--- tags: - sentence-transformers - cross-encoder - reranker - generated_from_trainer - dataset_size:18858 - loss:BinaryCrossEntropyLoss base_model: cross-encoder/ms-marco-MiniLM-L6-v2 pipeline_tag: text-ranking library_name: sentence-transformers --- # CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2 This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search. ## Model Details ### Model Description - **Model Type:** Cross Encoder - **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) - **Maximum Sequence Length:** 512 tokens - **Number of Output Labels:** 1 label - **Supported Modality:** Text ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder) ### Full Model Architecture ``` CrossEncoder( (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'BertForSequenceClassification'}) ) ``` ## 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 CrossEncoder # Download from the 🤗 Hub model = CrossEncoder("jmroth/nlp-reranker-finetuned") # Get scores for pairs of inputs pairs = [ ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients.'], ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Higher carbon dioxide concentrations will favourably affect plant growth and demand for water.'], ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse.'], ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Use of fertilizers are beneficial in providing nutrients to plants although they have some negative environmental effects.'], ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Studies have shown that higher CO2 levels lead to reduced plant uptake of nitrogen (and a smaller number showing the same for trace elements such as zinc) resulting in crops with lower nutritional value.'], ] scores = model.predict(pairs) print(scores) # [0.32 0.2035 0.4209 0.1389 0.4395] # Or rank different texts based on similarity to a single text ranks = model.rank( 'Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', [ 'Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients.', 'Higher carbon dioxide concentrations will favourably affect plant growth and demand for water.', 'At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse.', 'Use of fertilizers are beneficial in providing nutrients to plants although they have some negative environmental effects.', 'Studies have shown that higher CO2 levels lead to reduced plant uptake of nitrogen (and a smaller number showing the same for trace elements such as zinc) resulting in crops with lower nutritional value.', ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 18,858 