ModernBERT-base trained on GooAQ

This is a Cross Encoder model finetuned from answerdotai/ModernBERT-base using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

Model Type: Cross Encoder
Base model: answerdotai/ModernBERT-base
Maximum Sequence Length: 8192 tokens
Number of Output Labels: 1 label
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("ayushexel/reranker-ModernBERT-base-gooaq-bce-1995000")
# Get scores for pairs of texts
pairs = [
    ['what is the difference between a fixed and variable expense?', 'Variable costs vary based on the amount of output produced. Variable costs may include labor, commissions, and raw materials. Fixed costs remain the same regardless of production output. Fixed costs may include lease and rental payments, insurance, and interest payments.'],
    ['what is the difference between a fixed and variable expense?', 'In accounting, variable costs are costs that vary with production volume or business activity. ... Fixed costs include various indirect costs and fixed manufacturing overhead costs. Variable costs include direct labor, direct materials, and variable overhead.'],
    ['what is the difference between a fixed and variable expense?', 'Variable costs vary with increases or decreases in production. Fixed costs remain the same, whether production increases or decreases. Wages paid to workers for their regular hours are a fixed cost. Any extra time they spend on the job is a variable cost.'],
    ['what is the difference between a fixed and variable expense?', 'A dependent variable is a variable whose variations depend on another variable—usually the independent variable. An Independent variable is a variable whose variations do not depend on another variable but the researcher experimenting.'],
    ['what is the difference between a fixed and variable expense?', 'fixed cost. expenses that remain constant in total regardless of changes in activity within a relevant range. Examples are rent, insurance, and taxes. ... Fixed costs include salaries of executives, interest expense, rent, depreciation, and insurance expenses.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what is the difference between a fixed and variable expense?',
    [
        'Variable costs vary based on the amount of output produced. Variable costs may include labor, commissions, and raw materials. Fixed costs remain the same regardless of production output. Fixed costs may include lease and rental payments, insurance, and interest payments.',
        'In accounting, variable costs are costs that vary with production volume or business activity. ... Fixed costs include various indirect costs and fixed manufacturing overhead costs. Variable costs include direct labor, direct materials, and variable overhead.',
        'Variable costs vary with increases or decreases in production. Fixed costs remain the same, whether production increases or decreases. Wages paid to workers for their regular hours are a fixed cost. Any extra time they spend on the job is a variable cost.',
        'A dependent variable is a variable whose variations depend on another variable—usually the independent variable. An Independent variable is a variable whose variations do not depend on another variable but the researcher experimenting.',
        'fixed cost. expenses that remain constant in total regardless of changes in activity within a relevant range. Examples are rent, insurance, and taxes. ... Fixed costs include salaries of executives, interest expense, rent, depreciation, and insurance expenses.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Dataset: gooaq-dev

Evaluated with CrossEncoderRerankingEvaluator with these parameters:

{
    "at_k": 10,
    "always_rerank_positives": false
}

Metric	Value
map	0.4889 (+0.2193)
mrr@10	0.4886 (+0.2298)
ndcg@10	0.5296 (+0.2201)

Cross Encoder Reranking

Datasets: NanoMSMARCO_R100, NanoNFCorpus_R100 and NanoNQ_R100

Evaluated with CrossEncoderRerankingEvaluator with these parameters:

{
    "at_k": 10,
    "always_rerank_positives": true
}

Metric	NanoMSMARCO_R100	NanoNFCorpus_R100	NanoNQ_R100
map	0.3257 (-0.1639)	0.3598 (+0.0988)	0.3327 (-0.0869)
mrr@10	0.3138 (-0.1637)	0.5283 (+0.0285)	0.3309 (-0.0958)
ndcg@10	0.4190 (-0.1214)	0.4076 (+0.0826)	0.4068 (-0.0938)

Cross Encoder Nano BEIR

Dataset: NanoBEIR_R100_mean

Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco",
        "nfcorpus",
        "nq"
    ],
    "rerank_k": 100,
    "at_k": 10,
    "always_rerank_positives": true
}

Metric	Value
map	0.3394 (-0.0507)
mrr@10	0.3910 (-0.0770)
ndcg@10	0.4111 (-0.0442)

Training Details

Training Dataset

Unnamed Dataset

Size: 11,456,701 training samples
Columns: question, answer, and label

Approximate statistics based on the first 1000 samples:

	question	answer	label
type	string	string	int
details	min: 19 characters mean: 44.1 characters max: 86 characters	min: 51 characters mean: 247.82 characters max: 393 characters	0: ~82.60% 1: ~17.40%

Samples:

question	answer	label
`what is the difference between a fixed and variable expense?`	`Variable costs vary based on the amount of output produced. Variable costs may include labor, commissions, and raw materials. Fixed costs remain the same regardless of production output. Fixed costs may include lease and rental payments, insurance, and interest payments.`	`1`
`what is the difference between a fixed and variable expense?`	`In accounting, variable costs are costs that vary with production volume or business activity. ... Fixed costs include various indirect costs and fixed manufacturing overhead costs. Variable costs include direct labor, direct materials, and variable overhead.`	`0`
`what is the difference between a fixed and variable expense?`	`Variable costs vary with increases or decreases in production. Fixed costs remain the same, whether production increases or decreases. Wages paid to workers for their regular hours are a fixed cost. Any extra time they spend on the job is a variable cost.`	`0`

Loss: BinaryCrossEntropyLoss with these parameters:

