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ms-marco-ettin-68m-reranker

Text Ranking
sentence-transformers
Safetensors
English
modernbert
cross-encoder
reranker
Generated from Trainer
dataset_size:39770704
loss:MarginMSELoss
Eval Results
Model card Files
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ms-marco-ettin-68m-reranker / README.md
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metadata
language:
  - en
tags:
  - sentence-transformers
  - cross-encoder
  - reranker
  - generated_from_trainer
  - dataset_size:39770704
  - loss:MarginMSELoss
base_model: jhu-clsp/ettin-encoder-68m
datasets:
  - sentence-transformers/msmarco
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
  - map
  - mrr@10
  - ndcg@10
co2_eq_emissions:
  emissions: 4822.389375626856
  energy_consumed: 13.06404839121821
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: AMD EPYC 7R13 Processor
  ram_total_size: 1999.985553741455
  hours_used: 2.996
  hardware_used: 8 x NVIDIA H100 80GB HBM3
model-index:
  - name: CrossEncoder based on jhu-clsp/ettin-encoder-68m
    results:
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoMSMARCO R100
          type: NanoMSMARCO_R100
        metrics:
          - type: map
            value: 0.6474
            name: Map
          - type: mrr@10
            value: 0.6422
            name: Mrr@10
          - type: ndcg@10
            value: 0.7086
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNFCorpus R100
          type: NanoNFCorpus_R100
        metrics:
          - type: map
            value: 0.3581
            name: Map
          - type: mrr@10
            value: 0.5719
            name: Mrr@10
          - type: ndcg@10
            value: 0.4101
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNQ R100
          type: NanoNQ_R100
        metrics:
          - type: map
            value: 0.7449
            name: Map
          - type: mrr@10
            value: 0.769
            name: Mrr@10
          - type: ndcg@10
            value: 0.7878
            name: Ndcg@10
      - task:
          type: cross-encoder-nano-beir
          name: Cross Encoder Nano BEIR
        dataset:
          name: NanoBEIR R100 mean
          type: NanoBEIR_R100_mean
        metrics:
          - type: map
            value: 0.5834
            name: Map
          - type: mrr@10
            value: 0.661
            name: Mrr@10
          - type: ndcg@10
            value: 0.6355
            name: Ndcg@10

CrossEncoder based on jhu-clsp/ettin-encoder-68m

This is a Cross Encoder model finetuned from jhu-clsp/ettin-encoder-68m on the msmarco 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: jhu-clsp/ettin-encoder-68m
  • Maximum Sequence Length: 512 tokens
  • Number of Output Labels: 1 label
  • Training Dataset:
  • Language: en

Model Sources

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("tomaarsen/ms-marco-ettin-68m-reranker")
# Get scores for pairs of texts
pairs = [
    ['does a cars registered owner have to be the insurance policy holder?', 'For example when a person has a company car they may be registered as the keeper though they are not the owner. Named driver-this is a driver who has been added to a car insurance policy to be an additional one to the main driver.'],
    ['how to pronounce lucretia', "Lucretia /lu-cre-tia/ [3 sylls.] as a girls' name is pronounced loo-KREE-shah. It is of Latin origin, and the meaning of Lucretia is succeed. Also possibly of Etruscan origin and of uncertain meaning. The name of a Roman matron who committed suicide in public protest against dishonor."],
    ['average cost of gym equipment', 'When equipping a home-gym, itâ\x80\x99s critical to consider at least one piece of cardio equipment. The top four treadmill picks from Consumer Search range from $990 to $3,500, with the average costing around $2,000.Another piece of popular cardio equipment is an elliptical trainer, which averages $1,500.he top four treadmill picks from Consumer Search range from $990 to $3,500, with the average costing around $2,000. Another piece of popular cardio equipment is an elliptical trainer, which averages $1,500.'],
    ['is cerebral palsy a disease', 'It occurs in about 2.1 per 1,000 live births. Cerebral palsy has been documented throughout history with the first known descriptions occurring in the work of Hippocrates in the 5th century BCE. Extensive study of the condition began in the 19th century by William John Little, after whom it was called Little disease.'],
    ['what is a service in cherwell', 'Cherwell Service Management (CSM) is a cloud-based software product that helps an IT organization to deliver certified ITIL processes such as: Additionally, it has the ability to provide a customer-friendly IT self-service portal, powerful dashboards and reporting.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'does a cars registered owner have to be the insurance policy holder?',
    [
        'For example when a person has a company car they may be registered as the keeper though they are not the owner. Named driver-this is a driver who has been added to a car insurance policy to be an additional one to the main driver.',
        "Lucretia /lu-cre-tia/ [3 sylls.] as a girls' name is pronounced loo-KREE-shah. It is of Latin origin, and the meaning of Lucretia is succeed. Also possibly of Etruscan origin and of uncertain meaning. The name of a Roman matron who committed suicide in public protest against dishonor.",
        'When equipping a home-gym, itâ\x80\x99s critical to consider at least one piece of cardio equipment. The top four treadmill picks from Consumer Search range from $990 to $3,500, with the average costing around $2,000.Another piece of popular cardio equipment is an elliptical trainer, which averages $1,500.he top four treadmill picks from Consumer Search range from $990 to $3,500, with the average costing around $2,000. Another piece of popular cardio equipment is an elliptical trainer, which averages $1,500.',
        'It occurs in about 2.1 per 1,000 live births. Cerebral palsy has been documented throughout history with the first known descriptions occurring in the work of Hippocrates in the 5th century BCE. Extensive study of the condition began in the 19th century by William John Little, after whom it was called Little disease.',
        'Cherwell Service Management (CSM) is a cloud-based software product that helps an IT organization to deliver certified ITIL processes such as: Additionally, it has the ability to provide a customer-friendly IT self-service portal, powerful dashboards and reporting.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

