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Redis fine-tuned CrossEncoder model for semantic caching on LangCache

Name: langcache-reranker-v1-bce
Availability: InStock

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 Type: Cross Encoder
Base model: Alibaba-NLP/gte-reranker-modernbert-base
Maximum Sequence Length: 8192 tokens
Number of Output Labels: 1 label
Training Dataset:
- LangCache Sentence Pairs (all)
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Cross Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Cross Encoders on Hugging Face

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.

python

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

Datasets: val and test
Evaluated with CrossEncoderClassificationEvaluator

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:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": null 4}

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:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": null 4}

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

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",
}

	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%

redis/langcache-reranker-v1-bce

Redis fine-tuned CrossEncoder model for semantic caching on LangCache

Model Details

Model Description

Model Sources

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Cross Encoder Classification

Training Details

Training Dataset

LangCache Sentence Pairs (all)

Evaluation Dataset

LangCache Sentence Pairs (all)

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

redis/
langcache-reranker-v1-bce