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redis
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langcache-reranker-v1

Text Ranking
sentence-transformers
Safetensors
English
modernbert
cross-encoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:1056095
loss:BinaryCrossEntropyLoss
Eval Results
Model card Files
xet
Community

Redis fine-tuned CrossEncoder model for semantic caching on LangCache

This is a Cross Encoder model finetuned from Alibaba-NLP/gte-reranker-modernbert-base on the LangCache Sentence Pairs (all) dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for sentence pair classification.

Model Details

Model Description

Model Sources

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("aditeyabaral-redis/langcache-reranker-v1-test2")
# Get scores for pairs of texts
pairs = [
    ['The newer Punts are still very much in existence today and race in the same fleets as the older boats .', 'The newer punts are still very much in existence today and run in the same fleets as the older boats .'],
    ['Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .', 'Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .'],
    ['After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .', 'Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .'],
    ['She married Peter Haygarth on 29 May 1964 in Durban . Her second marriage , to Robin Osborne , took place in 1977 .', 'She married Robin Osborne on May 29 , 1964 in Durban , and her second marriage with Peter Haygarth took place in 1977 .'],
    ['In 2005 she moved to Norway , settled in Geilo and worked as a rafting guide , in 2006 she started mountain biking - races .', 'In 2005 , she moved to Geilo , settling in Norway and worked as a rafting guide . She started mountain bike races in 2006 .'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'The newer Punts are still very much in existence today and race in the same fleets as the older boats .',
    [
        'The newer punts are still very much in existence today and run in the same fleets as the older boats .',
        'Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .',
        'Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .',
        'She married Robin Osborne on May 29 , 1964 in Durban , and her second marriage with Peter Haygarth took place in 1977 .',
        'In 2005 , she moved to Geilo , settling in Norway and worked as a rafting guide . She started mountain bike races in 2006 .',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Metric val test
accuracy 0.7718 0.8932
accuracy_threshold 0.8927 0.7741
f1 0.6934 0.8793
f1_threshold 0.8759 0.1352
precision 0.6788 0.8524
recall 0.7086 0.908
average_precision 0.7676 0.9357

Training Details

Training Dataset

LangCache Sentence Pairs (all)

  • Dataset: LangCache Sentence Pairs (all)
  • Size: 62,021 training samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 label
    type string string int
    details
    • min: 27 characters
    • mean: 112.72 characters
    • max: 197 characters
    • min: 27 characters
    • mean: 112.54 characters
    • max: 198 characters
    • 0: ~50.30%
    • 1: ~49.70%
  • Samples:
    sentence1 sentence2 label
    The newer Punts are still very much in existence today and race in the same fleets as the older boats . The newer punts are still very much in existence today and run in the same fleets as the older boats . 1
    Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada . Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada . 0
    After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall . Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall . 1
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Evaluation Dataset

LangCache Sentence Pairs (all)

  • Dataset: LangCache Sentence Pairs (all)
  • Size: 62,021 evaluation samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 label
    type string string int
    details
    • min: 27 characters
    • mean: 112.72 characters
    • max: 197 characters
    • min: 27 characters
    • mean: 112.54 characters
    • max: 198 characters
    • 0: ~50.30%
    • 1: ~49.70%
  • Samples:
    sentence1 sentence2 label
    The newer Punts are still very much in existence today and race in the same fleets as the older boats . The newer punts are still very much in existence today and run in the same fleets as the older boats . 1
    Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada . Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada . 0
    After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall . Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall . 1
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 48
  • per_device_eval_batch_size: 48
  • learning_rate: 0.0002
  • num_train_epochs: 50
  • warmup_steps: 100
  • load_best_model_at_end: True
  • optim: adamw_torch
  • push_to_hub: True
  • hub_model_id: aditeyabaral-redis/langcache-reranker-v1-test2