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 0.6474 (+0.1578) 0.3581 (+0.0971) 0.7449 (+0.3253)
mrr@10 0.6422 (+0.1647) 0.5719 (+0.0720) 0.7690 (+0.3423)
ndcg@10 0.7086 (+0.1682) 0.4101 (+0.0850) 0.7878 (+0.2871)

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 0.5834 (+0.1934)
mrr@10 0.6610 (+0.1930)
ndcg@10 0.6355 (+0.1801)

Training Details

Training Dataset

msmarco

  • Dataset: msmarco at 9e329ed
  • Size: 39,770,704 training samples
  • Columns: query_id, positive_id, negative_id, and score
  • Approximate statistics based on the first 1000 samples:
    query_id positive_id negative_id score
    type string string string float
    details
    • min: 9 characters
    • mean: 34.31 characters
    • max: 135 characters
    • min: 52 characters
    • mean: 356.08 characters
    • max: 843 characters
    • min: 75 characters
    • mean: 350.32 characters
    • max: 990 characters
    • min: -3.52
    • mean: 13.57
    • max: 22.49
  • Samples:
    query_id positive_id negative_id score
    what is the salary for a supervisor at tyson foods? Tyson Foods Production Supervisor Salary. Tyson Foods Production Supervisor average salary is $38,288, median salary is $36,000 with a salary range from $31,000 to $71,200. Tyson Foods Production Supervisor salaries are collected from government agencies and companies. Each salary is associated with a real job position. Tyson Foods Production Supervisor salary statistics is not exclusive and is for reference only. They are presented as is and updated regularly. Internal Reporting. An employee should follow an employer's policy for making a report of discrimination. In general, the employee should report the discrimination to her supervisor. If the supervisor is responsible for the discrimination, the employee should report the incident to the supervisor's supervisor. 21.560779412587486
    what is a vulva The vulva is the outer part of the female genitals. The vulva includes the opening of the vagina (sometimes called the vestibule), the labia majora (outer lips), the labia minora (inner lips), and the clitoris.Around the opening of the vagina, there are 2 sets of skin folds.he Bartholin glands are found just inside the opening of the vagina -- one on each side. These glands produce a mucus-like fluid that acts as a lubricant during sex. At the front of the vagina, the labia minora meet to form a fold or small hood of skin called the prepuce. Vulvar cancer is a type of cancer that occurs on the outer surface area of the female genitalia. The vulva is the area of skin that surrounds the urethra and vagina, including the clitoris and labia. Vulvar cancer commonly forms as a lump or sore on the vulva that often causes itching. Though it can occur at any age, vulvar cancer is most commonly diagnosed in older women. Vulvar cancer treatment usually involves surgery to remove the cancer and a small amount of surrounding healthy tissue. Sometimes vulvar cancer surgery requires removing the entire vulva. The earlier vulvar cancer is diagnosed, the less likely an extensive surgery is needed for treatment. Symptoms. 1.783429940541585
    what is fighting type pokemon weak against Examples: 1 Electric-type attacks on a water/flying-type Pokemon mix for 4x damage. 2 This is because Electric-types are strong against both Water-and Flying-types. 3 Fighting-type attacks on a Flying/Poison-type Pokemon mix for .25x damage. The second Zapdos, an Electric-type Electric Pokemon. Pokémon zapdos first appeared in the video game series which the player can Capture zapdos in The Power. Plant the last one Is, moltres A-fire Type Flame pokemon pokémon and one of the Three bird pokemon pokémon that is considered as. the most legendary 12.374764601389566
  • Loss: MarginMSELoss with these parameters:
    {
    "activation_fn": "torch.nn.modules.linear.Identity"
}