{
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": 5
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 2e-05
num_train_epochs: 5
warmup_ratio: 0.1
seed: 12
bf16: True
dataloader_num_workers: 12
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
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: 2e-05
weight_decay: 0.0
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: 12
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
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: False
dataloader_num_workers: 12
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}
tp_size: 0
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: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
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
dispatch_batches: None
split_batches: 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
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	gooaq-dev_ndcg@10	NanoMSMARCO_R100_ndcg@10	NanoNFCorpus_R100_ndcg@10	NanoNQ_R100_ndcg@10	NanoBEIR_R100_mean_ndcg@10
-1	-1	-	0.1006 (-0.2090)	0.0483 (-0.4922)	0.2754 (-0.0496)	0.0164 (-0.4843)	0.1133 (-0.3420)
0.0000	1	1.1916	-	-	-	-	-
0.0045	200	1.2177	-	-	-	-	-
0.0089	400	1.1896	-	-	-	-	-
0.0134	600	1.1768	-	-	-	-	-
0.0179	800	1.1916	-	-	-	-	-
0.0223	1000	1.1658	-	-	-	-	-
0.0268	1200	1.1552	-	-	-	-	-
0.0313	1400	1.1286	-	-	-	-	-
0.0358	1600	1.0306	-	-	-	-	-
0.0402	1800	0.8748	-	-	-	-	-
0.0447	2000	0.8042	-	-	-	-	-
0.0492	2200	0.7559	-	-	-	-	-
0.0536	2400	0.7096	-	-	-	-	-
0.0581	2600	0.6915	-	-	-	-	-
0.0626	2800	0.6677	-	-	-	-	-
0.0670	3000	0.6585	-	-	-	-	-
0.0715	3200	0.6386	-	-	-	-	-
0.0760	3400	0.6361	-	-	-	-	-
0.0804	3600	0.6214	-	-	-	-	-
0.0849	3800	0.6082	-	-	-	-	-
0.0894	4000	0.6035	-	-	-	-	-
0.0938	4200	0.5856	-	-	-	-	-
0.0983	4400	0.5769	-	-	-	-	-
0.1028	4600	0.5737	-	-	-	-	-
0.1073	4800	0.5774	-	-	-	-	-
0.1117	5000	0.5596	-	-	-	-	-
0.1162	5200	0.5561	-	-	-	-	-
0.1207	5400	0.5461	-	-	-	-	-
0.1251	5600	0.5504	-	-	-	-	-
0.1296	5800	0.54	-	-	-	-	-
0.1341	6000	0.5365	-	-	-	-	-
0.1385	6200	0.5382	-	-	-	-	-
0.1430	6400	0.5275	-	-	-	-	-
0.1475	6600	0.5247	-	-	-	-	-
0.1519	6800	0.5242	-	-	-	-	-
0.1564	7000	0.5273	-	-	-	-	-
0.1609	7200	0.5144	-	-	-	-	-
0.1654	7400	0.519	-	-	-	-	-
0.1698	7600	0.5051	-	-	-	-	-
0.1743	7800	0.5117	-	-	-	-	-
0.1788	8000	0.5048	-	-	-	-	-
0.1832	8200	0.4997	-	-	-	-	-
0.1877	8400	0.4972	-	-	-	-	-
0.1922	8600	0.4969	-	-	-	-	-
0.1966	8800	0.4922	-	-	-	-	-
0.2011	9000	0.4933	-	-	-	-	-
0.2056	9200	0.4794	-	-	-	-	-
0.2100	9400	0.4843	-	-	-	-	-
0.2145	9600	0.4745	-	-	-	-	-
0.2190	9800	0.4855	-	-	-	-	-
0.2234	10000	0.4747	-	-	-	-	-
0.2279	10200	0.4732	-	-	-	-	-
0.2324	10400	0.4772	-	-	-	-	-
0.2369	10600	0.4678	-	-	-	-	-
0.2413	10800	0.464	-	-	-	-	-
0.2458	11000	0.4733	-	-	-	-	-
0.2503	11200	0.4617	-	-	-	-	-
0.2547	11400	0.457	-	-	-	-	-
0.2592	11600	0.4677	-	-	-	-	-
0.2637	11800	0.4561	-	-	-	-	-
0.2681	12000	0.4717	-	-	-	-	-
0.2726	12200	0.4501	-	-	-	-	-
0.2771	12400	0.4535	-	-	-	-	-
0.2815	12600	0.4571	-	-	-	-	-
0.2860	12800	0.4501	-	-	-	-	-
0.2905	13000	0.4573	-	-	-	-	-
0.2950	13200	0.4455	-	-	-	-	-
0.2994	13400	0.443	-	-	-	-	-
0.3039	13600	0.4446	-	-	-	-	-
0.3084	13800	0.4472	-	-	-	-	-
0.3128	14000	0.4364	-	-	-	-	-
0.3173	14200	0.4415	-	-	-	-	-
0.3218	14400	0.4471	-	-	-	-	-
0.3262	14600	0.4481	-	-	-	-	-
0.3307	14800	0.4383	-	-	-	-	-
0.3352	15000	0.4369	-	-	-	-	-
0.3396	15200	0.429	-	-	-	-	-
0.3441	15400	0.4354	-	-	-	-	-
0.3486	15600	0.4304	-	-	-	-	-
0.3530	15800	0.4262	-	-	-	-	-
0.3575	16000	0.4333	-	-	-	-	-
0.3620	16200	0.4326	-	-	-	-	-
0.3665	16400	0.4217	-	-	-	-	-
0.3709	16600	0.4253	-	-	-	-	-
0.3754	16800	0.4276	-	-	-	-	-
0.3799	17000	0.4183	-	-	-	-	-
0.3843	17200	0.4238	-	-	-	-	-
0.3888	17400	0.4291	-	-	-	-	-
0.3933	17600	0.4181	-	-	-	-	-
0.3977	17800	0.4222	-	-	-	-	-
0.4022	18000	0.4207	-	-	-	-	-
0.4067	18200	0.4176	-	-	-	-	-
0.4111	18400	0.4178	-	-	-	-	-
0.4156	18600	0.415	-	-	-	-	-
0.4201	18800	0.4113	-	-	-	-	-
0.4246	19000	0.4066	-	-	-	-	-
0.4290	19200	0.4118	-	-	-	-	-
0.4335	19400	0.4087	-	-	-	-	-
0.4380	19600	0.4034	-	-	-	-	-
0.4424	19800	0.4138	-	-	-	-	-
0.4469	20000	0.4227	-	-	-	-	-
0.4514	20200	0.409	-	-	-	-	-
0.4558	20400	0.4059	-	-	-	-	-
0.4603	20600	0.4102	-	-	-	-	-
0.4648	20800	0.4127	-	-	-	-	-
0.4692	21000	0.4024	-	-	-	-	-
0.4737	21200	0.4088	-	-	-	-	-
0.4782	21400	0.3943	-	-	-	-	-
0.4826	21600	0.4045	-	-	-	-	-
0.4871	21800	0.4043	-	-	-	-	-
0.4916	22000	0.4042	-	-	-	-	-
0.4961	22200	0.3983	-	-	-	-	-
0.5005	22400	0.4009	-	-	-	-	-
0.5050	22600	0.393	-	-	-	-	-
0.5095	22800	0.3911	-	-	-	-	-
0.5139	23000	0.4004	-	-	-	-	-
0.5184	23200	0.3973	-	-	-	-	-
0.5229	23400	0.4063	-	-	-	-	-
0.5273	23600	0.3943	-	-	-	-	-
0.5318	23800	0.3896	-	-	-	-	-
0.5363	24000	0.3938	-	-	-	-	-
0.5407	24200	0.3961	-	-	-	-	-
0.5452	24400	0.3888	-	-	-	-	-
0.5497	24600	0.3872	-	-	-	-	-
0.5542	24800	0.3887	-	-	-	-	-
0.5586	25000	0.3789	-	-	-	-	-
0.5631	25200	0.3927	-	-	-	-	-
0.5676	25400	0.3929	-	-	-	-	-
0.5720	25600	0.387	-	-	-	-	-
0.5765	25800	0.3834	-	-	-	-	-
0.5810	26000	0.3779	-	-	-	-	-
0.5854	26200	0.3897	-	-	-	-	-
0.5899	26400	0.3798	-	-	-	-	-
0.5944	26600	0.38	-	-	-	-	-
