Redis fine-tuned CrossEncoder model for semantic caching on LangCache

This is a Cross Encoder model finetuned from cross-encoder/ms-marco-MiniLM-L6-v2 on the LangCache Sentence Pairs (subsets=['all'], train+val=True) 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: cross-encoder/ms-marco-MiniLM-L6-v2
Maximum Sequence Length: 512 tokens
Number of Output Labels: 1 label
Training Dataset:
- LangCache Sentence Pairs (subsets=['all'], train+val=True)
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.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("redis/langcache-reranker-v2-miniL6-bce-eps0.5")
# 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

Dataset: test_cls
Evaluated with CrossEncoderClassificationEvaluator

Metric	Value
accuracy	0.8159
accuracy_threshold	-3.0
f1	0.8052
f1_threshold	-3.1719
precision	0.7496
recall	0.8697
average_precision	0.8059

Cross Encoder Reranking

Datasets: NanoQuoraRetrieval_R25, NanoMSMARCO_R25 and NanoNQ_R25

Evaluated with CrossEncoderRerankingEvaluator with these parameters:

{
    "at_k": 1,
    "always_rerank_positives": true
}

Metric	NanoQuoraRetrieval_R25	NanoMSMARCO_R25	NanoNQ_R25
map	0.7293 (-0.1011)	0.1875 (-0.3003)	0.1969 (-0.2231)
mrr@1	0.6400 (-0.1600)	0.0800 (-0.2600)	0.0400 (-0.2000)
ndcg@1	0.6500 (-0.1500)	0.0800 (-0.2600)	0.0400 (-0.2000)

Cross Encoder Nano BEIR

Dataset: NanoBEIR_R25_mean

Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "QuoraRetrieval",
        "MSMARCO",
        "NQ"
    ],
    "rerank_k": 25,
    "at_k": 1,
    "always_rerank_positives": true
}

Metric	Value
map	0.3712 (-0.2082)
mrr@1	0.2533 (-0.2067)
ndcg@1	0.2567 (-0.2033)

Training Details

Training Dataset

LangCache Sentence Pairs (subsets=['all'], train+val=True)

Dataset: LangCache Sentence Pairs (subsets=['all'], train+val=True)
Size: 40,015,318 training samples
Columns: sentence1, sentence2, and label

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	label
type	string	string	float
details	min: 27 characters mean: 110.99 characters max: 199 characters	min: 29 characters mean: 110.74 characters max: 191 characters	min: 0.25 mean: 0.49 max: 0.75

Samples:

sentence1	sentence2	label
`The film was remade in Telugu with the same name in 1981 by Chandra Mohan , Jayasudha , Chakravarthy and S. P. Balasubrahmanyam starring K. Vasu .`	`The film was written in Telugu with the same name in 1981 by Chandra Mohan , Jayasudha , Chakravarthy and S. P. Balasubrahmanyam with K. Vasu .`	`0.75`
`The Cauchy stress tensor has therefore the following form`	`Therefore , the cauchy tensor has the following form`	`0.75`
`John Jairo Culma , sometimes spelled as Jhon Jairo Culma ( born 17 March 1981 ) is a Colombian footballer .`	`John Jairo Culma , sometimes known as Jhon Jairo Culma ( born March 17 , 1981 ) , is a Colombian footballer .`	`0.75`

Loss: BinaryCrossEntropyLoss with these parameters:

{
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": 0.1028856709599495
}

Evaluation Dataset

LangCache Sentence Pairs (split=test)

Dataset: LangCache Sentence Pairs (split=test)
Size: 74,265 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": 0.1028856709599495
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 48
per_device_eval_batch_size: 48
learning_rate: 0.001
weight_decay: 0.001
num_train_epochs: 5
warmup_ratio: 0.1
load_best_model_at_end: True
optim: adamw_torch
ddp_find_unused_parameters: False
push_to_hub: True
hub_model_id: redis/langcache-reranker-v2-miniL6-bce-eps0.5
eval_on_start: True
batch_sampler: no_duplicates

