This is a Cross Encoder model finetuned from cross-encoder/msmarco-MiniLM-L6-en-de-v1 on the csv dataset 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: cross-encoder/msmarco-MiniLM-L6-en-de-v1
Maximum Sequence Length: 512 tokens
Number of Output Labels: 1 label
Training Dataset:
- csv

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("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ['Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.', "Direct or coordinate an organization's financial or budget activities to fund operations, maximize investments, or increase efficiency."],
    ['Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.', 'Confer with board members, organization officials, or staff members to discuss issues, coordinate activities, or resolve problems.'],
    ['Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.', 'Prepare budgets for approval, including those for funding or implementation of programs.'],
    ['Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.', 'Direct, plan, or implement policies, objectives, or activities of organizations or businesses to ensure continuing operations, to maximize returns on investments, or to increase productivity.'],
    ['Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.', 'Prepare or present reports concerning activities, expenses, budgets, government statutes or rulings, or other items affecting businesses or program services.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.',
    [
        "Direct or coordinate an organization's financial or budget activities to fund operations, maximize investments, or increase efficiency.",
        'Confer with board members, organization officials, or staff members to discuss issues, coordinate activities, or resolve problems.',
        'Prepare budgets for approval, including those for funding or implementation of programs.',
        'Direct, plan, or implement policies, objectives, or activities of organizations or businesses to ensure continuing operations, to maximize returns on investments, or to increase productivity.',
        'Prepare or present reports concerning activities, expenses, budgets, government statutes or rulings, or other items affecting businesses or program services.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Correlation

Dataset: onet-validation
Evaluated with CrossEncoderCorrelationEvaluator

Metric	Value
pearson	0.8247
spearman	0.8073

Cross Encoder Correlation

Dataset: onet-validation
Evaluated with CrossEncoderCorrelationEvaluator

Metric	Value
pearson	0.8081
spearman	0.7876

Training Details

Training Dataset

csv

Dataset: csv
Size: 17,639 training samples
Columns: query, task, and score
Approximate statistics based on the first 1000 samples:
query task score
type string string float
details
min: 105 characters
mean: 235.78 characters
max: 562 characters
min: 20 characters
mean: 103.24 characters
max: 317 characters
min: 0.42
mean: 0.8
max: 0.98

Samples:

query	task	score
`Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.`	`Direct or coordinate an organization's financial or budget activities to fund operations, maximize investments, or increase efficiency.`	`0.8242`
`Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.`	`Confer with board members, organization officials, or staff members to discuss issues, coordinate activities, or resolve problems.`	`0.84055`
`Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.`	`Prepare budgets for approval, including those for funding or implementation of programs.`	`0.89705`

Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": null 4}

Evaluation Dataset

csv

Dataset: csv
Size: 1,764 evaluation samples
Columns: query, task, and score
Approximate statistics based on the first 1000 samples:
query task score
type string string float
details
min: 105 characters
mean: 235.78 characters
max: 562 characters
min: 20 characters
mean: 103.24 characters
max: 317 characters
min: 0.42
mean: 0.8
max: 0.98

Samples:

query	task	score
`Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.`	`Direct or coordinate an organization's financial or budget activities to fund operations, maximize investments, or increase efficiency.`	`0.8242`
`Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.`	`Confer with board members, organization officials, or staff members to discuss issues, coordinate activities, or resolve problems.`	`0.84055`
`Chief Executives:Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.`	`Prepare budgets for approval, including those for funding or implementation of programs.`	`0.89705`

