--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10822 - loss:CosineSimilarityLoss base_model: neuralmind/bert-large-portuguese-cased widget: - source_sentence: plastificadora documento a4 sentences: - produto destinar colecionador figura acao selo moeda quadrinho raro item historico comumente vender loja especializar feira tematico - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel especial trabalho artistico academico - console videogame controle headsets cadeira gamer jogo diferentes plataforma pc playstation xbox - source_sentence: carrinho supermercado brinquedo sentences: - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel especial trabalho artistico academico - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel especial trabalho artistico academico - produto voltar publico adulto brinquedo sexual jogo adulto - source_sentence: kit prato iniciante sentences: - paes fresco pizza pre-assadas bolo torta produto artesanal cafe sobremeso - movel utensilio domestico item decorativo produto limpeza acessorio organizacao manutencao casa - violoes teclado microfone pedal efeito suporte acessorio corda afinador voltar musico iniciante profissional - source_sentence: capacete seguranca sentences: - artigo esportivo bola raquete acessorio academia roupa esportiva equipamento esporte outdoor escalada ciclismo - tinta cimento ferramenta construcao material reforma piso azulejo equipamento protecao individual - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel especial trabalho artistico academico - source_sentence: livro ficcao sentences: - produto voltar publico adulto brinquedo sexual jogo adulto - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel especial trabalho artistico academico - produto basico arroz feijao massa item mercearia snack alimento congelar dia dia situacoes emergencial 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: 0.9024497617955924 name: Pearson Cosine - type: spearman_cosine value: 0.8404221831399815 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/acho2-ptbr-e4-lr3e-05") # Run inference sentences = [ 'livro ficcao', 'produto basico arroz feijao massa item mercearia snack alimento congelar dia dia situacoes emergencial', 'produto voltar publico adulto brinquedo sexual jogo adulto', ] 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 | 0.9024 | | **spearman_cosine** | **0.8404** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,822 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 | |:---------------------------------|:----------------------------------------------------------------------------------------------------------------------------|:-----------------| | tenis nike | artigo esportivo bola raquete acessorio academia roupa esportiva equipamento esporte outdoor escalada ciclismo | 1.0 | | tapete Sao Carlos | tinta cimento ferramenta construcao material reforma piso azulejo equipamento protecao individual | 0.1 | | kit sensual lua Mel | produto voltar publico adulto brinquedo sexual jogo adulto | 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: 1,203 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 | |:----------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | carvao | tinta cimento ferramenta construcao material reforma piso azulejo equipamento protecao individual | 1.0 | | telha fibrocimento | produto basico arroz feijao massa item