--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:19697 - loss:CosineSimilarityLoss base_model: neuralmind/bert-large-portuguese-cased widget: - source_sentence: procurar sapato social masculino sentences: - beleza autocuidado - moda acessorio - doce chocolate - source_sentence: livro ultimo adeus cynthia hand sentences: - livro material literario - item colecao - joia bijuterio - source_sentence: relogio pulso sentences: - servico reparo eletronico - hortifruti - hortifruti - source_sentence: medicamento antipulga gato sentences: - produto pet animal domestico - hortifruti - padaria confeitaria - source_sentence: guitarra gibson Les Paul sentences: - moda acessorio - tinta - peixaria pescado pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on neuralmind/bert-large-portuguese-cased results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: eval similarity type: eval-similarity metrics: - type: pearson_cosine value: 0.932130151806209 name: Pearson Cosine - type: spearman_cosine value: 0.8467496824207882 name: Spearman Cosine --- # SentenceTransformer based on neuralmind/bert-large-portuguese-cased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased). It maps sentences & paragraphs to a 1024-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:** [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 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: BertModel (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}) ) ``` ## 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("SenhorDasMoscas/acho-ptbr-e3-lr0.0001-08-01-2025") # Run inference sentences = [ 'guitarra gibson Les Paul', 'tinta', 'peixaria pescado', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `eval-similarity` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.9321 | | **spearman_cosine** | **0.8467** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 19,697 training samples * Columns: text1, text2, and label * Approximate statistics based on the first 1000 samples: | | text1 | text2 | label | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | text1 | text2 | label | |:----------------------------------------------------|:----------------------------------|:-----------------| | fritadeira eletrico em esse loja festa | decoracao festa | 0.1 | | vinho | papelaria escritorio | 0.1 | | forno eletrico Fischer | eletrodomestico | 1.