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redis/langcache-reranker-v1-bce
langcache-reranker-v1-bce is a text ranking model from redis. Use it for the text ranking task on the model card, and read the license before you ship it in a product. It is set up for sentence-transformers. The card lists the license as apache-2.0.
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…
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From the Hugging Face model README
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.
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': ...}, ...]
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
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val and test| 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 |
| sentence1 | sentence2 | label | |
|---|---|---|---|
| type | string | string | int |
| details | <ul><li>min: 27 characters</li><li>mean: 112.72 characters</li><li>max: 197 characters</li></ul> | <ul><li>min: 27 characters</li><li>mean: 112.54 characters</li><li>max: 198 characters</li></ul> | <ul><li>0: ~50.30%</li><li>1: ~49.70%</li></ul> |
| sentence1 | sentence2 | label |
|---|---|---|
| <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>1</code> |
| <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | <code>0</code> |
| <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> |
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
| sentence1 | sentence2 | label | |
|---|---|---|---|
| type | string | string | int |
| details | <ul><li>min: 27 characters</li><li>mean: 112.72 characters</li><li>max: 197 characters</li></ul> | <ul><li>min: 27 characters</li><li>mean: 112.54 characters</li><li>max: 198 characters</li></ul> | <ul><li>0: ~50.30%</li><li>1: ~49.70%</li></ul> |
| sentence1 | sentence2 | label |
|---|---|---|
| <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>1</code> |
| <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | <code>0</code> |
| <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> |
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
eval_strategy: stepsper_device_train_batch_size: 48per_device_eval_batch_size: 48learning_rate: 0.0002num_train_epochs: 50warmup_steps: 100load_best_model_at_end: Trueoptim: adamw_torchpush_to_hub: Truehub_model_id: aditeyabaral-redis/langcache-reranker-v1-test2overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 48per_device_eval_batch_size: 48per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 0.0002weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 50max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.0warmup_steps: 100log_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: 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: 0dataloader_prefetch_factor: Nonepast_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, '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: Trueresume_from_checkpoint: Nonehub_model_id: aditeyabaral-redis/langcache-reranker-v1-test2hub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsehub_revision: Nonegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_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: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseliger_kernel_config: Noneeval_use_gather_object: Falseaverage_tokens_across_devices: Trueprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: proportionalrouter_mapping: {}learning_rate_mapping: {}| 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 |
@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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