SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1
This is a sentence-transformers model finetuned from 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
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
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:
pip install -U sentence-transformers
Then you can load this model and run inference.
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
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
: stepsper_device_train_batch_size
: 138per_device_eval_batch_size
: 138learning_rate
: 5e-07weight_decay
: 0.01num_train_epochs
: 50lr_scheduler_type
: constantbf16
: Truedataloader_drop_last
: Trueresume_from_checkpoint
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 138per_device_eval_batch_size
: 138per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-07weight_decay
: 0.01adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 50max_steps
: -1lr_scheduler_type
: constantlr_scheduler_kwargs
: {}warmup_ratio
: 0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Truedataloader_num_workers
: 0dataloader_prefetch_factor
: 2past_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Truehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_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
@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
@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}
}
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Evaluation results
- Cosine Accuracy on beatai devself-reported0.793
- Dot Accuracy on beatai devself-reported0.254
- Manhattan Accuracy on beatai devself-reported0.802
- Euclidean Accuracy on beatai devself-reported0.801
- Max Accuracy on beatai devself-reported0.802