mercearia snack alimento congelar dia dia situacoes emergencial | 0.1 | | racao cachorro pedigree loja decoracao | baloe paineis decorativo item tematico casamento aniversario luminaria bandeirola vela acessorio transformar ambiente festa ocasioes especial | 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`: 3e-05 - `weight_decay`: 0.1 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `warmup_steps`: 135 - `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`: 3e-05 - `weight_decay`: 0.1 - `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`: 135 - `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 | |:----------:|:--------:|:-------------:|:---------------:|:-------------------------------:| | 0.0147 | 5 | 0.2323 | - | - | | 0.0295 | 10 | 0.2056 | - | - | | 0.0442 | 15 | 0.2203 | - | - | | 0.0590 | 20 | 0.1947 | - | - | | 0.0737 | 25 | 0.1811 | - | - | | 0.0885 | 30 | 0.1526 | - | - | | 0.1032 | 35 | 0.1511 | - | - | | 0.1180 | 40 | 0.1543 | - | - | | 0.1327 | 45 | 0.1529 | - | - | | 0.1475 | 50 | 0.1296 | - | - | | 0.1622 | 55 | 0.1212 | - | - | | 0.1770 | 60 | 0.1023 | - | - | | 0.1917 | 65 | 0.1011 | - | - | | 0.2065 | 70 | 0.1047 | - | - | | 0.2212 | 75 | 0.1077 | - | - | | 0.2360 | 80 | 0.0909 | - | - | | 0.2507 | 85 | 0.0913 | - | - | | 0.2655 | 90 | 0.1045 | - | - | | 0.2802 | 95 | 0.0761 | - | - | | 0.2950 | 100 | 0.0705 | - | - | | 0.3097 | 105 | 0.086 | - | - | | 0.3245 | 110 | 0.0753 | - | - | | 0.3392 | 115 | 0.0652 | - | - | | 0.3540 | 120 | 0.0663 | - | - | | 0.3687 | 125 | 0.0862 | - | - | | 0.3835 | 130 | 0.085 | - | - | | 0.3982 | 135 | 0.0803 | - | - | | 0.4130 | 140 | 0.088 | - | - | | 0.4277 | 145 | 0.0569 | - | - | | 0.4425 | 150 | 0.0689 | - | - | | 0.4572 | 155 | 0.0746 | - | - | | 0.4720 | 160 | 0.069 | - | - | | 0.4867 | 165 | 0.0665 | - | - | | 0.5015 | 170 | 0.0778 | - | - | | 0.5162 | 175 | 0.0513 | - | - | | 0.5310 | 180 | 0.0525 | - | - | | 0.5457 | 185 | 0.0817 | - | - | | 0.5605 | 190 | 0.0731 | - | - | | 0.5752 | 195 | 0.0704 | - | - | | 0.5900 | 200 | 0.0742 | 0.0651 | 0.8003 | | 0.6047 | 205 | 0.0722 | - | - | | 0.6195 | 210 | 0.0894 | - | - | | 0.6342 | 215 | 0.0679 | - | - | | 0.6490 | 220 | 0.0532 | - | - | | 0.6637 | 225 | 0.0877 | - | - | | 0.6785 | 230 | 0.2859 | - | - | | 0.6932 | 235 | 0.3122 | - | - | | 0.7080 | 240 | 0.1166 | - | - | | 0.7227 | 245 | 0.0785 | - | - | | 0.7375 | 250 | 0.0636 | - | - | | 0.7522 | 255 | 0.0613 | - | - | | 0.7670 | 260 | 0.0648 | - | - | | 0.7817 | 265 | 0.0597 | - | - | | 0.7965 | 270 | 0.0597 | - | - | | 0.8112 | 275 | 0.0662 | - | - | | 0.8260 | 280 | 0.0581 | - | - | | 0.8407 | 285 | 0.0685 | - | - | | 0.8555 | 290 | 0.0629 | - | - | | 0.8702 | 295 | 0.0694 | - | - | | 0.8850 | 300 | 0.055 | - | - | | 0.8997 | 305 | 0.0647 | - | - | | 0.9145 | 310 | 0.0634 | - | - | | 0.9292 | 315 | 0.0724 | - | - | | 0.9440 | 320 | 0.0658 | - | - | | 0.9587 | 325 | 0.0594 | - | - | | 0.9735 | 330 | 0.053 | - | - | | 0.9882 | 335 | 0.0622 | - | - | | 1.0029 | 340 | 0.0622 | - | - | | 1.0177 | 345 | 0.0593 | - | - | | 1.0324 | 350 | 0.0541 | - | - | | 1.0472 | 355 | 0.0493 | - | - | | 1.0619 | 360 | 0.0504 | - | - | | 1.0767 | 365 | 0.0539 | - | - | | 1.0914 | 370 | 0.0439 | - | - | | 1.1062 | 375 | 0.0613 | - | - | | 1.1209 | 380 | 0.0432 | - | - | | 1.1357 | 385 | 0.0617 | - | - | | 1.1504 | 390 | 0.0546 | - | - | | 1.1652 | 395 | 0.0427 | - | - | | 