--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:170845 - loss:BatchHardTripletLoss base_model: Alibaba-NLP/gte-base-en-v1.5 widget: - source_sentence: Radionuclide therapy for targeted cancer treatment sentences: - Reinforced fish plates with a zinc coating for tropical climates - Tantalum alloy powders for hardened steel production. - Kraft pulp used extensively in the production of kraft paper and bags. - source_sentence: Reflective woven fabric, 100 cm width, 200 meters, designed for safety gear sentences: - Articulated link chains used in conveyor systems, made from high-carbon steel. - M5 x 45mm flat head wood screws, perfect for flush mounting. - 15" Mini Dovetail Saw - Small size for fine dovetail work. - source_sentence: Filament yarn, viscose rayon, not for retail, 64 dtex, twisted sentences: - 72.5kV Solid Dielectric Circuit Breaker - Eco-Friendly Solution - High-quality warp-knit fabric for technical applications, made from natural fibers. - Animal motifs silk handkerchief, luxurious, 18x18 cm - source_sentence: Kiln-dried hemlock, available for various craft applications. sentences: - Seasoned cockles for appetizers, 150g - Vitamin B12 + Calcium Tablets, 250 mcg, 100 Count - Boys' formal suit in burgundy, featuring a synthetic fabric with a slight luster. - source_sentence: Aluminium alloy wire, 10mm diameter, used in electrical transmission. sentences: - Cocoa mass, suitable for coating confectioneries. - Sunnah Dates - High-quality, nutrient-rich dates ideal for religious celebrations and dietary needs. - Zika Virus Antigen Detection Kit - A laboratory reagent for the rapid detection of Zika virus antigens in human samples. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 results: - task: type: triplet name: Triplet dataset: name: eval type: eval metrics: - type: cosine_accuracy value: 0.8725334869652888 name: Cosine Accuracy --- # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). 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:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("philmas/cese5020-contrastive-model") # Run inference sentences = [ 'Aluminium alloy wire, 10mm diameter, used in electrical transmission.', 'Sunnah Dates - High-quality, nutrient-rich dates ideal for religious celebrations and dietary needs.', 'Cocoa mass, suitable for coating confectioneries.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Triplet * Dataset: `eval` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.8725** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 170,845 training samples * Columns: text and label * Approximate statistics based on the first 1000 samples: | | text | label | 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| type | string | int | | details | | | * Samples: | text | label | |:--------------------------------------------------------------------------------------|:------------------| | Continuous Flow Liquid Analyzer | 5077 | | Spicy chili powder mix with salt and garlic. | 535 | | Brined and dried fillets of fine fish, suitable for a variety of dishes. | 246 | * Loss: [BatchHardTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchhardtripletloss) ### Evaluation Dataset #### Unnamed Dataset * Size: 10,682 evaluation samples * Columns: text and label * Approximate statistics based on the first 1000 samples: | | text | label | 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| type | string | int | | details | | | * Samples: | text | label | |:----------------------------------------------------------------------------------------------------------|:------------------| | Integrated radio receiver with sound recording capabilities, mobile battery-operated system. | 4681 | | Premium frozen turkey, 7-9 kg, antibiotic-free, processed within 24 hours. | 69 | | Glittering colored granules, under 7mm, used in cosmetics and decorative arts. | 3263 | * Loss: [BatchHardTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchhardtripletloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 1e-05 - `warmup_ratio`: 0.1 - `bf16`: True - `optim`: adamw_torch_fused - `hub_private_repo`: True - `batch_sampler`: group_by_label #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `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`: 1e-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`: 3 - `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_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`: True - `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`: group_by_label - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | eval_cosine_accuracy | |:------:|:----:|:-------------:|:---------------:|:--------------------:| | 0 | 0 | - | - | 0.8447 | | 0.0037 | 10 | 4.0341 | - | - | | 0.0075 | 20 | 3.8755 | - | - | | 0.0112 | 30 | 3.9669 | - | - | | 0.0150 | 40 | 4.6424 | - | - | | 0.0187 | 50 | 3.4661 | - | - | | 0.0225 | 60 | 3.7177 | - | - | | 0.0262 | 70 | 4.5446 | - | - | | 0.0300 | 80 | 3.2709 | - | - | | 0.0337 | 90 | 4.648 | - | - | | 0.0375 | 100 | 2.4594 | - | - | | 0.0412 | 110 | 2.4992 | - | - | | 0.0449 | 120 | 2.381 | - | - | | 0.0487 | 130 | 2.9117 | - | - | | 0.0524 | 140 | 2.0562 | - | - | | 0.0562 | 150 | 2.9831 | - | - | | 0.0599 | 160 | 3.3428 | - | - | | 0.0637 | 170 | 3.3217 | - | - | | 0.0674 | 180 | 3.5566 | - | - | | 0.0712 | 190 | 3.0018 | - | - | | 0.0749 | 200 | 2.4643 | - | - | | 0.0787 | 210 | 2.1375 | - | - | | 0.0824 | 220 | 2.7643 | - | - | | 0.0861 | 230 | 2.3066 | - | - | | 0.0899 | 240 | 2.0659 | - | - | | 0.0936 | 250 | 1.6675 | - | - | | 0.0974 | 260 | 2.516 | - | - | | 0.1011 | 270 | 1.4495 | - | - | | 0.1049 | 280 | 3.1037 | - | - | | 0.1086 | 290 | 2.9175 | - | - | | 0.1124 | 300 | 2.8179 | - | - | | 0.1161 | 310 | 1.8993 | - | - | | 0.1199 | 320 | 2.7167 | - | - | | 0.1236 | 330 | 2.0482 | - | - | | 0.1273 | 340 | 3.0799 | - | - | | 0.1311 | 350 | 3.0152 | - | - | | 0.1348 | 360 | 2.4402 | - | - | | 0.1386 | 370 | 1.7145 | - | - | | 0.1423 | 380 | 1.5029 | - | - | | 0.1461 | 390 | 2.3034 | - | - | | 0.1498 | 400 | 2.0296 | - | - | | 0.1536 | 410 | 2.3206 | - | - | | 0.1573 | 420 | 2.3162 | - | - | | 0.1610 | 430 | 1.3744 | - | - | | 0.1648 | 440 | 2.9439 | - | - | | 0.1685 | 450 | 2.5834 | - | - | | 0.1723 | 460 | 2.2302 | - | - | | 0.1760 | 470 | 1.5165 | - | - | | 0.1798 | 480 | 2.4553 | - | - | | 0.1835 | 490 | 1.6331 | - | - | | 0.1873 | 500 | 1.7502 | - | - | | 0.1910 | 510 | 2.3556 | - | - | | 0.1948 | 520 | 2.6268 | - | - | | 0.1985 | 530 | 2.3735 | - | - | | 0.2022 | 540 | 2.9494 | - | - | | 0.2060 | 550 | 1.7133 | - | - | | 0.2097 | 560 | 1.5455 | - | - | | 0.2135 | 570 | 1.8857 | - | - | | 0.2172 | 580 | 1.7242 | - | - | | 0.2210 | 590 | 0.9303 | - | - | | 0.2247 | 600 | 1.2073 | - | - | | 0.2285 | 610 | 1.9799 | - | - | | 0.2322 | 620 | 0.5134 | - | - | | 0.2360 | 630 | 1.7473 | - | - | | 0.2397 | 640 | 2.5535 | - | - | | 0.2434 | 650 | 2.2415 | - | - | | 0.2472 | 660 | 2.3361 | - | - | | 0.2509 | 670 | 2.1372 | - | - | | 0.2547 | 680 | 1.8236 | - | - | | 0.2584 | 690 | 1.7999 | - | - | | 0.2622 | 700 | 1.041 | - | - | | 0.2659 | 710 | 1.5633 | - | - | | 0.2697 | 720 | 1.475 | - | - | | 0.2734 | 730 | 2.6768 | - | - | | 0.2772 | 740 | 2.0162 | - | - | | 0.2809 | 750 | 2.8179 | - | - | | 0.2846 | 760 | 2.2107 | - | - | | 0.2884 | 770 | 1.4401 | - | - | | 0.2921 | 780 | 1.3463 | - | - | | 0.2959 | 790 | 1.4704 | - | - | | 0.2996 | 800 | 2.1911 | - | - | | 0.3034 | 810 | 1.4399 | - | - | | 0.3071 | 820 | 1.6818 | - | - | | 0.3109 | 830 | 1.3086 | - | - | | 0.3146 | 840 | 3.0084 | - | - | | 0.3184 | 850 | 1.5507 | - | - | | 0.3221 | 860 | 1.2379 | - | - | | 0.3258 | 870 | 1.6205 | - | - | | 0.3296 | 880 | 1.7312 | - | - | | 0.3333 | 890 | 1.2205 | - | - | | 0.3371 | 900 | 2.0977 | - | - | | 0.3408 | 910 | 2.1105 | - | - | | 0.3446 | 920 | 1.6375 | - | - | | 0.3483 | 930 | 1.7065 | - | - | | 0.3521 | 940 | 1.6578 | - | - | | 0.3558 | 950 | 1.9871 | - | - | | 0.3596 | 960 | 2.5589 | - | - | | 0.3633 | 970 | 1.5536 | - | - | | 0.3670 | 980 | 1.5662 | - | - | | 0.3708 | 990 | 2.3202 | - | - | | 0.3745 | 1000 | 1.6294 | - | - | | 0.3783 | 1010 | 2.0777 | - | - | | 0.3820 | 1020 | 1.5202 | - | - | | 0.3858 | 1030 | 1.8365 | - | - | | 0.3895 | 1040 | 0.9917 | - | - | | 0.3933 | 1050 | 1.3668 | - | - | | 0.3970 | 1060 | 1.0952 | - | - | | 0.4007 | 1070 | 0.6225 | - | - | | 0.4045 | 1080 | 0.9056 | - | - | | 0.4082 | 1090 | 2.5108 | - | - | | 0.4120 | 1100 | 0.8275 | - | - | | 0.4157 | 1110 | 0.8328 | - | - | | 0.4195 | 1120 | 1.6204 | - | - | | 0.4232 | 1130 | 1.4578 | - | - | | 0.4270 | 1140 | 0.985 | - | - | | 0.4307 | 1150 | 1.5583 | - | - | | 0.4345 | 1160 | 0.797 | - | - | | 0.4382 | 1170 | 1.2212 | - | - | | 0.4419 | 1180 | 1.3289 | - | - | | 0.4457 | 1190 | 1.4719 | - | - | | 0.4494 | 1200 | 0.9898 | - | - | | 0.4532 | 1210 | 1.5724 | - | - | | 0.4569 | 1220 | 2.4698 | - | - | | 0.4607 | 1230 | 1.7312 | - | - | | 0.4644 | 1240 | 0.8984 | - | - | | 0.4682 | 1250 | 1.4435 | - | - | | 0.4719 | 1260 | 0.4182 | - | - | | 0.4757 | 1270 | 2.5585 | - | - | | 0.4794 | 1280 | 2.1777 | - | - | | 0.4831 | 1290 | 1.8817 | - | - | | 0.4869 | 1300 | 1.3328 | - | - | | 0.4906 | 1310 | 1.1548 | - | - | | 0.4944 | 1320 | 1.8619 | - | - | | 0.4981 | 1330 | 1.8818 | - | - | | 0.5019 | 1340 | 1.2547 | - | - | | 0.5056 | 1350 | 1.1262 | - | - | | 0.5094 | 1360 | 2.4004 | - | - | | 0.5131 | 1370 | 0.5397 | - | - | | 0.5169 | 1380 | 1.1227 | - | - | | 0.5206 | 1390 | 2.1331 | - | - | | 0.5243 | 1400 | 0.8593 | - | - | | 0.5281 | 1410 | 1.7893 | - | - | | 0.5318 | 1420 | 0.5693 | - | - | | 0.5356 | 1430 | 1.0304 | - | - | | 0.5393 | 1440 | 0.7579 | - | - | | 0.5431 | 1450 | 1.5615 | - | - | | 0.5468 | 1460 | 0.6529 | - | - | | 0.5506 | 1470 | 0.5767 | - | - | | 0.5543 | 1480 | 1.3396 | - | - | | 0.5581 | 1490 | 1.2152 | - | - | | 0.5618 | 1500 | 0.8144 | - | - | | 0.5655 | 1510 | 2.0135 | - | - | | 0.5693 | 1520 | 2.5916 | - | - | | 0.5730 | 1530 | 1.553 | - | - | | 0.5768 | 1540 | 0.6537 | - | - | | 0.5805 | 1550 | 0.7982 | - | - | | 0.5843 | 1560 | 1.9476 | - | - | | 0.5880 | 1570 | 0.6488 | - | - | | 0.5918 | 1580 | 1.0492 | - | - | | 0.5955 | 1590 | 1.7359 | - | - | | 0.5993 | 1600 | 2.0695 | - | - | | 0.6030 | 1610 | 0.7046 | - | - | | 0.6067 | 1620 | 1.1444 | - | - | | 0.6105 | 1630 | 0.9934 | - | - | | 0.6142 | 1640 | 0.5541 | - | - | | 0.6180 | 1650 | 0.9048 | - | - | | 0.6217 | 1660 | 1.9154 | - | - | | 0.6255 | 1670 | 2.3706 | - | - | | 0.6292 | 1680 | 0.2856 | - | - | | 0.6330 | 1690 | 1.0283 | - | - | | 0.6367 | 1700 | 1.2681 | - | - | | 0.6404 | 1710 | 0.9028 | - | - | | 0.6442 | 1720 | 0.9902 | - | - | | 0.6479 | 1730 | 1.3535 | - | - | | 0.6517 | 1740 | 0.9419 | - | - | | 0.6554 | 1750 | 0.9893 | - | - | | 0.6592 | 1760 | 1.4345 | - | - | | 0.6629 | 1770 | 2.1841 | - | - | | 0.6667 | 1780 | 0.7408 | - | - | | 0.6704 | 1790 | 2.4774 | - | - | | 0.6742 | 1800 | 0.7757 | - | - | | 0.6779 | 1810 | 2.0088 | - | - | | 0.6816 | 1820 | 1.5048 | - | - | | 0.6854 | 1830 | 0.9138 | - | - | | 0.6891 | 1840 | 1.403 | - | - | | 0.6929 | 1850 | 1.5927 | - | - | | 0.6966 | 1860 | 1.0471 | - | - | | 0.7004 | 1870 | 1.6628 | - | - | | 0.7041 | 1880 | 0.6006 | - | - | | 0.7079 | 1890 | 0.2351 | - | - | | 0.7116 | 1900 | 0.9406 | - | - | | 0.7154 | 1910 | 1.5868 | - | - | | 0.7191 | 1920 | 1.1405 | - | - | | 0.7228 | 1930 | 0.2823 | - | - | | 0.7266 | 1940 | 1.7329 | - | - | | 0.7303 | 1950 | 1.7973 | - | - | | 0.7341 | 1960 | 0.9928 | - | - | | 0.7378 | 1970 | 1.8539 | - | - | | 0.7416 | 1980 | 1.7418 | - | - | | 0.7453 | 1990 | 1.7236 | - | - | | 0.7491 | 2000 | 0.7957 | - | - | | 0.7528 | 2010 | 0.0987 | - | - | | 0.7566 | 2020 | 1.7363 | - | - | | 0.7603 | 2030 | 0.8135 | - | - | | 0.7640 | 2040 | 1.7698 | - | - | | 0.7678 | 2050 | 1.4394 | - | - | | 0.7715 | 2060 | 0.7707 | - | - | | 0.7753 | 2070 | 2.7317 | - | - | | 0.7790 | 2080 | 0.3891 | - | - | | 0.7828 | 2090 | 2.6116 | - | - | | 0.7865 | 2100 | 1.1891 | - | - | | 0.7903 | 2110 | 1.5366 | - | - | | 0.7940 | 2120 | 0.4196 | - | - | | 0.7978 | 2130 | 0.745 | - | - | | 0.8015 | 2140 | 1.4042 | - | - | | 0.8052 | 2150 | 2.7567 | - | - | | 0.8090 | 2160 | 1.9903 | - | - | | 0.8127 | 2170 | 1.8249 | - | - | | 0.8165 | 2180 | 2.0049 | - | - | | 0.8202 | 2190 | 1.6193 | - | - | | 0.8240 | 2200 | 1.0768 | - | - | | 0.8277 | 2210 | 1.5331 | - | - | | 0.8315 | 