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ms-marco-ettin-32m-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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metadata
language:
  - en
tags:
  - sentence-transformers
  - cross-encoder
  - reranker
  - generated_from_trainer
  - dataset_size:39770704
  - loss:MarginMSELoss
base_model: jhu-clsp/ettin-encoder-32m
datasets:
  - sentence-transformers/msmarco
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
  - map
  - mrr@10
  - ndcg@10
co2_eq_emissions:
  emissions: 2412.1447613578944
  energy_consumed: 6.534597983371423
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: AMD EPYC 7R13 Processor
  ram_total_size: 1999.9855308532715
  hours_used: 1.932
  hardware_used: 8 x NVIDIA H100 80GB HBM3
model-index:
  - name: CrossEncoder based on jhu-clsp/ettin-encoder-32m
    results:
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoMSMARCO R100
          type: NanoMSMARCO_R100
        metrics:
          - type: map
            value: 0.638
            name: Map
          - type: mrr@10
            value: 0.6319
            name: Mrr@10
          - type: ndcg@10
            value: 0.6951
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNFCorpus R100
          type: NanoNFCorpus_R100
        metrics:
          - type: map
            value: 0.3547
            name: Map
          - type: mrr@10
            value: 0.6102
            name: Mrr@10
          - type: ndcg@10
            value: 0.4138
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNQ R100
          type: NanoNQ_R100
        metrics:
          - type: map
            value: 0.6748
            name: Map
          - type: mrr@10
            value: 0.6919
            name: Mrr@10
          - type: ndcg@10
            value: 0.7294
            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: 0.5558
            name: Map
          - type: mrr@10
            value: 0.6446
            name: Mrr@10
          - type: ndcg@10
            value: 0.6128
            name: Ndcg@10

CrossEncoder based on jhu-clsp/ettin-encoder-32m

This is a Cross Encoder model finetuned from jhu-clsp/ettin-encoder-32m 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-32m
  • 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-32m-reranker")
# Get scores for pairs of texts
pairs = [
    ['what is honor society', 'In the United States, an honor society is a rank organization that recognizes excellence among peers. Numerous societies recognize various fields and circumstances. The Order of the Arrow, for example, is the national honor society of the Boy Scouts of America.'],
    ['what happens to blood pressure when you raise your arm', 'Well, you measured blood pressure in that arm would drop because while the pressure that your heart puts out will stay pretty constant, your arm is now higher and requires more energy to reach the end, so the pressure seen there will be lower. The Doc · 7 years ago.'],
    ['what country is the name astrid from', 'Comment by silkfire. Astrid, Estrid, Ã\x86striðr æstriðr Ã\x81strÃ\xadðr astriðr ástrÃ\xadðr is a given Name Of north. Germanic origin it Comes From Ã\x81sfrÃ\xadðr (Norse Asfriðr), ásfrÃ\xadðr divine (beauty) + from (ass). áss god friðr FrÃ\xadðr beautiful names derived From, astrid include the name astrida which is a somewhat common name for Girls. in the country of latviat comes from Old Norse Ã\x81sfrÃ\xadðr (ásfrÃ\xadðr Divine), beauty from (ass) + áss (god). Friðr frÃ\xadðr beautiful Names derived from astrid Include, the name astrida which is a somewhat common name for girls in The. country of latvia'],
    ['where the latin people came from', 'Latina is a city in Italy and the Latin people come from southern Italy around Rome.'],
    ['how long do cocker spaniels', "the cocker spaniel's life span is 12-15 years. a dog that is kept fit and on nutritious dog food has a better chance of living longer than those who aren't. I would assume roughly 10-15 years. My cocker spaniel is 13.5 years old and just starting to lose her health."],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what is honor society',
    [
        'In the United States, an honor society is a rank organization that recognizes excellence among peers. Numerous societies recognize various fields and circumstances. The Order of the Arrow, for example, is the national honor society of the Boy Scouts of America.',
        'Well, you measured blood pressure in that arm would drop because while the pressure that your heart puts out will stay pretty constant, your arm is now higher and requires more energy to reach the end, so the pressure seen there will be lower. The Doc · 7 years ago.',
        'Comment by silkfire. Astrid, Estrid, Ã\x86striðr æstriðr Ã\x81strÃ\xadðr astriðr ástrÃ\xadðr is a given Name Of north. Germanic origin it Comes From Ã\x81sfrÃ\xadðr (Norse Asfriðr), ásfrÃ\xadðr divine (beauty) + from (ass). áss god friðr FrÃ\xadðr beautiful names derived From, astrid include the name astrida which is a somewhat common name for Girls. in the country of latviat comes from Old Norse Ã\x81sfrÃ\xadðr (ásfrÃ\xadðr Divine), beauty from (ass) + áss (god). Friðr frÃ\xadðr beautiful Names derived from astrid Include, the name astrida which is a somewhat common name for girls in The. country of latvia',
        'Latina is a city in Italy and the Latin people come from southern Italy around Rome.',
        "the cocker spaniel's life span is 12-15 years. a dog that is kept fit and on nutritious dog food has a better chance of living longer than those who aren't. I would assume roughly 10-15 years. My cocker spaniel is 13.5 years old and just starting to lose her health.",
    ]
)
# [{'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 0.6380 (+0.1484) 0.3547 (+0.0937) 0.6748 (+0.2552)
mrr@10 0.6319 (+0.1544) 0.6102 (+0.1104) 0.6919 (+0.2652)
ndcg@10 0.6951 (+0.1547) 0.4138 (+0.0888) 0.7294 (+0.2287)