0.5988	26800	0.3773	-	-	-	-	-
0.6033	27000	0.385	-	-	-	-	-
0.6078	27200	0.391	-	-	-	-	-
0.6122	27400	0.3798	-	-	-	-	-
0.6167	27600	0.3754	-	-	-	-	-
0.6212	27800	0.3845	-	-	-	-	-
0.6257	28000	0.3763	-	-	-	-	-
0.6301	28200	0.376	-	-	-	-	-
0.6346	28400	0.3786	-	-	-	-	-
0.6391	28600	0.3788	-	-	-	-	-
0.6435	28800	0.3796	-	-	-	-	-
0.6480	29000	0.38	-	-	-	-	-
0.6525	29200	0.3755	-	-	-	-	-
0.6569	29400	0.3718	-	-	-	-	-
0.6614	29600	0.3746	-	-	-	-	-
0.6659	29800	0.3697	-	-	-	-	-
0.6703	30000	0.3679	-	-	-	-	-
0.6748	30200	0.382	-	-	-	-	-
0.6793	30400	0.3683	-	-	-	-	-
0.6838	30600	0.3692	-	-	-	-	-
0.6882	30800	0.3715	-	-	-	-	-
0.6927	31000	0.3684	-	-	-	-	-
0.6972	31200	0.3649	-	-	-	-	-
0.7016	31400	0.376	-	-	-	-	-
0.7061	31600	0.3649	-	-	-	-	-
0.7106	31800	0.3718	-	-	-	-	-
0.7150	32000	0.3725	-	-	-	-	-
0.7195	32200	0.3587	-	-	-	-	-
0.7240	32400	0.3699	-	-	-	-	-
0.7284	32600	0.3585	-	-	-	-	-
0.7329	32800	0.3597	-	-	-	-	-
0.7374	33000	0.3631	-	-	-	-	-
0.7418	33200	0.3537	-	-	-	-	-
0.7463	33400	0.3565	-	-	-	-	-
0.7508	33600	0.3597	-	-	-	-	-
0.7553	33800	0.3559	-	-	-	-	-
0.7597	34000	0.3555	-	-	-	-	-
0.7642	34200	0.3486	-	-	-	-	-
0.7687	34400	0.3605	-	-	-	-	-
0.7731	34600	0.3588	-	-	-	-	-
0.7776	34800	0.3597	-	-	-	-	-
0.7821	35000	0.364	-	-	-	-	-
0.7865	35200	0.3509	-	-	-	-	-
0.7910	35400	0.3606	-	-	-	-	-
0.7955	35600	0.3567	-	-	-	-	-
0.7999	35800	0.3597	-	-	-	-	-
0.8044	36000	0.3508	-	-	-	-	-
0.8089	36200	0.3515	-	-	-	-	-
0.8134	36400	0.345	-	-	-	-	-
0.8178	36600	0.3493	-	-	-	-	-
0.8223	36800	0.3611	-	-	-	-	-
0.8268	37000	0.3467	-	-	-	-	-
0.8312	37200	0.3519	-	-	-	-	-
0.8357	37400	0.3505	-	-	-	-	-
0.8402	37600	0.3538	-	-	-	-	-
0.8446	37800	0.3553	-	-	-	-	-
0.8491	38000	0.3469	-	-	-	-	-
0.8536	38200	0.3481	-	-	-	-	-
0.8580	38400	0.3485	-	-	-	-	-
0.8625	38600	0.3459	-	-	-	-	-
0.8670	38800	0.3495	-	-	-	-	-
0.8714	39000	0.3527	-	-	-	-	-
0.8759	39200	0.3478	-	-	-	-	-
0.8804	39400	0.3461	-	-	-	-	-
0.8849	39600	0.3378	-	-	-	-	-
0.8893	39800	0.3503	-	-	-	-	-
0.8938	40000	0.3469	-	-	-	-	-
0.8983	40200	0.342	-	-	-	-	-
0.9027	40400	0.3446	-	-	-	-	-
0.9072	40600	0.3504	-	-	-	-	-
0.9117	40800	0.3482	-	-	-	-	-
0.9161	41000	0.3568	-	-	-	-	-
0.9206	41200	0.3503	-	-	-	-	-
0.9251	41400	0.3444	-	-	-	-	-
0.9295	41600	0.35	-	-	-	-	-
0.9340	41800	0.3386	-	-	-	-	-
0.9385	42000	0.339	-	-	-	-	-
0.9430	42200	0.3495	-	-	-	-	-
0.9474	42400	0.3498	-	-	-	-	-
0.9519	42600	0.3415	-	-	-	-	-
0.9564	42800	0.3443	-	-	-	-	-
0.9608	43000	0.337	-	-	-	-	-
0.9653	43200	0.3397	-	-	-	-	-
0.9698	43400	0.3368	-	-	-	-	-
0.9742	43600	0.3367	-	-	-	-	-
0.9787	43800	0.339	-	-	-	-	-
0.9832	44000	0.3382	-	-	-	-	-
0.9876	44200	0.3381	-	-	-	-	-
0.9921	44400	0.3374	-	-	-	-	-
0.9966	44600	0.3296	-	-	-	-	-
1.0011	44800	0.3298	-	-	-	-	-
1.0055	45000	0.2924	-	-	-	-	-
1.0100	45200	0.2886	-	-	-	-	-
1.0145	45400	0.2913	-	-	-	-	-
1.0189	45600	0.2919	-	-	-	-	-
1.0234	45800	0.289	-	-	-	-	-
1.0279	46000	0.2922	-	-	-	-	-
1.0323	46200	0.2865	-	-	-	-	-
1.0368	46400	0.2928	-	-	-	-	-
1.0413	46600	0.29	-	-	-	-	-
1.0457	46800	0.294	-	-	-	-	-
1.0502	47000	0.2949	-	-	-	-	-
1.0547	47200	0.2927	-	-	-	-	-
1.0591	47400	0.2922	-	-	-	-	-
1.0636	47600	0.2956	-	-	-	-	-
1.0681	47800	0.2917	-	-	-	-	-
1.0726	48000	0.2901	-	-	-	-	-
1.0770	48200	0.2869	-	-	-	-	-
1.0815	48400	0.2872	-	-	-	-	-
1.0860	48600	0.29	-	-	-	-	-
1.0904	48800	0.2897	-	-	-	-	-
1.0949	49000	0.2834	-	-	-	-	-
1.0994	49200	0.2849	-	-	-	-	-
1.1038	49400	0.288	-	-	-	-	-
1.1083	49600	0.2929	-	-	-	-	-
1.1128	49800	0.2931	-	-	-	-	-
1.1172	50000	0.2834	-	-	-	-	-
1.1217	50200	0.2928	-	-	-	-	-
1.1262	50400	0.2822	-	-	-	-	-
1.1307	50600	0.285	-	-	-	-	-
1.1351	50800	0.2941	-	-	-	-	-
1.1396	51000	0.2858	-	-	-	-	-
1.1441	51200	0.2909	-	-	-	-	-
1.1485	51400	0.2844	-	-	-	-	-
1.1530	51600	0.2874	-	-	-	-	-
1.1575	51800	0.2906	-	-	-	-	-
1.1619	52000	0.2831	-	-	-	-	-
1.1664	52200	0.29	-	-	-	-	-
1.1709	52400	0.294	-	-	-	-	-
1.1753	52600	0.2871	-	-	-	-	-
1.1798	52800	0.283	-	-	-	-	-
1.1843	53000	0.2898	-	-	-	-	-
1.1887	53200	0.287	-	-	-	-	-
1.1932	53400	0.2798	-	-	-	-	-
1.1977	53600	0.2864	-	-	-	-	-
1.2022	53800	0.2812	-	-	-	-	-
1.2066	54000	0.2842	-	-	-	-	-
1.2111	54200	0.285	-	-	-	-	-
1.2156	54400	0.2868	-	-	-	-	-
1.2200	54600	0.2912	-	-	-	-	-
1.2245	54800	0.2876	-	-	-	-	-
1.2290	55000	0.2984	-	-	-	-	-
1.2334	55200	0.2804	-	-	-	-	-
1.2379	55400	0.2757	-	-	-	-	-
1.2424	55600	0.2823	-	-	-	-	-
1.2468	55800	0.2893	-	-	-	-	-
1.2513	56000	0.2931	-	-	-	-	-
1.2558	56200	0.2865	-	-	-	-	-
1.2603	56400	0.2854	-	-	-	-	-
1.2647	56600	0.2942	-	-	-	-	-
1.2692	56800	0.2884	-	-	-	-	-
1.2737	57000	0.2838	-	-	-	-	-
1.2781	57200	0.2846	-	-	-	-	-
1.2826	57400	0.2872	-	-	-	-	-
1.2871	57600	0.2853	-	-	-	-	-
1.2915	57800	0.2884	-	-	-	-	-
1.2960	58000	0.2852	-	-	-	-	-
1.3005	58200	0.284	-	-	-	-	-
1.3049	58400	0.2802	-	-	-	-	-
1.3094	58600	0.2802	-	-	-	-	-
1.3139	58800	0.2845	-	-	-	-	-
1.3183	59000	0.2884	-	-	-	-	-
1.3228	59200	0.2804	-	-	-	-	-
1.3273	59400	0.2838	-	-	-	-	-
1.3318	59600	0.2822	-	-	-	-	-
1.3362	59800	0.2884	-	-	-	-	-
1.3407	60000	0.275	-	-	-	-	-
1.3452	60200	0.2814	-	-	-	-	-
1.3496	60400	0.2821	-	-	-	-	-
1.3541	60600	0.2832	-	-	-	-	-
1.3586	60800	0.2813	-	-	-	-	-