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: 0.001
weight_decay: 0.001
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
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}
parallelism_config: 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: False
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: redis/langcache-reranker-v2-miniL6-bce-eps0.5
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: True
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	test_cls_average_precision	NanoQuoraRetrieval_R25_ndcg@1	NanoMSMARCO_R25_ndcg@1	NanoNQ_R25_ndcg@1	NanoBEIR_R25_mean_ndcg@1
0	0	-	1.8794	0.5568	0.8800 (+0.0800)	0.5000 (+0.1600)	0.6600 (+0.4200)	0.6800 (+0.2200)
0.0048	1000	0.8567	-	-	-	-	-	-
0.0096	2000	0.2047	-	-	-	-	-	-
0.0144	3000	0.1884	-	-	-	-	-	-
0.0192	4000	0.1859	-	-	-	-	-	-
0.0240	5000	0.184	-	-	-	-	-	-
0.0288	6000	0.1824	-	-	-	-	-	-
0.0336	7000	0.1815	-	-	-	-	-	-
0.0384	8000	0.1809	-	-	-	-	-	-
0.0432	9000	0.1805	-	-	-	-	-	-
0.0480	10000	0.1802	-	-	-	-	-	-
0.0528	11000	0.1799	-	-	-	-	-	-
0.0576	12000	0.1797	-	-	-	-	-	-
0.0624	13000	0.1793	-	-	-	-	-	-
0.0672	14000	0.1788	-	-	-	-	-	-
0.0720	15000	0.1783	-	-	-	-	-	-
0.0768	16000	0.1778	-	-	-	-	-	-
0.0816	17000	0.1775	-	-	-	-	-	-
0.0864	18000	0.1772	-	-	-	-	-	-
0.0912	19000	0.1769	-	-	-	-	-	-
0.0960	20000	0.1768	0.1604	0.5657	0.3400 (-0.4600)	0.1000 (-0.2400)	0.1200 (-0.1200)	0.1867 (-0.2733)
0.1008	21000	0.1766	-	-	-	-	-	-
0.1056	22000	0.1764	-	-	-	-	-	-
0.1104	23000	0.1761	-	-	-	-	-	-
0.1152	24000	0.176	-	-	-	-	-	-
0.1200	25000	0.1758	-	-	-	-	-	-
0.1248	26000	0.1757	-	-	-	-	-	-
0.1296	27000	0.1756	-	-	-	-	-	-
0.1343	28000	0.1755	-	-	-	-	-	-
0.1391	29000	0.1754	-	-	-	-	-	-
0.1439	30000	0.1752	-	-	-	-	-	-
0.1487	31000	0.1751	-	-	-	-	-	-
0.1535	32000	0.1752	-	-	-	-	-	-
0.1583	33000	0.1751	-	-	-	-	-	-
0.1631	34000	0.175	-	-	-	-	-	-
0.1679	35000	0.1749	-	-	-	-	-	-
0.1727	36000	0.1749	-	-	-	-	-	-
0.1775	37000	0.1749	-	-	-	-	-	-
0.1823	38000	0.1748	-	-	-	-	-	-
0.1871	39000	0.1748	-	-	-	-	-	-
0.1919	40000	0.1747	0.1499	0.6555	0.4600 (-0.3400)	0.3000 (-0.0400)	0.1600 (-0.0800)	0.3067 (-0.1533)
0.1967	41000	0.1749	-	-	-	-	-	-
0.2015	42000	0.1747	-	-	-	-	-	-
0.2063	43000	0.1747	-	-	-	-	-	-
0.2111	44000	0.1747	-	-	-	-	-	-
0.2159	45000	0.1746	-	-	-	-	-	-
0.2207	46000	0.1746	-	-	-	-	-	-
0.2255	47000	0.1747	-	-	-	-	-	-
0.2303	48000	0.1746	-	-	-	-	-	-
0.2351	49000	0.1746	-	-	-	-	-	-
0.2399	50000	0.1745	-	-	-	-	-	-
0.2447	51000	0.1746	-	-	-	-	-	-
0.2495	52000	0.1746	-	-	-	-	-	-
0.2543	53000	0.1746	-	-	-	-	-	-
0.2591	54000	0.1746	-	-	-	-	-	-
0.2639	55000	0.1745	-	-	-	-	-	-
