--- base_model: dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1 library_name: sentence-transformers metrics: - cosine_accuracy - dot_accuracy - manhattan_accuracy - euclidean_accuracy - max_accuracy pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:9504 - loss:TripletLoss widget: - source_sentence: cap product sentences: - method of adjoining a chain of degree p with a co-chain of degree q, where q is less than or equal to p, to form a composite chain of degree p-q - 'Ontology ' - hat commodity - source_sentence: cognitivism sentences: - supporting cognitive science - study of changes in organisms caused by modification of gene expression rather than alteration of the genetic code - 'the idea that mind works like an algorithmic symbol manipulation ' - source_sentence: doxastic voluntarism sentences: - Land surrounded by water - belief one is free - the ability to will beliefs - source_sentence: conceptual role sentences: - concept - inferential role - 'Theory of knowledge ' - source_sentence: scientific revolutions sentences: - scientific realism - Universal moral principles govern legal systems - paradigm shifts model-index: - name: SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1 results: - task: type: triplet name: Triplet dataset: name: beatai dev type: beatai-dev metrics: - type: cosine_accuracy value: 0.7929292929292929 name: Cosine Accuracy - type: dot_accuracy value: 0.2542087542087542 name: Dot Accuracy - type: manhattan_accuracy value: 0.8021885521885522 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.8013468013468014 name: Euclidean Accuracy - type: max_accuracy value: 0.8021885521885522 name: Max Accuracy --- # SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1). 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:** [dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **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': 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("dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e") # Run inference sentences = [ 'scientific revolutions', 'paradigm shifts', 'scientific realism', ] 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 #### Triplet * Dataset: `beatai-dev` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.7929** | | dot_accuracy | 0.2542 | | manhattan_accuracy | 0.8022 | | euclidean_accuracy | 0.8013 | | max_accuracy | 0.8022 | ## Training Details ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 138 - `per_device_eval_batch_size`: 138 - `learning_rate`: 5e-07 - `weight_decay`: 0.01 - `num_train_epochs`: 50 - `lr_scheduler_type`: constant - `bf16`: True - `dataloader_drop_last`: True - `resume_from_checkpoint`: 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`: 