SentenceTransformer
This is a sentence-transformers model trained on the triplets dataset. 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
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- triplets
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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
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("lv12/esci-nomic-embed-text-v1_5_4")
# Run inference
sentences = [
'search_query: karoke set 2 microphone for adults',
'search_document: Starion KS829-B Bluetooth Karaoke Machine l Pedestal Design w/Light Show l Two Karaoke Microphones, Starion, Black',
'search_document: EARISE T26 Portable Karaoke Machine Bluetooth Speaker with Wireless Microphone, Rechargeable PA System with FM Radio, Audio Recording, Remote Control, Supports TF Card/USB, Perfect for Party, EARISE, ',
]
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
- Evaluated with
TripletEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.7298 |
dot_accuracy | 0.2832 |
manhattan_accuracy | 0.7282 |
euclidean_accuracy | 0.7299 |
max_accuracy | 0.7299 |
Semantic Similarity
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.4148 |
spearman_cosine | 0.3997 |
pearson_manhattan | 0.3771 |
spearman_manhattan | 0.3699 |
pearson_euclidean | 0.3778 |
spearman_euclidean | 0.3708 |
pearson_dot | 0.3814 |
spearman_dot | 0.3817 |
pearson_max | 0.4148 |
spearman_max | 0.3997 |
Information Retrieval
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@10 | 0.967 |
cosine_precision@10 | 0.6951 |
cosine_recall@10 | 0.6217 |
cosine_ndcg@10 | 0.83 |
cosine_mrr@10 | 0.9111 |
cosine_map@10 | 0.7758 |
dot_accuracy@10 | 0.946 |
dot_precision@10 | 0.6369 |
dot_recall@10 | 0.5693 |
dot_ndcg@10 | 0.7669 |
dot_mrr@10 | 0.8754 |
dot_map@10 | 0.6962 |
Training Details
Training Dataset
triplets
- Dataset: triplets
- Size: 1,600,000 training samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 7 tokens
- mean: 11.03 tokens
- max: 39 tokens
- min: 10 tokens
- mean: 39.86 tokens
- max: 104 tokens
- min: 9 tokens
- mean: 39.73 tokens
- max: 159 tokens
- Samples:
anchor positive negative search_query: udt hydraulic fluid
search_document: Triax Agra UTTO XL Synthetic Blend Tractor Transmission and Hydraulic Oil, 6,000 Hour Life, 50% Less wear, 36F Pour Point, Replaces All OEM Tractor Fluids (5 Gallon Pail), TRIAX,
search_document: Shell Rotella T5 Synthetic Blend 15W-40 Diesel Engine Oil (1-Gallon, Case of 3), Shell Rotella,
search_query: cheetah print iphone xs case
search_document: iPhone Xs Case, iPhone Xs Case,Doowear Leopard Cheetah Protective Cover Shell For Girls Women,Slim Fit Anti Scratch Shockproof Soft TPU Bumper Flexible Rubber Gel Silicone Case for iPhone Xs / X-1, Ebetterr, 1
search_document: iPhone Xs & iPhone X Case, J.west Luxury Sparkle Bling Translucent Leopard Print Soft Silicone Phone Case Cover for Girls Women Flex Slim Design Pattern Drop Protective Case for iPhone Xs/x 5.8 inch, J.west, Leopard
search_query: platform shoes