1.1799 | 400 | 0.0674 | 0.0488 | 0.8279 | | 1.1947 | 405 | 0.055 | - | - | | 1.2094 | 410 | 0.0393 | - | - | | 1.2242 | 415 | 0.0561 | - | - | | 1.2389 | 420 | 0.0531 | - | - | | 1.2537 | 425 | 0.0374 | - | - | | 1.2684 | 430 | 0.0374 | - | - | | 1.2832 | 435 | 0.0369 | - | - | | 1.2979 | 440 | 0.0408 | - | - | | 1.3127 | 445 | 0.0508 | - | - | | 1.3274 | 450 | 0.0558 | - | - | | 1.3422 | 455 | 0.0566 | - | - | | 1.3569 | 460 | 0.0466 | - | - | | 1.3717 | 465 | 0.0363 | - | - | | 1.3864 | 470 | 0.0489 | - | - | | 1.4012 | 475 | 0.0535 | - | - | | 1.4159 | 480 | 0.0502 | - | - | | 1.4307 | 485 | 0.0429 | - | - | | 1.4454 | 490 | 0.0541 | - | - | | 1.4602 | 495 | 0.057 | - | - | | 1.4749 | 500 | 0.0402 | - | - | | 1.4897 | 505 | 0.0464 | - | - | | 1.5044 | 510 | 0.0405 | - | - | | 1.5192 | 515 | 0.0469 | - | - | | 1.5339 | 520 | 0.0519 | - | - | | 1.5487 | 525 | 0.0338 | - | - | | 1.5634 | 530 | 0.0476 | - | - | | 1.5782 | 535 | 0.0385 | - | - | | 1.5929 | 540 | 0.0442 | - | - | | 1.6077 | 545 | 0.0379 | - | - | | 1.6224 | 550 | 0.0477 | - | - | | 1.6372 | 555 | 0.0525 | - | - | | 1.6519 | 560 | 0.0487 | - | - | | 1.6667 | 565 | 0.0499 | - | - | | 1.6814 | 570 | 0.0344 | - | - | | 1.6962 | 575 | 0.0503 | - | - | | 1.7109 | 580 | 0.0568 | - | - | | 1.7257 | 585 | 0.0465 | - | - | | 1.7404 | 590 | 0.0325 | - | - | | 1.7552 | 595 | 0.0479 | - | - | | 1.7699 | 600 | 0.046 | 0.0466 | 0.8309 | | 1.7847 | 605 | 0.0482 | - | - | | 1.7994 | 610 | 0.0546 | - | - | | 1.8142 | 615 | 0.0465 | - | - | | 1.8289 | 620 | 0.049 | - | - | | 1.8437 | 625 | 0.0422 | - | - | | 1.8584 | 630 | 0.0358 | - | - | | 1.8732 | 635 | 0.0519 | - | - | | 1.8879 | 640 | 0.0416 | - | - | | 1.9027 | 645 | 0.0344 | - | - | | 1.9174 | 650 | 0.0339 | - | - | | 1.9322 | 655 | 0.0365 | - | - | | 1.9469 | 660 | 0.038 | - | - | | 1.9617 | 665 | 0.0417 | - | - | | 1.9764 | 670 | 0.0521 | - | - | | 1.9912 | 675 | 0.0242 | - | - | | 2.0059 | 680 | 0.0405 | - | - | | 2.0206 | 685 | 0.0233 | - | - | | 2.0354 | 690 | 0.0299 | - | - | | 2.0501 | 695 | 0.0194 | - | - | | 2.0649 | 700 | 0.0424 | - | - | | 2.0796 | 705 | 0.0245 | - | - | | 2.0944 | 710 | 0.0374 | - | - | | 2.1091 | 715 | 0.0295 | - | - | | 2.1239 | 720 | 0.0236 | - | - | | 2.1386 | 725 | 0.0477 | - | - | | 2.1534 | 730 | 0.0211 | - | - | | 2.1681 | 735 | 0.0306 | - | - | | 2.1829 | 740 | 0.0265 | - | - | | 2.1976 | 745 | 0.0398 | - | - | | 2.2124 | 750 | 0.0468 | - | - | | 2.2271 | 755 | 0.0252 | - | - | | 2.2419 | 760 | 0.0329 | - | - | | 2.2566 | 765 | 0.0317 | - | - | | 2.2714 | 770 | 0.035 | - | - | | 2.2861 | 775 | 0.0387 | - | - | | 2.3009 | 780 | 0.037 | - | - | | 2.3156 | 785 | 0.0285 | - | - | | 2.3304 | 790 | 0.0377 | - | - | | 2.3451 | 795 | 0.0344 | - | - | | 2.3599 | 800 | 0.0335 | 0.0431 | 0.8360 | | 2.3746 | 805 | 0.0296 | - | - | | 2.3894 | 810 | 0.0357 | - | - | | 2.4041 | 815 | 0.0244 | - | - | | 2.4189 | 820 | 0.0373 | - | - | | 2.4336 | 825 | 0.0295 | - | - | | 2.4484 | 830 | 0.0353 | - | - | | 2.4631 | 835 | 0.0303 | - | - | | 2.4779 | 840 | 0.0206 | - | - | | 2.4926 | 845 | 0.0284 | - | - | | 2.5074 | 850 | 0.0293 | - | - | | 2.5221 | 855 | 0.035 | - | - | | 2.5369 | 860 | 0.0295 | - | - | | 2.5516 | 865 | 0.0349 | - | - | | 2.5664 | 870 | 0.0195 | - | - | | 2.5811 | 875 | 0.0265 | - | - | | 2.5959 | 880 | 0.0298 | - | - | | 2.6106 | 885 | 0.0321 | - | - | | 2.6254 | 890 | 0.0321 | - | - | | 