Evaluation Dataset

msmarco

  • Dataset: msmarco at 9e329ed
  • Size: 10,000 evaluation samples
  • Columns: query_id, positive_id, negative_id, and score
  • Approximate statistics based on the first 1000 samples:
    query_id positive_id negative_id score
    type string string string float
    details
    • min: 11 characters
    • mean: 34.73 characters
    • max: 158 characters
    • min: 76 characters
    • mean: 355.74 characters
    • max: 1217 characters
    • min: 59 characters
    • mean: 339.1 characters
    • max: 963 characters
    • min: -1.69
    • mean: 13.45
    • max: 22.14
  • Samples:
    query_id positive_id negative_id score
    does a cars registered owner have to be the insurance policy holder? For example when a person has a company car they may be registered as the keeper though they are not the owner. Named driver-this is a driver who has been added to a car insurance policy to be an additional one to the main driver. Features & Benefits Of Third Party Insurance Policy For Cars. 1 Death or bodily injury to a third party. 2 Damage to third party property. 3 Accidental death of the vehicle’s Owner or Driver. 4 Permanent Total Disability suffered by vehicle’s Owner or Driver. 5.590251604715983
    how to pronounce lucretia Lucretia /lu-cre-tia/ [3 sylls.] as a girls' name is pronounced loo-KREE-shah. It is of Latin origin, and the meaning of Lucretia is succeed. Also possibly of Etruscan origin and of uncertain meaning. The name of a Roman matron who committed suicide in public protest against dishonor. How To Pronounce Safat. English pronunciation for Safat is: Breaking a name down into syllables can make pronouncing it much easier. If you see the name Safat divided into smaller parts you can try to pronounce each part separately to get correct emphasis. 17.093592802683514
    average cost of gym equipment When equipping a home-gym, it’s critical to consider at least one piece of cardio equipment. The top four treadmill picks from Consumer Search range from $990 to $3,500, with the average costing around $2,000.Another piece of popular cardio equipment is an elliptical trainer, which averages $1,500.he top four treadmill picks from Consumer Search range from $990 to $3,500, with the average costing around $2,000. Another piece of popular cardio equipment is an elliptical trainer, which averages $1,500. “It’s what we all learned — or should have learned — in sixth-grade health class,” he said. “It’s all common sense. You need to keep yourself and your equipment clean. You never know who last used the equipment in a gym. It can be a great breeding ground for these bugs, some of which are pretty nasty.” 15.078913132349651
  • Loss: MarginMSELoss with these parameters:
    {
    "activation_fn": "torch.nn.modules.linear.Identity"
}

