--- base_model: TaylorAI/bge-micro datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3210255 - loss:CachedMultipleNegativesRankingLoss widget: - source_sentence: donepezil hydrochloride monohydrate sentences: - Cn1nccc1[C@H]1CC[C@H](O[Si](C)(C)C(C)(C)C)C[C@@H]1OC(=O)c1ccccc1 - COc1cc2c(cc1OC)C(=O)C(CC1CCN(Cc3ccccc3)CC1)C2.Cl.O - C(=O)(OC)C1=CC=C(C=C1)CC(C)=O - source_sentence: 6-Cyclopropylmethoxy-5-(3,3-difluoro-azetidin-1-yl)-pyridine-2-carboxylic acid tert-butyl-(5-methyl-[1,3,4]oxadiazol-2-ylmethyl)-amide sentences: - Cc1nnc(CN(C(=O)c2ccc(N3CC(F)(F)C3)c(OCC3CC3)n2)C(C)(C)C)o1 - COc1cccc(CCCC=C(Br)Br)c1 - CN(C)CCNC(=O)c1ccc2oc(=O)n(Cc3ccc4[nH]c(=O)[nH]c4c3)c2c1 - source_sentence: N-(2-chlorophenyl)-6,8-difluoro-N-methyl-4H-thieno[3,2-c]chromene-2-carboxamide sentences: - CN(C(=O)c1cc2c(s1)-c1cc(F)cc(F)c1OC2)c1ccccc1Cl - ClC(C(=O)OCCOCC1=CC=C(C=C1)F)C - C(C)OC(\C=C(/C)\OC1=C(C(=CC=C1F)OC(C)C)F)=O - source_sentence: 6-[2-[(3-chlorophenyl)methyl]-1,3,3a,4,6,6a-hexahydropyrrolo[3,4-c]pyrrol-5-yl]-3-(trifluoromethyl)-[1,2,4]triazolo[4,3-b]pyridazine sentences: - CC(=O)OCCOCn1cc(C)c(=O)[nH]c1=O - NC1=C(C(=NN1C1=C(C=C(C=C1Cl)C(F)(F)F)Cl)C#N)S(=O)(=O)C - ClC=1C=C(C=CC1)CN1CC2CN(CC2C1)C=1C=CC=2N(N1)C(=NN2)C(F)(F)F - source_sentence: (±)-cis-2-(4-methoxyphenyl)-3-acetoxy-5-[2-(dimethylamino)ethyl]-8-chloro-2,3-dihydro-1,5-benzothiazepin-4(5H)-one hydrochloride sentences: - N(=[N+]=[N-])C(C(=O)C1=NC(=C(C(=N1)C(C)(C)C)O)C(C)(C)C)C - O[C@@H]1[C@H](O)[C@@H](Oc2nc(N3CCNCC3)nc3ccccc23)C[C@H]1O - Cl.COC1=CC=C(C=C1)[C@@H]1SC2=C(N(C([C@@H]1OC(C)=O)=O)CCN(C)C)C=CC(=C2)Cl model-index: - name: MPNet base trained on AllNLI triplets results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: bge micro test type: bge-micro-test metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: .nan name: Pearson Manhattan - type: spearman_manhattan value: .nan name: Spearman Manhattan - type: pearson_euclidean value: .nan name: Pearson Euclidean - type: spearman_euclidean value: .nan name: Spearman Euclidean - type: pearson_dot value: .nan name: Pearson Dot - type: spearman_dot value: .nan name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max --- # MPNet base trained on AllNLI triplets This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [TaylorAI/bge-micro](https://huggingface.co/TaylorAI/bge-micro). 