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | label | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life. | Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients. | 1.0 | | Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life. | Higher carbon dioxide concentrations will favourably affect plant growth and demand for water. | 1.0 | | Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life. | At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse. | 1.0 | * Loss: [BinaryCrossEntropyLoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters: ```json { "activation_fn": "torch.nn.modules.linear.Identity", "pos_weight": null } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `learning_rate`: 1e-06 - `weight_decay`: 0.01 - `num_train_epochs`: 5 - `warmup_steps`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 8 - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 1e-06 - `weight_decay`: 0.01 - `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`: None - `warmup_ratio`: None - `warmup_steps`: 0.1 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `enable_jit_checkpoint`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `use_cpu`: False - `seed`: 42 - `data_seed`: None - `bf16`: False - `fp16`: True - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: -1 - `ddp_backend`: None - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `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 - `group_by_length`: False - `length_column_name`: length - `project`: huggingface - `trackio_space_id`: trackio - `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 - `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_for_metrics`: [] - `eval_do_concat_batches`: True - `auto_find_batch_size`: False - `full_determinism`: False - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_num_input_tokens_seen`: no - `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`: True - `use_cache`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs
Click to expand | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0085 | 10 | 1.0242 | | 0.0170 | 20 | 1.3311 | | 0.0254 | 30 | 1.2281 | | 0.0339 | 40 | 1.1490 | | 0.0424 | 50 | 1.3295 | | 0.0509 | 60 | 1.1771 | | 0.0594 | 70 | 1.2986 | | 0.0679 | 80 | 1.1451 | | 0.0763 | 90 | 0.9617 | | 0.0848 | 100 | 0.9573 | | 0.0933 | 110 | 1.1310 | | 0.1018 | 120 | 1.1037 | | 0.1103 | 130 | 0.8294 | | 0.1187 | 140 | 1.2412 | | 0.1272 | 150 | 1.5379 | | 0.1357 | 160 | 0.8937 | | 0.1442 | 170 | 1.0990 | | 0.1527 | 180 | 1.2441 | | 0.1612 | 190 | 0.9481 | | 0.1696 | 200 | 1.0538 | | 0.1781 | 210 | 1.2861 | | 0.1866 | 220 | 