138 - `per_device_eval_batch_size`: 138 - `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`: 5e-07 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 50 - `max_steps`: -1 - `lr_scheduler_type`: constant - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0 - `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`: True - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: 2 - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: True - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | loss | beatai-dev_cosine_accuracy | |:-------:|:----:|:-------------:|:------:|:--------------------------:| | 0 | 0 | - | - | 0.4764 | | 0.1471 | 10 | 0.2061 | - | - | | 0.2941 | 20 | 0.2048 | - | - | | 0.4412 | 30 | 0.204 | - | - | | 0.5882 | 40 | 0.202 | - | - | | 0.7353 | 50 | 0.2019 | 0.2010 | 0.5219 | | 0.8824 | 60 | 0.2017 | - | - | | 1.0294 | 70 | 0.1954 | - | - | | 1.1765 | 80 | 0.1959 | - | - | | 1.3235 | 90 | 0.1941 | - | - | | 1.4706 | 100 | 0.1937 | 0.1929 | 0.5598 | | 1.6176 | 110 | 0.1923 | - | - | | 1.7647 | 120 | 0.1893 | - | - | | 1.9118 | 130 | 0.1861 | - | - | | 2.0588 | 140 | 0.1842 | - | - | | 2.2059 | 150 | 0.1818 | 0.1814 | 0.5985 | | 2.3529 | 160 | 0.1834 | - | - | | 2.5 | 170 | 0.1729 | - | - | | 2.6471 | 180 | 0.1726 | - | - | | 2.7941 | 190 | 0.1668 | - | - | | 2.9412 | 200 | 0.1622 | 0.1653 | 0.6330 | | 3.0882 | 210 | 0.1604 | - | - | | 3.2353 | 220 | 0.1572 | - | - | | 3.3824 | 230 | 0.159 | - | - | | 3.5294 | 240 | 0.1567 | - | - | | 3.6765 | 250 | 0.1481 | 0.1562 | 0.6532 | | 3.8235 | 260 | 0.148 | - | - | | 3.9706 | 270 | 0.1492 | - | - | | 4.1176 | 280 | 0.1528 | - | - | | 4.2647 | 290 | 0.1437 | - | - | | 4.4118 | 300 | 0.1481 | 0.1490 | 0.6658 | | 4.5588 | 310 | 0.1386 | - | - | | 4.7059 | 320 | 0.1413 | - | - | | 4.8529 | 330 | 0.1407 | - | - | | 5.0 | 340 | 0.1387 | - | - | | 5.1471 | 350 | 0.1423 | 0.1438 | 0.6717 | | 5.2941 | 360 | 0.1376 | - | - | | 5.4412 | 370 | 0.1314 | - | - | | 5.5882 | 380 | 0.1416 | - | - | | 5.7353 | 390 | 0.1284 | - | - | | 5.8824 | 400 | 0.1375 | 0.1394 | 0.6801 | | 6.0294 | 410 | 0.1308 | - | - | | 6.1765 | 420 | 0.1286 | - | - | | 6.3235 | 430 | 0.1326 | - | - | | 6.4706 | 440 | 0.1356 | - | - | | 6.6176 | 450 | 0.1298 | 0.1361 | 0.6877 | | 6.7647 | 460 | 0.1242 | - | - | | 6.9118 | 470 | 0.1299 | - | - | | 7.0588 | 480 | 0.1279 | - | - | | 7.2059 | 490 | 0.1234 | - | - | | 7.3529 | 500 | 0.1298 | 0.1333 | 0.7045 | | 7.5 | 510 | 0.1252 | - | - | | 7.6471 | 520 | 0.1248 | - | - | | 7.7941 | 530 | 0.1241 | - | - | | 7.9412 | 540 | 0.126 | - | - | | 8.0882 | 550 | 0.1252 | 0.1316 | 0.7071 | | 8.2353 | 560 | 0.1237 | - | - | | 8.3824 | 570 | 0.1205 | - | - | | 8.5294 | 580 | 0.1195 | - | - | | 8.6765 | 