0.2687	56000	0.1745	-	-	-	-	-	-
0.2735	57000	0.1745	-	-	-	-	-	-
0.2783	58000	0.1744	-	-	-	-	-	-
0.2831	59000	0.1745	-	-	-	-	-	-
0.2879	60000	0.1744	0.1453	0.7049	0.5400 (-0.2600)	0.2800 (-0.0600)	0.2500 (+0.0100)	0.3567 (-0.1033)
0.2927	61000	0.1744	-	-	-	-	-	-
0.2975	62000	0.1744	-	-	-	-	-	-
0.3023	63000	0.1745	-	-	-	-	-	-
0.3071	64000	0.1744	-	-	-	-	-	-
0.3119	65000	0.1744	-	-	-	-	-	-
0.3167	66000	0.1744	-	-	-	-	-	-
0.3215	67000	0.1744	-	-	-	-	-	-
0.3263	68000	0.1744	-	-	-	-	-	-
0.3311	69000	0.1745	-	-	-	-	-	-
0.3359	70000	0.1743	-	-	-	-	-	-
0.3407	71000	0.1743	-	-	-	-	-	-
0.3455	72000	0.1743	-	-	-	-	-	-
0.3503	73000	0.1744	-	-	-	-	-	-
0.3551	74000	0.1743	-	-	-	-	-	-
0.3599	75000	0.1743	-	-	-	-	-	-
0.3647	76000	0.1743	-	-	-	-	-	-
0.3695	77000	0.1743	-	-	-	-	-	-
0.3743	78000	0.1743	-	-	-	-	-	-
0.3791	79000	0.1743	-	-	-	-	-	-
0.3839	80000	0.1743	0.1445	0.6867	0.4000 (-0.4000)	0.2000 (-0.1400)	0.2100 (-0.0300)	0.2700 (-0.1900)
0.3887	81000	0.1743	-	-	-	-	-	-
0.3934	82000	0.1743	-	-	-	-	-	-
0.3982	83000	0.1742	-	-	-	-	-	-
0.4030	84000	0.1743	-	-	-	-	-	-
0.4078	85000	0.1742	-	-	-	-	-	-
0.4126	86000	0.1742	-	-	-	-	-	-
0.4174	87000	0.1742	-	-	-	-	-	-
0.4222	88000	0.1742	-	-	-	-	-	-
0.4270	89000	0.1742	-	-	-	-	-	-
0.4318	90000	0.1742	-	-	-	-	-	-
0.4366	91000	0.1742	-	-	-	-	-	-
0.4414	92000	0.1742	-	-	-	-	-	-
0.4462	93000	0.1742	-	-	-	-	-	-
0.4510	94000	0.1741	-	-	-	-	-	-
0.4558	95000	0.1741	-	-	-	-	-	-
0.4606	96000	0.1742	-	-	-	-	-	-
0.4654	97000	0.1741	-	-	-	-	-	-
0.4702	98000	0.1741	-	-	-	-	-	-
0.4750	99000	0.1741	-	-	-	-	-	-
0.4798	100000	0.1742	0.1393	0.7193	0.4800 (-0.3200)	0.2400 (-0.1000)	0.1500 (-0.0900)	0.2900 (-0.1700)
0.4846	101000	0.1741	-	-	-	-	-	-
0.4894	102000	0.1741	-	-	-	-	-	-
0.4942	103000	0.1741	-	-	-	-	-	-
0.4990	104000	0.1741	-	-	-	-	-	-
0.5038	105000	0.1741	-	-	-	-	-	-
0.5086	106000	0.1741	-	-	-	-	-	-
0.5134	107000	0.1741	-	-	-	-	-	-
0.5182	108000	0.1741	-	-	-	-	-	-
0.5230	109000	0.174	-	-	-	-	-	-
0.5278	110000	0.1741	-	-	-	-	-	-
0.5326	111000	0.1741	-	-	-	-	-	-
0.5374	112000	0.174	-	-	-	-	-	-
0.5422	113000	0.1741	-	-	-	-	-	-
0.5470	114000	0.174	-	-	-	-	-	-
0.5518	115000	0.174	-	-	-	-	-	-
0.5566	116000	0.174	-	-	-	-	-	-
0.5614	117000	0.174	-	-	-	-	-	-
0.5662	118000	0.174	-	-	-	-	-	-
0.5710	119000	0.174	-	-	-	-	-	-
0.5758	120000	0.1739	0.1365	0.7192	0.3900 (-0.4100)	0.1700 (-0.1700)	0.2000 (-0.0400)	0.2533 (-0.2067)
0.5806	121000	0.174	-	-	-	-	-	-
0.5854	122000	0.174	-	-	-	-	-	-
0.5902	123000	0.174	-	-	-	-	-	-
0.5950	124000	0.174	-	-	-	-	-	-
0.5998	125000	0.1739	-	-	-	-	-	-
0.6046	126000	0.1739	-	-	-	-	-	-
