--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:116941 - loss:SoftmaxLoss base_model: google-bert/bert-base-cased widget: - source_sentence: O[C@@H]1CC(CCc2c(O)cc(Cl)cc2Cl)OC(=O)C1 sentences: - O[C@@H]1C[C@@H](CC[C@@H]2CCC[C@@H]3CCCC[C@H]23)OC(=O)C1 - O[C@@H]1CC(CCc2cccc3ccccc23)OC(=O)C1 - CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)NCc1ccccc1)-c1ccccn1)-c1ccc(F)cc1 - source_sentence: O[C@@H]1C[C@H](OC(=O)C1)\C=C\c1cnc2c(Sc3ccc(F)cc3)c(Sc3ccc(F)cc3)c(F)cc2c1Sc1ccc(F)cc1 sentences: - O[C@H](C[C@H](O)\C=C\c1c2CCCc2nn1-c1ccc(F)cc1)CC([O-])=O - C[C@H](CC\C=C(/C)C(O)=O)[C@H]1C[C@H](O)[C@@]2(C)C3=CC[C@H]4C(C)(C)C(=O)CC[C@]4(C)C3=CC[C@]12C - CC(C)c1ccc(Sc2c(\C=C\[C@@H]3C[C@@H](O)CC(=O)O3)cnc3cc(Cl)c(F)cc23)cc1 - source_sentence: O[C@H](C[C@H](O)\C=C\c1c2CCCCc2nn1-c1ccc(F)cc1)CC([O-])=O sentences: - O[C@@H]1C[C@H](OC(=O)C1)\C=C\c1cnc2cc(Sc3ccccc3)c(Sc3ccccc3)cc2c1Sc1ccccc1 - CC[C@H](C)[C@H](N)C(=O)N[C@@H](C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCC(O)=O)C(O)=O - CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC(O)=O)c(c(c1C(=O)N(C)Cc1ccccc1)-c1ccccc1)-c1ccc(F)cc1 - source_sentence: COc1ccc(CNC(=O)c2nc(-c3ccc(F)cc3)n(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c2C2CC2)cc1 sentences: - CC(C)c1nc(nc(-c2ccc(F)cc2)c1\C=C\[C@@H](O)C[C@@H](O)CC(O)=O)N(c1nnnn1C)S(C)(=O)=O - CC(C)c1c(CC[C@@H](O)C[C@@H](O)CC(O)=O)n(nc1C(=O)N(C)Cc1ccccc1)-c1ccc(F)cc1 - Cc1c(OCC(O)C[C@@H](O)CC([O-])=O)c(cc2ccccc12)C(c1ccc(F)cc1)c1ccc(F)cc1 - source_sentence: CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)NCc1cccc(c1)C(N)=O)-c1ccccc1)-c1ccc(F)cc1 sentences: - CC(C)c1nc(c(-c2ccc(F)cc2)n1\C=C\[C@@H](O)C[C@@H](O)CC(O)=O)-c1ccc(F)cc1 - CCn1nnc(n1)C(\C=C\[C@@H](O)C[C@@H](O)CC([O-])=O)=C(c1ccc(F)cc1)c1ccc(F)cc1 - CC(C)c1nc(nc(-c2ccc(F)cc2)c1\C=C\[C@@H](O)C[C@@H](O)CC(O)=O)N(C)c1ccnn1C pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on google-bert/bert-base-cased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the csv dataset. It maps sentences & paragraphs to a 768-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:** [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv ### 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, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 768, '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("cafierom/905_Statin_Contrastive") # Run inference sentences = [ 'CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)NCc1cccc(c1)C(N)=O)-c1ccccc1)-c1ccc(F)cc1', 'CC(C)c1nc(c(-c2ccc(F)cc2)n1\\C=C\\[C@@H](O)C[C@@H](O)CC(O)=O)-c1ccc(F)cc1', 'CCn1nnc(n1)C(\\C=C\\[C@@H](O)C[C@@H](O)CC([O-])=O)=C(c1ccc(F)cc1)c1ccc(F)cc1', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 1.0000, 0.9994, -0.0483], # [ 0.9994, 1.0000, -0.0453], # [-0.0483, -0.0453, 1.0000]]) ``` ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 116,941 training samples * Columns: premise, hypothesis, and label * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | premise | hypothesis | label | |:--------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:---------------| | CC[C@H](C)C(=O)O[C@H]1C[C@@H](C)C[C@@H]2C=C[C@H](C)[C@H](CCC(O)C[C@@H](O)CC(O)=O)C12 | CCCCCCCCCCCCCCCC1(O)CCOC(O)C1 | 2 | | O[C@H](C[C@H](O)\C=C\c1c(Cl)cc(Cl)cc1-c1ccc(F)cc1)CC([O-])=O | C[C@@]1(O)C[C@H](OC(=O)C1)\C=C\c1ccc(Cl)cc1Cl | 2 | | CC(C)c1nc(nc(-c2ccc(F)cc2)c1\C=C\[C@@H]1C[C@@H](O)CC(=O)O1)-c1ccc(F)cc1 | CC(C)C[C@H](NC(=O)CN)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCC(O)=O)C(=O)NCC(=O)NCC(O)=O | 2 | * Loss: [SoftmaxLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Evaluation Dataset #### csv * Dataset: csv * Size: 20,637 evaluation samples * Columns: premise, hypothesis, and label * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | premise | hypothesis | label | |:-----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | COC(=O)C[C@H](O)C[C@H](O)\C=C\n1c(C(C)C)c(Br)c(c1-c1ccc(F)cc1)-c1ccc(F)cc1 | C[C@H](CC(O)CC(O)CC([O-])=O)[C@H]1CC[C@H]2[C@@H]3[C@@H](C[C@@H]4C[C@@H](CC[C@]4(C)[C@H]3C[C@H](OC(C)=O)[C@]12C)OC(C)=O)OC(C)=O | 2 | | CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)Nc1ccc(O)cc1)-c1ccccc1)-c1ccc(F)cc1 | CC[C@H](C)C(=O)O[C@H]1C[C@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H]3C[C@@H](O)CC(=O)O3)[C@@H]12 | 0 | | CC(C)C(=O)O[C@H]1C[C@@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H]3C[C@@H](O)CC(=O)O3)C12 | CC(C)c1c(nc(-c2ccc(F)cc2)n1\C=C\[C@@H](O)C[C@@H](O)CC([O-])=O)-c1ccc(F)cc1 | 0 | * Loss: [SoftmaxLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `weight_decay`: 0.01 - `num_train_epochs`: 10 - `warmup_steps`: 100 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `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`: 5e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 100 - `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} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `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 - `hub_revision`: None - `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 - `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 - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.1094 | 100 | 0.4346 | | 0.2188 | 200 | 0.0656 | | 0.3282 | 300 | 0.0082 | | 0.4376 | 400 | 0.007 | | 0.5470 | 500 | 0.0056 | | 0.6565 | 600 | 0.0054 | | 0.7659 | 700 | 0.0006 | | 0.8753 | 800 | 0.0005 | | 0.9847 | 900 | 0.0004 | | 1.0941 | 1000 | 0.0004 | | 1.2035 | 1100 | 0.0003 | | 1.3129 | 1200 | 0.0003 | | 1.4223 | 1300 | 0.0003 | | 1.5317 | 1400 | 0.0003 | | 1.6411 | 1500 | 0.0002 | | 1.7505 | 1600 | 0.0002 | | 1.8600 | 1700 | 0.0002 | | 1.9694 | 1800 | 0.0002 | | 2.0788 | 1900 | 0.0002 | | 2.1882 | 2000 | 0.0002 | | 2.2976 | 2100 | 0.0001 | | 2.4070 | 2200 | 0.0001 | | 2.5164 | 2300 | 0.0001 | | 2.6258 | 2400 | 0.0001 | | 2.7352 | 2500 | 0.0001 | | 2.8446 | 2600 | 0.0001 | | 2.9540 | 2700 | 0.0001 | | 3.0635 | 2800 | 0.0001 | | 3.1729 | 2900 | 0.0001 | | 3.2823 | 3000 | 0.0001 | | 3.3917 | 3100 | 0.0001 | | 3.5011 | 3200 | 0.0001 | | 3.6105 | 3300 | 0.0001 | | 3.7199 | 3400 | 0.0001 | | 3.8293 | 3500 | 0.0001 | | 3.9387 | 3600 | 0.0001 | | 4.0481 | 3700 | 0.0001 | | 4.1575 | 3800 | 0.0001 | | 4.2670 | 3900 | 0.0001 | | 4.3764 | 4000 | 0.0 | | 4.4858 | 4100 | 0.0 | | 4.5952 | 4200 | 0.0 | | 4.7046 | 4300 | 0.0 | | 4.8140 | 4400 | 0.0 | | 4.9234 | 4500 | 0.0 | | 5.0328 | 4600 | 0.0 | | 5.1422 | 4700 | 0.0 | | 5.2516 | 4800 | 0.0 | | 5.3611 | 4900 | 0.0 | | 5.4705 | 5000 | 0.0 | | 5.5799 | 5100 | 0.0 | | 5.6893 | 5200 | 0.0 | | 5.7987 | 5300 | 0.0 | | 5.9081 | 5400 | 0.0 | | 6.0175 | 5500 | 0.0002 | | 6.1269 | 5600 | 0.0 | | 6.2363 | 5700 | 0.0 | | 6.3457 | 5800 | 0.0 | | 6.4551 | 5900 | 0.0 | | 6.5646 | 6000 | 0.0 | | 6.6740 | 6100 | 0.0 | | 6.7834 | 6200 | 0.0 | | 6.8928 | 6300 | 0.0 | | 7.0022 | 6400 | 0.0 | | 7.1116 | 6500 | 0.0 | | 7.2210 | 6600 | 0.0 | | 7.3304 | 6700 | 0.0 | | 7.4398 | 6800 | 0.0 | | 7.5492 | 6900 | 0.0 | | 7.6586 | 7000 | 0.0 | | 7.7681 | 7100 | 0.0 | | 7.8775 | 7200 | 0.0 | | 7.9869 | 7300 | 0.0 | | 8.0963 | 7400 | 0.0 | | 8.2057 | 7500 | 0.0 | | 8.3151 | 7600 | 0.0 | | 8.4245 | 7700 | 0.0 | | 8.5339 | 7800 | 0.0 | | 8.6433 | 7900 | 0.0 | | 8.7527 | 8000 | 0.0 | | 8.8621 | 8100 | 0.0 | | 8.9716 | 8200 | 0.0 | | 9.0810 | 8300 | 0.0022 | | 9.1904 | 8400 | 0.0019 | | 9.2998 | 8500 | 0.0001 | | 9.4092 | 8600 | 0.0 | | 9.5186 | 8700 | 0.0 | | 9.6280 | 8800 | 0.0 | | 9.7374 | 8900 | 0.0 | | 9.8468 | 9000 | 0.0 | | 9.9562 | 9100 | 0.0 | ### Framework Versions - Python: 3.12.11 - Sentence Transformers: 5.1.0 - Transformers: 4.56.0 - PyTorch: 2.8.0+cu126 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citation ### BibTeX #### Sentence Transformers and SoftmaxLoss ```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", } ```