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 2,189 evaluation samples * Columns: text1, text2, and label * Approximate statistics based on the first 1000 samples: | | text1 | text2 | label | |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | text1 | text2 | label | |:--------------------------------------------------|:------------------------------------|:-----------------| | querer salgado | comida rapido fastfood | 1.0 | | ervilha enlatar | movel | 0.1 | | preciso loja artigo esporte aquatico | servico area educacao | 0.1 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 0.0001 - `weight_decay`: 0.1 - `warmup_ratio`: 0.1 - `warmup_steps`: 246 - `fp16`: True - `load_best_model_at_end`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `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`: 0.0001 - `weight_decay`: 0.1 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 246 - `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`: True - `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`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | eval-similarity_spearman_cosine | |:-------:|:--------:|:-------------:|:---------------:|:-------------------------------:| | 0.0081 | 5 | 0.1965 | - | - | | 0.0162 | 10 | 0.2125 | - | - | | 0.0244 | 15 | 0.1944 | - | - | | 0.0325 | 20 | 0.1674 | - | - | | 0.0406 | 25 | 0.1518 | - | - | | 0.0487 | 30 | 0.1381 | - | - | | 0.0568 | 35 | 0.1385 | - | - | | 0.0649 | 40 | 0.109 | - | - | | 0.0731 | 45 | 0.1054 | - | - | | 0.0812 | 50 | 0.0963 | - | - | | 0.0893 | 55 | 0.0917 | - | - | | 0.0974 | 60 | 0.0797 | - | - | | 0.1055 | 65 | 0.0877 | - | - | | 0.1136 | 70 | 0.0755 | - | - | | 0.1218 | 75 | 0.0773 | - | - | | 0.1299 | 80 | 0.0605 | - | - | | 0.1380 | 85 | 0.0669 | - | - | | 0.1461 | 90 | 0.0698 | - | - | | 0.1542 | 95 | 0.0595 | - | - | | 0.1623 | 100 | 0.0382 | - | - | | 0.1705 | 105 | 0.0723 | - | - | | 0.1786 | 110 | 0.0448 | - | - | | 0.1867 | 115 | 0.0703 | - | - | | 0.1948 | 120 | 0.0694 | - | - | | 0.2029 | 125 | 0.0515 | - | - | | 0.2110 | 130 | 0.0581 | - | - | | 0.2192 | 135 | 0.0458 | - | - | | 0.2273 | 140 | 0.0643 | - | - | | 0.2354 | 145 | 0.0602 | - | - | | 0.2435 | 150 | 0.0651 | - | - | | 0.2516 | 155 | 0.0662 | - | - | | 0.2597 | 160 | 0.0712 | - | - | | 0.2679 | 165 | 0.0546 | - | - | | 0.2760 | 170 | 0.0419 | - | - | | 0.2841 | 175 | 0.061 | - | - | | 0.2922 | 180 | 0.0549 | - | - | | 0.3003 | 185 | 0.0523 | - | - | | 0.3084 | 190 | 0.0579 | - | - | | 0.3166 | 195 | 0.0595 | - | - | | 0.3247 | 200 | 0.0478 | - | - | | 0.3328 | 205 | 0.0507 | - | - | | 0.3409 | 210 | 0.0312 | - | - | | 0.3490 | 215 | 0.041 | - | - | | 0.3571 | 220 | 0.0528 | - | - | | 0.3653 | 225 | 0.0386 | - | - | | 0.3734 | 230 | 0.0656 | - | - | | 0.3815 | 235 | 0.0567 | - | - | | 0.3896 | 240 | 0.0673 | - | - | | 0.3977 | 245 | 0.103 | - | - | | 0.4058 | 250 | 0.1704 | - | - | | 0.4140 | 255 | 0.0844 | - | - | | 0.4221 | 260 | 0.0883 | - | - | | 0.4302 | 265 | 0.0728 | - | - | | 0.4383 | 270 | 0.0531 | - | - | | 0.4464 | 275 | 0.0715 | - | - | | 0.4545 | 280 | 0.0623 | - | - | | 0.4627 | 285 | 0.0679 | - | - | | 0.4708 | 290 | 0.0577 | - | - | | 