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: 1
  • warmup_ratio: 0.1
  • seed: 12
  • bf16: True
  • 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: 1
  • 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
  • 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: 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_fused
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • project: huggingface
  • trackio_space_id: trackio
  • 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
  • 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: no
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: True
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss Validation Loss NanoMSMARCO_R100_ndcg@10 NanoNFCorpus_R100_ndcg@10 NanoNQ_R100_ndcg@10 NanoBEIR_R100_mean_ndcg@10
-1 -1 - - 0.0191 (-0.5213) 0.2366 (-0.0884) 0.0332 (-0.4675) 0.0963 (-0.3591)
0.0001 1 205.7634 - - - - -
0.0020 39 209.0864 - - - - -
0.0040 78 205.3905 - - - - -
0.0060 117 198.5226 - - - - -
0.0080 156 187.3992 - - - - -
0.0100 195 173.062 163.0107 0.0826 (-0.4578) 0.2753 (-0.0497) 0.1163 (-0.3844) 0.1580 (-0.2973)
0.0121 234 154.6088 - - - - -
0.0141 273 129.1789 - - - - -
0.0161 312 85.9178 - - - - -
0.0181 351 48.0864 - - - - -
0.0201 390 33.2479 28.7226 0.5448 (+0.0043) 0.3598 (+0.0347) 0.4913 (-0.0093) 0.4653 (+0.0099)
0.0221 429 26.1395 - - - - -
0.0241 468 21.4156 - - - - -
0.0261 507 18.4675 - - - - -
0.0281 546 16.1435 - - - - -
0.0301 585 14.2426 13.7192 0.5958 (+0.0553) 0.3909 (+0.0659) 0.6268 (+0.1261) 0.5378 (+0.0825)
0.0321 624 13.0593 - - - - -
0.0341 663 11.9561 - - - - -
0.0362 702 11.3387 - - - - -
0.0382 741 10.3913 - - - - -
0.0402 780 9.6607 9.5505 0.5964 (+0.0560) 0.4018 (+0.0768) 0.6765 (+0.1759) 0.5582 (+0.1029)
0.0422 819 9.1261 - - - - -
0.0442 858 8.6014 - - - - -
0.0462 897 8.3014 - - - - -
0.0482 936 7.8924 - - - - -
0.0502 975 7.5679 7.5872 0.6055 (+0.0650) 0.4011 (+0.0761) 0.7020 (+0.2014) 0.5695 (+0.1142)
0.0522 1014 7.2393 - - - - -
0.0542 1053 6.9913 - - - - -
0.0562 1092 6.7492 - - - - -
0.0582 1131 6.5946 - - - - -
0.0603 1170 6.387 6.3510 0.6506 (+0.1102) 0.4084 (+0.0833) 0.7107 (+0.2100) 0.5899 (+0.1345)
0.0623 1209 6.1871 - - - - -
0.0643 1248 6.0328 - - - - -
0.0663 1287 6.0049 - - - - -
0.0683 1326 5.7902 - - - - -
0.0703 1365 5.6371 5.6318 0.6600 (+0.1196) 0.4063 (+0.0813) 0.7021 (+0.2014) 0.5895 (+0.1341)
0.0723 1404 5.5341 - - - - -
0.0743 1443 5.4093 - - - - -
0.0763 1482 5.4064 - - - - -
0.0783 1521 5.348 - - - - -
0.0803 1560 5.0726 5.0712 0.6755 (+0.1351) 0.4093 (+0.0843) 0.7006 (+0.1999) 0.5951 (+0.1398)
0.0823 1599 5.0875 - - - - -
0.0844 1638 5.0279 - - - - -
0.0864 1677 4.884 - - - - -
0.0884 1716 4.8179 - - - - -
0.0904 1755 4.8205 4.7885 0.6711 (+0.1307) 0.4048 (+0.0798) 0.7152 (+0.2146) 0.5971 (+0.1417)
0.0924 1794 4.6914 - - - - -
0.0944 1833 4.6565 - - - - -
0.0964 1872 4.5685 - - - - -
0.0984 1911 4.4617 - - - - -
0.1004 1950 4.4202 4.3938 0.6554 (+0.1150) 0.4093 (+0.0843) 0.7045 (+0.2039) 0.5897 (+0.1344)