search_document: Teva Women's Flatform Universal Platform Sandal, Black, 5 M US, Teva, Black
search_document: Vans Women's Old Skool Platform Trainers, (Black/White Y28), 5 UK 38 EU, Vans, Black/White
- Loss:
TripletLoss
with these parameters:{ "distance_metric": "TripletDistanceMetric.COSINE", "triplet_margin": 0.8 }
Evaluation Dataset
triplets
- Dataset: triplets
- Size: 16,000 evaluation samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 7 tokens
- mean: 11.02 tokens
- max: 29 tokens
- min: 10 tokens
- mean: 38.78 tokens
- max: 87 tokens
- min: 9 tokens
- mean: 38.81 tokens
- max: 91 tokens
- Samples:
anchor positive negative search_query: hogknobz
search_document: Black 2014-2015 HDsmallPARTS/LocEzy Saddlebag Mounting Hardware Knobs are replacement/compatible for Saddlebag Quick Release Pins on Harley Davidson Touring Motorcycles Theft Deterrent, LocEzy,
search_document: HANSWD Saddlebag Support Bars Brackets For SUZUKI YAMAHA KAWASAKI (Black), HANSWD, Black
search_query: tile sticker key finder
search_document: Tile Sticker (2020) 2-pack - Small, Adhesive Bluetooth Tracker, Item Locator and Finder for Remotes, Headphones, Gadgets and More, Tile,
search_document: Tile Pro Combo (2017) - 2 Pack (1 x Sport, 1 x Style) - Discontinued by Manufacturer, Tile, Graphite/Gold
search_query: adobe incense burner
search_document: AM Incense Burner Frankincense Resin - Luxury Globe Charcoal Bakhoor Burners for Office & Home Decor (Brown), AM, Brown
search_document: semli Large Incense Burner Backflow Incense Burner Holder Incense Stick Holder Home Office Decor, Semli,
- Loss:
TripletLoss
with these parameters:{ "distance_metric": "TripletDistanceMetric.COSINE", "triplet_margin": 0.8 }
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 64per_device_eval_batch_size
: 16gradient_accumulation_steps
: 2learning_rate
: 1e-07num_train_epochs
: 5lr_scheduler_type
: polynomiallr_scheduler_kwargs
: {'lr_end': 1e-08, 'power': 2.0}warmup_ratio
: 0.05dataloader_drop_last
: Truedataloader_num_workers
: 4dataloader_prefetch_factor
: 4load_best_model_at_end
: Truegradient_checkpointing
: Trueauto_find_batch_size
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 2eval_accumulation_steps
: Nonelearning_rate
: 1e-07weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: polynomiallr_scheduler_kwargs
: {'lr_end': 1e-08, 'power': 2.0}warmup_ratio
: 0.05warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: 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
: 4dataloader_prefetch_factor
: 4past_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_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}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
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Truegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falsefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Truefull_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
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | triplets loss | cosine_accuracy | cosine_map@10 | spearman_cosine |
---|---|---|---|---|---|---|