0.4789 | 295 | 0.0781 | - | - | | 0.4870 | 300 | 0.0541 | - | - | | 0.4951 | 305 | 0.0876 | - | - | | 0.5032 | 310 | 0.0648 | - | - | | 0.5114 | 315 | 0.0583 | - | - | | 0.5195 | 320 | 0.0506 | - | - | | 0.5276 | 325 | 0.051 | - | - | | 0.5357 | 330 | 0.0633 | - | - | | 0.5438 | 335 | 0.0764 | - | - | | 0.5519 | 340 | 0.0753 | - | - | | 0.5601 | 345 | 0.0701 | - | - | | 0.5682 | 350 | 0.0688 | - | - | | 0.5763 | 355 | 0.0691 | - | - | | 0.5844 | 360 | 0.0497 | - | - | | 0.5925 | 365 | 0.0606 | - | - | | 0.6006 | 370 | 0.0544 | - | - | | 0.6088 | 375 | 0.0587 | - | - | | 0.6169 | 380 | 0.0432 | - | - | | 0.625 | 385 | 0.0768 | - | - | | 0.6331 | 390 | 0.0701 | - | - | | 0.6412 | 395 | 0.0421 | - | - | | 0.6494 | 400 | 0.0415 | - | - | | 0.6575 | 405 | 0.0419 | - | - | | 0.6656 | 410 | 0.0613 | - | - | | 0.6737 | 415 | 0.0442 | - | - | | 0.6818 | 420 | 0.0487 | - | - | | 0.6899 | 425 | 0.0443 | - | - | | 0.6981 | 430 | 0.0493 | - | - | | 0.7062 | 435 | 0.0429 | - | - | | 0.7143 | 440 | 0.0464 | - | - | | 0.7224 | 445 | 0.0541 | - | - | | 0.7305 | 450 | 0.0539 | - | - | | 0.7386 | 455 | 0.0497 | - | - | | 0.7468 | 460 | 0.0471 | - | - | | 0.75 | 462 | - | 0.0457 | 0.8234 | | 0.7549 | 465 | 0.0514 | - | - | | 0.7630 | 470 | 0.0457 | - | - | | 0.7711 | 475 | 0.0315 | - | - | | 0.7792 | 480 | 0.0491 | - | - | | 0.7873 | 485 | 0.0619 | - | - | | 0.7955 | 490 | 0.0298 | - | - | | 0.8036 | 495 | 0.0725 | - | - | | 0.8117 | 500 | 0.043 | - | - | | 0.8198 | 505 | 0.0392 | - | - | | 0.8279 | 510 | 0.0275 | - | - | | 0.8360 | 515 | 0.0509 | - | - | | 0.8442 | 520 | 0.0508 | - | - | | 0.8523 | 525 | 0.0394 | - | - | | 0.8604 | 530 | 0.0309 | - | - | | 0.8685 | 535 | 0.0601 | - | - | | 0.8766 | 540 | 0.0524 | - | - | | 0.8847 | 545 | 0.0491 | - | - | | 0.8929 | 550 | 0.0626 | - | - | | 0.9010 | 555 | 0.0395 | - | - | | 0.9091 | 560 | 0.0655 | - | - | | 0.9172 | 565 | 0.045 | - | - | | 0.9253 | 570 | 0.0394 | - | - | | 0.9334 | 575 | 0.0521 | - | - | | 0.9416 | 580 | 0.0324 | - | - | | 0.9497 | 585 | 0.0426 | - | - | | 0.9578 | 590 | 0.032 | - | - | | 0.9659 | 595 | 0.0425 | - | - | | 0.9740 | 600 | 0.0458 | - | - | | 0.9821 | 605 | 0.0341 | - | - | | 0.9903 | 610 | 0.0339 | - | - | | 0.9984 | 615 | 0.0444 | - | - | | 1.0065 | 620 | 0.0364 | - | - | | 1.0146 | 625 | 0.0277 | - | - | | 1.0227 | 630 | 0.0372 | - | - | | 1.0308 | 635 | 0.0254 | - | - | | 1.0390 | 640 | 0.0382 | - | - | | 1.0471 | 645 | 0.0333 | - | - | | 1.0552 | 650 | 0.0312 | - | - | | 1.0633 | 655 | 0.0366 | - | - | | 1.0714 | 660 | 0.0341 | - | - | | 1.0795 | 665 | 0.0146 | - | - | | 1.0877 | 670 | 0.0362 | - | - | | 1.0958 | 675 | 0.0225 | - | - | | 1.1039 | 680 | 0.038 | - | - | | 1.1120 | 685 | 0.0406 | - | - | | 1.1201 | 690 | 0.0392 | - | - | | 1.1282 | 695 | 0.0343 | - | - | | 1.1364 | 700 | 0.0494 | - | - | | 1.1445 | 705 | 0.021 | - | - | | 1.1526 | 710 | 0.0358 | - | - | | 1.1607 | 715 | 0.034 | - | - | | 1.1688 | 720 | 0.0288 | - | - | | 1.1769 | 725 | 0.0224 | - | - | | 1.1851 | 730 | 0.0324 | - | - | | 1.1932 | 735 | 0.0378 | - | - | | 1.2013 | 740 | 0.0446 | - | - | | 1.2094 | 745 | 0.0293 | - | - | | 1.2175 | 750 | 0.0314 | - | - | | 1.2256 | 755 | 0.0444 | - | - | | 1.2338 | 760 | 0.0283 | - | - | | 1.2419 | 765 | 0.0207 | - | - | | 1.25 | 770 | 0.0413 | - | - | | 1.2581 | 775 | 0.0317 | - | - | | 1.2662 | 780 | 0.0382 | - | - | | 1.2744 | 785 | 0.0363 | - | - | | 1.2825 | 790 | 0.0324 | - | - | | 1.2906 | 795 | 0.0225 | - | - | | 1.2987 | 800 | 0.0316 | - | - | | 1.3068 | 805 | 0.0438 | - | - | | 1.3149 | 810 | 0.0298 | - | - | | 1.3231 | 815 | 0.0395 | - | - | | 1.3312 | 820 | 0.0388 | - | - | | 1.3393 | 825 | 0.0289 | - | - | | 1.3474 | 830 | 0.0233 | - | - | | 1.3555 | 835 | 0.022 | - | - | | 1.3636 | 840 | 0.016 | - | - | | 1.3718 | 845 | 0.0488 | - | - | | 1.3799 | 850 | 0.0519 | - | - | | 1.3880 | 855 | 0.033 | - | - | | 1.3961 | 860 | 0.025 | - | - | | 1.4042 | 865 | 0.0212 | - | - | | 1.4123 | 870 | 0.0184 | - | - | | 1.4205 | 875 | 0.0335 | - | - | | 1.4286 | 880 | 0.0308 | - | - | | 1.4367 | 885 | 0.028 | - | - | | 1.4448 | 890 | 0.0352 | - | - | | 1.4529 | 895 | 0.0255 | - | - | | 1.4610 | 900 | 0.0243 | - | - | | 1.4692 | 905 | 0.0355 | - | - | | 1.4773 | 910 | 0.0267 | - | - | | 1.4854 | 915 | 0.0263 | - | - | | 1.4935 | 920 | 0.0275 | - | - | | 1.5 | 924 | - | 0.0313 | 0.8414 | | 1.5016 | 925 | 0.0294 | - | - | | 1.5097 | 930 | 0.0514 | - | - | | 1.5179 | 935 | 0.0321 | - | - | | 1.5260 | 940 | 0.0306 | - | - | | 1.5341 | 945 | 0.0279 | - | - | | 1.5422 | 950 | 0.0334 | - | - | | 1.5503 | 955 | 0.0337 | - | - | | 1.5584 | 960 | 0.0266 | - | - | | 1.5666 | 965 | 0.036 | - | - | | 1.5747 | 970 | 0.0328 | - | - | | 1.5828 | 975 | 0.0224 | - | - | | 1.5909 | 980 | 0.0404 | - | - | | 1.5990 | 985 | 0.0293 | - | - | | 1.6071 | 990 | 0.016 | - | - | | 1.6153 | 995 | 0.0177 | - | - | | 1.6234 | 1000 | 0.0216 | - | - | | 1.6315 | 1005 | 0.029 | - | - | | 1.6396 | 1010 | 0.0306 | - | - | | 1.6477 | 1015 | 0.0291 | - | - | | 1.6558 | 1020 | 0.032 | - | - | | 1.6640 | 1025 | 0.0277 | - | - | | 1.6721 | 1030 | 0.0191 | - | - | | 1.6802 | 1035 | 0.0353 | - | - | | 1.6883 | 1040 | 0.0304 | - | - | | 1.6964 | 1045 | 0.0385 | - | - | | 1.7045 | 1050 | 0.0315 | - | - | | 1.7127 | 1055 | 0.0428 | - | - | | 1.7208 | 1060 | 0.0338 | - | - | | 1.7289 | 1065 | 0.0258 | - | - | | 1.7370 | 1070 | 0.0303 | - | - | | 1.7451 | 1075 | 0.0171 | - | - | | 1.7532 | 1080 | 0.0229 | - | - | | 1.7614 | 1085 | 0.0278 | - | - | | 1.7695 | 1090 | 0.0246 | - | - | | 1.7776 | 1095 | 0.0241 | - | - | | 1.7857 | 1100 | 0.0182 | - | - | | 1.7938 | 1105 | 0.0366 | - | - | | 1.8019 | 1110 | 0.0204 | - | - | | 1.8101 | 1115 | 0.0208 | - | - | | 1.8182 | 1120 | 0.01 | - | - | | 1.8263 | 1125 | 0.0239 | - | - | | 1.8344 | 1130 | 0.0228 | - | - | | 1.8425 | 1135 | 0.0228 | - | - | | 1.8506 | 1140 | 0.0176 | - | - | | 1.8588 | 1145 | 0.0278 | - | - | | 1.8669 | 1150 | 0.0242 | - | - | | 1.875 | 1155 | 0.0174 | - | - | | 1.8831 | 1160 | 0.0248 | - | - | | 1.8912 | 1165 | 0.0192 | - | - | | 1.8994 | 1170 | 0.0293 | - | - | | 1.9075 | 1175 | 0.017 | - | - | | 1.9156 | 1180 | 0.0212 | - | - | | 1.9237 | 1185 | 0.0214 | - | - | | 1.9318 | 1190 | 0.025 | - | - | | 1.9399 | 1195 | 0.0246 | - | - | | 1.9481 | 1200 | 0.0202 | - | - | | 1.9562 | 1205 | 0.021 | - | - | | 1.9643 | 1210 | 0.0183 | - | - | | 1.9724 | 1215 | 0.0313 | - | - | | 1.9805 | 1220 | 0.0211 | - | - | | 1.9886 | 1225 | 0.0299 | - | - | | 1.9968 | 1230 | 0.0222 | - | - | | 2.0049 | 1235 | 0.0154 | - | - | | 2.0130 | 1240 | 0.018 | - | - | | 2.0211 | 1245 | 0.0212 | - | - | | 2.0292 | 1250 | 0.0123 | - | - | | 2.0373 | 1255 | 0.013 | - | - | | 2.0455 | 1260 | 0.0213 | - | - | | 2.0536 | 1265 | 0.0125 | - | - | | 2.0617 | 1270 | 0.0175 | - | - | | 2.0698 | 1275 | 0.0092 | - | - | | 2.0779 | 1280 | 0.0209 | - | - | | 2.0860 | 1285 | 0.0135 | - | - | | 2.0942 | 1290 | 0.0295 | - | - | | 2.1023 | 1295 | 0.0175 | - | - | | 2.1104 | 1300 | 0.0252 | - | - | | 2.1185 | 1305 | 0.0071 | - | - | | 2.1266 | 1310 | 0.0139 | - | - | | 2.1347 | 1315 | 0.0104 | - | - | | 2.1429 | 1320 | 0.0125 | - | - | | 2.1510 | 1325 | 0.0103 | - | - | | 2.1591 | 1330 | 0.0171 | - | - | | 2.1672 | 1335 | 0.0083 | - | - | | 2.1753 | 1340 | 0.0185 | - | - | | 2.1834 | 1345 | 0.0141 | - | - | | 2.1916 | 1350 | 0.0177 | - | - | | 2.1997 | 1355 | 0.0189 | - | - | | 2.2078 | 1360 | 0.0254 | - | - | | 2.2159 | 1365 | 0.0198 | - | - | | 2.2240 | 1370 | 0.0162 | - | - | | 2.2321 | 1375 | 0.0139 | - | - | | 2.2403 | 1380 | 0.013 | - | - | | 2.2484 | 1385 | 0.0201 | - | - | | 2.25 | 1386 | - | 0.0292 | 0.8443 | | 2.2565 | 1390 | 0.0202 | - | - | | 2.2646 | 1395 | 0.0169 | - | - | | 2.2727 | 1400 | 0.0105 | - | - | | 2.2808 | 1405 | 0.0136 | - | - | | 2.2890 | 1410 | 0.0125 | - | - | | 2.2971 | 1415 | 0.0168 | - | - | | 2.3052 | 1420 | 0.0108 | - | - | | 2.3133 | 1425 | 0.0297 | - | - | | 2.3214 | 1430 | 0.0233 | - | - | | 2.3295 | 1435 | 0.0164 | - | - | | 2.3377 | 1440 | 0.0178 | - | - | | 2.3458 | 1445 | 0.0203 | - | - | | 2.3539 | 1450 | 0.0112 | - | - | | 2.3620 | 1455 | 0.0156 | - | - | | 2.3701 | 1460 | 0.0151 | - | - | | 2.3782 | 1465 | 0.0097 | - | - | | 2.3864 | 1470 | 0.0196 | - | - | | 2.3945 | 1475 | 0.0148 | - | - | | 2.4026 | 1480 | 0.0154 | - | - | | 2.4107 | 