0.1024 1989 4.3885 - - - - -
0.1044 2028 4.351 - - - - -
0.1064 2067 4.2522 - - - - -
0.1085 2106 4.2619 - - - - -
0.1105 2145 4.175 4.1509 0.6596 (+0.1192) 0.4149 (+0.0898) 0.7299 (+0.2293) 0.6015 (+0.1461)
0.1125 2184 4.1337 - - - - -
0.1145 2223 4.0883 - - - - -
0.1165 2262 4.0799 - - - - -
0.1185 2301 3.9775 - - - - -
0.1205 2340 3.9562 3.9644 0.6580 (+0.1176) 0.4136 (+0.0885) 0.7437 (+0.2430) 0.6051 (+0.1497)
0.1225 2379 3.8889 - - - - -
0.1245 2418 3.8587 - - - - -
0.1265 2457 3.822 - - - - -
0.1285 2496 3.7963 - - - - -
0.1305 2535 3.814 3.7850 0.6635 (+0.1231) 0.4036 (+0.0786) 0.7248 (+0.2242) 0.5973 (+0.1419)
0.1326 2574 3.7665 - - - - -
0.1346 2613 3.7226 - - - - -
0.1366 2652 3.7418 - - - - -
0.1386 2691 3.6575 - - - - -
0.1406 2730 3.6418 3.6521 0.6629 (+0.1225) 0.4101 (+0.0851) 0.7334 (+0.2328) 0.6021 (+0.1468)
0.1426 2769 3.5892 - - - - -
0.1446 2808 3.5589 - - - - -
0.1466 2847 3.5254 - - - - -
0.1486 2886 3.5035 - - - - -
0.1506 2925 3.5073 3.4462 0.6679 (+0.1274) 0.4056 (+0.0805) 0.7469 (+0.2462) 0.6068 (+0.1514)
0.1526 2964 3.4131 - - - - -
0.1546 3003 3.4122 - - - - -
0.1567 3042 3.3845 - - - - -
0.1587 3081 3.3994 - - - - -
0.1607 3120 3.3618 3.3692 0.6680 (+0.1276) 0.4109 (+0.0858) 0.7231 (+0.2224) 0.6007 (+0.1453)
0.1627 3159 3.3318 - - - - -
0.1647 3198 3.3173 - - - - -
0.1667 3237 3.3207 - - - - -
0.1687 3276 3.2827 - - - - -
0.1707 3315 3.2371 3.2761 0.6502 (+0.1097) 0.4074 (+0.0823) 0.7402 (+0.2395) 0.5992 (+0.1439)
0.1727 3354 3.21 - - - - -
0.1747 3393 3.2402 - - - - -
0.1767 3432 3.1845 - - - - -
0.1787 3471 3.1894 - - - - -
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0.8435 16380 1.7118 1.7440 0.6715 (+0.1311) 0.4188 (+0.0937) 0.7858 (+0.2851) 0.6254 (+0.1700)
0.8455 16419 1.6945 - - - - -
0.8475 16458 1.6611 - - - - -
0.8495 16497 1.7015 - - - - -
0.8515 16536 1.6846 - - - - -
0.8535 16575 1.6866 1.7254 0.6719 (+0.1315) 0.4201 (+0.0951) 0.7813 (+0.2806) 0.6244 (+0.1691)
0.8556 16614 1.6721 - - - - -
0.8576 16653 1.6785 - - - - -
0.8596 16692 1.693 - - - - -
0.8616 16731 1.674 - - - - -
0.8636 16770 1.6973 1.7288 0.6737 (+0.1332) 0.4158 (+0.0907) 0.7899 (+0.2893) 0.6265 (+0.1711)
0.8656 16809 1.6654 - - - - -
0.8676 16848 1.6531 - - - - -
0.8696 16887 1.6858 - - - - -
0.8716 16926 1.6835 - - - - -
0.8736 16965 1.6908 1.7180 0.6741 (+0.1337) 0.4182 (+0.0932) 0.7851 (+0.2845) 0.6258 (+0.1705)
0.8756 17004 1.6721 - - - - -
0.8776 17043 1.6642 - - - - -
0.8797 17082 1.6769 - - - - -
0.8817 17121 1.6559 - - - - -
0.8837 17160 1.6478 1.7080 0.6737 (+0.1332) 0.4169 (+0.0919) 0.7836 (+0.2829) 0.6247 (+0.1693)
0.8857 17199 1.6801 - - - - -
0.8877 17238 1.6622 - - - - -
0.8897 17277 1.6741 - - - - -
0.8917 17316 1.6748 - - - - -
0.8937 17355 1.6895 1.6944 0.6728 (+0.1324) 0.4198 (+0.0948) 0.7800 (+0.2794) 0.6242 (+0.1688)
0.8957 17394 1.6604 - - - - -
0.8977 17433 1.6739 - - - - -
0.8997 17472 1.6865 - - - - -
0.9017 17511 1.6773 - - - - -