0.0008 | 10 | 0.7505 | - | - | - | - |
0.0016 | 20 | 0.7499 | - | - | - | - |
0.0024 | 30 | 0.7524 | - | - | - | - |
0.0032 | 40 | 0.7486 | - | - | - | - |
0.004 | 50 | 0.7493 | - | - | - | - |
0.0048 | 60 | 0.7476 | - | - | - | - |
0.0056 | 70 | 0.7483 | - | - | - | - |
0.0064 | 80 | 0.7487 | - | - | - | - |
0.0072 | 90 | 0.7496 | - | - | - | - |
0.008 | 100 | 0.7515 | 0.7559 | 0.7263 | 0.7684 | 0.3941 |
0.0088 | 110 | 0.7523 | - | - | - | - |
0.0096 | 120 | 0.7517 | - | - | - | - |
0.0104 | 130 | 0.7534 | - | - | - | - |
0.0112 | 140 | 0.746 | - | - | - | - |
0.012 | 150 | 0.7528 | - | - | - | - |
0.0128 | 160 | 0.7511 | - | - | - | - |
0.0136 | 170 | 0.7491 | - | - | - | - |
0.0144 | 180 | 0.752 | - | - | - | - |
0.0152 | 190 | 0.7512 | - | - | - | - |
0.016 | 200 | 0.7513 | 0.7557 | 0.7259 | 0.7688 | 0.3942 |
0.0168 | 210 | 0.7505 | - | - | - | - |
0.0176 | 220 | 0.7481 | - | - | - | - |
0.0184 | 230 | 0.7516 | - | - | - | - |
0.0192 | 240 | 0.7504 | - | - | - | - |
0.02 | 250 | 0.7498 | - | - | - | - |
0.0208 | 260 | 0.7506 | - | - | - | - |
0.0216 | 270 | 0.7486 | - | - | - | - |
0.0224 | 280 | 0.7471 | - | - | - | - |
0.0232 | 290 | 0.7511 | - | - | - | - |
0.024 | 300 | 0.7506 | 0.7553 | 0.7258 | 0.7692 | 0.3943 |
0.0248 | 310 | 0.7485 | - | - | - | - |
0.0256 | 320 | 0.7504 | - | - | - | - |
0.0264 | 330 | 0.7456 | - | - | - | - |
0.0272 | 340 | 0.7461 | - | - | - | - |
0.028 | 350 | 0.7496 | - | - | - | - |
0.0288 | 360 | 0.7518 | - | - | - | - |
0.0296 | 370 | 0.7514 | - | - | - | - |
0.0304 | 380 | 0.7479 | - | - | - | - |
0.0312 | 390 | 0.7507 | - | - | - | - |
0.032 | 400 | 0.7511 | 0.7547 | 0.7258 | 0.7695 | 0.3945 |
0.0328 | 410 | 0.7491 | - | - | - | - |
0.0336 | 420 | 0.7487 | - | - | - | - |
0.0344 | 430 | 0.7496 | - | - | - | - |
0.0352 | 440 | 0.7464 | - | - | - | - |
0.036 | 450 | 0.7518 | - | - | - | - |
0.0368 | 460 | 0.7481 | - | - | - | - |
0.0376 | 470 | 0.7493 | - | - | - | - |
0.0384 | 480 | 0.753 | - | - | - | - |
0.0392 | 490 | 0.7475 | - | - | - | - |
0.04 | 500 | 0.7498 | 0.7540 | 0.7262 | 0.7700 | 0.3948 |
0.0408 | 510 | 0.7464 | - | - | - | - |
0.0416 | 520 | 0.7506 | - | - | - | - |
0.0424 | 530 | 0.747 | - | - | - | - |
0.0432 | 540 | 0.7462 | - | - | - | - |
0.044 | 550 | 0.75 | - | - | - | - |
0.0448 | 560 | 0.7522 | - | - | - | - |
0.0456 | 570 | 0.7452 | - | - | - | - |
0.0464 | 580 | 0.7475 | - | - | - | - |
0.0472 | 590 | 0.7507 | - | - | - | - |
0.048 | 600 | 0.7494 | 0.7531 | 0.7269 | 0.7707 | 0.3951 |
0.0488 | 610 | 0.7525 | - | - | - | - |
0.0496 | 620 | 0.7446 | - | - | - | - |
0.0504 | 630 | 0.7457 | - | - | - | - |
0.0512 | 640 | 0.7462 | - | - | - | - |
0.052 | 650 | 0.7478 | - | - | - | - |
0.0528 | 660 | 0.7459 | - | - | - | - |
0.0536 | 670 | 0.7465 | - | - | - | - |
0.0544 | 680 | 0.7495 | - | - | - | - |
0.0552 | 690 | 0.7513 | - | - | - | - |
0.056 | 700 | 0.7445 | 0.7520 | 0.7274 | 0.7705 | 0.3954 |
0.0568 | 710 | 0.7446 | - | - | - | - |
0.0576 | 720 | 0.746 | - | - | - | - |
0.0584 | 730 | 0.7452 | - | - | - | - |