1.3630	61000	0.2834	-	-	-	-	-
1.3675	61200	0.2782	-	-	-	-	-
1.3720	61400	0.2761	-	-	-	-	-
1.3764	61600	0.283	-	-	-	-	-
1.3809	61800	0.2779	-	-	-	-	-
1.3854	62000	0.2899	-	-	-	-	-
1.3899	62200	0.2841	-	-	-	-	-
1.3943	62400	0.2808	-	-	-	-	-
1.3988	62600	0.2799	-	-	-	-	-
1.4033	62800	0.272	-	-	-	-	-
1.4077	63000	0.2834	-	-	-	-	-
1.4122	63200	0.2791	-	-	-	-	-
1.4167	63400	0.2842	-	-	-	-	-
1.4211	63600	0.283	-	-	-	-	-
1.4256	63800	0.278	-	-	-	-	-
1.4301	64000	0.2818	-	-	-	-	-
1.4345	64200	0.2757	-	-	-	-	-
1.4390	64400	0.2812	-	-	-	-	-
1.4435	64600	0.2886	-	-	-	-	-
1.4479	64800	0.2885	-	-	-	-	-
1.4524	65000	0.2784	-	-	-	-	-
1.4569	65200	0.2893	-	-	-	-	-
1.4614	65400	0.2728	-	-	-	-	-
1.4658	65600	0.2825	-	-	-	-	-
1.4703	65800	0.2891	-	-	-	-	-
1.4748	66000	0.2789	-	-	-	-	-
1.4792	66200	0.2753	-	-	-	-	-
1.4837	66400	0.2805	-	-	-	-	-
1.4882	66600	0.2789	-	-	-	-	-
1.4926	66800	0.2769	-	-	-	-	-
1.4971	67000	0.282	-	-	-	-	-
1.5016	67200	0.2847	-	-	-	-	-
1.5060	67400	0.2744	-	-	-	-	-
1.5105	67600	0.2746	-	-	-	-	-
1.5150	67800	0.2747	-	-	-	-	-
1.5195	68000	0.2754	-	-	-	-	-
1.5239	68200	0.2785	-	-	-	-	-
1.5284	68400	0.2755	-	-	-	-	-
1.5329	68600	0.2807	-	-	-	-	-
1.5373	68800	0.2815	-	-	-	-	-
1.5418	69000	0.2794	-	-	-	-	-
1.5463	69200	0.2847	-	-	-	-	-
1.5507	69400	0.2786	-	-	-	-	-
1.5552	69600	0.2781	-	-	-	-	-
1.5597	69800	0.2785	-	-	-	-	-
1.5641	70000	0.2764	-	-	-	-	-
1.5686	70200	0.282	-	-	-	-	-
1.5731	70400	0.2773	-	-	-	-	-
1.5775	70600	0.2769	-	-	-	-	-
1.5820	70800	0.2783	-	-	-	-	-
1.5865	71000	0.2774	-	-	-	-	-
1.5910	71200	0.2684	-	-	-	-	-
1.5954	71400	0.2736	-	-	-	-	-
1.5999	71600	0.2683	-	-	-	-	-
1.6044	71800	0.2748	-	-	-	-	-
1.6088	72000	0.2736	-	-	-	-	-
1.6133	72200	0.2725	-	-	-	-	-
1.6178	72400	0.2747	-	-	-	-	-
1.6222	72600	0.2702	-	-	-	-	-
1.6267	72800	0.2733	-	-	-	-	-
1.6312	73000	0.2727	-	-	-	-	-
1.6356	73200	0.2727	-	-	-	-	-
1.6401	73400	0.2749	-	-	-	-	-
1.6446	73600	0.2729	-	-	-	-	-
1.6491	73800	0.2736	-	-	-	-	-
1.6535	74000	0.2745	-	-	-	-	-
1.6580	74200	0.2757	-	-	-	-	-
1.6625	74400	0.2757	-	-	-	-	-
1.6669	74600	0.2754	-	-	-	-	-
1.6714	74800	0.2757	-	-	-	-	-
1.6759	75000	0.2762	-	-	-	-	-
1.6803	75200	0.2723	-	-	-	-	-
1.6848	75400	0.2825	-	-	-	-	-
1.6893	75600	0.2744	-	-	-	-	-
1.6937	75800	0.2727	-	-	-	-	-
1.6982	76000	0.2709	-	-	-	-	-
1.7027	76200	0.2652	-	-	-	-	-
1.7071	76400	0.2728	-	-	-	-	-
1.7116	76600	0.2794	-	-	-	-	-
1.7161	76800	0.2828	-	-	-	-	-
1.7206	77000	0.2696	-	-	-	-	-
1.7250	77200	0.2786	-	-	-	-	-
1.7295	77400	0.2727	-	-	-	-	-
1.7340	77600	0.2586	-	-	-	-	-
1.7384	77800	0.2757	-	-	-	-	-
1.7429	78000	0.2709	-	-	-	-	-
1.7474	78200	0.2754	-	-	-	-	-
1.7518	78400	0.2678	-	-	-	-	-
1.7563	78600	0.2756	-	-	-	-	-
1.7608	78800	0.2671	-	-	-	-	-
1.7652	79000	0.2734	-	-	-	-	-
1.7697	79200	0.2712	-	-	-	-	-
1.7742	79400	0.267	-	-	-	-	-
1.7787	79600	0.2789	-	-	-	-	-
1.7831	79800	0.2737	-	-	-	-	-
1.7876	80000	0.2703	0.5337 (+0.2241)	0.5404 (-0.0001)	0.3883 (+0.0632)	0.5146 (+0.0139)	0.4811 (+0.0257)
1.7921	80200	0.2714	-	-	-	-	-
1.7965	80400	0.2627	-	-	-	-	-
1.8010	80600	0.2706	-	-	-	-	-
1.8055	80800	0.2751	-	-	-	-	-
1.8099	81000	0.2659	-	-	-	-	-
1.8144	81200	0.2751	-	-	-	-	-
1.8189	81400	0.2759	-	-	-	-	-
1.8233	81600	0.2659	-	-	-	-	-
1.8278	81800	0.2618	-	-	-	-	-
1.8323	82000	0.2694	-	-	-	-	-
1.8367	82200	0.2772	-	-	-	-	-
1.8412	82400	0.2672	-	-	-	-	-
1.8457	82600	0.2667	-	-	-	-	-
1.8502	82800	0.2633	-	-	-	-	-
1.8546	83000	0.2698	-	-	-	-	-
1.8591	83200	0.2691	-	-	-	-	-
1.8636	83400	0.2789	-	-	-	-	-
1.8680	83600	0.2625	-	-	-	-	-
1.8725	83800	0.2752	-	-	-	-	-
1.8770	84000	0.2641	-	-	-	-	-
1.8814	84200	0.2668	-	-	-	-	-
1.8859	84400	0.2754	-	-	-	-	-
1.8904	84600	0.2681	-	-	-	-	-
1.8948	84800	0.2701	-	-	-	-	-
1.8993	85000	0.2761	-	-	-	-	-
1.9038	85200	0.273	-	-	-	-	-
1.9083	85400	0.2682	-	-	-	-	-
1.9127	85600	0.2635	-	-	-	-	-
1.9172	85800	0.2685	-	-	-	-	-
1.9217	86000	0.2618	-	-	-	-	-
1.9261	86200	0.2705	-	-	-	-	-
1.9306	86400	0.2675	-	-	-	-	-
1.9351	86600	0.2587	-	-	-	-	-
1.9395	86800	0.2682	-	-	-	-	-
1.9440	87000	0.2656	-	-	-	-	-
1.9485	87200	0.2684	-	-	-	-	-
1.9529	87400	0.2661	-	-	-	-	-
1.9574	87600	0.2649	-	-	-	-	-
1.9619	87800	0.2643	-	-	-	-	-
1.9663	88000	0.27	-	-	-	-	-
1.9708	88200	0.2706	-	-	-	-	-
1.9753	88400	0.2631	-	-	-	-	-
1.9798	88600	0.2551	-	-	-	-	-
1.9842	88800	0.2644	-	-	-	-	-
1.9887	89000	0.2724	-	-	-	-	-
1.9932	89200	0.2665	-	-	-	-	-
1.9976	89400	0.2555	-	-	-	-	-
2.0021	89600	0.2401	-	-	-	-	-
2.0066	89800	0.1957	-	-	-	-	-
2.0110	90000	0.1985	-	-	-	-	-
2.0155	90200	0.1968	-	-	-	-	-
2.0200	90400	0.1963	-	-	-	-	-
2.0244	90600	0.1941	-	-	-	-	-
2.0289	90800	0.1984	-	-	-	-	-
2.0334	91000	0.2029	-	-	-	-	-
2.0379	91200	0.1968	-	-	-	-	-
2.0423	91400	0.1978	-	-	-	-	-
2.0468	91600	0.1986	-	-	-	-	-
2.0513	91800	0.1968	-	-	-	-	-
2.0557	92000	0.2023	-	-	-	-	-
2.0602	92200	0.1996	-	-	-	-	-
2.0647	92400	0.1978	-	-	-	-	-
2.0691	92600	0.2028	-	-	-	-	-
2.0736	92800	0.1987	-	-	-	-	-
2.0781	93000	0.1962	-	-	-	-	-
2.0825	93200	0.2015	-	-	-	-	-
2.0870	93400	0.2005	-	-	-	-	-
2.0915	93600	0.1988	-	-	-	-	-
2.0959	93800	0.1949	-	-	-	-	-
2.1004	94000	0.1955	-	-	-	-	-
2.1049	94200	0.1981	-	-	-	-	-