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
num_train_epochs: 4
warmup_ratio: 0.1
bf16: 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: 8
per_device_eval_batch_size: 8
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: 5e-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: 4
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: 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: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
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
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	Validation Loss	onet-validation_spearman
-1	-1	-	-	0.0090
0.0091	20	1.2693	-	-
0.0181	40	1.3548	-	-
0.0272	60	1.0328	-	-
0.0363	80	0.9504	0.8379	0.0108
0.0454	100	0.9183	-	-
0.0544	120	0.697	-	-
0.0635	140	0.625	-	-
0.0726	160	0.5337	0.5489	0.0368
0.0816	180	0.5008	-	-
0.0907	200	0.4894	-	-
0.0998	220	0.5352	-	-
0.1088	240	0.4994	0.5040	0.0981
0.1179	260	0.4857	-	-
0.1270	280	0.5122	-	-
0.1361	300	0.517	-	-
0.1451	320	0.5156	0.4994	0.1470
0.1542	340	0.4859	-	-
0.1633	360	0.4842	-	-
0.1723	380	0.5138	-	-
0.1814	400	0.5142	0.5011	0.1403
0.1905	420	0.4948	-	-
0.1995	440	0.499	-	-
0.2086	460	0.4732	-	-
0.2177	480	0.5066	0.5007	0.1681
0.2268	500	0.5014	-	-
0.2358	520	0.4819	-	-
0.2449	540	0.4937	-	-
0.2540	560	0.5056	0.4974	0.2359
0.2630	580	0.4986	-	-
0.2721	600	0.5066	-	-
0.2812	620	0.4776	-	-
0.2902	640	0.4845	0.5024	0.2607
0.2993	660	0.4991	-	-
0.3084	680	0.5034	-	-
0.3175	700	0.4655	-	-
0.3265	720	0.5191	0.5107	0.2969
0.3356	740	0.509	-	-
0.3447	760	0.484	-	-
0.3537	780	0.5113	-	-
0.3628	800	0.4968	0.4966	0.3645
0.3719	820	0.4713	-	-
0.3810	840	0.507	-	-
0.3900	860	0.5041	-	-
0.3991	880	0.4868	0.4953	0.3896
0.4082	900	0.4985	-	-
0.4172	920	0.477	-	-
0.4263	940	0.4888	-	-
0.4354	960	0.4791	0.4916	0.4280
0.4444	980	0.4969	-	-
0.4535	1000	0.4757	-	-
0.4626	1020	0.4978	-	-
0.4717	1040	0.4998	0.4966	0.4299
0.4807	1060	0.5062	-	-
0.4898	1080	0.4876	-	-
0.4989	1100	0.4836	-	-
0.5079	1120	0.5034	0.4908	0.4404
0.5170	1140	0.4788	-	-
0.5261	1160	0.5037	-	-
0.5351	1180	0.467	-	-
0.5442	1200	0.4785	0.4942	0.4701
0.5533	1220	0.502	-	-
0.5624	1240	0.5223	-	-
0.5714	1260	0.4755	-	-
0.5805	1280	0.4826	0.4888	0.4685
0.5896	1300	0.493	-	-
0.5986	1320	0.4935	-	-
0.6077	1340	0.4851	-	-
0.6168	1360	0.4884	0.4908	0.5028
0.6259	1380	0.4966	-	-
0.6349	1400	0.4769	-	-
0.6440	1420	0.4965	-	-
0.6531	1440	0.492	0.4869	0.5234
0.6621	1460	0.487	-	-
0.6712	1480	0.5045	-	-
0.6803	1500	0.4638	-	-
0.6893	1520	0.4622	0.4874	0.5281