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 48
  • per_device_eval_batch_size: 48
  • 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: 0.0002
  • weight_decay: 0.0
  • 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: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • warmup_steps: 100
  • 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: False
  • 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: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: True
  • 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: True
  • resume_from_checkpoint: None
  • hub_model_id: aditeyabaral-redis/langcache-reranker-v1-test2
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • hub_revision: None
  • 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
  • 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
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: True
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss Validation Loss val_average_precision test_average_precision
-1 -1 - - 0.7676 0.6907
0.1818 1000 0.3058 0.3991 - 0.8742
0.3636 2000 0.2417 0.3729 - 0.8962
0.5455 3000 0.2287 0.3356 - 0.9039
0.7273 4000 0.2183 0.3729 - 0.9109
0.9091 5000 0.212 0.3404 - 0.9215
1.0909 6000 0.1999 0.3171 - 0.9240
1.2727 7000 0.1944 0.3131 - 0.9263
1.4545 8000 0.1933 0.3116 - 0.9264
1.6364 9000 0.193 0.3211 - 0.9243
1.8182 10000 0.1879 0.2984 - 0.9291
2.0 11000 0.1869 0.2983 - 0.9305
2.1818 12000 0.1726 0.3009 - 0.9252
2.3636 13000 0.1725 0.3058 - 0.9277
2.5455 14000 0.1743 0.2991 - 0.9239
2.7273 15000 0.1724 0.2875 - 0.9317
2.9091 16000 0.173 0.2872 - 0.9369
3.0909 17000 0.1652 0.2840 - 0.9364
3.2727 18000 0.1597 0.2825 - 0.9360
3.4545 19000 0.1613 0.2988 - 0.9375
3.6364 20000 0.1587 0.2868 - 0.9311
3.8182 21000 0.16 0.2863 - 0.9327
4.0 22000 0.1599 0.2853 - 0.9351
4.1818 23000 0.146 0.2876 - 0.9376
4.3636 24000 0.1481 0.2813 - 0.9424
4.5455 25000 0.1477 0.2831 - 0.9403
4.7273 26000 0.148 0.2769 - 0.9405
4.9091 27000 0.1485 0.2758 - 0.9418
5.0909 28000 0.142 0.2771 - 0.9420
5.2727 29000 0.1359 0.2878 - 0.9392
5.4545 30000 0.1368 0.2777 - 0.9402
5.6364 31000 0.1397 0.2797 - 0.9370
5.8182 32000 0.1395 0.2771 - 0.9433
6.0 33000 0.1393 0.2883 - 0.9421
6.1818 34000 0.1249 0.2822 - 0.9410
6.3636 35000 0.127 0.2739 - 0.9431
6.5455 36000 0.13 0.2797 - 0.9409
6.7273 37000 0.1304 0.2796 - 0.9414
6.9091 38000 0.1306 0.2750 - 0.9455
7.0909 39000 0.1236 0.2820 - 0.9417
7.2727 40000 0.1185 0.2898 - 0.9434
7.4545 41000 0.119 0.2863 - 0.9393
7.6364 42000 0.1206 0.2761 - 0.9412
7.8182 43000 0.1216 0.2702 - 0.9433
8.0 44000 0.1221 0.2771 - 0.9447
8.1818 45000 0.1083 0.2836 - 0.9418
8.3636 46000 0.1124 0.2889 - 0.9430
8.5455 47000 0.1106 0.2755 - 0.9406
8.7273 48000 0.1134 0.2853 - 0.9439
8.9091 49000 0.114 0.2845 - 0.9446
9.0909 50000 0.1095 0.2852 - 0.9421
9.2727 51000 0.1028 0.2880 - 0.9452
9.4545 52000 0.103 0.2796 - 0.9434