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:** [TaylorAI/bge-micro](https://huggingface.co/TaylorAI/bge-micro) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity - **Language:** en - **License:** apache-2.0 ### 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: BertModel (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("fpc/bge-micro-smiles") # Run inference sentences = [ '(±)-cis-2-(4-methoxyphenyl)-3-acetoxy-5-[2-(dimethylamino)ethyl]-8-chloro-2,3-dihydro-1,5-benzothiazepin-4(5H)-one hydrochloride', 'Cl.COC1=CC=C(C=C1)[C@@H]1SC2=C(N(C([C@@H]1OC(C)=O)=O)CCN(C)C)C=CC(=C2)Cl', 'O[C@@H]1[C@H](O)[C@@H](Oc2nc(N3CCNCC3)nc3ccccc23)C[C@H]1O', ] 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] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 3,210,255 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:--------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------| | 4-t-butylbromobenzene | C(C)(C)(C)C1=CC=C(C=C1)Br | | 1-methyl-4-(morpholine-4-carbonyl)-N-(2-phenyl-[1,2,4]triazolo[1,5-a]pyridin-7-yl)-1H-pyrazole-5-carboxamide | CN1N=CC(=C1C(=O)NC1=CC=2N(C=C1)N=C(N2)C2=CC=CC=C2)C(=O)N2CCOCC2 | | Phthalimide | C1(C=2C(C(N1)=O)=CC=CC2)=O | * Loss: [CachedMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 512 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 512 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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`: 4 - `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`: True - `fp16`: False - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | bge-micro-test_spearman_cosine | |:------:|:-----:|:-------------:|:------------------------------:| | 0.0159 | 100 | 6.1861 | - | | 0.0319 | 200 | 6.0547 | - | | 0.0478 | 300 | 5.6041 | - | | 0.0638 | 400 | 4.9367 | - | | 0.0797 | 500 | 4.3412 | - | | 0.0957 | 600 | 3.8245 | - | | 0.1116 | 700 | 3.3188 | - | | 0.1276 | 800 | 2.869 | - | | 0.1435 | 900 | 2.5149 | - | | 0.1595 | 1000 | 2.2282 | - | | 0.1754 | 1100 | 2.0046 | - | | 0.1914 | 1200 | 1.8032 | - | | 0.2073 | 1300 | 1.6289 | - | | 0.2232 | 1400 | 1.4567 | - | | 0.2392 | 1500 | 1.3326 | - | | 0.2551 | 1600 | 1.2127 | - | | 0.2711 | 1700 | 1.0909 | - | | 0.2870 | 1800 | 1.0021 | - | | 0.3030 | 1900 | 0.9135 | - | | 0.3189 | 2000 | 0.8378 | - | | 0.3349 | 2100 | 0.7758 | - | | 0.3508 | 2200 | 0.7031 | - | | 0.3668 | 2300 | 0.6418 | - | | 0.3827 | 2400 | 0.5965 | - | | 0.3987 | 2500 | 0.5461 | - | | 0.4146 | 2600 | 0.5039 | - | | 0.4306 | 2700 | 0.4674 | - | | 0.4465 | 2800 | 0.4339 | - | | 0.4624 | 2900 | 0.4045 | - | | 0.4784 | 3000 | 0.373 | - | | 0.4943 | 3100 | 