2220 | 0.8109 | - | - | | 0.8352 | 2230 | 0.6081 | - | - | | 0.8390 | 2240 | 1.3533 | - | - | | 0.8427 | 2250 | 2.0449 | - | - | | 0.8464 | 2260 | 1.1876 | - | - | | 0.8502 | 2270 | 0.7197 | - | - | | 0.8539 | 2280 | 0.9462 | - | - | | 0.8577 | 2290 | 0.7562 | - | - | | 0.8614 | 2300 | 0.9699 | - | - | | 0.8652 | 2310 | 1.115 | - | - | | 0.8689 | 2320 | 0.9679 | - | - | | 0.8727 | 2330 | 2.0255 | - | - | | 0.8764 | 2340 | 0.7457 | - | - | | 0.8801 | 2350 | 0.7221 | - | - | | 0.8839 | 2360 | 1.4877 | - | - | | 0.8876 | 2370 | 1.0071 | - | - | | 0.8914 | 2380 | 1.0958 | - | - | | 0.8951 | 2390 | 1.2945 | - | - | | 0.8989 | 2400 | 1.5245 | - | - | | 0.9026 | 2410 | 0.7008 | - | - | | 0.9064 | 2420 | 1.5043 | - | - | | 0.9101 | 2430 | 1.3202 | - | - | | 0.9139 | 2440 | 1.2748 | - | - | | 0.9176 | 2450 | 1.3845 | - | - | | 0.9213 | 2460 | 1.2619 | - | - | | 0.9251 | 2470 | 1.1196 | - | - | | 0.9288 | 2480 | 2.1311 | - | - | | 0.9326 | 2490 | 1.0909 | - | - | | 0.9363 | 2500 | 2.2843 | - | - | | 0.9401 | 2510 | 0.4763 | - | - | | 0.9438 | 2520 | 1.4252 | - | - | | 0.9476 | 2530 | 1.6419 | - | - | | 0.9513 | 2540 | 1.6628 | - | - | | 0.9551 | 2550 | 1.0937 | - | - | | 0.9588 | 2560 | 2.9318 | - | - | | 0.9625 | 2570 | 1.3356 | - | - | | 0.9663 | 2580 | 1.6431 | - | - | | 0.9700 | 2590 | 2.4785 | - | - | | 0.9738 | 2600 | 2.0959 | - | - | | 0.9775 | 2610 | 1.021 | - | - | | 0.9813 | 2620 | 1.5177 | - | - | | 0.9850 | 2630 | 1.1866 | - | - | | 0.9888 | 2640 | 1.5121 | - | - | | 0.9925 | 2650 | 0.9855 | - | - | | 0.9963 | 2660 | 0.9257 | - | - | | 1.0 | 2670 | 0.9811 | 1.0522 | 0.8691 | | 1.0037 | 2680 | 0.4769 | - | - | | 1.0075 | 2690 | 0.0788 | - | - | | 1.0112 | 2700 | 0.4716 | - | - | | 1.0150 | 2710 | 0.6434 | - | - | | 1.0187 | 2720 | 0.3483 | - | - | | 1.0225 | 2730 | 1.1349 | - | - | | 1.0262 | 2740 | 1.4718 | - | - | | 1.0300 | 2750 | 0.6267 | - | - | | 1.0337 | 2760 | 0.7566 | - | - | | 1.0375 | 2770 | 0.5439 | - | - | | 1.0412 | 2780 | 1.3736 | - | - | | 1.0449 | 2790 | 0.482 | - | - | | 1.0487 | 2800 | 0.8668 | - | - | | 1.0524 | 2810 | 1.5906 | - | - | | 1.0562 | 2820 | 1.5024 | - | - | | 1.0599 | 2830 | 1.1421 | - | - | | 1.0637 | 2840 | 0.951 | - | - | | 1.0674 | 2850 | 1.2362 | - | - | | 1.0712 | 2860 | 0.9021 | - | - | | 1.0749 | 2870 | 0.6175 | - | - | | 1.0787 | 2880 | 0.5354 | - | - | | 1.0824 | 2890 | 0.8739 | - | - | | 1.0861 | 2900 | 1.2778 | - | - | | 1.0899 | 2910 | 1.1148 | - | - | | 1.0936 | 2920 | 1.2744 | - | - | | 1.0974 | 2930 | 2.8342 | - | - | | 1.1011 | 2940 | 0.8226 | - | - | | 1.1049 | 2950 | 0.7788 | - | - | | 1.1086 | 2960 | 0.2087 | - | - | | 1.1124 | 2970 | 2.0295 | - | - | | 1.1161 | 2980 | 0.7227 | - | - | | 1.1199 | 2990 | 0.3996 | - | - | | 1.1236 | 3000 | 1.081 | - | - | | 1.1273 | 3010 | 1.1544 | - | - | | 1.1311 | 3020 | 1.4191 | - | - | | 1.1348 | 3030 | 0.9023 | - | - | | 1.1386 | 3040 | 1.2946 | - | - | | 1.1423 | 3050 | 0.7664 | - | - | | 1.1461 | 3060 | 1.8775 | - | - | | 1.1498 | 3070 | 1.1414 | - | - | | 1.1536 | 3080 | 1.4882 | - | - | | 1.1573 | 3090 | 0.9656 | - | - | | 1.1610 | 3100 | 0.254 | - | - | | 1.1648 | 3110 | 2.8362 | - | - | | 1.1685 | 3120 | 1.5211 | - | - | | 1.1723 | 3130 | 0.5995 | - | - | | 1.1760 | 3140 | 1.192 | - | - | | 1.1798 | 3150 | 0.5996 | - | - | | 1.1835 | 3160 | 0.9875 | - | - | | 1.1873 | 3170 | 0.9348 | - | - | | 1.1910 | 3180 | 0.8946 | - | - | | 1.1948 | 3190 | 1.2509 | - | - | | 1.1985 | 3200 | 1.5223 | - | - | | 1.2022 | 3210 | 1.6398 | - | - | | 1.2060 | 3220 | 1.2502 | - | - | | 1.2097 | 3230 | 1.713 | - | - | | 1.2135 | 3240 | 0.2114 | - | - | | 1.2172 | 3250 | 0.7086 | - | - | | 1.2210 | 3260 | 1.3041 | - | - | | 1.2247 | 3270 | 1.2593 | - | - | | 1.2285 | 3280 | 0.4046 | - | - | | 1.2322 | 3290 | 1.2122 | - | - | | 1.2360 | 3300 | 1.3019 | - | - | | 1.2397 | 3310 | 0.7197 | - | - | | 1.2434 | 3320 | 0.6891 | - | - | | 1.2472 | 3330 | 0.7012 | - | - | | 1.2509 | 3340 | 1.0261 | - | - | | 1.2547 | 3350 | 1.2433 | - | - | | 1.2584 | 3360 | 0.1486 | - | - | | 1.2622 | 3370 | 0.1235 | - | - | | 1.2659 | 3380 | 1.5325 | - | - | | 1.2697 | 3390 | 0.7763 | - | - | | 1.2734 | 3400 | 1.6514 | - | - | | 1.2772 | 3410 | 1.3432 | - | - | | 1.2809 | 3420 | 0.9633 | - | - | | 1.2846 | 3430 | 0.5197 | - | - | | 1.2884 | 3440 | 1.5208 | - | - | | 1.2921 | 3450 | 0.1065 | - | - | | 1.2959 | 3460 | 1.158 | - | - | | 1.2996 | 3470 | 0.1859 | - | - | | 1.3034 | 3480 | 0.5727 | - | - | | 1.3071 | 3490 | 0.4956 | - | - | | 1.3109 | 3500 | 1.7412 | - | - | | 1.3146 | 3510 | 1.0473 | - | - | | 1.3184 | 3520 | 1.1178 | - | - | | 1.3221 | 3530 | 2.0815 | - | - | | 1.3258 | 3540 | 2.2776 | - | - | | 1.3296 | 3550 | 0.7169 | - | - | | 1.3333 | 3560 | 1.3027 | - | - | | 1.3371 | 3570 | 1.7225 | - | - | | 1.3408 | 3580 | 0.7588 | - | - | | 1.3446 | 3590 | 0.7847 | - | - | | 1.3483 | 3600 | 0.9037 | - | - | | 1.3521 | 3610 | 1.3455 | - | - | | 1.3558 | 3620 | 0.9022 | - | - | | 1.3596 | 3630 | 0.1956 | - | - | | 1.3633 | 3640 | 1.0445 | - | - | | 1.3670 | 3650 | 0.8999 | - | - | | 1.3708 | 3660 | 0.439 | - | - | | 1.3745 | 3670 | 1.1256 | - | - | | 1.3783 | 3680 | 0.8729 | - | - | | 1.3820 | 3690 | 2.2068 | - | - | | 1.3858 | 3700 | 1.6487 | - | - | | 1.3895 | 3710 | 0.9254 | - | - | | 1.3933 | 3720 | 0.2883 | - | - | | 1.3970 | 3730 | 0.8981 | - | - | | 1.4007 | 3740 | 1.2252 | - | - | | 1.4045 | 3750 | 0.8682 | - | - | | 1.4082 | 3760 | 0.8365 | - | - | | 1.4120 | 3770 | 1.8876 | - | - | | 1.4157 | 3780 | 0.6073 | - | - | | 1.4195 | 3790 | 0.9617 | - | - | | 1.4232 | 3800 | 0.2706 | - | - | | 1.4270 | 3810 | 0.3518 | - | - | | 1.4307 | 3820 | 1.1181 | - | - | | 1.4345 | 3830 | 1.2088 | - | - | | 1.4382 | 3840 | 0.8219 | - | - | | 1.4419 | 3850 | 1.0337 | - | - | | 1.4457 | 3860 | 1.5798 | - | - | | 1.4494 | 3870 | 0.293 | - | - | | 1.4532 | 3880 | 0.577 | - | - | | 1.4569 | 3890 | 1.1591 | - | - | | 1.4607 | 3900 | 0.677 | - | - | | 1.4644 | 3910 | 0.2807 | - | - | | 1.4682 | 3920 | 0.8355 | - | - | | 1.4719 | 3930 | 1.1842 | - | - | | 1.4757 | 3940 | 1.1249 | - | - | | 1.4794 | 3950 | 0.9494 | - | - | | 1.4831 | 3960 | 0.3435 | - | - | | 1.4869 | 3970 | 0.491 | - | - | | 1.4906 | 3980 | 0.024 | - | - | | 1.4944 | 3990 | 0.4431 | - | - | | 1.4981 | 4000 | 0.3127 | - | - | | 1.5019 | 4010 | 1.1624 | - | - | | 1.5056 | 4020 | 0.7637 | - | - | | 1.5094 | 4030 | 0.2917 | - | - | | 1.5131 | 4040 | 0.5337 | - | - | | 1.5169 | 4050 | 0.4679 | - | - | | 1.5206 | 4060 | 1.1765 | - | - | | 1.5243 | 4070 | 1.5454 | - | - | | 1.5281 | 4080 | 1.1035 | - | - | | 1.5318 | 4090 | 0.4787 | - | - | | 1.5356 | 4100 | 1.1475 | - | - | | 1.5393 | 4110 | 2.5765 | - | - | | 1.5431 | 4120 | 0.8925 | - | - | | 1.5468 | 4130 | 1.1461 | - | - | | 1.5506 | 4140 | 1.0587 | - | - | | 1.5543 | 4150 | 0.8122 | - | - | | 1.5581 | 4160 | 1.197 | - | - | | 1.5618 | 4170 | 1.5496 | - | - | | 1.5655 | 4180 | 0.5243 | - | - | | 1.5693 | 4190 | 1.1577 | - | - | | 1.5730 | 4200 | 0.8121 | - | - | | 1.5768 | 4210 | 0.623 | - | - | | 1.5805 | 4220 | 0.7428 | - | - | | 1.5843 | 4230 | 1.3538 | - | - | | 1.5880 | 4240 | 0.5452 | - | - | | 1.5918 | 4250 | 0.6693 | - | - | | 1.5955 | 4260 | 0.5567 | - | - | | 1.5993 | 4270 | 1.1811 | - | - | | 1.6030 | 4280 | 0.5026 | - | - | | 1.6067 | 4290 | 0.8282 | - | - | | 1.6105 | 4300 | 1.3515 | - | - | | 1.6142 | 4310 | 1.0876 | - | - | | 1.6180 | 4320 | 1.3355 | - | - | | 1.6217 | 4330 | 0.7432 | - | - | | 1.6255 | 4340 | 0.7268 | - | - | | 1.6292 | 4350 | 2.156 | - | - | | 1.6330 | 4360 | 0.5804 | - | - | | 1.6367 | 4370 | 0.5645 | - | - | | 1.6404 | 4380 | 0.3972 | - | - | | 1.6442 | 4390 | 0.3717 | - | - | | 1.6479 | 4400 | 0.3682 | - | - | | 1.6517 | 4410 | 0.8165 | - | - | | 1.6554 | 4420 | 0.4629 | - | - | | 1.6592 | 4430 | 0.4669 | - | - | | 1.6629 | 4440 | 1.4872 | - | - | | 1.6667 | 4450 | 0.0391 | - | - | | 1.6704 | 4460 | 0.5723 | - | - | | 1.6742 | 4470 | 0.1429 | - | - | | 1.6779 | 4480 | 1.3683 | - | - | | 1.6816 | 4490 | 0.2154 | - | - | | 1.6854 | 4500 | 0.486 | - | - | | 1.6891 | 4510 | 0.57 | - | - | | 1.6929 | 4520 | 0.4862 | - | - | | 1.6966 | 4530 | 0.7939 | - | - | | 1.7004 | 4540 | 1.6848 | - | - | | 1.7041 | 4550 | 0.7317 | - | - | | 1.7079 | 4560 | 0.9226 | - | - | | 1.7116 | 4570 | 