0.6094	127000	0.1739	-	-	-	-	-	-
0.6142	128000	0.174	-	-	-	-	-	-
0.6190	129000	0.1739	-	-	-	-	-	-
0.6238	130000	0.1739	-	-	-	-	-	-
0.6286	131000	0.1739	-	-	-	-	-	-
0.6334	132000	0.1739	-	-	-	-	-	-
0.6382	133000	0.1738	-	-	-	-	-	-
0.6430	134000	0.1739	-	-	-	-	-	-
0.6478	135000	0.1739	-	-	-	-	-	-
0.6526	136000	0.1739	-	-	-	-	-	-
0.6573	137000	0.1739	-	-	-	-	-	-
0.6621	138000	0.1738	-	-	-	-	-	-
0.6669	139000	0.1739	-	-	-	-	-	-
0.6717	140000	0.1739	0.1345	0.7220	0.3800 (-0.4200)	0.1400 (-0.2000)	0.1000 (-0.1400)	0.2067 (-0.2533)
0.6765	141000	0.1738	-	-	-	-	-	-
0.6813	142000	0.1739	-	-	-	-	-	-
0.6861	143000	0.1738	-	-	-	-	-	-
0.6909	144000	0.1738	-	-	-	-	-	-
0.6957	145000	0.1738	-	-	-	-	-	-
0.7005	146000	0.1738	-	-	-	-	-	-
0.7053	147000	0.1738	-	-	-	-	-	-
0.7101	148000	0.1738	-	-	-	-	-	-
0.7149	149000	0.1738	-	-	-	-	-	-
0.7197	150000	0.1739	-	-	-	-	-	-
0.7245	151000	0.1738	-	-	-	-	-	-
0.7293	152000	0.1738	-	-	-	-	-	-
0.7341	153000	0.1738	-	-	-	-	-	-
0.7389	154000	0.1738	-	-	-	-	-	-
0.7437	155000	0.1737	-	-	-	-	-	-
0.7485	156000	0.1738	-	-	-	-	-	-
0.7533	157000	0.1738	-	-	-	-	-	-
0.7581	158000	0.1738	-	-	-	-	-	-
0.7629	159000	0.1737	-	-	-	-	-	-
0.7677	160000	0.1738	0.1336	0.7288	0.3000 (-0.5000)	0.1200 (-0.2200)	0.1100 (-0.1300)	0.1767 (-0.2833)
0.7725	161000	0.1738	-	-	-	-	-	-
0.7773	162000	0.1737	-	-	-	-	-	-
0.7821	163000	0.1738	-	-	-	-	-	-
0.7869	164000	0.1739	-	-	-	-	-	-
0.7917	165000	0.1737	-	-	-	-	-	-
0.7965	166000	0.1737	-	-	-	-	-	-
0.8013	167000	0.1738	-	-	-	-	-	-
0.8061	168000	0.1738	-	-	-	-	-	-
0.8109	169000	0.1738	-	-	-	-	-	-
0.8157	170000	0.1737	-	-	-	-	-	-
0.8205	171000	0.1738	-	-	-	-	-	-
0.8253	172000	0.1737	-	-	-	-	-	-
0.8301	173000	0.1738	-	-	-	-	-	-
0.8349	174000	0.1738	-	-	-	-	-	-
0.8397	175000	0.1738	-	-	-	-	-	-
0.8445	176000	0.1737	-	-	-	-	-	-
0.8493	177000	0.1738	-	-	-	-	-	-
0.8541	178000	0.1737	-	-	-	-	-	-
0.8589	179000	0.1737	-	-	-	-	-	-
0.8637	180000	0.1737	0.1331	0.7524	0.4600 (-0.3400)	0.0600 (-0.2800)	0.1000 (-0.1400)	0.2067 (-0.2533)
0.8685	181000	0.1737	-	-	-	-	-	-
0.8733	182000	0.1737	-	-	-	-	-	-
0.8781	183000	0.1737	-	-	-	-	-	-
0.8829	184000	0.1737	-	-	-	-	-	-
0.8877	185000	0.1738	-	-	-	-	-	-
0.8925	186000	0.1738	-	-	-	-	-	-
0.8973	187000	0.1737	-	-	-	-	-	-
0.9021	188000	0.1738	-	-	-	-	-	-
0.9069	189000	0.1737	-	-	-	-	-	-
0.9117	190000	0.1738	-	-	-	-	-	-
0.9164	191000	0.1738	-	-	-	-	-	-
0.9212	192000	0.1737	-	-	-	-	-	-
0.9260	193000	0.1737	-	-	-	-	-	-
0.9308	194000	0.1737	-	-	-	-	-	-
0.9356	195000	0.1737	-	-	-	-	-	-
0.9404	196000	0.1737	-	-	-	-	-	-
0.9452	197000	0.1737	-	-	-	-	-	-
0.9500	198000	0.1737	-	-	-	-	-	-