9.6364 53000 0.1048 0.2794 - 0.9439
9.8182 54000 0.105 0.2838 - 0.9454
10.0 55000 0.1086 0.2866 - 0.9436
10.1818 56000 0.0938 0.2809 - 0.9436
10.3636 57000 0.0969 0.3018 - 0.9421
10.5455 58000 0.0975 0.2823 - 0.9436
10.7273 59000 0.0971 0.2943 - 0.9403
10.9091 60000 0.1002 0.2915 - 0.9425
11.0909 61000 0.0939 0.2980 - 0.9433
11.2727 62000 0.0869 0.2932 - 0.9460
11.4545 63000 0.0888 0.2885 - 0.9442
11.6364 64000 0.0915 0.2844 - 0.9431
11.8182 65000 0.092 0.3085 - 0.9444
12.0 66000 0.0933 0.2833 - 0.9456
12.1818 67000 0.0796 0.3031 - 0.9457
12.3636 68000 0.082 0.2934 - 0.9445
12.5455 69000 0.0865 0.3030 - 0.9426
12.7273 70000 0.0858 0.3028 - 0.9448
12.9091 71000 0.0877 0.2930 - 0.9456
13.0909 72000 0.0791 0.3105 - 0.9460
13.2727 73000 0.0754 0.3189 - 0.9468
13.4545 74000 0.0777 0.2985 - 0.9454
13.6364 75000 0.0796 0.3170 - 0.9445
13.8182 76000 0.0799 0.2851 - 0.9468
14.0 77000 0.0816 0.3022 - 0.9468
14.1818 78000 0.0706 0.3169 - 0.9449
14.3636 79000 0.0727 0.3114 - 0.9464
14.5455 80000 0.073 0.3059 - 0.9464
14.7273 81000 0.0745 0.3108 - 0.9427
14.9091 82000 0.0741 0.3149 - 0.9447
15.0909 83000 0.0704 0.3213 - 0.9441
15.2727 84000 0.0649 0.3245 - 0.9450
15.4545 85000 0.0684 0.3180 - 0.9452
15.6364 86000 0.0694 0.3320 - 0.9425
15.8182 87000 0.0681 0.3138 - 0.9449
16.0 88000 0.0691 0.3158 - 0.9460
16.1818 89000 0.0608 0.3317 - 0.9457
16.3636 90000 0.0609 0.3253 - 0.9438
16.5455 91000 0.0621 0.3298 - 0.9458
16.7273 92000 0.0648 0.3246 - 0.9428
16.9091 93000 0.0657 0.3229 - 0.9432
17.0909 94000 0.0596 0.3327 - 0.9446
17.2727 95000 0.0579 0.3186 - 0.9419
17.4545 96000 0.0581 0.3272 - 0.9459
17.6364 97000 0.0592 0.3344 - 0.9423
17.8182 98000 0.06 0.3446 - 0.9409
18.0 99000 0.0598 0.3280 - 0.9452
18.1818 100000 0.0515 0.3577 - 0.9428
18.3636 101000 0.0539 0.3418 - 0.9459
18.5455 102000 0.0544 0.3365 - 0.9426
18.7273 103000 0.054 0.3294 - 0.9451
18.9091 104000 0.0568 0.3420 - 0.9391
19.0909 105000 0.0516 0.3650 - 0.9443
19.2727 106000 0.0482 0.3546 - 0.9458
19.4545 107000 0.0497 0.3338 - 0.9455
19.6364 108000 0.0495 0.3524 - 0.9426
19.8182 109000 0.051 0.3556 - 0.9436
20.0 110000 0.0512 0.3323 - 0.9412
20.1818 111000 0.0433 0.3572 - 0.9423
20.3636 112000 0.045 0.3678 - 0.9431
20.5455 113000 0.0466 0.3450 - 0.9437
20.7273 114000 0.047 0.3616 - 0.9406
20.9091 115000 0.048 0.3538 - 0.9453
21.0909 116000 0.044 0.3638 - 0.9454
21.2727 117000 0.0417 0.3767 - 0.9448
21.4545 118000 0.0428 0.3773 - 0.9455
21.6364 119000 0.0421 0.3613 - 0.9453
21.8182 120000 0.0442 0.3795 - 0.9426
22.0 121000 0.0453 0.3758 - 0.9442
22.1818 122000 0.0379 0.3819 - 0.9436
22.3636 123000 0.0397 0.3665 - 0.9396
22.5455 124000 0.039 0.3871 - 0.9436
22.7273 125000 0.0398 0.3752 - 0.9443
22.9091 126000 0.0408 0.3755 - 0.9424
23.0909 127000 0.0388 0.3698 - 0.9464
23.2727 128000 0.0348 0.3828 - 0.9400
23.4545 129000 0.0353 0.3814 - 0.9426
23.6364 130000 0.0375 0.3907 - 0.9423