1.1759 | | 0.1951 | 230 | 1.0645 | | 0.2036 | 240 | 1.3782 | | 0.2120 | 250 | 1.1335 | | 0.2205 | 260 | 1.1956 | | 0.2290 | 270 | 1.2743 | | 0.2375 | 280 | 1.3976 | | 0.2460 | 290 | 1.2965 | | 0.2545 | 300 | 1.3064 | | 0.2629 | 310 | 1.4721 | | 0.2714 | 320 | 1.0270 | | 0.2799 | 330 | 0.8853 | | 0.2884 | 340 | 1.1295 | | 0.2969 | 350 | 1.0856 | | 0.3053 | 360 | 1.0361 | | 0.3138 | 370 | 0.9704 | | 0.3223 | 380 | 0.9981 | | 0.3308 | 390 | 1.1587 | | 0.3393 | 400 | 1.0416 | | 0.3478 | 410 | 1.0385 | | 0.3562 | 420 | 0.9801 | | 0.3647 | 430 | 1.0559 | | 0.3732 | 440 | 0.9274 | | 0.3817 | 450 | 0.8217 | | 0.3902 | 460 | 0.8266 | | 0.3986 | 470 | 1.0941 | | 0.4071 | 480 | 0.8873 | | 0.4156 | 490 | 1.0712 | | 0.4241 | 500 | 1.0303 | | 0.4326 | 510 | 1.0132 | | 0.4411 | 520 | 1.0600 | | 0.4495 | 530 | 0.8903 | | 0.4580 | 540 | 0.8773 | | 0.4665 | 550 | 1.0966 | | 0.4750 | 560 | 1.1267 | | 0.4835 | 570 | 0.9524 | | 0.4919 | 580 | 1.0614 | | 0.5004 | 590 | 1.0250 | | 0.5089 | 600 | 0.7776 | | 0.5174 | 610 | 0.8945 | | 0.5259 | 620 | 0.5462 | | 0.5344 | 630 | 0.9012 | | 0.5428 | 640 | 1.0679 | | 0.5513 | 650 | 0.7123 | | 0.5598 | 660 | 0.8103 | | 0.5683 | 670 | 0.6745 | | 0.5768 | 680 | 0.8479 | | 0.5852 | 690 | 0.8473 | | 0.5937 | 700 | 0.9150 | | 0.6022 | 710 | 0.5468 | | 0.6107 | 720 | 0.7483 | | 0.6192 | 730 | 0.9425 | | 0.6277 | 740 | 0.7295 | | 0.6361 | 750 | 0.8385 | | 0.6446 | 760 | 0.8177 | | 0.6531 | 770 | 0.7989 | | 0.6616 | 780 | 0.7910 | | 0.6701 | 790 | 0.6544 | | 0.6785 | 800 | 0.9887 | | 0.6870 | 810 | 0.6404 | | 0.6955 | 820 | 0.8134 | | 0.7040 | 830 | 0.5477 | | 0.7125 | 840 | 0.7031 | | 0.7209 | 850 | 0.7191 | | 0.7294 | 860 | 0.7349 | | 0.7379 | 870 | 0.8676 | | 0.7464 | 880 | 0.6788 | | 0.7549 | 890 | 0.7849 | | 0.7634 | 900 | 0.7795 | | 0.7718 | 910 | 0.8199 | | 0.7803 | 920 | 0.7006 | | 0.7888 | 930 | 0.6766 | | 0.7973 | 940 | 0.7082 | | 0.8058 | 950 | 0.7763 | | 0.8142 | 960 | 0.7876 | | 0.8227 | 970 | 0.8169 | | 0.8312 | 980 | 0.6610 | | 0.8397 | 990 | 0.8538 | | 0.8482 | 1000 | 0.5989 | | 0.8567 | 1010 | 0.7383 | | 0.8651 | 1020 | 0.7147 | | 0.8736 | 1030 | 0.7304 | | 0.8821 | 1040 | 0.7192 | | 0.8906 | 1050 | 0.7289 | | 0.8991 | 1060 | 0.6387 | | 0.9075 | 1070 | 0.5964 | | 0.9160 | 1080 | 0.8195 | | 0.9245 | 1090 | 0.6337 | | 0.9330 | 1100 | 0.6402 | | 0.9415 | 1110 | 0.7337 | | 0.9500 | 1120 | 0.6287 | | 0.9584 | 1130 | 0.6445 | | 0.9669 | 1140 | 0.7259 | | 0.9754 | 1150 | 0.7839 | | 0.9839 | 1160 | 0.6223 | | 0.9924 | 1170 | 0.7045 | | 1.0008 | 1180 | 0.5581 | | 1.0093 | 1190 | 0.5762 | | 1.0178 | 1200 | 0.6059 | | 1.0263 | 1210 | 0.6403 | | 1.0348 | 1220 | 0.6196 | | 1.0433 | 1230 | 0.6916 | | 1.0517 | 1240 | 0.7460 | | 1.0602 | 1250 | 0.5768 | | 1.0687 | 1260 | 0.5439 | | 1.0772 | 1270 | 0.6749 | | 1.0857 | 1280 | 0.6286 | | 1.0941 | 1290 | 0.7275 | | 1.1026 | 1300 | 0.5483 | | 1.1111 | 1310 | 0.5651 | | 1.1196 | 1320 | 0.7014 | | 1.1281 | 1330 | 0.6378 | | 1.1366 | 1340 | 0.5440 | | 1.1450 | 1350 | 0.7049 | | 1.1535 | 1360 | 0.5390 | | 1.1620 | 1370 | 0.6372 | | 1.1705 | 1380 | 0.7674 | | 1.1790 | 1390 | 0.5778 | | 1.1874 | 1400 | 0.6669 | | 1.1959 | 1410 | 0.6366 | | 1.2044 | 1420 | 0.5297 | | 1.2129 | 1430 | 0.6731 | | 1.2214 | 1440 | 0.7272 | | 1.2299 | 1450 | 0.5835 | | 1.2383 | 1460 | 0.5759 | | 1.2468 | 1470 | 0.6544 | | 1.2553 | 1480 | 0.5855 | | 1.2638 | 1490 | 0.6161 | | 1.2723 | 1500 | 0.5341 | | 1.2807 | 1510 | 0.7101 | | 1.2892 | 1520 | 0.5991 | | 1.2977 | 1530 | 0.6181 | | 1.3062 | 1540 | 0.5412 | | 1.3147 | 1550 | 0.6335 | | 1.3232 | 1560 | 0.4617 | | 1.3316 | 1570 | 0.6078 | | 1.3401 | 1580 | 0.5664 | | 1.3486 | 1590 | 0.4548 | | 1.3571 | 1600 | 0.5480 | | 1.3656 | 1610 | 0.6777 | | 1.3740 | 1620 | 0.4901 | | 1.3825 | 1630 | 0.5732 | | 1.3910 | 1640 | 0.6267 | | 1.3995 | 1650 | 0.6211 | | 1.4080 | 1660 | 0.5107 | | 1.4165 | 1670 | 0.4965 | | 1.4249 | 1680 | 0.4756 | | 1.4334 | 1690 | 0.6201 | | 1.4419 | 1700 | 0.6060 | | 1.4504 | 1710 | 0.5225 | | 1.4589 | 1720 | 0.5140 | | 1.4673 | 1730 | 0.5546 | | 1.4758 | 1740 | 0.6196 | | 1.4843 | 1750 | 0.5900 | | 1.4928 | 1760 | 0.5839 | | 1.5013 | 1770 | 0.5367 | | 1.5098 | 1780 | 0.6249 | | 1.5182 | 1790 | 0.4742 | | 1.5267 | 1800 | 0.6129 | | 1.5352 | 1810 | 0.5100 | | 1.5437 | 1820 | 0.6051 | | 1.5522 | 1830 | 0.5335 | | 1.5606 | 1840 | 0.5096 | | 1.5691 | 1850 | 0.5355 | | 1.5776 | 1860 | 0.5281 | | 1.5861 | 1870 | 0.6472 | | 1.5946 | 1880 | 0.6264 | | 1.6031 | 1890 | 0.4981 | | 1.6115 | 1900 | 0.4401 | | 1.6200 | 1910 | 0.4959 | | 1.6285 | 1920 | 0.6478 | | 1.6370 | 1930 | 0.5109 | | 1.6455 | 1940 | 0.6202 | | 1.6539 | 1950 | 0.6286 | | 1.6624 | 1960 | 0.5406 | | 1.6709 | 1970 | 0.5257 | | 1.6794 | 1980 | 0.6998 | | 1.6879 | 1990 | 0.5036 | | 1.6964 | 2000 | 0.5581 | | 1.7048 | 2010 | 0.5586 | | 1.7133 | 2020 | 0.5199 | | 1.7218 | 2030 | 0.5346 | | 1.7303 | 2040 | 0.5688 | | 1.7388 | 2050 | 0.5654 | | 1.7472 | 2060 | 0.5735 | | 1.7557 | 2070 | 0.4618 | | 1.7642 | 2080 | 0.4923 | | 1.7727 | 2090 | 0.4617 | | 1.7812 | 2100 | 0.5193 | | 1.7897 | 2110 | 0.6116 | | 1.7981 | 2120 | 0.6135 | | 1.8066 | 2130 | 0.4818 | | 1.8151 | 2140 | 0.5521 | | 1.8236 | 2150 | 0.5664 | | 1.8321 | 2160 | 0.5185 | | 1.8405 | 2170 | 0.4654 | | 1.8490 | 2180 | 0.4675 | | 1.8575 | 2190 | 0.4681 | | 1.8660 | 2200 | 0.5867 | | 1.8745 | 2210 | 0.4690 | | 1.8830 | 2220 | 0.5602 | | 1.8914 | 2230 | 0.5059 | | 1.8999 | 2240 | 0.5971 | | 1.9084 | 2250 | 0.5671 | | 1.9169 | 2260 | 0.4636 | | 1.9254 | 2270 | 0.4128 | | 1.9338 | 2280 | 0.5535 | | 1.9423 | 2290 | 0.5211 | | 1.9508 | 2300 | 0.4354 | | 1.9593 | 2310 | 0.5711 | | 1.9678 | 2320 | 0.5789 | | 1.9763 | 2330 | 0.5064 | | 1.9847 | 2340 | 0.5665 | | 1.9932 | 2350 | 0.5486 | | 2.0017 | 2360 | 0.4541 | | 2.0102 | 2370 | 0.4996 | | 2.0187 | 2380 | 0.4554 | | 2.0271 | 2390 | 0.5296 | | 2.0356 | 2400 | 0.6290 | | 2.0441 | 2410 | 0.5294 | | 2.0526 | 2420 | 0.4837 | | 2.0611 | 2430 | 0.5640 | | 2.0696 | 2440 | 0.4845 | | 2.0780 | 2450 | 0.5184 | | 2.0865 | 2460 | 0.5202 | | 2.0950 | 2470 | 0.5436 | | 2.1035 | 2480 | 0.5087 | | 2.1120 | 2490 | 0.4930 | | 2.1204 | 2500 | 0.5346 | | 2.1289 | 2510 | 0.4438 | | 2.1374 | 2520 | 0.5001 | | 2.1459 | 2530 | 0.5827 | | 2.1544 | 2540 | 0.5700 | | 2.1628 | 2550 | 0.4941 | | 2.1713 | 2560 | 0.4896 | | 2.1798 | 2570 | 0.5766 | | 2.1883 | 2580 | 0.4674 | | 2.1968 | 2590 | 0.5692 | | 2.2053 | 2600 | 0.4569 | | 2.2137 | 2610 | 0.5488 | | 2.2222 | 2620 | 0.5426 | | 2.2307 | 2630 | 0.5298 | | 2.2392 | 2640 | 0.5127 | | 2.2477 | 2650 | 0.5043 | | 2.2561 | 2660 | 0.4980 | | 2.2646 | 2670 | 0.5604 | | 2.2731 | 2680 | 0.5107 | | 2.2816 | 2690 | 0.4833 | | 2.2901 | 2700 | 0.6057 | | 2.2986 | 2710 | 0.5487 | | 2.3070 | 2720 | 0.6312 | | 2.3155 | 2730 | 0.4802 | | 2.3240 | 2740 | 0.5626 | | 2.3325 | 2750 | 0.5361 | | 2.3410 | 2760 | 0.5074 | | 2.3494 | 2770 | 0.5846 | | 2.3579 | 2780 | 0.4857 | | 2.3664 | 2790 | 0.5881 | | 2.3749 | 2800 | 0.3761 | | 2.3834 | 2810 | 0.4919 | | 2.3919 | 2820 | 0.5354 | | 2.4003 | 2830 | 0.4923 | | 2.4088 | 2840 | 0.5929 | | 2.4173 | 2850 | 0.4572 | | 2.4258 | 2860 | 0.5131 | | 2.4343 | 2870 | 0.4850 | | 2.4427 | 2880 | 0.5409 | | 2.4512 | 2890 | 0.5483 | | 2.4597 | 2900 | 0.5252 | | 2.4682 | 2910 | 0.5181 | | 2.4767 | 2920 | 0.4834 | | 2.4852 | 2930 | 0.4996 | | 2.4936 | 2940 | 0.4852 | | 2.5021 | 2950 | 0.5059 | | 2.5106 | 2960 | 0.5016 | | 2.5191 | 2970 | 0.4697 | | 2.5276 | 2980 | 0.6227 | | 2.5360 | 2990 | 0.4147 | | 2.5445 | 3000 | 0.4348 | | 2.5530 | 3010 | 0.4935 | | 2.5615 | 3020 | 0.4841 | | 2.5700 | 3030 | 0.5299 | | 2.5785 | 3040 | 0.5956 | | 2.5869 | 3050 | 0.5880 | | 2.5954 | 3060 | 0.5062 | | 2.6039 | 3070 | 0.5179 | | 2.6124 | 3080 | 0.5290 | | 2.6209 | 3090 | 0.4372 | | 2.6293 | 3100 | 0.5652 | | 2.6378 | 3110 | 0.5222 | | 2.6463 | 3120 | 0.5589 | | 