0.6984	1540	0.468	-	-
0.7075	1560	0.4627	-	-
0.7166	1580	0.4892	-	-
0.7256	1600	0.5044	0.4885	0.5219
0.7347	1620	0.4941	-	-
0.7438	1640	0.4857	-	-
0.7528	1660	0.497	-	-
0.7619	1680	0.5007	0.4925	0.5146
0.7710	1700	0.5038	-	-
0.7800	1720	0.4702	-	-
0.7891	1740	0.4754	-	-
0.7982	1760	0.4852	0.4874	0.5402
0.8073	1780	0.4858	-	-
0.8163	1800	0.493	-	-
0.8254	1820	0.4802	-	-
0.8345	1840	0.4905	0.4865	0.5370
0.8435	1860	0.5	-	-
0.8526	1880	0.4888	-	-
0.8617	1900	0.4764	-	-
0.8707	1920	0.4647	0.4885	0.5100
0.8798	1940	0.4714	-	-
0.8889	1960	0.497	-	-
0.8980	1980	0.4878	-	-
0.9070	2000	0.4906	0.4855	0.5633
0.9161	2020	0.5018	-	-
0.9252	2040	0.4998	-	-
0.9342	2060	0.4619	-	-
0.9433	2080	0.4722	0.4855	0.5575
0.9524	2100	0.487	-	-
0.9615	2120	0.4798	-	-
0.9705	2140	0.46	-	-
0.9796	2160	0.4683	0.4844	0.5710
0.9887	2180	0.5026	-	-
0.9977	2200	0.4905	-	-
1.0068	2220	0.5008	-	-
1.0159	2240	0.4918	0.4832	0.5951
1.0249	2260	0.4809	-	-
1.0340	2280	0.4964	-	-
1.0431	2300	0.4562	-	-
1.0522	2320	0.4529	0.4862	0.5884
1.0612	2340	0.4689	-	-
1.0703	2360	0.4811	-	-
1.0794	2380	0.4822	-	-
1.0884	2400	0.4944	0.4832	0.5892
1.0975	2420	0.5001	-	-
1.1066	2440	0.4912	-	-
1.1156	2460	0.4826	-	-
1.1247	2480	0.47	0.4834	0.5988
1.1338	2500	0.4818	-	-
1.1429	2520	0.4648	-	-
1.1519	2540	0.4687	-	-
1.1610	2560	0.4737	0.4837	0.5984
1.1701	2580	0.4789	-	-
1.1791	2600	0.4876	-	-
1.1882	2620	0.4952	-	-
1.1973	2640	0.4861	0.4823	0.5981
1.2063	2660	0.4758	-	-
1.2154	2680	0.4927	-	-
1.2245	2700	0.4897	-	-
1.2336	2720	0.4785	0.4835	0.6037
1.2426	2740	0.5027	-	-
1.2517	2760	0.4776	-	-
1.2608	2780	0.445	-	-
1.2698	2800	0.4675	0.4844	0.6264
1.2789	2820	0.4646	-	-
1.2880	2840	0.4822	-	-
1.2971	2860	0.4669	-	-
1.3061	2880	0.4817	0.4823	0.6375
1.3152	2900	0.4759	-	-
1.3243	2920	0.4876	-	-
1.3333	2940	0.4689	-	-
1.3424	2960	0.4751	0.4807	0.6520
1.3515	2980	0.4872	-	-
1.3605	3000	0.4543	-	-
1.3696	3020	0.4687	-	-
1.3787	3040	0.4759	0.4819	0.6136
1.3878	3060	0.4827	-	-
1.3968	3080	0.4876	-	-
1.4059	3100	0.4791	-	-
1.4150	3120	0.4887	0.4818	0.6314
1.4240	3140	0.4863	-	-
1.4331	3160	0.4864	-	-
1.4422	3180	0.4824	-	-
1.4512	3200	0.4974	0.4829	0.6645
1.4603	3220	0.4554	-	-
1.4694	3240	0.484	-	-
1.4785	3260	0.4735	-	-
1.4875	3280	0.504	0.4832	0.6617
1.4966	3300	0.4758	-	-
1.5057	3320	0.4711	-	-
1.5147	3340	0.486	-	-
1.5238	3360	0.4751	0.4815	0.6502
1.5329	3380	0.4761	-	-
1.5420	3400	0.467	-	-
1.5510	3420	0.4706	-	-
1.5601	3440	0.4894	0.4798	0.6549
1.5692	3460	0.4795	-	-
1.5782	3480	0.4922	-	-
1.5873	3500	0.4763	-	-