2.1094	94400	0.1989	-	-	-	-	-
2.1138	94600	0.1991	-	-	-	-	-
2.1183	94800	0.1939	-	-	-	-	-
2.1228	95000	0.2004	-	-	-	-	-
2.1272	95200	0.2028	-	-	-	-	-
2.1317	95400	0.1955	-	-	-	-	-
2.1362	95600	0.1961	-	-	-	-	-
2.1406	95800	0.2038	-	-	-	-	-
2.1451	96000	0.1972	-	-	-	-	-
2.1496	96200	0.1964	-	-	-	-	-
2.1540	96400	0.2058	-	-	-	-	-
2.1585	96600	0.199	-	-	-	-	-
2.1630	96800	0.1964	-	-	-	-	-
2.1675	97000	0.2037	-	-	-	-	-
2.1719	97200	0.2014	-	-	-	-	-
2.1764	97400	0.1975	-	-	-	-	-
2.1809	97600	0.2006	-	-	-	-	-
2.1853	97800	0.2008	-	-	-	-	-
2.1898	98000	0.2021	-	-	-	-	-
2.1943	98200	0.1977	-	-	-	-	-
2.1987	98400	0.2002	-	-	-	-	-
2.2032	98600	0.1952	-	-	-	-	-
2.2077	98800	0.2012	-	-	-	-	-
2.2121	99000	0.1989	-	-	-	-	-
2.2166	99200	0.1981	-	-	-	-	-
2.2211	99400	0.2041	-	-	-	-	-
2.2255	99600	0.2014	-	-	-	-	-
2.2300	99800	0.1916	-	-	-	-	-
2.2345	100000	0.2035	-	-	-	-	-
2.2390	100200	0.2003	-	-	-	-	-
2.2434	100400	0.201	-	-	-	-	-
2.2479	100600	0.1973	-	-	-	-	-
2.2524	100800	0.1989	-	-	-	-	-
2.2568	101000	0.1993	-	-	-	-	-
2.2613	101200	0.1989	-	-	-	-	-
2.2658	101400	0.1982	-	-	-	-	-
2.2702	101600	0.202	-	-	-	-	-
2.2747	101800	0.2029	-	-	-	-	-
2.2792	102000	0.2053	-	-	-	-	-
2.2836	102200	0.2063	-	-	-	-	-
2.2881	102400	0.1977	-	-	-	-	-
2.2926	102600	0.1987	-	-	-	-	-
2.2971	102800	0.1977	-	-	-	-	-
2.3015	103000	0.1997	-	-	-	-	-
2.3060	103200	0.1976	-	-	-	-	-
2.3105	103400	0.203	-	-	-	-	-
2.3149	103600	0.1993	-	-	-	-	-
2.3194	103800	0.2084	-	-	-	-	-
2.3239	104000	0.1982	-	-	-	-	-
2.3283	104200	0.2017	-	-	-	-	-
2.3328	104400	0.1986	-	-	-	-	-
2.3373	104600	0.2035	-	-	-	-	-
2.3417	104800	0.1963	-	-	-	-	-
2.3462	105000	0.2104	-	-	-	-	-
2.3507	105200	0.2038	-	-	-	-	-
2.3551	105400	0.1983	-	-	-	-	-
2.3596	105600	0.2016	-	-	-	-	-
2.3641	105800	0.2009	-	-	-	-	-
2.3686	106000	0.2037	-	-	-	-	-
2.3730	106200	0.2059	-	-	-	-	-
2.3775	106400	0.2002	-	-	-	-	-
2.3820	106600	0.2003	-	-	-	-	-
2.3864	106800	0.2008	-	-	-	-	-
2.3909	107000	0.1984	-	-	-	-	-
2.3954	107200	0.1944	-	-	-	-	-
2.3998	107400	0.2085	-	-	-	-	-
2.4043	107600	0.203	-	-	-	-	-
2.4088	107800	0.2028	-	-	-	-	-
2.4132	108000	0.1969	-	-	-	-	-
2.4177	108200	0.1964	-	-	-	-	-
2.4222	108400	0.1973	-	-	-	-	-
2.4267	108600	0.2052	-	-	-	-	-
2.4311	108800	0.1967	-	-	-	-	-
2.4356	109000	0.2021	-	-	-	-	-
2.4401	109200	0.1917	-	-	-	-	-
2.4445	109400	0.2016	-	-	-	-	-
2.4490	109600	0.2016	-	-	-	-	-
2.4535	109800	0.2042	-	-	-	-	-
2.4579	110000	0.2008	-	-	-	-	-
2.4624	110200	0.1969	-	-	-	-	-
2.4669	110400	0.1933	-	-	-	-	-
2.4713	110600	0.1958	-	-	-	-	-
2.4758	110800	0.2006	-	-	-	-	-
2.4803	111000	0.2002	-	-	-	-	-
2.4847	111200	0.2043	-	-	-	-	-
2.4892	111400	0.2055	-	-	-	-	-
2.4937	111600	0.2013	-	-	-	-	-
2.4982	111800	0.2032	-	-	-	-	-
2.5026	112000	0.2058	-	-	-	-	-
2.5071	112200	0.1985	-	-	-	-	-
2.5116	112400	0.1981	-	-	-	-	-
2.5160	112600	0.202	-	-	-	-	-
2.5205	112800	0.1985	-	-	-	-	-
2.5250	113000	0.1956	-	-	-	-	-
2.5294	113200	0.2021	-	-	-	-	-
2.5339	113400	0.1997	-	-	-	-	-
2.5384	113600	0.2057	-	-	-	-	-
2.5428	113800	0.1968	-	-	-	-	-
2.5473	114000	0.1963	-	-	-	-	-
2.5518	114200	0.2017	-	-	-	-	-
2.5563	114400	0.1976	-	-	-	-	-
2.5607	114600	0.1979	-	-	-	-	-
2.5652	114800	0.2006	-	-	-	-	-
2.5697	115000	0.2019	-	-	-	-	-
2.5741	115200	0.1946	-	-	-	-	-
2.5786	115400	0.2056	-	-	-	-	-
2.5831	115600	0.1951	-	-	-	-	-
2.5875	115800	0.2037	-	-	-	-	-
2.5920	116000	0.2045	-	-	-	-	-
2.5965	116200	0.2043	-	-	-	-	-
2.6009	116400	0.1997	-	-	-	-	-
2.6054	116600	0.1974	-	-	-	-	-
2.6099	116800	0.2039	-	-	-	-	-
2.6143	117000	0.198	-	-	-	-	-
2.6188	117200	0.1974	-	-	-	-	-
2.6233	117400	0.2005	-	-	-	-	-
2.6278	117600	0.1979	-	-	-	-	-
2.6322	117800	0.1993	-	-	-	-	-
2.6367	118000	0.194	-	-	-	-	-
2.6412	118200	0.1979	-	-	-	-	-
2.6456	118400	0.2063	-	-	-	-	-
2.6501	118600	0.1984	-	-	-	-	-
2.6546	118800	0.1965	-	-	-	-	-
2.6590	119000	0.1919	-	-	-	-	-
2.6635	119200	0.204	-	-	-	-	-
2.6680	119400	0.1993	-	-	-	-	-
2.6724	119600	0.1948	-	-	-	-	-
2.6769	119800	0.2	-	-	-	-	-
2.6814	120000	0.2072	-	-	-	-	-
2.6859	120200	0.1965	-	-	-	-	-
2.6903	120400	0.2042	-	-	-	-	-
2.6948	120600	0.1959	-	-	-	-	-
2.6993	120800	0.199	-	-	-	-	-
2.7037	121000	0.1867	-	-	-	-	-
2.7082	121200	0.1969	-	-	-	-	-
2.7127	121400	0.1952	-	-	-	-	-
2.7171	121600	0.1983	-	-	-	-	-
2.7216	121800	0.1963	-	-	-	-	-
2.7261	122000	0.1937	-	-	-	-	-
2.7305	122200	0.2024	-	-	-	-	-
2.7350	122400	0.1968	-	-	-	-	-
2.7395	122600	0.1962	-	-	-	-	-
2.7440	122800	0.2004	-	-	-	-	-
2.7484	123000	0.2023	-	-	-	-	-
2.7529	123200	0.1999	-	-	-	-	-
2.7574	123400	0.2028	-	-	-	-	-
2.7618	123600	0.2004	-	-	-	-	-
2.7663	123800	0.1971	-	-	-	-	-
2.7708	124000	0.1939	-	-	-	-	-
2.7752	124200	0.1968	-	-	-	-	-
2.7797	124400	0.1956	-	-	-	-	-
2.7842	124600	0.1981	-	-	-	-	-
2.7886	124800	0.1967	-	-	-	-	-
2.7931	125000	0.1995	-	-	-	-	-
2.7976	125200	0.1994	-	-	-	-	-
2.8020	125400	0.1938	-	-	-	-	-
2.8065	125600	0.1995	-	-	-	-	-
2.8110	125800	0.2034	-	-	-	-	-
2.8155	126000	0.1947	-	-	-	-	-
2.8199	126200	0.1941	-	-	-	-	-
2.8244	126400	0.2017	-	-	-	-	-
2.8289	126600	0.1987	-	-	-	-	-
2.8333	126800	0.1976	-	-	-	-	-
2.8378	127000	0.1957	-	-	-	-	-
2.8423	127200	0.2006	-	-	-	-	-
2.8467	127400	0.1959	-	-	-	-	-