0.3566 | - | | 0.5103 | 3200 | 0.3348 | - | | 0.5262 | 3300 | 0.3215 | - | | 0.5422 | 3400 | 0.302 | - | | 0.5581 | 3500 | 0.2826 | - | | 0.5741 | 3600 | 0.2803 | - | | 0.5900 | 3700 | 0.2616 | - | | 0.6060 | 3800 | 0.2554 | - | | 0.6219 | 3900 | 0.234 | - | | 0.6379 | 4000 | 0.2306 | - | | 0.6538 | 4100 | 0.2224 | - | | 0.6697 | 4200 | 0.2141 | - | | 0.6857 | 4300 | 0.2117 | - | | 0.7016 | 4400 | 0.204 | - | | 0.7176 | 4500 | 0.198 | - | | 0.7335 | 4600 | 0.1986 | - | | 0.7495 | 4700 | 0.1821 | - | | 0.7654 | 4800 | 0.1813 | - | | 0.7814 | 4900 | 0.1741 | - | | 0.7973 | 5000 | 0.1697 | - | | 0.8133 | 5100 | 0.1655 | - | | 0.8292 | 5200 | 0.1623 | - | | 0.8452 | 5300 | 0.1593 | - | | 0.8611 | 5400 | 0.1566 | - | | 0.8771 | 5500 | 0.151 | - | | 0.8930 | 5600 | 0.1526 | - | | 0.9089 | 5700 | 0.1453 | - | | 0.9249 | 5800 | 0.1448 | - | | 0.9408 | 5900 | 0.1369 | - | | 0.9568 | 6000 | 0.1409 | - | | 0.9727 | 6100 | 0.1373 | - | | 0.9887 | 6200 | 0.133 | - | | 1.0046 | 6300 | 0.1269 | - | | 1.0206 | 6400 | 0.1274 | - | | 1.0365 | 6500 | 0.1271 | - | | 1.0525 | 6600 | 0.1216 | - | | 1.0684 | 6700 | 0.1176 | - | | 1.0844 | 6800 | 0.1208 | - | | 1.1003 | 6900 | 0.1177 | - | | 1.1162 | 7000 | 0.1175 | - | | 1.1322 | 7100 | 0.1109 | - | | 1.1481 | 7200 | 0.1118 | - | | 1.1641 | 7300 | 0.1085 | - | | 1.1800 | 7400 | 0.1155 | - | | 1.1960 | 7500 | 0.1079 | - | | 1.2119 | 7600 | 0.1087 | - | | 1.2279 | 7700 | 0.1004 | - | | 1.2438 | 7800 | 0.1084 | - | | 1.2598 | 7900 | 0.1089 | - | | 1.2757 | 8000 | 0.1012 | - | | 1.2917 | 8100 | 0.1037 | - | | 1.3076 | 8200 | 0.1004 | - | | 1.3236 | 8300 | 0.0979 | - | | 1.3395 | 8400 | 0.1007 | - | | 1.3554 | 8500 | 0.0956 | - | | 1.3714 | 8600 | 0.0972 | - | | 1.3873 | 8700 | 0.0947 | - | | 1.4033 | 8800 | 0.0931 | - | | 1.4192 | 8900 | 0.0948 | - | | 1.4352 | 9000 | 0.0925 | - | | 1.4511 | 9100 | 0.0933 | - | | 1.4671 | 9200 | 0.0888 | - | | 1.4830 | 9300 | 0.0877 | - | | 1.4990 | 9400 | 0.0889 | - | | 1.5149 | 9500 | 0.0895 | - | | 1.5309 | 9600 | 0.0892 | - | | 1.5468 | 9700 | 0.089 | - | | 1.5627 | 9800 | 0.0828 | - | | 1.5787 | 9900 | 0.0906 | - | | 1.5946 | 10000 | 0.0893 | - | | 1.6106 | 10100 | 0.0849 | - | | 1.6265 | 10200 | 0.0811 | - | | 1.6425 | 10300 | 0.0823 | - | | 1.6584 | 10400 | 0.0806 | - | | 1.6744 | 10500 | 0.0815 | - | | 1.6903 | 10600 | 0.0832 | - | | 1.7063 | 10700 | 0.0856 | - | | 1.7222 | 10800 | 0.081 | - | | 1.7382 | 10900 | 0.0831 | - | | 1.7541 | 11000 | 0.0767 | - | | 1.7701 | 11100 | 0.0779 | - | | 1.7860 | 11200 | 0.0792 | - | | 1.8019 | 11300 | 0.0771 | - | | 1.8179 | 11400 | 0.0783 | - | | 1.8338 | 11500 | 0.0749 | - | | 1.8498 | 11600 | 0.0755 | - | | 1.8657 | 11700 | 0.0778 | - | | 1.8817 | 11800 | 0.0753 | - | | 1.8976 | 11900 | 0.0767 | - | | 1.9136 | 12000 | 0.0725 | - | | 1.9295 | 12100 | 0.0744 | - | | 1.9455 | 12200 | 0.0743 | - | | 1.9614 | 12300 | 0.0722 | - | | 1.9774 | 12400 | 0.0712 | - | | 1.9933 | 12500 | 0.0709 | - | | 2.0092 | 12600 | 0.0694 | - | | 2.0252 | 12700 | 0.0705 | - | | 2.0411 | 12800 | 0.0715 | - | | 2.0571 | 12900 | 0.0705 | - | | 2.0730 | 13000 | 0.0653 | - | | 2.0890 | 13100 | 0.0698 | - | | 2.1049 | 13200 | 0.0676 | - | | 2.1209 | 13300 | 0.0684 | - | | 2.1368 | 13400 | 0.0644 | - | | 2.1528 | 13500 | 0.0652 | - | | 2.1687 | 13600 | 0.0673 | - | | 2.1847 | 13700 | 0.067 | - | | 2.2006 | 13800 | 0.0645 | - | | 2.2166 | 13900 | 0.0633 | - | | 2.2325 | 14000 | 0.0645 | - | | 2.2484 | 14100 | 0.0698 | - | | 2.2644 | 14200 | 0.0655 | - | | 2.2803 | 14300 | 0.0654 | - | | 2.2963 | 14400 | 0.0656 | - | | 2.3122 | 14500 | 0.0631 | - | | 2.3282 | 14600 | 0.0628 | - | | 2.3441 | 14700 | 0.0671 | - | | 2.3601 | 14800 | 0.0659 | - | | 2.3760 | 14900 | 0.0619 | - | | 2.3920 | 15000 | 0.0618 | - | | 2.4079 | 15100 | 0.0624 | - | | 2.4239 | 15200 | 0.0616 | - | | 2.4398 | 15300 | 0.0631 | - | | 2.4557 | 15400 | 0.0639 | - | | 2.4717 | 15500 | 0.0585 | - | | 2.4876 | 15600 | 0.0607 | - | | 2.5036 | 15700 | 0.0615 | - | | 2.5195 | 15800 | 0.062 | - | | 2.5355 | 15900 | 0.0621 | - | | 2.5514 | 16000 | 0.0608 | - | | 2.5674 | 16100 | 0.0594 | - | | 2.5833 | 16200 | 0.0631 | - | | 2.5993 | 16300 | 0.0635 | - | | 2.6152 | 16400 | 0.06 | - | | 2.6312 | 16500 | 0.0581 | - | | 2.6471 | 16600 | 0.0607 | - | | 2.6631 | 16700 | 0.0577 | - | | 2.6790 | 16800 | 0.0592 | - | | 2.6949 | 16900 | 0.0625 | - | | 2.7109 | 17000 | 0.0622 | - | | 2.7268 | 17100 | 0.0573 | - | | 2.7428 | 17200 | 0.0613 | - | | 2.7587 | 17300 | 0.0587 | - | | 2.7747 | 17400 | 0.0587 | - | | 2.7906 | 17500 | 0.0588 | - | | 2.8066 | 17600 | 0.0568 | - | | 2.8225 | 17700 | 0.0573 | - | | 2.8385 | 17800 | 0.0575 | - | | 2.8544 | 17900 | 0.0575 | - | | 2.8704 | 18000 | 0.0582 | - | | 2.8863 | 18100 | 0.0577 | - | | 2.9022 | 18200 | 0.057 | - | | 2.9182 | 18300 | 0.0572 | - | | 2.9341 | 18400 | 0.0558 | - | | 2.9501 | 18500 | 0.0578 | - | | 2.9660 | 18600 | 0.0567 | - | | 2.9820 | 18700 | 0.0569 | - | | 2.9979 | 18800 | 0.0547 | - | | 3.0139 | 18900 | 0.0542 | - | | 3.0298 | 19000 | 0.0563 | - | | 3.0458 | 19100 | 0.0549 | - | | 3.0617 | 19200 | 0.0531 | - | | 3.0777 | 19300 | 0.053 | - | | 3.0936 | 19400 | 0.0557 | - | | 3.1096 | 19500 | 0.0546 | - | | 3.1255 | 19600 | 0.0518 | - | | 3.1414 | 19700 | 0.0517 | - | | 3.1574 | 19800 | 0.0528 | - | | 3.1733 | 19900 | 0.0551 | - | | 3.1893 | 20000 | 0.0544 | - | | 3.2052 | 20100 | 0.0526 | - | | 3.2212 | 20200 | 0.0494 | - | | 3.2371 | 20300 | 0.0537 | - | | 3.2531 | 20400 | 0.0568 | - | | 3.2690 | 20500 | 0.0525 | - | | 3.2850 | 20600 | 0.0566 | - | | 3.3009 | 20700 | 0.0539 | - | | 3.3169 | 20800 | 0.0531 | - | | 3.3328 | 20900 | 0.0524 | - | | 3.3487 | 21000 | 0.0543 | - | | 3.3647 | 21100 | 0.0537 | - | | 3.3806 | 21200 | 0.0524 | - | | 3.3966 | 21300 | 0.0516 | - | | 3.4125 | 21400 | 0.0537 | - | | 3.4285 | 21500 | 0.0515 | - | | 3.4444 | 21600 | 0.0537 | - | | 3.4604 | 21700 | 0.0526 | - | | 3.4763 | 21800 | 0.0508 | - | | 3.4923 | 21900 | 0.0526 | - | | 3.5082 | 22000 | 0.0521 | - | | 3.5242 | 22100 | 0.054 | - | | 3.5401 | 22200 | 0.053 | - | | 3.5561 | 22300 | 0.0509 | - | | 3.5720 | 22400 | 0.0526 | - | | 3.5879 | 22500 | 0.0551 | - | | 3.6039 | 22600 | 0.0556 | - | | 3.6198 | 22700 | 0.0497 | - | | 3.6358 | 22800 | 0.0515 | - | | 3.6517 | 22900 | 0.0514 | - | | 3.6677 | 23000 | 0.0503 | - | | 3.6836 | 23100 | 0.0515 | - | | 3.6996 | 23200 | 0.0553 | - | | 3.7155 | 23300 | 0.0519 | - | | 3.7315 | 23400 | 0.0549 | - | | 3.7474 | 23500 | 0.0522 | - | | 3.7634 | 23600 | 0.0526 | - | | 3.7793 | 23700 | 0.0525 | - | | 3.7952 | 23800 | 0.051 | - | | 3.8112 | 23900 | 0.0509 | - | | 3.8271 | 24000 | 0.0503 | - | | 3.8431 | 24100 | 0.0524 | - | | 3.8590 | 24200 | 0.0526 | - | | 3.8750 | 24300 | 0.0512 | - | | 3.8909 | 24400 | 0.0518 | - | | 3.9069 | 24500 | 0.0521 | - | | 3.9228 | 24600 | 0.0524 | - | | 3.9388 | 24700 | 0.051 | - | | 3.9547 | 24800 | 0.0535 | - | | 3.9707 | 24900 | 0.0508 | - | | 3.9866 | 25000 | 0.0514 | - | | 4.0 | 25084 | - | nan |
### Framework Versions - Python: 3.10.9 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.4.1+cu124 - Accelerate: 0.33.0 - Datasets: 2.18.0 - Tokenizers: 0.19.1 ## 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", } ``` #### CachedMultipleNegativesRankingLoss ```bibtex @misc{gao2021scaling, title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, year={2021}, eprint={2101.06983}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```