1.5964	3520	0.4801	0.4804	0.6608
1.6054	3540	0.4692	-	-
1.6145	3560	0.4886	-	-
1.6236	3580	0.4758	-	-
1.6327	3600	0.456	0.4801	0.6651
1.6417	3620	0.496	-	-
1.6508	3640	0.5179	-	-
1.6599	3660	0.4729	-	-
1.6689	3680	0.4612	0.4785	0.6767
1.6780	3700	0.4628	-	-
1.6871	3720	0.4516	-	-
1.6961	3740	0.4773	-	-
1.7052	3760	0.4732	0.4781	0.6798
1.7143	3780	0.5025	-	-
1.7234	3800	0.4843	-	-
1.7324	3820	0.4799	-	-
1.7415	3840	0.4753	0.4781	0.6837
1.7506	3860	0.4568	-	-
1.7596	3880	0.4782	-	-
1.7687	3900	0.4855	-	-
1.7778	3920	0.4699	0.4791	0.6913
1.7868	3940	0.48	-	-
1.7959	3960	0.4743	-	-
1.8050	3980	0.453	-	-
1.8141	4000	0.4755	0.4816	0.6937
1.8231	4020	0.4419	-	-
1.8322	4040	0.4724	-	-
1.8413	4060	0.4892	-	-
1.8503	4080	0.4779	0.4829	0.6903
1.8594	4100	0.4748	-	-
1.8685	4120	0.4909	-	-
1.8776	4140	0.5026	-	-
1.8866	4160	0.4668	0.4795	0.7060
1.8957	4180	0.47	-	-
1.9048	4200	0.4977	-	-
1.9138	4220	0.4644	-	-
1.9229	4240	0.4745	0.4777	0.7053
1.9320	4260	0.455	-	-
1.9410	4280	0.4864	-	-
1.9501	4300	0.4987	-	-
1.9592	4320	0.4716	0.4770	0.6948
1.9683	4340	0.4877	-	-
1.9773	4360	0.4741	-	-
1.9864	4380	0.4969	-	-
1.9955	4400	0.4733	0.4767	0.7052
2.0045	4420	0.4842	-	-
2.0136	4440	0.48	-	-
2.0227	4460	0.4985	-	-
2.0317	4480	0.483	0.4760	0.7110
2.0408	4500	0.482	-	-
2.0499	4520	0.4687	-	-
2.0590	4540	0.4595	-	-
2.0680	4560	0.4699	0.4764	0.7095
2.0771	4580	0.4426	-	-
2.0862	4600	0.4691	-	-
2.0952	4620	0.4568	-	-
2.1043	4640	0.4716	0.4774	0.7124
2.1134	4660	0.4696	-	-
2.1224	4680	0.4737	-	-
2.1315	4700	0.4925	-	-
2.1406	4720	0.4708	0.4754	0.7274
2.1497	4740	0.4531	-	-
2.1587	4760	0.473	-	-
2.1678	4780	0.4824	-	-
2.1769	4800	0.4573	0.4760	0.7291
2.1859	4820	0.4774	-	-
2.1950	4840	0.4776	-	-
2.2041	4860	0.4764	-	-
2.2132	4880	0.4893	0.4749	0.7352
2.2222	4900	0.4793	-	-
2.2313	4920	0.4473	-	-
2.2404	4940	0.4851	-	-
2.2494	4960	0.4787	0.4757	0.7261
2.2585	4980	0.4676	-	-
2.2676	5000	0.4621	-	-
2.2766	5020	0.4714	-	-
2.2857	5040	0.4758	0.4762	0.7230
2.2948	5060	0.4754	-	-
2.3039	5080	0.4305	-	-
2.3129	5100	0.4752	-	-
2.3220	5120	0.4606	0.4759	0.7355
2.3311	5140	0.4936	-	-
2.3401	5160	0.4456	-	-
2.3492	5180	0.489	-	-
2.3583	5200	0.4633	0.4779	0.7319
2.3673	5220	0.4909	-	-
2.3764	5240	0.4601	-	-
2.3855	5260	0.476	-	-
2.3946	5280	0.4793	0.4739	0.7454
2.4036	5300	0.4618	-	-
2.4127	5320	0.4668	-	-
2.4218	5340	0.4621	-	-
2.4308	5360	0.4732	0.4749	0.7412
2.4399	5380	0.4683	-	-
2.4490	5400	0.4902	-	-
2.4580	5420	0.4629	-	-
2.4671	5440	0.4917	0.4748	0.7353
2.4762	5460	0.4783	-	-