590 | 0.1187 | - | - | | 8.8235 | 600 | 0.1187 | 0.1293 | 0.7138 | | 8.9706 | 610 | 0.1269 | - | - | | 9.1176 | 620 | 0.1261 | - | - | | 9.2647 | 630 | 0.1182 | - | - | | 9.4118 | 640 | 0.1219 | - | - | | 9.5588 | 650 | 0.1173 | 0.1276 | 0.7172 | | 9.7059 | 660 | 0.1182 | - | - | | 9.8529 | 670 | 0.122 | - | - | | 10.0 | 680 | 0.1179 | - | - | | 10.1471 | 690 | 0.1137 | - | - | | 10.2941 | 700 | 0.1248 | 0.1261 | 0.7247 | | 10.4412 | 710 | 0.1162 | - | - | | 10.5882 | 720 | 0.1166 | - | - | | 10.7353 | 730 | 0.1111 | - | - | | 10.8824 | 740 | 0.115 | - | - | | 11.0294 | 750 | 0.1175 | 0.1247 | 0.7298 | | 11.1765 | 760 | 0.1136 | - | - | | 11.3235 | 770 | 0.1172 | - | - | | 11.4706 | 780 | 0.1158 | - | - | | 11.6176 | 790 | 0.1142 | - | - | | 11.7647 | 800 | 0.1097 | 0.1236 | 0.7332 | | 11.9118 | 810 | 0.1161 | - | - | | 12.0588 | 820 | 0.1153 | - | - | | 12.2059 | 830 | 0.1114 | - | - | | 12.3529 | 840 | 0.1133 | - | - | | 12.5 | 850 | 0.1104 | 0.1226 | 0.7332 | | 12.6471 | 860 | 0.1093 | - | - | | 12.7941 | 870 | 0.1157 | - | - | | 12.9412 | 880 | 0.1127 | - | - | | 13.0882 | 890 | 0.1115 | - | - | | 13.2353 | 900 | 0.1109 | 0.1214 | 0.7323 | | 13.3824 | 910 | 0.1125 | - | - | | 13.5294 | 920 | 0.1097 | - | - | | 13.6765 | 930 | 0.1124 | - | - | | 13.8235 | 940 | 0.114 | - | - | | 13.9706 | 950 | 0.11 | 0.1204 | 0.7382 | | 14.1176 | 960 | 0.1049 | - | - | | 14.2647 | 970 | 0.1128 | - | - | | 14.4118 | 980 | 0.1109 | - | - | | 14.5588 | 990 | 0.1087 | - | - | | 14.7059 | 1000 | 0.1079 | 0.1196 | 0.7382 | | 14.8529 | 1010 | 0.1077 | - | - | | 15.0 | 1020 | 0.1061 | - | - | | 15.1471 | 1030 | 0.1101 | - | - | | 15.2941 | 1040 | 0.1087 | - | - | | 15.4412 | 1050 | 0.106 | 0.1186 | 0.7399 | | 15.5882 | 1060 | 0.1047 | - | - | | 15.7353 | 1070 | 0.1048 | - | - | | 15.8824 | 1080 | 0.103 | - | - | | 16.0294 | 1090 | 0.1064 | - | - | | 16.1765 | 1100 | 0.1029 | 0.1179 | 0.7433 | | 16.3235 | 1110 | 0.1033 | - | - | | 16.4706 | 1120 | 0.1066 | - | - | | 16.6176 | 1130 | 0.1095 | - | - | | 16.7647 | 1140 | 0.1031 | - | - | | 16.9118 | 1150 | 0.1 | 0.1172 | 0.7466 | | 17.0588 | 1160 | 0.1056 | - | - | | 17.2059 | 1170 | 0.1033 | - | - | | 17.3529 | 1180 | 0.102 | - | - | | 17.5 | 1190 | 0.1083 | - | - | | 17.6471 | 1200 | 0.0971 | 0.1164 | 0.7458 | | 17.7941 | 1210 | 0.1016 | - | - | | 17.9412 | 1220 | 0.1033 | - | - | | 18.0882 | 1230 | 0.0987 | - | - | | 18.2353 | 1240 | 0.1062 | - | - | | 18.3824 | 1250 | 0.0925 | 0.1157 | 0.7475 | | 18.5294 | 1260 | 0.1028 | - | - | | 18.6765 | 1270 | 0.1012 | - | - | | 18.8235 | 1280 | 0.1027 | - | - | | 18.9706 | 1290 | 0.1026 | - | - | | 19.1176 | 1300 | 0.1023 | 0.1148 | 0.7508 | | 19.2647 | 1310 | 0.1053 | - | - | | 19.4118 | 1320 | 0.0981 | - | - | | 