0.9461 | - | - | | 1.7154 | 4580 | 0.5289 | - | - | | 1.7191 | 4590 | 0.9467 | - | - | | 1.7228 | 4600 | 0.4374 | - | - | | 1.7266 | 4610 | 0.8408 | - | - | | 1.7303 | 4620 | 0.7935 | - | - | | 1.7341 | 4630 | 0.8529 | - | - | | 1.7378 | 4640 | 0.9103 | - | - | | 1.7416 | 4650 | 0.8169 | - | - | | 1.7453 | 4660 | 0.7316 | - | - | | 1.7491 | 4670 | 0.3014 | - | - | | 1.7528 | 4680 | 1.0149 | - | - | | 1.7566 | 4690 | 1.1554 | - | - | | 1.7603 | 4700 | 0.9175 | - | - | | 1.7640 | 4710 | 0.332 | - | - | | 1.7678 | 4720 | 1.0431 | - | - | | 1.7715 | 4730 | 0.4539 | - | - | | 1.7753 | 4740 | 0.3434 | - | - | | 1.7790 | 4750 | 1.6847 | - | - | | 1.7828 | 4760 | 0.6125 | - | - | | 1.7865 | 4770 | 0.6509 | - | - | | 1.7903 | 4780 | 2.1171 | - | - | | 1.7940 | 4790 | 0.1296 | - | - | | 1.7978 | 4800 | 0.8468 | - | - | | 1.8015 | 4810 | 0.8887 | - | - | | 1.8052 | 4820 | 0.475 | - | - | | 1.8090 | 4830 | 1.1306 | - | - | | 1.8127 | 4840 | 1.56 | - | - | | 1.8165 | 4850 | 1.446 | - | - | | 1.8202 | 4860 | 1.1175 | - | - | | 1.8240 | 4870 | 1.5735 | - | - | | 1.8277 | 4880 | 1.749 | - | - | | 1.8315 | 4890 | 0.6597 | - | - | | 1.8352 | 4900 | 0.8736 | - | - | | 1.8390 | 4910 | 0.2586 | - | - | | 1.8427 | 4920 | 1.0175 | - | - | | 1.8464 | 4930 | 1.0651 | - | - | | 1.8502 | 4940 | 0.3644 | - | - | | 1.8539 | 4950 | 0.7849 | - | - | | 1.8577 | 4960 | 1.4129 | - | - | | 1.8614 | 4970 | 1.3896 | - | - | | 1.8652 | 4980 | 0.5037 | - | - | | 1.8689 | 4990 | 0.2482 | - | - | | 1.8727 | 5000 | 1.1326 | - | - | | 1.8764 | 5010 | 0.7214 | - | - | | 1.8801 | 5020 | 0.7837 | - | - | | 1.8839 | 5030 | 1.9915 | - | - | | 1.8876 | 5040 | 1.0516 | - | - | | 1.8914 | 5050 | 0.8879 | - | - | | 1.8951 | 5060 | 1.6854 | - | - | | 1.8989 | 5070 | 1.313 | - | - | | 1.9026 | 5080 | 0.5719 | - | - | | 1.9064 | 5090 | 0.2045 | - | - | | 1.9101 | 5100 | 0.4238 | - | - | | 1.9139 | 5110 | 0.8916 | - | - | | 1.9176 | 5120 | 0.9572 | - | - | | 1.9213 | 5130 | 0.9926 | - | - | | 1.9251 | 5140 | 1.3111 | - | - | | 1.9288 | 5150 | 0.7925 | - | - | | 1.9326 | 5160 | 0.8453 | - | - | | 1.9363 | 5170 | 0.2731 | - | - | | 1.9401 | 5180 | 1.3019 | - | - | | 1.9438 | 5190 | 1.2677 | - | - | | 1.9476 | 5200 | 1.5136 | - | - | | 1.9513 | 5210 | 1.4283 | - | - | | 1.9551 | 5220 | 1.4765 | - | - | | 1.9588 | 5230 | 0.3049 | - | - | | 1.9625 | 5240 | 0.988 | - | - | | 1.9663 | 5250 | 1.7154 | - | - | | 1.9700 | 5260 | 0.5865 | - | - | | 1.9738 | 5270 | 0.8685 | - | - | | 1.9775 | 5280 | 2.1119 | - | - | | 1.9813 | 5290 | 1.6986 | - | - | | 1.9850 | 5300 | 0.9968 | - | - | | 1.9888 | 5310 | 0.6045 | - | - | | 1.9925 | 5320 | 0.7844 | - | - | | 1.9963 | 5330 | 0.7483 | - | - | | 2.0 | 5340 | 2.4421 | 0.8997 | 0.8694 | | 2.0037 | 5350 | 0.3721 | - | - | | 2.0075 | 5360 | 0.7311 | - | - | | 2.0112 | 5370 | 0.4219 | - | - | | 2.0150 | 5380 | 0.5756 | - | - | | 2.0187 | 5390 | 0.2848 | - | - | | 2.0225 | 5400 | 0.7341 | - | - | | 2.0262 | 5410 | 0.4964 | - | - | | 2.0300 | 5420 | 0.1535 | - | - | | 2.0337 | 5430 | 0.4309 | - | - | | 2.0375 | 5440 | 0.3544 | - | - | | 2.0412 | 5450 | 0.2336 | - | - | | 2.0449 | 5460 | 1.212 | - | - | | 2.0487 | 5470 | 0.5154 | - | - | | 2.0524 | 5480 | 0.1163 | - | - | | 2.0562 | 5490 | 0.9765 | - | - | | 2.0599 | 5500 | 0.2086 | - | - | | 2.0637 | 5510 | 0.2978 | - | - | | 2.0674 | 5520 | 1.9357 | - | - | | 2.0712 | 5530 | 0.6232 | - | - | | 2.0749 | 5540 | 0.6823 | - | - | | 2.0787 | 5550 | 0.0296 | - | - | | 2.0824 | 5560 | 0.9172 | - | - | | 2.0861 | 5570 | 0.3007 | - | - | | 2.0899 | 5580 | 0.4675 | - | - | | 2.0936 | 5590 | 0.1491 | - | - | | 2.0974 | 5600 | 1.1711 | - | - | | 2.1011 | 5610 | 0.6131 | - | - | | 2.1049 | 5620 | 0.0001 | - | - | | 2.1086 | 5630 | 0.408 | - | - | | 2.1124 | 5640 | 0.0041 | - | - | | 2.1161 | 5650 | 0.2059 | - | - | | 2.1199 | 5660 | 0.675 | - | - | | 2.1236 | 5670 | 0.6992 | - | - | | 2.1273 | 5680 | 0.3526 | - | - | | 2.1311 | 5690 | 0.2875 | - | - | | 2.1348 | 5700 | 0.6462 | - | - | | 2.1386 | 5710 | 0.3409 | - | - | | 2.1423 | 5720 | 0.4659 | - | - | | 2.1461 | 5730 | 0.4404 | - | - | | 2.1498 | 5740 | 0.9744 | - | - | | 