1485 | 0.0069 | - | - | | 2.4188 | 1490 | 0.0145 | - | - | | 2.4269 | 1495 | 0.0204 | - | - | | 2.4351 | 1500 | 0.0225 | - | - | | 2.4432 | 1505 | 0.0165 | - | - | | 2.4513 | 1510 | 0.0079 | - | - | | 2.4594 | 1515 | 0.0183 | - | - | | 2.4675 | 1520 | 0.0196 | - | - | | 2.4756 | 1525 | 0.0085 | - | - | | 2.4838 | 1530 | 0.0109 | - | - | | 2.4919 | 1535 | 0.0168 | - | - | | 2.5 | 1540 | 0.0124 | - | - | | 2.5081 | 1545 | 0.0218 | - | - | | 2.5162 | 1550 | 0.0164 | - | - | | 2.5244 | 1555 | 0.0234 | - | - | | 2.5325 | 1560 | 0.0115 | - | - | | 2.5406 | 1565 | 0.0135 | - | - | | 2.5487 | 1570 | 0.0179 | - | - | | 2.5568 | 1575 | 0.0104 | - | - | | 2.5649 | 1580 | 0.0188 | - | - | | 2.5731 | 1585 | 0.0166 | - | - | | 2.5812 | 1590 | 0.0228 | - | - | | 2.5893 | 1595 | 0.015 | - | - | | 2.5974 | 1600 | 0.0171 | - | - | | 2.6055 | 1605 | 0.0207 | - | - | | 2.6136 | 1610 | 0.009 | - | - | | 2.6218 | 1615 | 0.0111 | - | - | | 2.6299 | 1620 | 0.0109 | - | - | | 2.6380 | 1625 | 0.0175 | - | - | | 2.6461 | 1630 | 0.0155 | - | - | | 2.6542 | 1635 | 0.0193 | - | - | | 2.6623 | 1640 | 0.0189 | - | - | | 2.6705 | 1645 | 0.0123 | - | - | | 2.6786 | 1650 | 0.0102 | - | - | | 2.6867 | 1655 | 0.0097 | - | - | | 2.6948 | 1660 | 0.0116 | - | - | | 2.7029 | 1665 | 0.0134 | - | - | | 2.7110 | 1670 | 0.0218 | - | - | | 2.7192 | 1675 | 0.0148 | - | - | | 2.7273 | 1680 | 0.0137 | - | - | | 2.7354 | 1685 | 0.0062 | - | - | | 2.7435 | 1690 | 0.0075 | - | - | | 2.7516 | 1695 | 0.0078 | - | - | | 2.7597 | 1700 | 0.0151 | - | - | | 2.7679 | 1705 | 0.0157 | - | - | | 2.7760 | 1710 | 0.0153 | - | - | | 2.7841 | 1715 | 0.0088 | - | - | | 2.7922 | 1720 | 0.0093 | - | - | | 2.8003 | 1725 | 0.0154 | - | - | | 2.8084 | 1730 | 0.0124 | - | - | | 2.8166 | 1735 | 0.0128 | - | - | | 2.8247 | 1740 | 0.0088 | - | - | | 2.8328 | 1745 | 0.0144 | - | - | | 2.8409 | 1750 | 0.0184 | - | - | | 2.8490 | 1755 | 0.0114 | - | - | | 2.8571 | 1760 | 0.0043 | - | - | | 2.8653 | 1765 | 0.0151 | - | - | | 2.8734 | 1770 | 0.0089 | - | - | | 2.8815 | 1775 | 0.014 | - | - | | 2.8896 | 1780 | 0.0095 | - | - | | 2.8977 | 1785 | 0.0106 | - | - | | 2.9058 | 1790 | 0.007 | - | - | | 2.9140 | 1795 | 0.0275 | - | - | | 2.9221 | 1800 | 0.0185 | - | - | | 2.9302 | 1805 | 0.0158 | - | - | | 2.9383 | 1810 | 0.0134 | - | - | | 2.9464 | 1815 | 0.0068 | - | - | | 2.9545 | 1820 | 0.0144 | - | - | | 2.9627 | 1825 | 0.0134 | - | - | | 2.9708 | 1830 | 0.0109 | - | - | | 2.9789 | 1835 | 0.0114 | - | - | | 2.9870 | 1840 | 0.0097 | - | - | | 2.9951 | 1845 | 0.0076 | - | - | | **3.0** | **1848** | **-** | **0.0269** | **0.8467** | * The bold row denotes the saved checkpoint.
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 2.14.4 - 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", } ```