0.9548	199000	0.1737	-	-	-	-	-	-
0.9596	200000	0.1736	0.1321	0.7659	0.5800 (-0.2200)	0.0200 (-0.3200)	0.0600 (-0.1800)	0.2200 (-0.2400)
0.9644	201000	0.1738	-	-	-	-	-	-
0.9692	202000	0.1737	-	-	-	-	-	-
0.9740	203000	0.1737	-	-	-	-	-	-
0.9788	204000	0.1737	-	-	-	-	-	-
0.9836	205000	0.1737	-	-	-	-	-	-
0.9884	206000	0.1737	-	-	-	-	-	-
0.9932	207000	0.1737	-	-	-	-	-	-
0.9980	208000	0.1736	-	-	-	-	-	-
1.0028	209000	0.1736	-	-	-	-	-	-
1.0076	210000	0.1736	-	-	-	-	-	-
1.0124	211000	0.1737	-	-	-	-	-	-
1.0172	212000	0.1737	-	-	-	-	-	-
1.0220	213000	0.1736	-	-	-	-	-	-
1.0268	214000	0.1737	-	-	-	-	-	-
1.0316	215000	0.1736	-	-	-	-	-	-
1.0364	216000	0.1736	-	-	-	-	-	-
1.0412	217000	0.1737	-	-	-	-	-	-
1.0460	218000	0.1736	-	-	-	-	-	-
1.0508	219000	0.1736	-	-	-	-	-	-
1.0556	220000	0.1737	0.1308	0.7535	0.4200 (-0.3800)	0.0600 (-0.2800)	0.1100 (-0.1300)	0.1967 (-0.2633)
1.0604	221000	0.1737	-	-	-	-	-	-
1.0652	222000	0.1736	-	-	-	-	-	-
1.0700	223000	0.1736	-	-	-	-	-	-
1.0748	224000	0.1736	-	-	-	-	-	-
1.0796	225000	0.1737	-	-	-	-	-	-
1.0844	226000	0.1736	-	-	-	-	-	-
1.0892	227000	0.1736	-	-	-	-	-	-
1.0940	228000	0.1736	-	-	-	-	-	-
1.0988	229000	0.1737	-	-	-	-	-	-
1.1036	230000	0.1736	-	-	-	-	-	-
1.1084	231000	0.1737	-	-	-	-	-	-
1.1132	232000	0.1736	-	-	-	-	-	-
1.1180	233000	0.1737	-	-	-	-	-	-
1.1228	234000	0.1737	-	-	-	-	-	-
1.1276	235000	0.1736	-	-	-	-	-	-
1.1324	236000	0.1737	-	-	-	-	-	-
1.1372	237000	0.1737	-	-	-	-	-	-
1.1420	238000	0.1736	-	-	-	-	-	-
1.1468	239000	0.1736	-	-	-	-	-	-
1.1516	240000	0.1736	0.1307	0.7831	0.5800 (-0.2200)	0.0400 (-0.3000)	0.1300 (-0.1100)	0.2500 (-0.2100)
1.1564	241000	0.1737	-	-	-	-	-	-
1.1612	242000	0.1736	-	-	-	-	-	-
1.1660	243000	0.1735	-	-	-	-	-	-
1.1708	244000	0.1736	-	-	-	-	-	-
1.1756	245000	0.1736	-	-	-	-	-	-
1.1803	246000	0.1735	-	-	-	-	-	-
1.1851	247000	0.1736	-	-	-	-	-	-
1.1899	248000	0.1736	-	-	-	-	-	-
1.1947	249000	0.1736	-	-	-	-	-	-
1.1995	250000	0.1736	-	-	-	-	-	-
1.2043	251000	0.1735	-	-	-	-	-	-
1.2091	252000	0.1735	-	-	-	-	-	-
1.2139	253000	0.1736	-	-	-	-	-	-
1.2187	254000	0.1736	-	-	-	-	-	-
1.2235	255000	0.1736	-	-	-	-	-	-
1.2283	256000	0.1736	-	-	-	-	-	-
1.2331	257000	0.1736	-	-	-	-	-	-
1.2379	258000	0.1736	-	-	-	-	-	-
1.2427	259000	0.1735	-	-	-	-	-	-
1.2475	260000	0.1735	0.1294	0.7997	0.5600 (-0.2400)	0.0600 (-0.2800)	0.0600 (-0.1800)	0.2267 (-0.2333)
1.2523	261000	0.1736	-	-	-	-	-	-
1.2571	262000	0.1736	-	-	-	-	-	-
1.2619	263000	0.1735	-	-	-	-	-	-
1.2667	264000	0.1735	-	-	-	-	-	-
1.2715	265000	0.1736	-	-	-	-	-	-
1.2763	266000	0.1735	-	-	-	-	-	-
1.2811	267000	0.1736	-	-	-	-	-	-
1.2859	268000	0.1736	-	-	-	-	-	-