0.0592 | 740 | 0.7459 | - | - | - | - |
0.06 | 750 | 0.7419 | - | - | - | - |
0.0608 | 760 | 0.7462 | - | - | - | - |
0.0616 | 770 | 0.7414 | - | - | - | - |
0.0624 | 780 | 0.7444 | - | - | - | - |
0.0632 | 790 | 0.7419 | - | - | - | - |
0.064 | 800 | 0.7438 | 0.7508 | 0.7273 | 0.7712 | 0.3957 |
0.0648 | 810 | 0.7503 | - | - | - | - |
0.0656 | 820 | 0.7402 | - | - | - | - |
0.0664 | 830 | 0.7435 | - | - | - | - |
0.0672 | 840 | 0.741 | - | - | - | - |
0.068 | 850 | 0.7386 | - | - | - | - |
0.0688 | 860 | 0.7416 | - | - | - | - |
0.0696 | 870 | 0.7473 | - | - | - | - |
0.0704 | 880 | 0.7438 | - | - | - | - |
0.0712 | 890 | 0.7458 | - | - | - | - |
0.072 | 900 | 0.7446 | 0.7494 | 0.7279 | 0.7718 | 0.3961 |
0.0728 | 910 | 0.7483 | - | - | - | - |
0.0736 | 920 | 0.7458 | - | - | - | - |
0.0744 | 930 | 0.7473 | - | - | - | - |
0.0752 | 940 | 0.7431 | - | - | - | - |
0.076 | 950 | 0.7428 | - | - | - | - |
0.0768 | 960 | 0.7385 | - | - | - | - |
0.0776 | 970 | 0.7438 | - | - | - | - |
0.0784 | 980 | 0.7406 | - | - | - | - |
0.0792 | 990 | 0.7426 | - | - | - | - |
0.08 | 1000 | 0.7372 | 0.7478 | 0.7282 | 0.7725 | 0.3965 |
0.0808 | 1010 | 0.7396 | - | - | - | - |
0.0816 | 1020 | 0.7398 | - | - | - | - |
0.0824 | 1030 | 0.7376 | - | - | - | - |
0.0832 | 1040 | 0.7417 | - | - | - | - |
0.084 | 1050 | 0.7408 | - | - | - | - |
0.0848 | 1060 | 0.7415 | - | - | - | - |
0.0856 | 1070 | 0.7468 | - | - | - | - |
0.0864 | 1080 | 0.7427 | - | - | - | - |
0.0872 | 1090 | 0.7371 | - | - | - | - |
0.088 | 1100 | 0.7375 | 0.7460 | 0.7279 | 0.7742 | 0.3970 |
0.0888 | 1110 | 0.7434 | - | - | - | - |
0.0896 | 1120 | 0.7441 | - | - | - | - |
0.0904 | 1130 | 0.7378 | - | - | - | - |
0.0912 | 1140 | 0.735 | - | - | - | - |
0.092 | 1150 | 0.739 | - | - | - | - |
0.0928 | 1160 | 0.7408 | - | - | - | - |
0.0936 | 1170 | 0.7346 | - | - | - | - |
0.0944 | 1180 | 0.7389 | - | - | - | - |
0.0952 | 1190 | 0.7367 | - | - | - | - |
0.096 | 1200 | 0.7358 | 0.7440 | 0.729 | 0.7747 | 0.3975 |
0.0968 | 1210 | 0.7381 | - | - | - | - |
0.0976 | 1220 | 0.7405 | - | - | - | - |
0.0984 | 1230 | 0.7348 | - | - | - | - |
0.0992 | 1240 | 0.737 | - | - | - | - |
0.1 | 1250 | 0.7393 | - | - | - | - |
0.1008 | 1260 | 0.7411 | - | - | - | - |
0.1016 | 1270 | 0.7359 | - | - | - | - |
0.1024 | 1280 | 0.7276 | - | - | - | - |
0.1032 | 1290 | 0.7364 | - | - | - | - |
0.104 | 1300 | 0.7333 | 0.7418 | 0.7293 | 0.7747 | 0.3979 |
0.1048 | 1310 | 0.7367 | - | - | - | - |
0.1056 | 1320 | 0.7352 | - | - | - | - |
0.1064 | 1330 | 0.7333 | - | - | - | - |
0.1072 | 1340 | 0.737 | - | - | - | - |
0.108 | 1350 | 0.7361 | - | - | - | - |
0.1088 | 1360 | 0.7299 | - | - | - | - |
0.1096 | 1370 | 0.7339 | - | - | - | - |
0.1104 | 1380 | 0.7349 | - | - | - | - |
0.1112 | 1390 | 0.7318 | - | - | - | - |
0.112 | 1400 | 0.7336 | 0.7394 | 0.7292 | 0.7749 | 0.3983 |
0.1128 | 1410 | 0.7326 | - | - | - | - |
0.1136 | 1420 | 0.7317 | - | - | - | - |
0.1144 | 1430 | 0.7315 | - | - | - | - |
0.1152 | 1440 | 0.7321 | - | - | - | - |
0.116 | 1450 | 0.7284 | - | - | - | - |