19.5588 | 1330 | 0.0975 | - | - | | 19.7059 | 1340 | 0.1006 | - | - | | 19.8529 | 1350 | 0.0991 | 0.1141 | 0.7508 | | 20.0 | 1360 | 0.0994 | - | - | | 20.1471 | 1370 | 0.0998 | - | - | | 20.2941 | 1380 | 0.1014 | - | - | | 20.4412 | 1390 | 0.0986 | - | - | | 20.5882 | 1400 | 0.098 | 0.1133 | 0.7525 | | 20.7353 | 1410 | 0.101 | - | - | | 20.8824 | 1420 | 0.098 | - | - | | 21.0294 | 1430 | 0.1041 | - | - | | 21.1765 | 1440 | 0.0979 | - | - | | 21.3235 | 1450 | 0.1006 | 0.1126 | 0.7559 | | 21.4706 | 1460 | 0.097 | - | - | | 21.6176 | 1470 | 0.0985 | - | - | | 21.7647 | 1480 | 0.0956 | - | - | | 21.9118 | 1490 | 0.0993 | - | - | | 22.0588 | 1500 | 0.0943 | 0.1120 | 0.7551 | | 22.2059 | 1510 | 0.0977 | - | - | | 22.3529 | 1520 | 0.0998 | - | - | | 22.5 | 1530 | 0.0977 | - | - | | 22.6471 | 1540 | 0.099 | - | - | | 22.7941 | 1550 | 0.0925 | 0.1113 | 0.7576 | | 22.9412 | 1560 | 0.0929 | - | - | | 23.0882 | 1570 | 0.0965 | - | - | | 23.2353 | 1580 | 0.0896 | - | - | | 23.3824 | 1590 | 0.0993 | - | - | | 23.5294 | 1600 | 0.0941 | 0.1109 | 0.7576 | | 23.6765 | 1610 | 0.0927 | - | - | | 23.8235 | 1620 | 0.0994 | - | - | | 23.9706 | 1630 | 0.0956 | - | - | | 24.1176 | 1640 | 0.0947 | - | - | | 24.2647 | 1650 | 0.0927 | 0.1103 | 0.7576 | | 24.4118 | 1660 | 0.0935 | - | - | | 24.5588 | 1670 | 0.0996 | - | - | | 24.7059 | 1680 | 0.0903 | - | - | | 24.8529 | 1690 | 0.0916 | - | - | | 25.0 | 1700 | 0.0951 | 0.1096 | 0.7584 | | 25.1471 | 1710 | 0.0924 | - | - | | 25.2941 | 1720 | 0.0952 | - | - | | 25.4412 | 1730 | 0.0954 | - | - | | 25.5882 | 1740 | 0.0968 | - | - | | 25.7353 | 1750 | 0.0942 | 0.1090 | 0.7593 | | 25.8824 | 1760 | 0.0913 | - | - | | 26.0294 | 1770 | 0.0931 | - | - | | 26.1765 | 1780 | 0.0872 | - | - | | 26.3235 | 1790 | 0.0915 | - | - | | 26.4706 | 1800 | 0.0937 | 0.1085 | 0.7601 | | 26.6176 | 1810 | 0.0971 | - | - | | 26.7647 | 1820 | 0.0944 | - | - | | 26.9118 | 1830 | 0.0908 | - | - | | 27.0588 | 1840 | 0.089 | - | - | | 27.2059 | 1850 | 0.0944 | 0.1082 | 0.7626 | | 27.3529 | 1860 | 0.0926 | - | - | | 27.5 | 1870 | 0.087 | - | - | | 27.6471 | 1880 | 0.0904 | - | - | | 27.7941 | 1890 | 0.0886 | - | - | | 27.9412 | 1900 | 0.0942 | 0.1077 | 0.7635 | | 28.0882 | 1910 | 0.0947 | - | - | | 28.2353 | 1920 | 0.0857 | - | - | | 28.3824 | 1930 | 0.0908 | - | - | | 28.5294 | 1940 | 0.0943 | - | - | | 28.6765 | 1950 | 0.0902 | 0.1071 | 0.7668 | | 28.8235 | 1960 | 0.0909 | - | - | | 28.9706 | 1970 | 0.0897 | - | - | | 29.1176 | 1980 | 0.0924 | - | - | | 29.2647 | 1990 | 0.0909 | - | - | | 29.4118 | 2000 | 0.0895 | 0.1066 | 0.7652 | | 29.5588 | 2010 | 0.0832 | - | - | | 29.7059 | 2020 | 0.0883 | - | - | | 29.8529 | 2030 | 0.0935 | - | - | | 30.0 | 2040 | 0.09 | - | - | | 30.1471 | 