2.6401 | 895 | 0.0299 | - | - | | 2.6549 | 900 | 0.0216 | - | - | | 2.6696 | 905 | 0.02 | - | - | | 2.6844 | 910 | 0.0277 | - | - | | 2.6991 | 915 | 0.0381 | - | - | | 2.7139 | 920 | 0.0296 | - | - | | 2.7286 | 925 | 0.0339 | - | - | | 2.7434 | 930 | 0.035 | - | - | | 2.7581 | 935 | 0.0293 | - | - | | 2.7729 | 940 | 0.038 | - | - | | 2.7876 | 945 | 0.0291 | - | - | | 2.8024 | 950 | 0.0411 | - | - | | 2.8171 | 955 | 0.0377 | - | - | | 2.8319 | 960 | 0.0282 | - | - | | 2.8466 | 965 | 0.0388 | - | - | | 2.8614 | 970 | 0.0286 | - | - | | 2.8761 | 975 | 0.0177 | - | - | | 2.8909 | 980 | 0.0352 | - | - | | 2.9056 | 985 | 0.0329 | - | - | | 2.9204 | 990 | 0.0265 | - | - | | 2.9351 | 995 | 0.0363 | - | - | | 2.9499 | 1000 | 0.021 | 0.0404 | 0.8374 | | 2.9646 | 1005 | 0.0342 | - | - | | 2.9794 | 1010 | 0.0415 | - | - | | 2.9941 | 1015 | 0.0232 | - | - | | 3.0088 | 1020 | 0.0251 | - | - | | 3.0236 | 1025 | 0.0317 | - | - | | 3.0383 | 1030 | 0.0344 | - | - | | 3.0531 | 1035 | 0.021 | - | - | | 3.0678 | 1040 | 0.0271 | - | - | | 3.0826 | 1045 | 0.021 | - | - | | 3.0973 | 1050 | 0.0151 | - | - | | 3.1121 | 1055 | 0.0222 | - | - | | 3.1268 | 1060 | 0.0186 | - | - | | 3.1416 | 1065 | 0.0357 | - | - | | 3.1563 | 1070 | 0.0179 | - | - | | 3.1711 | 1075 | 0.0291 | - | - | | 3.1858 | 1080 | 0.0313 | - | - | | 3.2006 | 1085 | 0.0349 | - | - | | 3.2153 | 1090 | 0.0181 | - | - | | 3.2301 | 1095 | 0.0294 | - | - | | 3.2448 | 1100 | 0.0216 | - | - | | 3.2596 | 1105 | 0.0334 | - | - | | 3.2743 | 1110 | 0.0256 | - | - | | 3.2891 | 1115 | 0.026 | - | - | | 3.3038 | 1120 | 0.0176 | - | - | | 3.3186 | 1125 | 0.0231 | - | - | | 3.3333 | 1130 | 0.0164 | - | - | | 3.3481 | 1135 | 0.0226 | - | - | | 3.3628 | 1140 | 0.0286 | - | - | | 3.3776 | 1145 | 0.02 | - | - | | 3.3923 | 1150 | 0.0229 | - | - | | 3.4071 | 1155 | 0.0231 | - | - | | 3.4218 | 1160 | 0.0289 | - | - | | 3.4366 | 1165 | 0.0188 | - | - | | 3.4513 | 1170 | 0.0313 | - | - | | 3.4661 | 1175 | 0.0179 | - | - | | 3.4808 | 1180 | 0.0157 | - | - | | 3.4956 | 1185 | 0.0252 | - | - | | 3.5103 | 1190 | 0.019 | - | - | | 3.5251 | 1195 | 0.0251 | - | - | | **3.5398** | **1200** | **0.021** | **0.0399** | **0.8404** | | 3.5546 | 1205 | 0.0154 | - | - | | 3.5693 | 1210 | 0.0187 | - | - | | 3.5841 | 1215 | 0.0221 | - | - | | 3.5988 | 1220 | 0.0148 | - | - | | 3.6136 | 1225 | 0.0168 | - | - | | 3.6283 | 1230 | 0.0236 | - | - | | 3.6431 | 1235 | 0.0194 | - | - | | 3.6578 | 1240 | 0.0245 | - | - | | 3.6726 | 1245 | 0.0171 | - | - | | 3.6873 | 1250 | 0.0235 | - | - | | 3.7021 | 1255 | 0.0243 | - | - | | 3.7168 | 1260 | 0.0325 | - | - | | 3.7316 | 1265 | 0.0196 | - | - | | 3.7463 | 1270 | 0.0362 | - | - | | 3.7611 | 1275 | 0.0188 | - | - | | 3.7758 | 1280 | 0.0151 | - | - | | 3.7906 | 1285 | 0.0189 | - | - | | 3.8053 | 1290 | 0.0286 | - | - | | 3.8201 | 1295 | 0.0266 | - | - | | 3.8348 | 1300 | 0.0216 | - | - | | 3.8496 | 1305 | 0.0218 | - | - | | 3.8643 | 1310 | 0.0214 | - | - | | 3.8791 | 1315 | 0.0224 | - | - | | 3.8938 | 1320 | 0.0213 | - | - | | 3.9086 | 1325 | 0.0302 | - | - | | 3.9233 | 1330 | 0.0196 | - | - | | 3.9381 | 1335 | 0.0218 | - | - | | 3.9528 | 1340 | 0.0226 | - | - | | 3.9676 | 1345 | 0.0204 | - | - | | 3.9823 | 1350 | 0.0215 | - | - | | 3.9971 | 1355 | 0.0258 | - | - | * 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: 1.1.1 - 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", } ```