1.2907	269000	0.1736	-	-	-	-	-	-
1.2955	270000	0.1734	-	-	-	-	-	-
1.3003	271000	0.1736	-	-	-	-	-	-
1.3051	272000	0.1735	-	-	-	-	-	-
1.3099	273000	0.1735	-	-	-	-	-	-
1.3147	274000	0.1736	-	-	-	-	-	-
1.3195	275000	0.1736	-	-	-	-	-	-
1.3243	276000	0.1735	-	-	-	-	-	-
1.3291	277000	0.1736	-	-	-	-	-	-
1.3339	278000	0.1735	-	-	-	-	-	-
1.3387	279000	0.1735	-	-	-	-	-	-
1.3435	280000	0.1734	0.1303	0.7794	0.5800 (-0.2200)	0.0400 (-0.3000)	0.0800 (-0.1600)	0.2333 (-0.2267)
1.3483	281000	0.1736	-	-	-	-	-	-
1.3531	282000	0.1735	-	-	-	-	-	-
1.3579	283000	0.1734	-	-	-	-	-	-
1.3627	284000	0.1735	-	-	-	-	-	-
1.3675	285000	0.1735	-	-	-	-	-	-
1.3723	286000	0.1735	-	-	-	-	-	-
1.3771	287000	0.1735	-	-	-	-	-	-
1.3819	288000	0.1735	-	-	-	-	-	-
1.3867	289000	0.1735	-	-	-	-	-	-
1.3915	290000	0.1734	-	-	-	-	-	-
1.3963	291000	0.1734	-	-	-	-	-	-
1.4011	292000	0.1735	-	-	-	-	-	-
1.4059	293000	0.1734	-	-	-	-	-	-
1.4107	294000	0.1734	-	-	-	-	-	-
1.4155	295000	0.1734	-	-	-	-	-	-
1.4203	296000	0.1735	-	-	-	-	-	-
1.4251	297000	0.1734	-	-	-	-	-	-
1.4299	298000	0.1735	-	-	-	-	-	-
1.4347	299000	0.1735	-	-	-	-	-	-
1.4394	300000	0.1735	0.1298	0.7744	0.5000 (-0.3000)	0.0300 (-0.3100)	0.1100 (-0.1300)	0.2133 (-0.2467)
1.4442	301000	0.1735	-	-	-	-	-	-
1.4490	302000	0.1734	-	-	-	-	-	-
1.4538	303000	0.1734	-	-	-	-	-	-
1.4586	304000	0.1735	-	-	-	-	-	-
1.4634	305000	0.1734	-	-	-	-	-	-
1.4682	306000	0.1734	-	-	-	-	-	-
1.4730	307000	0.1734	-	-	-	-	-	-
1.4778	308000	0.1735	-	-	-	-	-	-
1.4826	309000	0.1734	-	-	-	-	-	-
1.4874	310000	0.1734	-	-	-	-	-	-
1.4922	311000	0.1734	-	-	-	-	-	-
1.4970	312000	0.1734	-	-	-	-	-	-
1.5018	313000	0.1734	-	-	-	-	-	-
1.5066	314000	0.1734	-	-	-	-	-	-
1.5114	315000	0.1735	-	-	-	-	-	-
1.5162	316000	0.1735	-	-	-	-	-	-
1.5210	317000	0.1734	-	-	-	-	-	-
1.5258	318000	0.1734	-	-	-	-	-	-
1.5306	319000	0.1735	-	-	-	-	-	-
1.5354	320000	0.1734	0.1288	0.8021	0.6300 (-0.1700)	0.0000 (-0.3400)	0.0800 (-0.1600)	0.2367 (-0.2233)
1.5402	321000	0.1734	-	-	-	-	-	-
1.5450	322000	0.1735	-	-	-	-	-	-
1.5498	323000	0.1734	-	-	-	-	-	-
1.5546	324000	0.1735	-	-	-	-	-	-
1.5594	325000	0.1734	-	-	-	-	-	-
1.5642	326000	0.1734	-	-	-	-	-	-
1.5690	327000	0.1734	-	-	-	-	-	-
1.5738	328000	0.1734	-	-	-	-	-	-
1.5786	329000	0.1734	-	-	-	-	-	-
1.5834	330000	0.1735	-	-	-	-	-	-
1.5882	331000	0.1735	-	-	-	-	-	-
1.5930	332000	0.1734	-	-	-	-	-	-
1.5978	333000	0.1734	-	-	-	-	-	-
1.6026	334000	0.1734	-	-	-	-	-	-
1.6074	335000	0.1734	-	-	-	-	-	-
1.6122	336000	0.1734	-	-	-	-	-	-
1.6170	337000	0.1735	-	-	-	-	-	-
1.6218	338000	0.1735	-	-	-	-	-	-
1.6266	339000	0.1734	-	-	-	-	-	-