0.9038 17550 1.6481 1.6944 0.6724 (+0.1320) 0.4167 (+0.0917) 0.7836 (+0.2829) 0.6242 (+0.1689)
0.9058 17589 1.6395 - - - - -
0.9078 17628 1.6714 - - - - -
0.9098 17667 1.6506 - - - - -
0.9118 17706 1.6712 - - - - -
0.9138 17745 1.6525 1.6816 0.6734 (+0.1330) 0.4173 (+0.0923) 0.7836 (+0.2829) 0.6248 (+0.1694)
0.9158 17784 1.642 - - - - -
0.9178 17823 1.65 - - - - -
0.9198 17862 1.6658 - - - - -
0.9218 17901 1.6394 - - - - -
0.9238 17940 1.669 1.6846 0.6733 (+0.1329) 0.4171 (+0.0920) 0.7836 (+0.2829) 0.6246 (+0.1693)
0.9258 17979 1.6404 - - - - -
0.9279 18018 1.6499 - - - - -
0.9299 18057 1.6403 - - - - -
0.9319 18096 1.6645 - - - - -
0.9339 18135 1.6535 1.6847 0.6724 (+0.1320) 0.4129 (+0.0879) 0.7840 (+0.2834) 0.6231 (+0.1678)
0.9359 18174 1.6469 - - - - -
0.9379 18213 1.6519 - - - - -
0.9399 18252 1.6528 - - - - -
0.9419 18291 1.6473 - - - - -
0.9439 18330 1.6154 1.6870 0.6782 (+0.1378) 0.4158 (+0.0908) 0.7848 (+0.2842) 0.6263 (+0.1709)
0.9459 18369 1.6364 - - - - -
0.9479 18408 1.6484 - - - - -
0.9499 18447 1.6479 - - - - -
0.9520 18486 1.6468 - - - - -
0.9540 18525 1.637 1.6772 0.6802 (+0.1397) 0.4172 (+0.0922) 0.7838 (+0.2832) 0.6271 (+0.1717)
0.9560 18564 1.6481 - - - - -
0.9580 18603 1.6474 - - - - -
0.9600 18642 1.634 - - - - -
0.9620 18681 1.6476 - - - - -
0.9640 18720 1.6195 1.6834 0.6810 (+0.1406) 0.4159 (+0.0909) 0.7837 (+0.2830) 0.6269 (+0.1715)
0.9660 18759 1.6725 - - - - -
0.9680 18798 1.6448 - - - - -
0.9700 18837 1.6292 - - - - -
0.9720 18876 1.6631 - - - - -
0.9740 18915 1.6573 1.6776 0.6724 (+0.1320) 0.4133 (+0.0882) 0.7838 (+0.2832) 0.6232 (+0.1678)
0.9761 18954 1.6358 - - - - -
0.9781 18993 1.6256 - - - - -
0.9801 19032 1.6126 - - - - -
0.9821 19071 1.6428 - - - - -
0.9841 19110 1.6498 1.6770 0.6724 (+0.1320) 0.4148 (+0.0898) 0.7832 (+0.2825) 0.6235 (+0.1681)
0.9861 19149 1.6528 - - - - -
0.9881 19188 1.6417 - - - - -
0.9901 19227 1.6341 - - - - -
0.9921 19266 1.6444 - - - - -
0.9941 19305 1.6509 1.6735 0.6724 (+0.1320) 0.4148 (+0.0898) 0.7838 (+0.2832) 0.6237 (+0.1683)
0.9961 19344 1.6705 - - - - -
0.9981 19383 1.6433 - - - - -
-1 -1 - - 0.7086 (+0.1682) 0.4101 (+0.0850) 0.7878 (+0.2871) 0.6355 (+0.1801)
  • The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Energy Consumed: 13.064 kWh
  • Carbon Emitted: 4.822 kg of CO2
  • Hours Used: 2.996 hours

Training Hardware

  • On Cloud: No
  • GPU Model: 8 x NVIDIA H100 80GB HBM3
  • CPU Model: AMD EPYC 7R13 Processor
  • RAM Size: 1999.99 GB

Framework Versions

  • Python: 3.10.14
  • Sentence Transformers: 5.1.2
  • Transformers: 4.57.1
  • PyTorch: 2.9.1+cu126
  • Accelerate: 1.12.0
  • Datasets: 4.4.1
  • Tokenizers: 0.22.1

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",
}

MarginMSELoss 

@misc{hofstätter2021improving,
    title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
    author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
    year={2021},
    eprint={2010.02666},
    archivePrefix={arXiv},
    primaryClass={cs.IR}
}