23.8182 131000 0.0366 0.4085 - 0.9412
24.0 132000 0.0388 0.3734 - 0.9350
24.1818 133000 0.0321 0.4105 - 0.9442
24.3636 134000 0.0329 0.4038 - 0.9433
24.5455 135000 0.0335 0.4123 - 0.9431
24.7273 136000 0.0351 0.3945 - 0.9431
24.9091 137000 0.0347 0.3995 - 0.9446
25.0909 138000 0.0322 0.4154 - 0.9442
25.2727 139000 0.0312 0.3900 - 0.9432
25.4545 140000 0.0301 0.4083 - 0.9417
25.6364 141000 0.0318 0.4146 - 0.9445
25.8182 142000 0.0321 0.4198 - 0.9437
26.0 143000 0.032 0.4168 - 0.9405
26.1818 144000 0.0266 0.4293 - 0.9396
26.3636 145000 0.0277 0.4234 - 0.9421
26.5455 146000 0.0288 0.4309 - 0.9438
26.7273 147000 0.0292 0.4215 - 0.9398
26.9091 148000 0.0294 0.4020 - 0.9412
27.0909 149000 0.0272 0.4342 - 0.9438
27.2727 150000 0.025 0.4434 - 0.9402
27.4545 151000 0.027 0.4178 - 0.9435
27.6364 152000 0.0257 0.4396 - 0.9428
27.8182 153000 0.028 0.4099 - 0.9405
28.0 154000 0.0275 0.4185 - 0.9443
28.1818 155000 0.0236 0.4375 - 0.9456
28.3636 156000 0.0237 0.4232 - 0.9409
28.5455 157000 0.0237 0.4642 - 0.9430
28.7273 158000 0.0249 0.4374 - 0.9447
28.9091 159000 0.0258 0.4329 - 0.9451
29.0909 160000 0.0219 0.4867 - 0.9454
29.2727 161000 0.0216 0.4737 - 0.9435
29.4545 162000 0.0218 0.4577 - 0.9449
29.6364 163000 0.0223 0.4589 - 0.9424
29.8182 164000 0.0223 0.4410 - 0.9452
30.0 165000 0.0236 0.4477 - 0.9432
30.1818 166000 0.0203 0.4798 - 0.9459
30.3636 167000 0.02 0.4600 - 0.9453
30.5455 168000 0.0206 0.4492 - 0.9419
30.7273 169000 0.0203 0.4839 - 0.9435
30.9091 170000 0.0212 0.4731 - 0.9438
31.0909 171000 0.0196 0.4621 - 0.9434
31.2727 172000 0.0178 0.4986 - 0.9441
31.4545 173000 0.0177 0.4871 - 0.9431
31.6364 174000 0.0201 0.4520 - 0.9445
31.8182 175000 0.0191 0.4571 - 0.9429
32.0 176000 0.0201 0.4871 - 0.9453
32.1818 177000 0.0156 0.5061 - 0.9440
32.3636 178000 0.0174 0.4704 - 0.9444
32.5455 179000 0.0175 0.4900 - 0.9430
32.7273 180000 0.0175 0.4861 - 0.9386
32.9091 181000 0.0178 0.5005 - 0.9436
33.0909 182000 0.0163 0.4934 - 0.9441
33.2727 183000 0.0149 0.5065 - 0.9426
33.4545 184000 0.0157 0.4973 - 0.9444
33.6364 185000 0.0164 0.4993 - 0.9410
33.8182 186000 0.0164 0.4904 - 0.9411
34.0 187000 0.0167 0.5096 - 0.9432
34.1818 188000 0.0136 0.4960 - 0.9399
34.3636 189000 0.0142 0.5188 - 0.9447
34.5455 190000 0.0144 0.5139 - 0.9434
34.7273 191000 0.0149 0.4919 - 0.9416
34.9091 192000 0.0149 0.4775 - 0.9453
35.0909 193000 0.0127 0.5546 - 0.9437
35.2727 194000 0.0124 0.5440 - 0.9444
35.4545 195000 0.0126 0.5571 - 0.9435
35.6364 196000 0.0131 0.5127 - 0.9428
35.8182 197000 0.0134 0.5167 - 0.9415
36.0 198000 0.0134 0.4939 - 0.9394
36.1818 199000 0.011 0.5279 - 0.9421
36.3636 200000 0.0115 0.5336 - 0.9434
36.5455 201000 0.0113 0.5626 - 0.9437
36.7273 202000 0.012 0.5316 - 0.9421
36.9091 203000 0.0121 0.5222 - 0.9428
37.0909 204000 0.0107 0.5618 - 0.9450
37.2727 205000 0.0107 0.5508 - 0.9430
37.4545 206000 0.0106 0.5414 - 0.9432
37.6364 207000 0.0106 0.5522 - 0.9432