2.8512	127600	0.195	-	-	-	-	-
2.8557	127800	0.1973	-	-	-	-	-
2.8601	128000	0.1986	-	-	-	-	-
2.8646	128200	0.1979	-	-	-	-	-
2.8691	128400	0.2053	-	-	-	-	-
2.8736	128600	0.2008	-	-	-	-	-
2.8780	128800	0.203	-	-	-	-	-
2.8825	129000	0.203	-	-	-	-	-
2.8870	129200	0.1928	-	-	-	-	-
2.8914	129400	0.2054	-	-	-	-	-
2.8959	129600	0.1957	-	-	-	-	-
2.9004	129800	0.1986	-	-	-	-	-
2.9048	130000	0.2023	-	-	-	-	-
2.9093	130200	0.1975	-	-	-	-	-
2.9138	130400	0.1915	-	-	-	-	-
2.9182	130600	0.1976	-	-	-	-	-
2.9227	130800	0.191	-	-	-	-	-
2.9272	131000	0.2068	-	-	-	-	-
2.9316	131200	0.1948	-	-	-	-	-
2.9361	131400	0.1909	-	-	-	-	-
2.9406	131600	0.1971	-	-	-	-	-
2.9451	131800	0.1959	-	-	-	-	-
2.9495	132000	0.194	-	-	-	-	-
2.9540	132200	0.1975	-	-	-	-	-
2.9585	132400	0.1935	-	-	-	-	-
2.9629	132600	0.1933	-	-	-	-	-
2.9674	132800	0.1943	-	-	-	-	-
2.9719	133000	0.1936	-	-	-	-	-
2.9763	133200	0.1919	-	-	-	-	-
2.9808	133400	0.2008	-	-	-	-	-
2.9853	133600	0.1936	-	-	-	-	-
2.9897	133800	0.1989	-	-	-	-	-
2.9942	134000	0.1924	-	-	-	-	-
2.9987	134200	0.1963	-	-	-	-	-
3.0032	134400	0.1556	-	-	-	-	-
3.0076	134600	0.1417	-	-	-	-	-
3.0121	134800	0.144	-	-	-	-	-
3.0166	135000	0.1387	-	-	-	-	-
3.0210	135200	0.1384	-	-	-	-	-
3.0255	135400	0.1397	-	-	-	-	-
3.0300	135600	0.1431	-	-	-	-	-
3.0344	135800	0.1345	-	-	-	-	-
3.0389	136000	0.1339	-	-	-	-	-
3.0434	136200	0.1402	-	-	-	-	-
3.0478	136400	0.1431	-	-	-	-	-
3.0523	136600	0.148	-	-	-	-	-
3.0568	136800	0.1434	-	-	-	-	-
3.0612	137000	0.1431	-	-	-	-	-
3.0657	137200	0.1386	-	-	-	-	-
3.0702	137400	0.1424	-	-	-	-	-
3.0747	137600	0.1421	-	-	-	-	-
3.0791	137800	0.1431	-	-	-	-	-
3.0836	138000	0.1411	-	-	-	-	-
3.0881	138200	0.146	-	-	-	-	-
3.0925	138400	0.1456	-	-	-	-	-
3.0970	138600	0.1436	-	-	-	-	-
3.1015	138800	0.1484	-	-	-	-	-
3.1059	139000	0.1386	-	-	-	-	-
3.1104	139200	0.1422	-	-	-	-	-
3.1149	139400	0.1439	-	-	-	-	-
3.1193	139600	0.1482	-	-	-	-	-
3.1238	139800	0.141	-	-	-	-	-
3.1283	140000	0.1424	-	-	-	-	-
3.1328	140200	0.1425	-	-	-	-	-
3.1372	140400	0.144	-	-	-	-	-
3.1417	140600	0.1391	-	-	-	-	-
3.1462	140800	0.1436	-	-	-	-	-
3.1506	141000	0.1466	-	-	-	-	-
3.1551	141200	0.1361	-	-	-	-	-
3.1596	141400	0.141	-	-	-	-	-
3.1640	141600	0.143	-	-	-	-	-
3.1685	141800	0.1407	-	-	-	-	-
3.1730	142000	0.1416	-	-	-	-	-
3.1774	142200	0.1377	-	-	-	-	-
3.1819	142400	0.1399	-	-	-	-	-
3.1864	142600	0.1473	-	-	-	-	-
3.1908	142800	0.1448	-	-	-	-	-
3.1953	143000	0.1435	-	-	-	-	-
3.1998	143200	0.1476	-	-	-	-	-
3.2043	143400	0.1396	-	-	-	-	-
3.2087	143600	0.1373	-	-	-	-	-
3.2132	143800	0.1443	-	-	-	-	-
3.2177	144000	0.1434	-	-	-	-	-
3.2221	144200	0.1383	-	-	-	-	-
3.2266	144400	0.1445	-	-	-	-	-
3.2311	144600	0.14	-	-	-	-	-
3.2355	144800	0.1384	-	-	-	-	-
3.2400	145000	0.143	-	-	-	-	-
3.2445	145200	0.14	-	-	-	-	-
3.2489	145400	0.1428	-	-	-	-	-
3.2534	145600	0.1461	-	-	-	-	-
3.2579	145800	0.1445	-	-	-	-	-
3.2624	146000	0.1383	-	-	-	-	-
3.2668	146200	0.1407	-	-	-	-	-
3.2713	146400	0.1387	-	-	-	-	-
3.2758	146600	0.1382	-	-	-	-	-
3.2802	146800	0.1397	-	-	-	-	-
3.2847	147000	0.1429	-	-	-	-	-
3.2892	147200	0.1456	-	-	-	-	-
3.2936	147400	0.136	-	-	-	-	-
3.2981	147600	0.1415	-	-	-	-	-
3.3026	147800	0.1469	-	-	-	-	-
3.3070	148000	0.1451	-	-	-	-	-
3.3115	148200	0.1436	-	-	-	-	-
3.3160	148400	0.1399	-	-	-	-	-
3.3204	148600	0.1464	-	-	-	-	-
3.3249	148800	0.1405	-	-	-	-	-
3.3294	149000	0.1424	-	-	-	-	-
3.3339	149200	0.1437	-	-	-	-	-
3.3383	149400	0.1487	-	-	-	-	-
3.3428	149600	0.1418	-	-	-	-	-
3.3473	149800	0.136	-	-	-	-	-
3.3517	150000	0.1447	-	-	-	-	-
3.3562	150200	0.1437	-	-	-	-	-
3.3607	150400	0.1381	-	-	-	-	-
3.3651	150600	0.1391	-	-	-	-	-
3.3696	150800	0.1458	-	-	-	-	-
3.3741	151000	0.1436	-	-	-	-	-
3.3785	151200	0.1495	-	-	-	-	-
3.3830	151400	0.1461	-	-	-	-	-
3.3875	151600	0.1446	-	-	-	-	-
3.3920	151800	0.1383	-	-	-	-	-
3.3964	152000	0.1413	-	-	-	-	-
3.4009	152200	0.1401	-	-	-	-	-
3.4054	152400	0.1455	-	-	-	-	-
3.4098	152600	0.1457	-	-	-	-	-
3.4143	152800	0.1406	-	-	-	-	-
3.4188	153000	0.1402	-	-	-	-	-
3.4232	153200	0.1436	-	-	-	-	-
3.4277	153400	0.1398	-	-	-	-	-
3.4322	153600	0.1426	-	-	-	-	-
3.4366	153800	0.143	-	-	-	-	-
3.4411	154000	0.1462	-	-	-	-	-
3.4456	154200	0.1459	-	-	-	-	-
3.4500	154400	0.1414	-	-	-	-	-
3.4545	154600	0.1399	-	-	-	-	-
3.4590	154800	0.1488	-	-	-	-	-
3.4635	155000	0.1422	-	-	-	-	-
3.4679	155200	0.1451	-	-	-	-	-
3.4724	155400	0.1448	-	-	-	-	-
3.4769	155600	0.1371	-	-	-	-	-
3.4813	155800	0.1393	-	-	-	-	-
3.4858	156000	0.1418	-	-	-	-	-
3.4903	156200	0.1435	-	-	-	-	-
3.4947	156400	0.1392	-	-	-	-	-
3.4992	156600	0.1383	-	-	-	-	-
3.5037	156800	0.1431	-	-	-	-	-
3.5081	157000	0.1467	-	-	-	-	-
3.5126	157200	0.1363	-	-	-	-	-
3.5171	157400	0.1409	-	-	-	-	-
3.5216	157600	0.144	-	-	-	-	-
3.5260	157800	0.1408	-	-	-	-	-
3.5305	158000	0.1436	-	-	-	-	-
3.5350	158200	0.142	-	-	-	-	-
3.5394	158400	0.143	-	-	-	-	-
3.5439	158600	0.1466	-	-	-	-	-
3.5484	158800	0.1447	-	-	-	-	-
3.5528	159000	0.1424	-	-	-	-	-
3.5573	159200	0.1402	-	-	-	-	-
3.5618	159400	0.1467	-	-	-	-	-
3.5662	159600	0.1418	-	-	-	-	-
3.5707	159800	0.1433	-	-	-	-	-
3.5752	160000	0.1387	0.5327 (+0.2232)	0.5004 (-0.0401)	0.3898 (+0.0648)	0.4664 (-0.0342)	0.4522 (-0.0032)