1.6314	340000	0.1734	0.1281	0.7941	0.5600 (-0.2400)	0.0400 (-0.3000)	0.1000 (-0.1400)	0.2333 (-0.2267)
1.6362	341000	0.1734	-	-	-	-	-	-
1.6410	342000	0.1734	-	-	-	-	-	-
1.6458	343000	0.1733	-	-	-	-	-	-
1.6506	344000	0.1734	-	-	-	-	-	-
1.6554	345000	0.1734	-	-	-	-	-	-
1.6602	346000	0.1734	-	-	-	-	-	-
1.6650	347000	0.1734	-	-	-	-	-	-
1.6698	348000	0.1735	-	-	-	-	-	-
1.6746	349000	0.1734	-	-	-	-	-	-
1.6794	350000	0.1735	-	-	-	-	-	-
1.6842	351000	0.1734	-	-	-	-	-	-
1.6890	352000	0.1734	-	-	-	-	-	-
1.6938	353000	0.1734	-	-	-	-	-	-
1.6986	354000	0.1734	-	-	-	-	-	-
1.7033	355000	0.1734	-	-	-	-	-	-
1.7081	356000	0.1734	-	-	-	-	-	-
1.7129	357000	0.1735	-	-	-	-	-	-
1.7177	358000	0.1734	-	-	-	-	-	-
1.7225	359000	0.1734	-	-	-	-	-	-
1.7273	360000	0.1734	0.1283	0.7790	0.5400 (-0.2600)	0.1000 (-0.2400)	0.1200 (-0.1200)	0.2533 (-0.2067)
1.7321	361000	0.1734	-	-	-	-	-	-
1.7369	362000	0.1734	-	-	-	-	-	-
1.7417	363000	0.1734	-	-	-	-	-	-
1.7465	364000	0.1733	-	-	-	-	-	-
1.7513	365000	0.1734	-	-	-	-	-	-
1.7561	366000	0.1734	-	-	-	-	-	-
1.7609	367000	0.1734	-	-	-	-	-	-
1.7657	368000	0.1735	-	-	-	-	-	-
1.7705	369000	0.1734	-	-	-	-	-	-
1.7753	370000	0.1734	-	-	-	-	-	-
1.7801	371000	0.1734	-	-	-	-	-	-
1.7849	372000	0.1734	-	-	-	-	-	-
1.7897	373000	0.1734	-	-	-	-	-	-
1.7945	374000	0.1734	-	-	-	-	-	-
1.7993	375000	0.1733	-	-	-	-	-	-
1.8041	376000	0.1735	-	-	-	-	-	-
1.8089	377000	0.1734	-	-	-	-	-	-
1.8137	378000	0.1734	-	-	-	-	-	-
1.8185	379000	0.1735	-	-	-	-	-	-
1.8233	380000	0.1734	0.1275	0.8094	0.5800 (-0.2200)	0.0400 (-0.3000)	0.1000 (-0.1400)	0.2400 (-0.2200)
1.8281	381000	0.1734	-	-	-	-	-	-
1.8329	382000	0.1734	-	-	-	-	-	-
1.8377	383000	0.1735	-	-	-	-	-	-
1.8425	384000	0.1734	-	-	-	-	-	-
1.8473	385000	0.1734	-	-	-	-	-	-
1.8521	386000	0.1735	-	-	-	-	-	-
1.8569	387000	0.1733	-	-	-	-	-	-
1.8617	388000	0.1734	-	-	-	-	-	-
1.8665	389000	0.1733	-	-	-	-	-	-
1.8713	390000	0.1735	-	-	-	-	-	-
1.8761	391000	0.1734	-	-	-	-	-	-
1.8809	392000	0.1734	-	-	-	-	-	-
1.8857	393000	0.1735	-	-	-	-	-	-
1.8905	394000	0.1735	-	-	-	-	-	-
1.8953	395000	0.1734	-	-	-	-	-	-
1.9001	396000	0.1734	-	-	-	-	-	-
1.9049	397000	0.1735	-	-	-	-	-	-
1.9097	398000	0.1734	-	-	-	-	-	-
1.9145	399000	0.1735	-	-	-	-	-	-
1.9193	400000	0.1734	0.1277	0.7945	0.6400 (-0.1600)	0.0200 (-0.3200)	0.0800 (-0.1600)	0.2467 (-0.2133)
1.9241	401000	0.1734	-	-	-	-	-	-
1.9289	402000	0.1734	-	-	-	-	-	-
1.9337	403000	0.1734	-	-	-	-	-	-
1.9385	404000	0.1733	-	-	-	-	-	-
1.9433	405000	0.1734	-	-	-	-	-	-
1.9481	406000	0.1733	-	-	-	-	-	-
1.9529	407000	0.1734	-	-	-	-	-	-
1.9577	408000	0.1734	-	-	-	-	-	-
1.9624	409000	0.1734	-	-	-	-	-	-