2.6548 | 3130 | 0.4665 | | 2.6633 | 3140 | 0.5182 | | 2.6718 | 3150 | 0.6048 | | 2.6802 | 3160 | 0.5209 | | 2.6887 | 3170 | 0.4951 | | 2.6972 | 3180 | 0.4705 | | 2.7057 | 3190 | 0.4557 | | 2.7142 | 3200 | 0.5273 | | 2.7226 | 3210 | 0.4899 | | 2.7311 | 3220 | 0.5317 | | 2.7396 | 3230 | 0.5155 | | 2.7481 | 3240 | 0.4674 | | 2.7566 | 3250 | 0.4932 | | 2.7651 | 3260 | 0.5774 | | 2.7735 | 3270 | 0.4896 | | 2.7820 | 3280 | 0.4601 | | 2.7905 | 3290 | 0.5037 | | 2.7990 | 3300 | 0.5724 | | 2.8075 | 3310 | 0.4780 | | 2.8159 | 3320 | 0.5556 | | 2.8244 | 3330 | 0.4529 | | 2.8329 | 3340 | 0.4963 | | 2.8414 | 3350 | 0.4756 | | 2.8499 | 3360 | 0.5187 | | 2.8584 | 3370 | 0.5676 | | 2.8668 | 3380 | 0.4204 | | 2.8753 | 3390 | 0.4987 | | 2.8838 | 3400 | 0.5173 | | 2.8923 | 3410 | 0.5848 | | 2.9008 | 3420 | 0.5046 | | 2.9092 | 3430 | 0.5195 | | 2.9177 | 3440 | 0.4605 | | 2.9262 | 3450 | 0.4491 | | 2.9347 | 3460 | 0.5506 | | 2.9432 | 3470 | 0.4951 | | 2.9517 | 3480 | 0.5290 | | 2.9601 | 3490 | 0.4743 | | 2.9686 | 3500 | 0.5898 | | 2.9771 | 3510 | 0.4446 | | 2.9856 | 3520 | 0.5059 | | 2.9941 | 3530 | 0.5211 | | 3.0025 | 3540 | 0.5688 | | 3.0110 | 3550 | 0.5058 | | 3.0195 | 3560 | 0.5003 | | 3.0280 | 3570 | 0.5212 | | 3.0365 | 3580 | 0.4837 | | 3.0450 | 3590 | 0.4858 | | 3.0534 | 3600 | 0.4779 | | 3.0619 | 3610 | 0.5734 | | 3.0704 | 3620 | 0.4780 | | 3.0789 | 3630 | 0.4251 | | 3.0874 | 3640 | 0.5297 | | 3.0958 | 3650 | 0.4301 | | 3.1043 | 3660 | 0.5491 | | 3.1128 | 3670 | 0.5540 | | 3.1213 | 3680 | 0.4716 | | 3.1298 | 3690 | 0.4535 | | 3.1383 | 3700 | 0.4965 | | 3.1467 | 3710 | 0.5208 | | 3.1552 | 3720 | 0.5274 | | 3.1637 | 3730 | 0.5401 | | 3.1722 | 3740 | 0.5869 | | 3.1807 | 3750 | 0.5500 | | 3.1891 | 3760 | 0.4793 | | 3.1976 | 3770 | 0.5151 | | 3.2061 | 3780 | 0.5416 | | 3.2146 | 3790 | 0.5109 | | 3.2231 | 3800 | 0.5302 | | 3.2316 | 3810 | 0.4950 | | 3.2400 | 3820 | 0.5823 | | 3.2485 | 3830 | 0.4943 | | 3.2570 | 3840 | 0.5190 | | 3.2655 | 3850 | 0.4694 | | 3.2740 | 3860 | 0.4608 | | 3.2824 | 3870 | 0.5052 | | 3.2909 | 3880 | 0.5065 | | 3.2994 | 3890 | 0.5035 | | 3.3079 | 3900 | 0.4862 | | 3.3164 | 3910 | 0.5370 | | 3.3249 | 3920 | 0.4426 | | 3.3333 | 3930 | 0.4011 | | 3.3418 | 3940 | 0.5025 | | 3.3503 | 3950 | 0.5379 | | 3.3588 | 3960 | 0.4854 | | 3.3673 | 3970 | 0.4738 | | 3.3757 | 3980 | 0.4677 | | 3.3842 | 3990 | 0.4966 | | 3.3927 | 4000 | 0.5211 | | 3.4012 | 4010 | 0.4247 | | 3.4097 | 4020 | 0.4725 | | 3.4182 | 4030 | 0.4111 | | 3.4266 | 4040 | 0.5796 | | 3.4351 | 4050 | 0.4761 | | 3.4436 | 4060 | 0.5138 | | 3.4521 | 4070 | 0.5575 | | 3.4606 | 4080 | 0.4206 | | 3.4690 | 4090 | 0.4705 | | 3.4775 | 4100 | 0.5302 | | 3.4860 | 4110 | 0.5233 | | 3.4945 | 4120 | 0.5259 | | 3.5030 | 4130 | 0.5036 | | 3.5115 | 4140 | 0.4798 | | 3.5199 | 4150 | 0.4725 | | 3.5284 | 4160 | 0.4625 | | 3.5369 | 4170 | 0.5474 | | 3.5454 | 4180 | 0.4464 | | 3.5539 | 4190 | 0.5536 | | 3.5623 | 4200 | 0.5558 | | 3.5708 | 4210 | 0.5189 | | 3.5793 | 4220 | 0.4841 | | 3.5878 | 4230 | 0.4088 | | 3.5963 | 4240 | 0.4857 | | 3.6047 | 4250 | 0.4165 | | 3.6132 | 4260 | 0.4420 | | 3.6217 | 4270 | 0.5389 | | 3.6302 | 4280 | 0.5001 | | 3.6387 | 4290 | 0.4812 | | 3.6472 | 4300 | 0.5215 | | 3.6556 | 4310 | 0.5679 | | 3.6641 | 4320 | 0.4882 | | 3.6726 | 4330 | 0.5787 | | 3.6811 | 4340 | 0.4132 | | 3.6896 | 4350 | 0.4370 | | 3.6980 | 4360 | 0.6031 | | 3.7065 | 4370 | 0.5009 | | 3.7150 | 4380 | 0.5619 | | 3.7235 | 4390 | 0.4756 | | 3.7320 | 4400 | 0.5222 | | 3.7405 | 4410 | 0.5216 | | 3.7489 | 4420 | 0.4569 | | 3.7574 | 4430 | 0.4932 | | 3.7659 | 4440 | 0.4852 | | 3.7744 | 4450 | 0.5125 | | 3.7829 | 4460 | 0.4744 | | 3.7913 | 4470 | 0.5071 | | 3.7998 | 4480 | 0.5042 | | 3.8083 | 4490 | 0.4642 | | 3.8168 | 4500 | 0.5199 | | 3.8253 | 4510 | 0.5304 | | 3.8338 | 4520 | 0.5035 | | 3.8422 | 4530 | 0.5588 | | 3.8507 | 4540 | 0.4863 | | 3.8592 | 4550 | 0.4673 | | 3.8677 | 4560 | 0.5302 | | 3.8762 | 4570 | 0.4694 | | 3.8846 | 4580 | 0.5405 | | 3.8931 | 4590 | 0.4841 | | 3.9016 | 4600 | 0.5430 | | 3.9101 | 4610 | 0.5335 | | 3.9186 | 4620 | 0.5308 | | 3.9271 | 4630 | 0.5791 | | 3.9355 | 4640 | 0.5085 | | 3.9440 | 4650 | 0.5260 | | 3.9525 | 4660 | 0.5110 | | 3.9610 | 4670 | 0.4480 | | 3.9695 | 4680 | 0.5914 | | 3.9779 | 4690 | 0.4881 | | 3.9864 | 4700 | 0.4869 | | 3.9949 | 4710 | 0.4970 | | 4.0034 | 4720 | 0.5309 | | 4.0119 | 4730 | 0.4695 | | 4.0204 | 4740 | 0.5106 | | 4.0288 | 4750 | 0.5100 | | 4.0373 | 4760 | 0.5291 | | 4.0458 | 4770 | 0.4728 | | 4.0543 | 4780 | 0.5145 | | 4.0628 | 4790 | 0.5550 | | 4.0712 | 4800 | 0.5192 | | 4.0797 | 4810 | 0.4818 | | 4.0882 | 4820 | 0.5379 | | 4.0967 | 4830 | 0.4530 | | 4.1052 | 4840 | 0.4719 | | 4.1137 | 4850 | 0.5031 | | 4.1221 | 4860 | 0.4867 | | 4.1306 | 4870 | 0.4639 | | 4.1391 | 4880 | 0.4390 | | 4.1476 | 4890 | 0.4926 | | 4.1561 | 4900 | 0.5361 | | 4.1645 | 4910 | 0.4889 | | 4.1730 | 4920 | 0.3853 | | 4.1815 | 4930 | 0.5471 | | 4.1900 | 4940 | 0.5115 | | 4.1985 | 4950 | 0.5586 | | 4.2070 | 4960 | 0.5272 | | 4.2154 | 4970 | 0.5271 | | 4.2239 | 4980 | 0.4684 | | 4.2324 | 4990 | 0.5133 | | 4.2409 | 5000 | 0.5973 | | 4.2494 | 5010 | 0.4923 | | 4.2578 | 5020 | 0.4629 | | 4.2663 | 5030 | 0.4673 | | 4.2748 | 5040 | 0.5141 | | 4.2833 | 5050 | 0.5163 | | 4.2918 | 5060 | 0.4898 | | 4.3003 | 5070 | 0.4746 | | 4.3087 | 5080 | 0.5307 | | 4.3172 | 5090 | 0.4081 | | 4.3257 | 5100 | 0.5066 | | 4.3342 | 5110 | 0.5164 | | 4.3427 | 5120 | 0.5030 | | 4.3511 | 5130 | 0.5765 | | 4.3596 | 5140 | 0.4530 | | 4.3681 | 5150 | 0.5085 | | 4.3766 | 5160 | 0.4275 | | 4.3851 | 5170 | 0.4456 | | 4.3936 | 5180 | 0.4491 | | 4.4020 | 5190 | 0.4807 | | 4.4105 | 5200 | 0.4843 | | 4.4190 | 5210 | 0.5107 | | 4.4275 | 5220 | 0.5204 | | 4.4360 | 5230 | 0.5156 | | 4.4444 | 5240 | 0.5067 | | 4.4529 | 5250 | 0.4483 | | 4.4614 | 5260 | 0.5092 | | 4.4699 | 5270 | 0.4658 | | 4.4784 | 5280 | 0.5014 | | 4.4869 | 5290 | 0.4676 | | 4.4953 | 5300 | 0.5119 | | 4.5038 | 5310 | 0.5185 | | 4.5123 | 5320 | 0.4460 | | 4.5208 | 5330 | 0.4716 | | 4.5293 | 5340 | 0.4775 | | 4.5377 | 5350 | 0.4890 | | 4.5462 | 5360 | 0.5026 | | 4.5547 | 5370 | 0.5491 | | 4.5632 | 5380 | 0.4686 | | 4.5717 | 5390 | 0.3902 | | 4.5802 | 5400 | 0.4809 | | 4.5886 | 5410 | 0.4224 | | 4.5971 | 5420 | 0.5018 | | 4.6056 | 5430 | 0.4811 | | 4.6141 | 5440 | 0.5390 | | 4.6226 | 5450 | 0.4938 | | 4.6310 | 5460 | 0.5181 | | 4.6395 | 5470 | 0.4338 | | 4.6480 | 5480 | 0.5351 | | 4.6565 | 5490 | 0.4894 | | 4.6650 | 5500 | 0.5117 | | 4.6735 | 5510 | 0.4754 | | 4.6819 | 5520 | 0.4154 | | 4.6904 | 5530 | 0.5504 | | 4.6989 | 5540 | 0.5428 | | 4.7074 | 5550 | 0.5355 | | 4.7159 | 5560 | 0.6427 | | 4.7243 | 5570 | 0.4598 | | 4.7328 | 5580 | 0.4769 | | 4.7413 | 5590 | 0.4372 | | 4.7498 | 5600 | 0.5851 | | 4.7583 | 5610 | 0.4962 | | 4.7668 | 5620 | 0.5619 | | 4.7752 | 5630 | 0.4341 | | 4.7837 | 5640 | 0.5026 | | 4.7922 | 5650 | 0.5412 | | 4.8007 | 5660 | 0.5126 | | 4.8092 | 5670 | 0.4497 | | 4.8176 | 5680 | 0.4663 | | 4.8261 | 5690 | 0.5648 | | 4.8346 | 5700 | 0.4492 | | 4.8431 | 5710 | 0.3832 | | 4.8516 | 5720 | 0.4533 | | 4.8601 | 5730 | 0.5143 | | 4.8685 | 5740 | 0.4881 | | 4.8770 | 5750 | 0.4936 | | 4.8855 | 5760 | 0.4669 | | 4.8940 | 5770 | 0.5239 | | 4.9025 | 5780 | 0.4696 | | 4.9109 | 5790 | 0.5213 | | 4.9194 | 5800 | 0.4941 | | 4.9279 | 5810 | 0.4204 | | 4.9364 | 5820 | 0.5003 | | 4.9449 | 5830 | 0.5530 | | 4.9534 | 5840 | 0.4430 | | 4.9618 | 5850 | 0.5279 | | 4.9703 | 5860 | 0.5658 | | 4.9788 | 5870 | 0.4906 | | 4.9873 | 5880 | 0.4429 | | 4.9958 | 5890 | 0.5412 |
### Training Time - **Training**: 5.6 minutes ### Framework Versions - Python: 3.12.13 - Sentence Transformers: 5.4.1 - Transformers: 5.0.0 - PyTorch: 2.10.0+cu128 - Accelerate: 1.13.0 - Datasets: 4.0.0 - Tokenizers: 0.22.2 ## 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", } ```