37.8182 208000 0.0111 0.5524 - 0.9434
38.0 209000 0.012 0.5176 - 0.9434
38.1818 210000 0.0087 0.5742 - 0.9432
38.3636 211000 0.0092 0.5686 - 0.9441
38.5455 212000 0.0099 0.5699 - 0.9437
38.7273 213000 0.0094 0.5733 - 0.9435
38.9091 214000 0.0097 0.5516 - 0.9431
39.0909 215000 0.009 0.5923 - 0.9425
39.2727 216000 0.0078 0.5925 - 0.9427
39.4545 217000 0.0086 0.5703 - 0.9433
39.6364 218000 0.0087 0.5921 - 0.9444
39.8182 219000 0.0085 0.5859 - 0.9436
40.0 220000 0.0091 0.5577 - 0.9430
40.1818 221000 0.0077 0.5844 - 0.9425
40.3636 222000 0.0077 0.5691 - 0.9396
40.5455 223000 0.008 0.5794 - 0.9398
40.7273 224000 0.0073 0.6036 - 0.9370
40.9091 225000 0.0083 0.5754 - 0.9419
41.0909 226000 0.0078 0.6141 - 0.9416
41.2727 227000 0.0069 0.6332 - 0.9407
41.4545 228000 0.007 0.6220 - 0.9417
41.6364 229000 0.0075 0.6110 - 0.9413
41.8182 230000 0.007 0.6248 - 0.9423
42.0 231000 0.0072 0.5950 - 0.9396
42.1818 232000 0.0059 0.6428 - 0.9363
42.3636 233000 0.0065 0.6298 - 0.9410
42.5455 234000 0.0065 0.6166 - 0.9424
42.7273 235000 0.0066 0.5990 - 0.9424
42.9091 236000 0.0065 0.6297 - 0.9405
43.0909 237000 0.0057 0.6483 - 0.9376
43.2727 238000 0.0058 0.6077 - 0.9407
43.4545 239000 0.0056 0.6420 - 0.9376
43.6364 240000 0.0059 0.6574 - 0.9400
43.8182 241000 0.0051 0.6819 - 0.9372
44.0 242000 0.0055 0.6567 - 0.9348
44.1818 243000 0.0051 0.6697 - 0.9297
44.3636 244000 0.005 0.6459 - 0.9377
44.5455 245000 0.0047 0.6693 - 0.9353
44.7273 246000 0.0054 0.6589 - 0.9274
44.9091 247000 0.0051 0.6886 - 0.9318
45.0909 248000 0.0047 0.6886 - 0.9388
45.2727 249000 0.0045 0.6959 - 0.9320
45.4545 250000 0.0045 0.6827 - 0.9347
45.6364 251000 0.0042 0.6706 - 0.9314
45.8182 252000 0.0043 0.6858 - 0.9350
46.0 253000 0.0045 0.6926 - 0.9310
46.1818 254000 0.0041 0.7091 - 0.9341
46.3636 255000 0.0038 0.711 - 0.932
46.5455 256000 0.0039 0.6991 - 0.9277
46.7273 257000 0.0038 0.7068 - 0.9213
46.9091 258000 0.0043 0.7026 - 0.9295
47.0909 259000 0.0041 0.7031 - 0.9390
47.2727 260000 0.0037 0.6905 - 0.9388
47.4545 261000 0.0035 0.7044 - 0.9386
47.6364 262000 0.0038 0.7005 - 0.9378
47.8182 263000 0.0039 0.7064 - 0.9398
48.0 264000 0.0034 0.7112 - 0.9378
48.1818 265000 0.0034 0.7092 - 0.9377
48.3636 266000 0.0029 0.7286 - 0.9376
48.5455 267000 0.0034 0.7270 - 0.9386
48.7273 268000 0.0035 0.7101 - 0.9382
48.9091 269000 0.0035 0.7127 - 0.9374
49.0909 270000 0.0031 0.7228 - 0.9357
49.2727 271000 0.0029 0.7289 - 0.9356
49.4545 272000 0.003 0.7309 - 0.9356
49.6364 273000 0.003 0.7308 - 0.9358
49.8182 274000 0.0033 0.7293 - 0.9355
50.0 275000 0.003 0.7302 - 0.9357
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.12.3
  • Sentence Transformers: 5.1.0
  • Transformers: 4.55.0
  • PyTorch: 2.8.0+cu128
  • Accelerate: 1.10.0
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

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",
}
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