1.9672	410000	0.1734	-	-	-	-	-	-
1.9720	411000	0.1734	-	-	-	-	-	-
1.9768	412000	0.1734	-	-	-	-	-	-
1.9816	413000	0.1734	-	-	-	-	-	-
1.9864	414000	0.1734	-	-	-	-	-	-
1.9912	415000	0.1735	-	-	-	-	-	-
1.9960	416000	0.1733	-	-	-	-	-	-
2.0008	417000	0.1733	-	-	-	-	-	-
2.0056	418000	0.1733	-	-	-	-	-	-
2.0104	419000	0.1734	-	-	-	-	-	-
2.0152	420000	0.1733	0.1267	0.7897	0.7000 (-0.1000)	0.0200 (-0.3200)	0.1400 (-0.1000)	0.2867 (-0.1733)
2.0200	421000	0.1734	-	-	-	-	-	-
2.0248	422000	0.1733	-	-	-	-	-	-
2.0296	423000	0.1734	-	-	-	-	-	-
2.0344	424000	0.1734	-	-	-	-	-	-
2.0392	425000	0.1734	-	-	-	-	-	-
2.0440	426000	0.1734	-	-	-	-	-	-
2.0488	427000	0.1734	-	-	-	-	-	-
2.0536	428000	0.1734	-	-	-	-	-	-
2.0584	429000	0.1735	-	-	-	-	-	-
2.0632	430000	0.1734	-	-	-	-	-	-
2.0680	431000	0.1733	-	-	-	-	-	-
2.0728	432000	0.1734	-	-	-	-	-	-
2.0776	433000	0.1733	-	-	-	-	-	-
2.0824	434000	0.1734	-	-	-	-	-	-
2.0872	435000	0.1734	-	-	-	-	-	-
2.0920	436000	0.1733	-	-	-	-	-	-
2.0968	437000	0.1735	-	-	-	-	-	-
2.1016	438000	0.1734	-	-	-	-	-	-
2.1064	439000	0.1734	-	-	-	-	-	-
2.1112	440000	0.1734	0.1272	0.8012	0.6500 (-0.1500)	0.0000 (-0.3400)	0.0600 (-0.1800)	0.2367 (-0.2233)
2.1160	441000	0.1734	-	-	-	-	-	-
2.1208	442000	0.1734	-	-	-	-	-	-
2.1256	443000	0.1735	-	-	-	-	-	-
2.1304	444000	0.1734	-	-	-	-	-	-
2.1352	445000	0.1734	-	-	-	-	-	-
2.1400	446000	0.1735	-	-	-	-	-	-
2.1448	447000	0.1733	-	-	-	-	-	-
2.1496	448000	0.1733	-	-	-	-	-	-
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3.1716	661000	0.1733	-	-	-	-	-	-
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3.2676	681000	0.1733	-	-	-	-	-	-
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4.9085	1023000	0.1733	-	-	-	-	-	-
4.9133	1024000	0.1734	-	-	-	-	-	-
4.9181	1025000	0.1733	-	-	-	-	-	-
4.9229	1026000	0.1734	-	-	-	-	-	-
4.9277	1027000	0.1733	-	-	-	-	-	-
4.9325	1028000	0.1733	-	-	-	-	-	-
4.9373	1029000	0.1733	-	-	-	-	-	-
4.9421	1030000	0.1733	-	-	-	-	-	-
4.9469	1031000	0.1733	-	-	-	-	-	-
4.9517	1032000	0.1733	-	-	-	-	-	-
4.9565	1033000	0.1733	-	-	-	-	-	-
4.9613	1034000	0.1733	-	-	-	-	-	-
4.9661	1035000	0.1733	-	-	-	-	-	-
4.9709	1036000	0.1734	-	-	-	-	-	-
4.9757	1037000	0.1733	-	-	-	-	-	-
4.9805	1038000	0.1733	-	-	-	-	-	-
4.9853	1039000	0.1733	-	-	-	-	-	-
4.9901	1040000	0.1733	0.1325	0.8059	0.6500 (-0.1500)	0.0800 (-0.2600)	0.0400 (-0.2000)	0.2567 (-0.2033)
4.9949	1041000	0.1733	-	-	-	-	-	-
4.9997	1042000	0.1733	-	-	-	-	-	-

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.1.0
Transformers: 4.56.0
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

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",
}