3.5796	160200	0.147	-	-	-	-	-
3.5841	160400	0.1449	-	-	-	-	-
3.5886	160600	0.1353	-	-	-	-	-
3.5931	160800	0.1393	-	-	-	-	-
3.5975	161000	0.1402	-	-	-	-	-
3.6020	161200	0.1482	-	-	-	-	-
3.6065	161400	0.1448	-	-	-	-	-
3.6109	161600	0.1445	-	-	-	-	-
3.6154	161800	0.1444	-	-	-	-	-
3.6199	162000	0.1408	-	-	-	-	-
3.6243	162200	0.1446	-	-	-	-	-
3.6288	162400	0.1425	-	-	-	-	-
3.6333	162600	0.1435	-	-	-	-	-
3.6377	162800	0.1382	-	-	-	-	-
3.6422	163000	0.1461	-	-	-	-	-
3.6467	163200	0.1468	-	-	-	-	-
3.6512	163400	0.1462	-	-	-	-	-
3.6556	163600	0.1401	-	-	-	-	-
3.6601	163800	0.1414	-	-	-	-	-
3.6646	164000	0.1435	-	-	-	-	-
3.6690	164200	0.1398	-	-	-	-	-
3.6735	164400	0.1424	-	-	-	-	-
3.6780	164600	0.1424	-	-	-	-	-
3.6824	164800	0.1401	-	-	-	-	-
3.6869	165000	0.1454	-	-	-	-	-
3.6914	165200	0.1467	-	-	-	-	-
3.6958	165400	0.1416	-	-	-	-	-
3.7003	165600	0.1419	-	-	-	-	-
3.7048	165800	0.1438	-	-	-	-	-
3.7092	166000	0.1468	-	-	-	-	-
3.7137	166200	0.1472	-	-	-	-	-
3.7182	166400	0.1394	-	-	-	-	-
3.7227	166600	0.1455	-	-	-	-	-
3.7271	166800	0.1398	-	-	-	-	-
3.7316	167000	0.1365	-	-	-	-	-
3.7361	167200	0.1391	-	-	-	-	-
3.7405	167400	0.1435	-	-	-	-	-
3.7450	167600	0.1417	-	-	-	-	-
3.7495	167800	0.1436	-	-	-	-	-
3.7539	168000	0.1459	-	-	-	-	-
3.7584	168200	0.1427	-	-	-	-	-
3.7629	168400	0.1458	-	-	-	-	-
3.7673	168600	0.1399	-	-	-	-	-
3.7718	168800	0.1381	-	-	-	-	-
3.7763	169000	0.1365	-	-	-	-	-
3.7808	169200	0.1422	-	-	-	-	-
3.7852	169400	0.1458	-	-	-	-	-
3.7897	169600	0.1379	-	-	-	-	-
3.7942	169800	0.146	-	-	-	-	-
3.7986	170000	0.1366	-	-	-	-	-
3.8031	170200	0.1397	-	-	-	-	-
3.8076	170400	0.1444	-	-	-	-	-
3.8120	170600	0.1399	-	-	-	-	-
3.8165	170800	0.1427	-	-	-	-	-
3.8210	171000	0.1365	-	-	-	-	-
3.8254	171200	0.1357	-	-	-	-	-
3.8299	171400	0.1412	-	-	-	-	-
3.8344	171600	0.1424	-	-	-	-	-
3.8388	171800	0.1401	-	-	-	-	-
3.8433	172000	0.1455	-	-	-	-	-
3.8478	172200	0.1349	-	-	-	-	-
3.8523	172400	0.1361	-	-	-	-	-
3.8567	172600	0.1438	-	-	-	-	-
3.8612	172800	0.1364	-	-	-	-	-
3.8657	173000	0.1417	-	-	-	-	-
3.8701	173200	0.1404	-	-	-	-	-
3.8746	173400	0.14	-	-	-	-	-
3.8791	173600	0.1402	-	-	-	-	-
3.8835	173800	0.1381	-	-	-	-	-
3.8880	174000	0.1377	-	-	-	-	-
3.8925	174200	0.141	-	-	-	-	-
3.8969	174400	0.1444	-	-	-	-	-
3.9014	174600	0.1384	-	-	-	-	-
3.9059	174800	0.1388	-	-	-	-	-
3.9104	175000	0.1397	-	-	-	-	-
3.9148	175200	0.1376	-	-	-	-	-
3.9193	175400	0.1395	-	-	-	-	-
3.9238	175600	0.1388	-	-	-	-	-
3.9282	175800	0.1384	-	-	-	-	-
3.9327	176000	0.1382	-	-	-	-	-
3.9372	176200	0.1411	-	-	-	-	-
3.9416	176400	0.1396	-	-	-	-	-
3.9461	176600	0.1403	-	-	-	-	-
3.9506	176800	0.1444	-	-	-	-	-
3.9550	177000	0.1377	-	-	-	-	-
3.9595	177200	0.1409	-	-	-	-	-
3.9640	177400	0.1424	-	-	-	-	-
3.9684	177600	0.1419	-	-	-	-	-
3.9729	177800	0.1386	-	-	-	-	-
3.9774	178000	0.1375	-	-	-	-	-
3.9819	178200	0.1376	-	-	-	-	-
3.9863	178400	0.1417	-	-	-	-	-
3.9908	178600	0.1411	-	-	-	-	-
3.9953	178800	0.1478	-	-	-	-	-
3.9997	179000	0.1419	-	-	-	-	-
4.0042	179200	0.0962	-	-	-	-	-
4.0087	179400	0.0976	-	-	-	-	-
4.0131	179600	0.0998	-	-	-	-	-
4.0176	179800	0.0975	-	-	-	-	-
4.0221	180000	0.0969	-	-	-	-	-
4.0265	180200	0.0942	-	-	-	-	-
4.0310	180400	0.0974	-	-	-	-	-
4.0355	180600	0.0985	-	-	-	-	-
4.0400	180800	0.0996	-	-	-	-	-
4.0444	181000	0.098	-	-	-	-	-
4.0489	181200	0.0986	-	-	-	-	-
4.0534	181400	0.0962	-	-	-	-	-
4.0578	181600	0.0988	-	-	-	-	-
4.0623	181800	0.0965	-	-	-	-	-
4.0668	182000	0.0987	-	-	-	-	-
4.0712	182200	0.0955	-	-	-	-	-
4.0757	182400	0.0975	-	-	-	-	-
4.0802	182600	0.0947	-	-	-	-	-
4.0846	182800	0.098	-	-	-	-	-
4.0891	183000	0.0967	-	-	-	-	-
4.0936	183200	0.1006	-	-	-	-	-
4.0980	183400	0.0997	-	-	-	-	-
4.1025	183600	0.0999	-	-	-	-	-
4.1070	183800	0.1022	-	-	-	-	-
4.1115	184000	0.1019	-	-	-	-	-
4.1159	184200	0.0964	-	-	-	-	-
4.1204	184400	0.0987	-	-	-	-	-
4.1249	184600	0.098	-	-	-	-	-
4.1293	184800	0.0936	-	-	-	-	-
4.1338	185000	0.0978	-	-	-	-	-
4.1383	185200	0.095	-	-	-	-	-
4.1427	185400	0.0937	-	-	-	-	-
4.1472	185600	0.094	-	-	-	-	-
4.1517	185800	0.0999	-	-	-	-	-
4.1561	186000	0.0973	-	-	-	-	-
4.1606	186200	0.0951	-	-	-	-	-
4.1651	186400	0.1012	-	-	-	-	-
4.1696	186600	0.0974	-	-	-	-	-
4.1740	186800	0.0995	-	-	-	-	-
4.1785	187000	0.101	-	-	-	-	-
4.1830	187200	0.1016	-	-	-	-	-
4.1874	187400	0.0981	-	-	-	-	-
4.1919	187600	0.0996	-	-	-	-	-
4.1964	187800	0.0969	-	-	-	-	-
4.2008	188000	0.0937	-	-	-	-	-
4.2053	188200	0.0957	-	-	-	-	-
4.2098	188400	0.1011	-	-	-	-	-
4.2142	188600	0.0974	-	-	-	-	-
4.2187	188800	0.0943	-	-	-	-	-
4.2232	189000	0.0989	-	-	-	-	-
4.2276	189200	0.0959	-	-	-	-	-
4.2321	189400	0.0966	-	-	-	-	-
4.2366	189600	0.0991	-	-	-	-	-
4.2411	189800	0.102	-	-	-	-	-
4.2455	190000	0.0986	-	-	-	-	-
4.2500	190200	0.1004	-	-	-	-	-
4.2545	190400	0.0945	-	-	-	-	-
4.2589	190600	0.0985	-	-	-	-	-
4.2634	190800	0.0972	-	-	-	-	-
4.2679	191000	0.1022	-	-	-	-	-
4.2723	191200	0.1001	-	-	-	-	-
4.2768	191400	0.0977	-	-	-	-	-
4.2813	191600	0.1015	-	-	-	-	-
4.2857	191800	0.099	-	-	-	-	-
4.2902	192000	0.0942	-	-	-	-	-
4.2947	192200	0.0964	-	-	-	-	-
4.2992	192400	0.0972	-	-	-	-	-
4.3036	192600	0.0955	-	-	-	-	-