2.4853	5480	0.4865	-	-
2.4943	5500	0.4838	-	-
2.5034	5520	0.4486	0.4759	0.7376
2.5125	5540	0.4705	-	-
2.5215	5560	0.4713	-	-
2.5306	5580	0.5	-	-
2.5397	5600	0.4645	0.4753	0.7415
2.5488	5620	0.4655	-	-
2.5578	5640	0.4646	-	-
2.5669	5660	0.4608	-	-
2.5760	5680	0.4752	0.4741	0.7406
2.5850	5700	0.4608	-	-
2.5941	5720	0.46	-	-
2.6032	5740	0.4603	-	-
2.6122	5760	0.4686	0.4734	0.7438
2.6213	5780	0.4542	-	-
2.6304	5800	0.4718	-	-
2.6395	5820	0.4615	-	-
2.6485	5840	0.4749	0.4729	0.7552
2.6576	5860	0.4951	-	-
2.6667	5880	0.4944	-	-
2.6757	5900	0.459	-	-
2.6848	5920	0.4593	0.4743	0.7567
2.6939	5940	0.474	-	-
2.7029	5960	0.4555	-	-
2.7120	5980	0.465	-	-
2.7211	6000	0.4583	0.4730	0.7560
2.7302	6020	0.4528	-	-
2.7392	6040	0.4375	-	-
2.7483	6060	0.4838	-	-
2.7574	6080	0.4995	0.4729	0.7552
2.7664	6100	0.4687	-	-
2.7755	6120	0.4615	-	-
2.7846	6140	0.4619	-	-
2.7937	6160	0.4726	0.4734	0.7589
2.8027	6180	0.4699	-	-
2.8118	6200	0.4772	-	-
2.8209	6220	0.469	-	-
2.8299	6240	0.4592	0.4728	0.7640
2.8390	6260	0.4599	-	-
2.8481	6280	0.4642	-	-
2.8571	6300	0.4658	-	-
2.8662	6320	0.4786	0.4724	0.7655
2.8753	6340	0.4537	-	-
2.8844	6360	0.4984	-	-
2.8934	6380	0.4816	-	-
2.9025	6400	0.4598	0.4726	0.7649
2.9116	6420	0.4775	-	-
2.9206	6440	0.4802	-	-
2.9297	6460	0.4556	-	-
2.9388	6480	0.4787	0.4744	0.7737
2.9478	6500	0.4835	-	-
2.9569	6520	0.4638	-	-
2.9660	6540	0.4912	-	-
2.9751	6560	0.4727	0.4718	0.7725
2.9841	6580	0.4637	-	-
2.9932	6600	0.4934	-	-
3.0023	6620	0.4632	-	-
3.0113	6640	0.4772	0.4720	0.7786
3.0204	6660	0.4565	-	-
3.0295	6680	0.4433	-	-
3.0385	6700	0.4642	-	-
3.0476	6720	0.4603	0.4737	0.7768
3.0567	6740	0.466	-	-
3.0658	6760	0.4509	-	-
3.0748	6780	0.4455	-	-
3.0839	6800	0.4808	0.4718	0.7777
3.0930	6820	0.4836	-	-
3.1020	6840	0.4823	-	-
3.1111	6860	0.469	-	-
3.1202	6880	0.4654	0.4715	0.7777
3.1293	6900	0.4705	-	-
3.1383	6920	0.4869	-	-
3.1474	6940	0.4964	-	-
3.1565	6960	0.4346	0.4729	0.7823
3.1655	6980	0.478	-	-
3.1746	7000	0.4691	-	-
3.1837	7020	0.45	-	-
3.1927	7040	0.4821	0.4715	0.7868
3.2018	7060	0.4652	-	-
3.2109	7080	0.4654	-	-
3.2200	7100	0.4561	-	-
3.2290	7120	0.4657	0.4713	0.7860
3.2381	7140	0.4431	-	-
3.2472	7160	0.448	-	-
3.2562	7180	0.478	-	-
3.2653	7200	0.4574	0.4707	0.7913
3.2744	7220	0.4589	-	-
3.2834	7240	0.4759	-	-
3.2925	7260	0.4703	-	-
3.3016	7280	0.4683	0.4712	0.7943
3.3107	7300	0.4576	-	-
3.3197	7320	0.4517	-	-
3.3288	7340	0.4498	-	-
3.3379	7360	0.4782	0.4705	0.7961
3.3469	7380	0.476	-	-
3.3560	7400	0.4642	-	-
3.3651	7420	0.4923	-	-
3.3741	7440	0.4637	0.4706	0.7949