0.1168 | 1460 | 0.7308 | - | - | - | - |
0.1176 | 1470 | 0.7287 | - | - | - | - |
0.1184 | 1480 | 0.727 | - | - | - | - |
0.1192 | 1490 | 0.7298 | - | - | - | - |
0.12 | 1500 | 0.7306 | 0.7368 | 0.7301 | 0.7755 | 0.3988 |
0.1208 | 1510 | 0.7269 | - | - | - | - |
0.1216 | 1520 | 0.7299 | - | - | - | - |
0.1224 | 1530 | 0.7256 | - | - | - | - |
0.1232 | 1540 | 0.721 | - | - | - | - |
0.124 | 1550 | 0.7274 | - | - | - | - |
0.1248 | 1560 | 0.7251 | - | - | - | - |
0.1256 | 1570 | 0.7248 | - | - | - | - |
0.1264 | 1580 | 0.7244 | - | - | - | - |
0.1272 | 1590 | 0.7275 | - | - | - | - |
0.128 | 1600 | 0.7264 | 0.7339 | 0.7298 | 0.7756 | 0.3991 |
0.1288 | 1610 | 0.7252 | - | - | - | - |
0.1296 | 1620 | 0.7287 | - | - | - | - |
0.1304 | 1630 | 0.7263 | - | - | - | - |
0.1312 | 1640 | 0.7216 | - | - | - | - |
0.132 | 1650 | 0.7231 | - | - | - | - |
0.1328 | 1660 | 0.728 | - | - | - | - |
0.1336 | 1670 | 0.7309 | - | - | - | - |
0.1344 | 1680 | 0.7243 | - | - | - | - |
0.1352 | 1690 | 0.7239 | - | - | - | - |
0.136 | 1700 | 0.7219 | 0.7309 | 0.7302 | 0.7768 | 0.3994 |
0.1368 | 1710 | 0.7212 | - | - | - | - |
0.1376 | 1720 | 0.7217 | - | - | - | - |
0.1384 | 1730 | 0.7118 | - | - | - | - |
0.1392 | 1740 | 0.7226 | - | - | - | - |
0.14 | 1750 | 0.7185 | - | - | - | - |
0.1408 | 1760 | 0.7228 | - | - | - | - |
0.1416 | 1770 | 0.7257 | - | - | - | - |
0.1424 | 1780 | 0.7177 | - | - | - | - |
0.1432 | 1790 | 0.722 | - | - | - | - |
0.144 | 1800 | 0.712 | 0.7276 | 0.7307 | 0.7763 | 0.3997 |
0.1448 | 1810 | 0.7193 | - | - | - | - |
0.1456 | 1820 | 0.7138 | - | - | - | - |
0.1464 | 1830 | 0.7171 | - | - | - | - |
0.1472 | 1840 | 0.7191 | - | - | - | - |
0.148 | 1850 | 0.7172 | - | - | - | - |
0.1488 | 1860 | 0.7168 | - | - | - | - |
0.1496 | 1870 | 0.7111 | - | - | - | - |
0.1504 | 1880 | 0.7203 | - | - | - | - |
0.1512 | 1890 | 0.7095 | - | - | - | - |
0.152 | 1900 | 0.7064 | 0.7240 | 0.7301 | 0.7762 | 0.3998 |
0.1528 | 1910 | 0.7147 | - | - | - | - |
0.1536 | 1920 | 0.7098 | - | - | - | - |
0.1544 | 1930 | 0.7193 | - | - | - | - |
0.1552 | 1940 | 0.7096 | - | - | - | - |
0.156 | 1950 | 0.7107 | - | - | - | - |
0.1568 | 1960 | 0.7146 | - | - | - | - |
0.1576 | 1970 | 0.7106 | - | - | - | - |
0.1584 | 1980 | 0.7079 | - | - | - | - |
0.1592 | 1990 | 0.7097 | - | - | - | - |
0.16 | 2000 | 0.71 | 0.7202 | 0.7298 | 0.7758 | 0.3997 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.38.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.27.2
- Datasets: 2.19.1
- Tokenizers: 0.15.2
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 Unknownself-reported0.730
- Dot Accuracy on Unknownself-reported0.283
- Manhattan Accuracy on Unknownself-reported0.728
- Euclidean Accuracy on Unknownself-reported0.730
- Max Accuracy on Unknownself-reported0.730
- Pearson Cosine on Unknownself-reported0.415
- Spearman Cosine on Unknownself-reported0.400
- Pearson Manhattan on Unknownself-reported0.377
- Spearman Manhattan on Unknownself-reported0.370
- Pearson Euclidean on Unknownself-reported0.378