2.1536 | 5750 | 0.5018 | - | - | | 2.1573 | 5760 | 0.5624 | - | - | | 2.1610 | 5770 | 0.9155 | - | - | | 2.1648 | 5780 | 0.1129 | - | - | | 2.1685 | 5790 | 0.0347 | - | - | | 2.1723 | 5800 | 1.0591 | - | - | | 2.1760 | 5810 | 0.0432 | - | - | | 2.1798 | 5820 | 0.041 | - | - | | 2.1835 | 5830 | 0.2072 | - | - | | 2.1873 | 5840 | 0.3379 | - | - | | 2.1910 | 5850 | 0.3583 | - | - | | 2.1948 | 5860 | 0.4276 | - | - | | 2.1985 | 5870 | 0.4756 | - | - | | 2.2022 | 5880 | 1.0452 | - | - | | 2.2060 | 5890 | 0.1116 | - | - | | 2.2097 | 5900 | 0.199 | - | - | | 2.2135 | 5910 | 0.8664 | - | - | | 2.2172 | 5920 | 0.5747 | - | - | | 2.2210 | 5930 | 1.655 | - | - | | 2.2247 | 5940 | 0.8386 | - | - | | 2.2285 | 5950 | 0.5677 | - | - | | 2.2322 | 5960 | 0.3334 | - | - | | 2.2360 | 5970 | 0.3344 | - | - | | 2.2397 | 5980 | 0.7777 | - | - | | 2.2434 | 5990 | 0.359 | - | - | | 2.2472 | 6000 | 0.8495 | - | - | | 2.2509 | 6010 | 1.5789 | - | - | | 2.2547 | 6020 | 0.3153 | - | - | | 2.2584 | 6030 | 1.2525 | - | - | | 2.2622 | 6040 | 0.9709 | - | - | | 2.2659 | 6050 | 0.6815 | - | - | | 2.2697 | 6060 | 0.5785 | - | - | | 2.2734 | 6070 | 0.605 | - | - | | 2.2772 | 6080 | 0.7768 | - | - | | 2.2809 | 6090 | 0.2885 | - | - | | 2.2846 | 6100 | 0.0714 | - | - | | 2.2884 | 6110 | 1.5183 | - | - | | 2.2921 | 6120 | 0.8578 | - | - | | 2.2959 | 6130 | 0.4251 | - | - | | 2.2996 | 6140 | 1.1994 | - | - | | 2.3034 | 6150 | 0.5432 | - | - | | 2.3071 | 6160 | 1.3702 | - | - | | 2.3109 | 6170 | 1.3417 | - | - | | 2.3146 | 6180 | 0.9556 | - | - | | 2.3184 | 6190 | 0.3698 | - | - | | 2.3221 | 6200 | 0.6896 | - | - | | 2.3258 | 6210 | 0.6184 | - | - | | 2.3296 | 6220 | 0.2338 | - | - | | 2.3333 | 6230 | 0.342 | - | - | | 2.3371 | 6240 | 0.8367 | - | - | | 2.3408 | 6250 | 1.1024 | - | - | | 2.3446 | 6260 | 1.0722 | - | - | | 2.3483 | 6270 | 0.479 | - | - | | 2.3521 | 6280 | 0.8954 | - | - | | 2.3558 | 6290 | 0.8496 | - | - | | 2.3596 | 6300 | 1.2729 | - | - | | 2.3633 | 6310 | 0.1914 | - | - | | 2.3670 | 6320 | 0.8034 | - | - | | 2.3708 | 6330 | 0.9549 | - | - | | 2.3745 | 6340 | 1.1065 | - | - | | 2.3783 | 6350 | 1.5595 | - | - | | 2.3820 | 6360 | 0.2028 | - | - | | 2.3858 | 6370 | 0.2768 | - | - | | 2.3895 | 6380 | 1.6269 | - | - | | 2.3933 | 6390 | 0.8796 | - | - | | 2.3970 | 6400 | 0.6491 | - | - | | 2.4007 | 6410 | 0.8528 | - | - | | 2.4045 | 6420 | 0.9181 | - | - | | 2.4082 | 6430 | 1.2277 | - | - | | 2.4120 | 6440 | 0.7166 | - | - | | 2.4157 | 6450 | 1.0847 | - | - | | 2.4195 | 6460 | 0.5212 | - | - | | 2.4232 | 6470 | 0.0076 | - | - | | 2.4270 | 6480 | 0.7151 | - | - | | 2.4307 | 6490 | 0.4312 | - | - | | 2.4345 | 6500 | 0.7043 | - | - | | 2.4382 | 6510 | 0.3567 | - | - | | 2.4419 | 6520 | 1.5934 | - | - | | 2.4457 | 6530 | 0.3477 | - | - | | 2.4494 | 6540 | 0.218 | - | - | | 2.4532 | 6550 | 1.3359 | - | - | | 2.4569 | 6560 | 1.4008 | - | - | | 2.4607 | 6570 | 0.3827 | - | - | | 2.4644 | 6580 | 0.4915 | - | - | | 2.4682 | 6590 | 0.8245 | - | - | | 2.4719 | 6600 | 0.7731 | - | - | | 2.4757 | 6610 | 0.5343 | - | - | | 2.4794 | 6620 | 0.9325 | - | - | | 2.4831 | 6630 | 1.1319 | - | - | | 2.4869 | 6640 | 0.3839 | - | - | | 2.4906 | 6650 | 1.1552 | - | - | | 2.4944 | 6660 | 1.2688 | - | - | | 2.4981 | 6670 | 1.3912 | - | - | | 2.5019 | 6680 | 1.1167 | - | - | | 2.5056 | 6690 | 0.9472 | - | - | | 2.5094 | 6700 | 0.2292 | - | - | | 2.5131 | 6710 | 0.9339 | - | - | | 2.5169 | 6720 | 1.5937 | - | - | | 2.5206 | 6730 | 0.7239 | - | - | | 2.5243 | 6740 | 0.3039 | - | - | | 2.5281 | 6750 | 0.7881 | - | - | | 2.5318 | 6760 | 1.1695 | - | - | | 2.5356 | 6770 | 0.5547 | - | - | | 2.5393 | 6780 | 1.5877 | - | - | | 2.5431 | 6790 | 1.1356 | - | - | | 2.5468 | 6800 | 2.0128 | - | - | | 2.5506 | 6810 | 0.4398 | - | - | | 2.5543 | 6820 | 0.2939 | - | - | | 2.5581 | 6830 | 0.3846 | - | - | | 2.5618 | 6840 | 0.5616 | - | - | | 2.5655 | 6850 | 0.2842 | - | - | | 2.5693 | 6860 | 0.5759 | - | - | | 2.5730 | 6870 | 0.9994 | - | - | | 2.5768 | 6880 | 0.7186 | - | - | | 2.5805 | 6890 | 0.6981 | - | - | | 2.5843 | 6900 | 0.9311 | - | - | | 2.5880 | 6910 | 0.5467 | - | - | | 2.5918 | 6920 | 0.4206 | - | - | | 2.5955 | 6930 | 1.2741 | - | - | | 2.5993 | 6940 | 0.6233 | - | - | | 2.6030 | 6950 | 1.0366 | - | - | | 2.6067 | 6960 | 1.3207 | - | - | | 2.6105 | 6970 | 0.8609 | - | - | | 2.6142 | 6980 | 0.1173 | - | - | | 2.6180 | 6990 | 0.2675 | - | - | | 2.6217 | 7000 | 0.223 | - | - | | 2.6255 | 7010 | 1.3045 | - | - | | 2.6292 | 7020 | 0.5441 | - | - | | 2.6330 | 7030 | 0.7044 | - | - | | 2.6367 | 7040 | 0.3714 | - | - | | 2.6404 | 7050 | 1.1058 | - | - | | 2.6442 | 7060 | 0.7746 | - | - | | 2.6479 | 7070 | 0.447 | - | - | | 2.6517 | 7080 | 0.2873 | - | - | | 2.6554 | 7090 | 0.4244 | - | - | | 2.6592 | 7100 | 0.1731 | - | - | | 2.6629 | 7110 | 0.6776 | - | - | | 2.6667 | 7120 | 0.7168 | - | - | | 2.6704 | 7130 | 0.3992 | - | - | | 2.6742 | 7140 | 1.2154 | - | - | | 2.6779 | 7150 | 0.0108 | - | - | | 2.6816 | 7160 | 0.1631 | - | - | | 2.6854 | 7170 | 0.8925 | - | - | | 2.6891 | 7180 | 0.4143 | - | - | | 2.6929 | 7190 | 0.7408 | - | - | | 2.6966 | 7200 | 0.385 | - | - | | 2.7004 | 7210 | 0.2634 | - | - | | 2.7041 | 7220 | 0.5994 | - | - | | 2.7079 | 7230 | 1.6884 | - | - | | 2.7116 | 7240 | 0.8603 | - | - | | 2.7154 | 7250 | 1.6581 | - | - | | 2.7191 | 7260 | 1.9287 | - | - | | 2.7228 | 7270 | 0.7015 | - | - | | 2.7266 | 7280 | 0.5795 | - | - | | 2.7303 | 7290 | 0.3925 | - | - | | 2.7341 | 7300 | 0.951 | - | - | | 2.7378 | 7310 | 1.3277 | - | - | | 2.7416 | 7320 | 0.8554 | - | - | | 2.7453 | 7330 | 1.7586 | - | - | | 2.7491 | 7340 | 0.6161 | - | - | | 2.7528 | 7350 | 0.4676 | - | - | | 2.7566 | 7360 | 1.1006 | - | - | | 2.7603 | 7370 | 3.297 | - | - | | 2.7640 | 7380 | 0.9984 | - | - | | 2.7678 | 7390 | 0.3403 | - | - | | 2.7715 | 7400 | 1.1086 | - | - | | 2.7753 | 7410 | 2.0014 | - | - | | 2.7790 | 7420 | 0.9262 | - | - | | 2.7828 | 7430 | 0.3025 | - | - | | 2.7865 | 7440 | 1.5384 | - | - | | 2.7903 | 7450 | 0.3463 | - | - | | 2.7940 | 7460 | 0.4185 | - | - | | 2.7978 | 7470 | 0.2442 | - | - | | 2.8015 | 7480 | 0.5406 | - | - | | 2.8052 | 7490 | 1.0035 | - | - | | 2.8090 | 7500 | 0.6287 | - | - | | 2.8127 | 7510 | 0.8633 | - | - | | 2.8165 | 7520 | 0.3335 | - | - | | 2.8202 | 7530 | 0.5338 | - | - | | 2.8240 | 7540 | 1.2167 | - | - | | 2.8277 | 7550 | 0.2822 | - | - | | 2.8315 | 7560 | 1.4419 | - | - | | 2.8352 | 7570 | 0.4631 | - | - | | 2.8390 | 7580 | 1.0021 | - | - | | 2.8427 | 7590 | 0.7075 | - | - | | 2.8464 | 7600 | 0.3762 | - | - | | 2.8502 | 7610 | 0.6409 | - | - | | 2.8539 | 7620 | 0.87 | - | - | | 2.8577 | 7630 | 0.5116 | - | - | | 2.8614 | 7640 | 0.5592 | - | - | | 2.8652 | 7650 | 1.3226 | - | - | | 2.8689 | 7660 | 0.7833 | - | - | | 2.8727 | 7670 | 0.1119 | - | - | | 2.8764 | 7680 | 0.8755 | - | - | | 2.8801 | 7690 | 0.9209 | - | - | | 2.8839 | 7700 | 1.042 | - | - | | 2.8876 | 7710 | 0.261 | - | - | | 2.8914 | 7720 | 1.2104 | - | - | | 2.8951 | 7730 | 0.2092 | - | - | | 2.8989 | 7740 | 0.8415 | - | - | | 2.9026 | 7750 | 0.8367 | - | - | | 2.9064 | 7760 | 1.4419 | - | - | | 2.9101 | 7770 | 0.6172 | - | - | | 2.9139 | 7780 | 0.5117 | - | - | | 2.9176 | 7790 | 1.3995 | - | - | | 2.9213 | 7800 | 0.3404 | - | - | | 2.9251 | 7810 | 0.5748 | - | - | | 2.9288 | 7820 | 0.9675 | - | - | | 2.9326 | 7830 | 0.385 | - | - | | 2.9363 | 7840 | 0.0263 | - | - | | 2.9401 | 7850 | 0.6896 | - | - | | 2.9438 | 7860 | 0.2887 | - | - | | 2.9476 | 7870 | 1.1583 | - | - | | 2.9513 | 7880 | 0.5359 | - | - | | 2.9551 | 7890 | 0.1456 | - | - | | 2.9588 | 7900 | 0.3246 | - | - | | 2.9625 | 7910 | 1.0288 | - | - | | 2.9663 | 7920 | 0.2144 | - | - | | 2.9700 | 7930 | 1.2785 | - | - | | 2.9738 | 7940 | 1.2584 | - | - | | 2.9775 | 7950 | 0.7309 | - | - | | 2.9813 | 7960 | 0.1397 | - | - | | 2.9850 | 7970 | 0.2638 | - | - | | 2.9888 | 7980 | 0.7613 | - | - | | 2.9925 | 7990 | 0.3807 | - | - | | 2.9963 | 8000 | 1.2176 | - | - | | 3.0 | 8010 | 1.2772 | 0.9109 | 0.8725 |
### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.3.1 - Transformers: 4.48.0.dev0 - PyTorch: 2.5.1+cu124 - Accelerate: 1.2.1 - Datasets: 3.1.0 - 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", } ``` #### BatchHardTripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```