3.3832	7460	0.4512	-	-
3.3923	7480	0.4392	-	-
3.4014	7500	0.486	-	-
3.4104	7520	0.4632	0.4709	0.7950
3.4195	7540	0.4816	-	-
3.4286	7560	0.46	-	-
3.4376	7580	0.473	-	-
3.4467	7600	0.4618	0.4708	0.7953
3.4558	7620	0.4584	-	-
3.4649	7640	0.46	-	-
3.4739	7660	0.4655	-	-
3.4830	7680	0.4461	0.4713	0.7981
3.4921	7700	0.4521	-	-
3.5011	7720	0.4802	-	-
3.5102	7740	0.464	-	-
3.5193	7760	0.481	0.4699	0.7991
3.5283	7780	0.4719	-	-
3.5374	7800	0.4615	-	-
3.5465	7820	0.458	-	-
3.5556	7840	0.4659	0.4700	0.7996
3.5646	7860	0.4688	-	-
3.5737	7880	0.457	-	-
3.5828	7900	0.4821	-	-
3.5918	7920	0.4567	0.4707	0.8018
3.6009	7940	0.4722	-	-
3.6100	7960	0.4785	-	-
3.6190	7980	0.4904	-	-
3.6281	8000	0.483	0.4698	0.8012
3.6372	8020	0.4756	-	-
3.6463	8040	0.4689	-	-
3.6553	8060	0.4774	-	-
3.6644	8080	0.4638	0.4707	0.8015
3.6735	8100	0.4648	-	-
3.6825	8120	0.478	-	-
3.6916	8140	0.4839	-	-
3.7007	8160	0.4623	0.4696	0.8036
3.7098	8180	0.4558	-	-
3.7188	8200	0.4575	-	-
3.7279	8220	0.4628	-	-
3.7370	8240	0.4552	0.4702	0.8042
3.7460	8260	0.4655	-	-
3.7551	8280	0.4687	-	-
3.7642	8300	0.4549	-	-
3.7732	8320	0.4522	0.4700	0.8052
3.7823	8340	0.4424	-	-
3.7914	8360	0.4612	-	-
3.8005	8380	0.4606	-	-
3.8095	8400	0.4553	0.4695	0.8058
3.8186	8420	0.4661	-	-
3.8277	8440	0.4811	-	-
3.8367	8460	0.4661	-	-
3.8458	8480	0.4805	0.4695	0.8065
3.8549	8500	0.4761	-	-
3.8639	8520	0.4778	-	-
3.8730	8540	0.4566	-	-
3.8821	8560	0.4359	0.4695	0.8067
3.8912	8580	0.47	-	-
3.9002	8600	0.4851	-	-
3.9093	8620	0.4609	-	-
3.9184	8640	0.4675	0.4695	0.8068
3.9274	8660	0.4628	-	-
3.9365	8680	0.4604	-	-
3.9456	8700	0.4829	-	-
3.9546	8720	0.462	0.4695	0.8071
3.9637	8740	0.4554	-	-
3.9728	8760	0.4674	-	-
3.9819	8780	0.4721	-	-
3.9909	8800	0.4534	0.4695	0.8073
4.0	8820	0.4667	-	-
-1	-1	-	-	0.7876

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.5.0
Tokenizers: 0.21.1

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",
}

	query	task	score
type	string	string	float
details	min: 105 characters mean: 235.78 characters max: 562 characters	min: 20 characters mean: 103.24 characters max: 317 characters	min: 0.42 mean: 0.8 max: 0.98

vaibhav-ceew/onet-msmacroL6

CrossEncoder based on cross-encoder/msmarco-MiniLM-L6-en-de-v1

Model Details

Model Description

Model Sources

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Cross Encoder Correlation

Cross Encoder Correlation

Training Details

Training Dataset

csv

Evaluation Dataset

csv

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

vaibhav-ceew/
onet-msmacroL6