2050 | 0.0891 | 0.1060 | 0.7677 | | 30.2941 | 2060 | 0.0978 | - | - | | 30.4412 | 2070 | 0.0894 | - | - | | 30.5882 | 2080 | 0.0893 | - | - | | 30.7353 | 2090 | 0.0815 | - | - | | 30.8824 | 2100 | 0.0889 | 0.1058 | 0.7660 | | 31.0294 | 2110 | 0.0801 | - | - | | 31.1765 | 2120 | 0.0922 | - | - | | 31.3235 | 2130 | 0.0868 | - | - | | 31.4706 | 2140 | 0.0858 | - | - | | 31.6176 | 2150 | 0.0862 | 0.1055 | 0.7685 | | 31.7647 | 2160 | 0.0861 | - | - | | 31.9118 | 2170 | 0.0896 | - | - | | 32.0588 | 2180 | 0.0877 | - | - | | 32.2059 | 2190 | 0.0864 | - | - | | 32.3529 | 2200 | 0.0921 | 0.1050 | 0.7694 | | 32.5 | 2210 | 0.082 | - | - | | 32.6471 | 2220 | 0.0902 | - | - | | 32.7941 | 2230 | 0.0825 | - | - | | 32.9412 | 2240 | 0.0829 | - | - | | 33.0882 | 2250 | 0.0859 | 0.1046 | 0.7694 | | 33.2353 | 2260 | 0.0847 | - | - | | 33.3824 | 2270 | 0.0829 | - | - | | 33.5294 | 2280 | 0.0841 | - | - | | 33.6765 | 2290 | 0.0833 | - | - | | 33.8235 | 2300 | 0.0899 | 0.1042 | 0.7710 | | 33.9706 | 2310 | 0.0789 | - | - | | 34.1176 | 2320 | 0.0809 | - | - | | 34.2647 | 2330 | 0.0835 | - | - | | 34.4118 | 2340 | 0.0816 | - | - | | 34.5588 | 2350 | 0.0803 | 0.1038 | 0.7744 | | 34.7059 | 2360 | 0.0808 | - | - | | 34.8529 | 2370 | 0.0867 | - | - | | 35.0 | 2380 | 0.0878 | - | - | | 35.1471 | 2390 | 0.0869 | - | - | | 35.2941 | 2400 | 0.0785 | 0.1034 | 0.7753 | | 35.4412 | 2410 | 0.0849 | - | - | | 35.5882 | 2420 | 0.0832 | - | - | | 35.7353 | 2430 | 0.0799 | - | - | | 35.8824 | 2440 | 0.0813 | - | - | | 36.0294 | 2450 | 0.0801 | 0.1029 | 0.7753 | | 36.1765 | 2460 | 0.0771 | - | - | | 36.3235 | 2470 | 0.0828 | - | - | | 36.4706 | 2480 | 0.0837 | - | - | | 36.6176 | 2490 | 0.0774 | - | - | | 36.7647 | 2500 | 0.0822 | 0.1026 | 0.7769 | | 36.9118 | 2510 | 0.0845 | - | - | | 37.0588 | 2520 | 0.0882 | - | - | | 37.2059 | 2530 | 0.0802 | - | - | | 37.3529 | 2540 | 0.0806 | - | - | | 37.5 | 2550 | 0.0809 | 0.1022 | 0.7795 | | 37.6471 | 2560 | 0.0806 | - | - | | 37.7941 | 2570 | 0.0788 | - | - | | 37.9412 | 2580 | 0.0858 | - | - | | 38.0882 | 2590 | 0.0791 | - | - | | 38.2353 | 2600 | 0.0842 | 0.1018 | 0.7795 | | 38.3824 | 2610 | 0.0799 | - | - | | 38.5294 | 2620 | 0.0769 | - | - | | 38.6765 | 2630 | 0.0823 | - | - | | 38.8235 | 2640 | 0.0784 | - | - | | 38.9706 | 2650 | 0.0863 | 0.1016 | 0.7795 | | 39.1176 | 2660 | 0.0751 | - | - | | 39.2647 | 2670 | 0.0847 | - | - | | 39.4118 | 2680 | 0.0784 | - | - | | 39.5588 | 2690 | 0.0799 | - | - | | 39.7059 | 2700 | 0.0771 | 0.1013 | 0.7811 | | 39.8529 | 2710 | 0.0763 | - | - | | 40.0 | 2720 | 0.0783 | - | - | | 40.1471 | 2730 | 0.0784 | - | - | | 40.2941 | 2740 | 0.0761 | - | - | | 40.4412 | 2750 | 0.0797 | 0.1011 | 0.7837 | | 40.5882 | 2760 | 0.0809 | - | - | | 40.7353 | 2770 | 0.0758 | - | - | | 40.8824 | 2780 | 0.0777 | - | - | | 41.0294 | 2790 | 0.0777 | - | - | | 41.1765 | 2800 | 0.0806 | 0.1006 | 0.7786 | | 41.3235 | 2810 | 0.0852 | - | - | | 41.4706 | 2820 | 0.079 | - | - | | 41.6176 | 2830 | 0.0749 | - | - | | 41.7647 | 2840 | 0.0805 | - | - | | 41.9118 | 2850 | 0.0779 | 0.1003 | 0.7854 | | 42.0588 | 2860 | 0.0759 | - | - | | 42.2059 | 2870 | 0.0794 | - | - | | 42.3529 | 2880 | 0.0811 | - | - | | 42.5 | 2890 | 0.0772 | - | - | | 42.6471 | 2900 | 0.0757 | 0.1001 | 0.7828 | | 42.7941 | 2910 | 0.0781 | - | - | | 42.9412 | 2920 | 0.0751 | - | - | | 43.0882 | 2930 | 0.0752 | - | - | | 43.2353 | 2940 | 0.079 | - | - | | 43.3824 | 2950 | 0.076 | 0.0997 | 0.7811 | | 43.5294 | 2960 | 0.0783 | - | - | | 43.6765 | 2970 | 0.0774 | - | - | | 43.8235 | 2980 | 0.07 | - | - | | 43.9706 | 2990 | 0.073 | - | - | | 44.1176 | 3000 | 0.0762 | 0.0993 | 0.7854 | | 44.2647 | 3010 | 0.0749 | - | - | | 44.4118 | 3020 | 0.0782 | - | - | | 44.5588 | 3030 | 0.0764 | - | - | | 44.7059 | 3040 | 0.0759 | - | - | | 44.8529 | 3050 | 0.0769 | 0.0991 | 0.7887 | | 45.0 | 3060 | 0.0754 | - | - | | 45.1471 | 3070 | 0.0744 | - | - | | 45.2941 | 3080 | 0.0767 | - | - | | 45.4412 | 3090 | 0.0724 | - | - | | 45.5882 | 3100 | 0.0742 | 0.0989 | 0.7870 | | 45.7353 | 3110 | 0.0745 | - | - | | 45.8824 | 3120 | 0.076 | - | - | | 46.0294 | 3130 | 0.0666 | - | - | | 46.1765 | 3140 | 0.0801 | - | - | | 46.3235 | 3150 | 0.0734 | 0.0985 | 0.7887 | | 46.4706 | 3160 | 0.0703 | - | - | | 46.6176 | 3170 | 0.0772 | - | - | | 46.7647 | 3180 | 0.0763 | - | - | | 46.9118 | 3190 | 0.0718 | - | - | | 47.0588 | 3200 | 0.0724 | 0.0981 | 0.7904 | | 47.2059 | 3210 | 0.0755 | - | - | | 47.3529 | 3220 | 0.0719 | - | - | | 47.5 | 3230 | 0.0742 | - | - | | 47.6471 | 3240 | 0.074 | - | - | | 47.7941 | 3250 | 0.0758 | 0.0980 | 0.7921 | | 47.9412 | 3260 | 0.0727 | - | - | | 48.0882 | 3270 | 0.0676 | - | - | | 48.2353 | 3280 | 0.0791 | - | - | | 48.3824 | 3290 | 0.0751 | - | - | | 48.5294 | 3300 | 0.075 | 0.0977 | 0.7887 | | 48.6765 | 3310 | 0.0738 | - | - | | 48.8235 | 3320 | 0.0689 | - | - | | 48.9706 | 3330 | 0.0706 | - | - | | 49.1176 | 3340 | 0.0671 | - | - | | 49.2647 | 3350 | 0.0744 | 0.0974 | 0.7971 | | 49.4118 | 3360 | 0.0739 | - | - | | 49.5588 | 3370 | 0.0721 | - | - | | 49.7059 | 3380 | 0.073 | - | - | | 49.8529 | 3390 | 0.0707 | - | - | | 50.0 | 3400 | 0.0689 | 0.0972 | 0.7929 |
### Framework Versions - Python: 3.8.18 - Sentence Transformers: 3.1.1 - Transformers: 4.45.1 - PyTorch: 1.13.1+cu117 - Accelerate: 0.34.2 - Datasets: 3.0.0 - Tokenizers: 0.20.0 ## 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", } ``` #### TripletLoss ```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} } ```