 | C[NH+](C)CCC=C1c2ccccc2COc2ccc(CC(=O)[O-])cc21 | CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1 | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### 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_accuracy | |:------:|:-----:|:-------------:|:---------------:|:-----------------------:| | 0.0676 | 500 | 5.0821 | 5.1737 | 0.4047 | | 0.1351 | 1000 | 4.9869 | 5.1766 | 0.4230 | | 0.2027 | 1500 | 4.5562 | 4.9102 | 0.5345 | | 0.2703 | 2000 | 3.2364 | 4.3712 | 0.6534 | | 0.3378 | 2500 | 2.0738 | 4.0704 | 0.6736 | | 0.4054 | 3000 | 1.4239 | 4.0200 | 0.6635 | | 0.4730 | 3500 | 1.1578 | 3.7202 | 0.6791 | | 0.5405 | 4000 | 0.9669 | 3.7197 | 0.6831 | | 0.6081 | 4500 | 0.714 | 3.8818 | 0.6547 | | 0.6757 | 5000 | 0.5359 | 4.0987 | 0.6243 | | 0.7432 | 5500 | 0.5663 | 3.8127 | 0.6500 | | 0.8108 | 6000 | 0.4827 | 3.8346 | 0.6676 | | 0.8784 | 6500 | 0.4758 | 3.8333 | 0.6507 | | 0.9459 | 7000 | 0.4759 | 3.6872 | 0.6912 | | 1.0135 | 7500 | 0.4651 | 3.7229 | 0.6831 | | 1.0811 | 8000 | 0.4739 | 3.8041 | 0.6662 | | 1.1486 | 8500 | 0.4458 | 3.8235 | 0.6703 | | 1.2162 | 9000 | 0.4189 | 3.7957 | 0.6716 | | 1.2838 | 9500 | 0.4504 | 3.7422 | 0.6784 | | 1.3514 | 10000 | 0.413 | 3.7588 | 0.6770 | | 1.4189 | 10500 | 0.3808 | 3.9750 | 0.6615 | | 1.4865 | 11000 | 0.3853 | 3.7417 | 0.6953 | | 1.5541 | 11500 | 0.379 | 3.7319 | 0.6993 | | 1.6216 | 12000 | 0.429 | 3.5620 | 0.7209 | | 1.6892 | 12500 | 0.3735 | 3.6900 | 0.7020 | | 1.7568 | 13000 | 0.3908 | 3.8182 | 0.6932 | | 1.8243 | 13500 | 0.3848 | 3.7228 | 0.7101 | | 1.8919 | 14000 | 0.3777 | 3.6604 | 0.7149 | | 1.9595 | 14500 | 0.3912 | 3.7849 | 0.6946 | | 2.0269 | 15000 | 0.3282 | 3.8607 | 0.7014 | | 2.0945 | 15500 | 0.3324 | 3.8573 | 0.6953 | | 2.1620 | 16000 | 0.3852 | 3.9420 | 0.7000 | | 2.2296 | 16500 | 0.3633 | 3.7928 | 0.7189 | | 2.2972 | 17000 | 0.3493 | 3.8217 | 0.7216 | | 2.3647 | 17500 | 0.3554 | 3.8546 | 0.6993 | | 2.4323 | 18000 | 0.3363 | 3.7764 | 0.6993 | | 2.4999 | 18500 | 0.377 | 3.8224 | 0.6959 | | 2.5674 | 19000 | 0.3569 | 3.8376 | 0.7155 | | 2.635 | 19500 | 0.3414 | 4.0017 | 0.7034 | | 2.7026 | 20000 | 0.3567 | 3.7405 | 0.7135 | | 2.7701 | 20500 | 0.3524 | 3.9446 | 0.7189 | | 2.8377 | 21000 | 0.3347 | 3.8140 | 0.7169 | | 2.9053 | 21500 | 0.3458 | 4.0700 | 0.7088 | | 2.9728 | 22000 | 0.3632 | 3.7930 | 0.7081 | | 3.0404 | 22500 | 0.3496 | 3.9884 | 0.7236 | | 3.1080 | 23000 | 0.3426 | 3.7102 | 0.7155 | | 3.1755 | 23500 | 0.3579 | 3.9201 | 0.7135 | | 3.2431 | 24000 | 0.3553 | 4.2237 | 0.7270 | | 3.3107 | 24500 | 0.345 | 3.8090 | 0.7189 | | 3.3782 | 25000 | 0.3475 | 3.7802 | 0.7284 | | 3.4458 | 25500 | 0.3326 | 3.7549 | 0.7250 | | 3.5134 | 26000 | 0.3228 | 3.6717 | 0.7216 | | 3.5809 | 26500 | 0.3311 | 3.8241 | 0.7155 | | 3.6485 | 27000 | 0.3215 | 3.8151 | 0.7142 | | 3.7161 | 27500 | 0.3534 | 3.8639 | 0.7149 | | 3.7836 | 28000 | 0.3369 | 4.0947 | 0.7101 | | 3.8512 | 28500 | 0.3229 | 4.0495 | 0.7101 | | 3.9188 | 29000 | 0.3442 | 4.0408 | 0.7169 | | 3.9864 | 29500 | 0.3059 | 3.9493 | 0.6959 | | 4.0538 | 30000 | 0.3349 | 4.0431 | 0.7108 | | 4.1214 | 30500 | 0.3266 | 4.0224 | 0.7189 | | 4.1889 | 31000 | 0.3501 | 3.9502 | 0.7169 | | 4.2565 | 31500 | 0.3676 | 3.8903 | 0.7196 | | 4.3241 | 32000 | 0.3191 | 3.7994 | 0.7162 | | 4.3916 | 32500 | 0.3317 | 3.7889 | 0.7182 | | 4.4592 | 33000 | 0.3304 | 3.8661 | 0.7108 | | 4.5268 | 33500 | 0.3332 | 3.8822 | 0.7115 | | 4.5943 | 34000 | 0.3435 | 3.7945 | 0.7088 | | 4.6619 | 34500 | 0.317 | 3.8721 | 0.7243 | | 4.7295 | 35000 | 0.3038 | 3.8615 | 0.7209 | | 4.7970 | 35500 | 0.3093 | 3.8360 | 0.7162 | | 4.8646 | 36000 | 0.3309 | 3.8277 | 0.7155 | | 4.9322 | 36500 | 0.3378 | 3.7988 | 0.7128 | | 4.9997 | 37000 | 0.311 | 3.8015 | 0.7135 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - 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", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```