4.3081	192800	0.0965	-	-	-	-	-
4.3126	193000	0.0995	-	-	-	-	-
4.3170	193200	0.0949	-	-	-	-	-
4.3215	193400	0.0936	-	-	-	-	-
4.3260	193600	0.0989	-	-	-	-	-
4.3304	193800	0.096	-	-	-	-	-
4.3349	194000	0.0967	-	-	-	-	-
4.3394	194200	0.0999	-	-	-	-	-
4.3438	194400	0.1017	-	-	-	-	-
4.3483	194600	0.0962	-	-	-	-	-
4.3528	194800	0.1016	-	-	-	-	-
4.3572	195000	0.0968	-	-	-	-	-
4.3617	195200	0.0959	-	-	-	-	-
4.3662	195400	0.0969	-	-	-	-	-
4.3707	195600	0.1001	-	-	-	-	-
4.3751	195800	0.095	-	-	-	-	-
4.3796	196000	0.0968	-	-	-	-	-
4.3841	196200	0.099	-	-	-	-	-
4.3885	196400	0.0954	-	-	-	-	-
4.3930	196600	0.1016	-	-	-	-	-
4.3975	196800	0.0978	-	-	-	-	-
4.4019	197000	0.0918	-	-	-	-	-
4.4064	197200	0.0979	-	-	-	-	-
4.4109	197400	0.0996	-	-	-	-	-
4.4153	197600	0.0953	-	-	-	-	-
4.4198	197800	0.099	-	-	-	-	-
4.4243	198000	0.0967	-	-	-	-	-
4.4288	198200	0.1004	-	-	-	-	-
4.4332	198400	0.0988	-	-	-	-	-
4.4377	198600	0.0967	-	-	-	-	-
4.4422	198800	0.099	-	-	-	-	-
4.4466	199000	0.0944	-	-	-	-	-
4.4511	199200	0.0941	-	-	-	-	-
4.4556	199400	0.1	-	-	-	-	-
4.4600	199600	0.097	-	-	-	-	-
4.4645	199800	0.097	-	-	-	-	-
4.4690	200000	0.0964	-	-	-	-	-
4.4734	200200	0.0972	-	-	-	-	-
4.4779	200400	0.0934	-	-	-	-	-
4.4824	200600	0.0977	-	-	-	-	-
4.4869	200800	0.0995	-	-	-	-	-
4.4913	201000	0.0952	-	-	-	-	-
4.4958	201200	0.0956	-	-	-	-	-
4.5003	201400	0.1001	-	-	-	-	-
4.5047	201600	0.0963	-	-	-	-	-
4.5092	201800	0.1014	-	-	-	-	-
4.5137	202000	0.0993	-	-	-	-	-
4.5181	202200	0.1008	-	-	-	-	-
4.5226	202400	0.0969	-	-	-	-	-
4.5271	202600	0.0952	-	-	-	-	-
4.5315	202800	0.0994	-	-	-	-	-
4.5360	203000	0.0952	-	-	-	-	-
4.5405	203200	0.1012	-	-	-	-	-
4.5449	203400	0.0954	-	-	-	-	-
4.5494	203600	0.0945	-	-	-	-	-
4.5539	203800	0.0976	-	-	-	-	-
4.5584	204000	0.102	-	-	-	-	-
4.5628	204200	0.0948	-	-	-	-	-
4.5673	204400	0.098	-	-	-	-	-
4.5718	204600	0.0991	-	-	-	-	-
4.5762	204800	0.0951	-	-	-	-	-
4.5807	205000	0.0978	-	-	-	-	-
4.5852	205200	0.0949	-	-	-	-	-
4.5896	205400	0.0907	-	-	-	-	-
4.5941	205600	0.0979	-	-	-	-	-
4.5986	205800	0.098	-	-	-	-	-
4.6030	206000	0.0958	-	-	-	-	-
4.6075	206200	0.0928	-	-	-	-	-
4.6120	206400	0.0976	-	-	-	-	-
4.6165	206600	0.0981	-	-	-	-	-
4.6209	206800	0.0909	-	-	-	-	-
4.6254	207000	0.0979	-	-	-	-	-
4.6299	207200	0.0943	-	-	-	-	-
4.6343	207400	0.0956	-	-	-	-	-
4.6388	207600	0.0949	-	-	-	-	-
4.6433	207800	0.1015	-	-	-	-	-
4.6477	208000	0.0954	-	-	-	-	-
4.6522	208200	0.0964	-	-	-	-	-
4.6567	208400	0.0955	-	-	-	-	-
4.6611	208600	0.0977	-	-	-	-	-
4.6656	208800	0.097	-	-	-	-	-
4.6701	209000	0.0943	-	-	-	-	-
4.6745	209200	0.0957	-	-	-	-	-
4.6790	209400	0.0985	-	-	-	-	-
4.6835	209600	0.0972	-	-	-	-	-
4.6880	209800	0.0926	-	-	-	-	-
4.6924	210000	0.0994	-	-	-	-	-
4.6969	210200	0.1006	-	-	-	-	-
4.7014	210400	0.0983	-	-	-	-	-
4.7058	210600	0.0966	-	-	-	-	-
4.7103	210800	0.0964	-	-	-	-	-
4.7148	211000	0.0962	-	-	-	-	-
4.7192	211200	0.0942	-	-	-	-	-
4.7237	211400	0.1032	-	-	-	-	-
4.7282	211600	0.0979	-	-	-	-	-
4.7326	211800	0.096	-	-	-	-	-
4.7371	212000	0.0943	-	-	-	-	-
4.7416	212200	0.0924	-	-	-	-	-
4.7461	212400	0.0917	-	-	-	-	-
4.7505	212600	0.0932	-	-	-	-	-
4.7550	212800	0.0925	-	-	-	-	-
4.7595	213000	0.0932	-	-	-	-	-
4.7639	213200	0.0951	-	-	-	-	-
4.7684	213400	0.095	-	-	-	-	-
4.7729	213600	0.0969	-	-	-	-	-
4.7773	213800	0.0944	-	-	-	-	-
4.7818	214000	0.0947	-	-	-	-	-
4.7863	214200	0.0965	-	-	-	-	-
4.7907	214400	0.0937	-	-	-	-	-
4.7952	214600	0.0957	-	-	-	-	-
4.7997	214800	0.0992	-	-	-	-	-
4.8041	215000	0.0979	-	-	-	-	-
4.8086	215200	0.1003	-	-	-	-	-
4.8131	215400	0.0933	-	-	-	-	-
4.8176	215600	0.0925	-	-	-	-	-
4.8220	215800	0.0954	-	-	-	-	-
4.8265	216000	0.0941	-	-	-	-	-
4.8310	216200	0.0917	-	-	-	-	-
4.8354	216400	0.0952	-	-	-	-	-
4.8399	216600	0.0907	-	-	-	-	-
4.8444	216800	0.0953	-	-	-	-	-
4.8488	217000	0.094	-	-	-	-	-
4.8533	217200	0.0967	-	-	-	-	-
4.8578	217400	0.0927	-	-	-	-	-
4.8622	217600	0.097	-	-	-	-	-
4.8667	217800	0.0871	-	-	-	-	-
4.8712	218000	0.0934	-	-	-	-	-
4.8757	218200	0.0923	-	-	-	-	-
4.8801	218400	0.0904	-	-	-	-	-
4.8846	218600	0.0975	-	-	-	-	-
4.8891	218800	0.0908	-	-	-	-	-
4.8935	219000	0.0918	-	-	-	-	-
4.8980	219200	0.0997	-	-	-	-	-
4.9025	219400	0.0982	-	-	-	-	-
4.9069	219600	0.0977	-	-	-	-	-
4.9114	219800	0.094	-	-	-	-	-
4.9159	220000	0.0914	-	-	-	-	-
4.9203	220200	0.0919	-	-	-	-	-
4.9248	220400	0.0937	-	-	-	-	-
4.9293	220600	0.0893	-	-	-	-	-
4.9337	220800	0.0945	-	-	-	-	-
4.9382	221000	0.0926	-	-	-	-	-
4.9427	221200	0.0906	-	-	-	-	-
4.9472	221400	0.0958	-	-	-	-	-
4.9516	221600	0.0956	-	-	-	-	-
4.9561	221800	0.098	-	-	-	-	-
4.9606	222000	0.0991	-	-	-	-	-
4.9650	222200	0.0945	-	-	-	-	-
4.9695	222400	0.0927	-	-	-	-	-
4.9740	222600	0.092	-	-	-	-	-
4.9784	222800	0.09	-	-	-	-	-
4.9829	223000	0.0991	-	-	-	-	-
4.9874	223200	0.0961	-	-	-	-	-
4.9918	223400	0.0941	-	-	-	-	-
4.9963	223600	0.0976	-	-	-	-	-
-1	-1	-	0.5296 (+0.2201)	0.4190 (-0.1214)	0.4076 (+0.0826)	0.4068 (-0.0938)	0.4111 (-0.0442)

Framework Versions

Python: 3.11.0
Sentence Transformers: 4.0.1
Transformers: 4.50.3
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.5.0
Tokenizers: 0.21.1

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",
}