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 0.5558 (+0.1658)
mrr@10 0.6446 (+0.1766)
ndcg@10 0.6128 (+0.1574)

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: 11 characters
    • mean: 33.98 characters
    • max: 124 characters
    • min: 55 characters
    • mean: 348.53 characters
    • max: 926 characters
    • min: 80 characters
    • mean: 340.54 characters
    • max: 818 characters
    • min: -3.12
    • mean: 13.18
    • max: 22.49
  • Samples:
    query_id positive_id negative_id score
    which artist started fauves movement Les Fauves (Wild Beasts) • Fauvism was the first twentieth-century movement in modern art. Inspired by. the examples of van Gogh, Gauguin, Seurat, and Cézanne. • It grew out of a loosely allied group of French painters with shared interests. Henri Matisse was eventually recognized as the leader of Les Fauves, or The. Wild Beasts”. Let’s get a reflection onto your glass, for added realism. Architectural 2D to 3D visualizer and graphic artist Jonathan Pagaduan Ignas shows you how in this great little tutorial he has contributed to us here at SketchUpArtists.Here are a few easy steps to a glass reflection effect using Google SketchUp and V-Ray for SketchUp..let’s get started!Step 1 - Import Image File. Step 2 - Place Image in Glass. Step 3 - Position Texture. Step 4 - Sample Texture with Dropper.rchitectural 2D to 3D visualizer and graphic artist Jonathan Pagaduan Ignas shows you how in this great little tutorial he has contributed to us here at SketchUpArtists. Here are a few easy steps to a glass reflection effect using Google SketchUp and V-Ray for SketchUp..let’s get started! 18.45222806930542
    what foods are high in uric acid list Avoid high-purine foods. See attached lists. Avoid or limit alcohol. Alcohol increases purine production, leading to higher uric acid levels in your blood and urine. Limit meat to 3 ounces per meal. Limit high-fat foods such as salad dressings, ice cream, fried foods, gravies, and dressings.Fat holds onto uric acid in your kidneys.lcohol increases purine production, leading to higher uric acid levels in your blood and urine. Limit meat to 3 ounces per meal. Limit high-fat foods such as salad dressings, ice cream, fried foods, gravies, and dressings. Fat holds onto uric acid in your kidneys. You can develop gout if you have too much uric acid in your body. Uric acid is a chemical that everyone has in their blood. It's a waste product that forms from substances called purines. These are found in every cell in your body, and in certain foods, such as seafood and liver. 7.823118130366007
    what battle was the turning point in the pacific theatre The campaign lasted for nearly six months and cost thousands of American and Japanese lives. This campaign, from the naval perspective, and indeed from a strategic perspective, was the turning point of World War Two in the Pacific Theater. The experienced and well trained Japanese fleet was still superior to the United States fleet after the Battle of Midway, and was still a threat to the US fleet until it was checked in the actions off Guadalcanal. It is famous as the setting of the February–March 1945 Battle of Iwo Jima involving the United States and a small number of elements of the British Pacific Fleet versus the Empire of Japan during World War II. 7.8578096230824785
  • 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: 34.75 characters
    • max: 124 characters
    • min: 69 characters
    • mean: 346.11 characters
    • max: 920 characters
    • min: 79 characters
    • mean: 350.68 characters
    • max: 1014 characters
    • min: -2.41
    • mean: 13.41
    • max: 22.48
  • Samples:
    query_id positive_id negative_id score
    what is honor society In the United States, an honor society is a rank organization that recognizes excellence among peers. Numerous societies recognize various fields and circumstances. The Order of the Arrow, for example, is the national honor society of the Boy Scouts of America. Pi Gamma Mu Headlines. Pi Gamma Mu is the oldest and preeminent honor society in the social sciences. Our mission is to encourage and recognize superior scholarship in social science disciplines and to foster cooperation and social service among its members. 7.578772008419037
    what happens to blood pressure when you raise your arm Well, you measured blood pressure in that arm would drop because while the pressure that your heart puts out will stay pretty constant, your arm is now higher and requires more energy to reach the end, so the pressure seen there will be lower. The Doc · 7 years ago. Constriction of blood vessels and increase in heart rate does raise blood pressure, but only temporarily; when the stress reaction goes away, blood pressure returns to its pre-stress level.This is called situational stress, and its effects are generally short-lived and disappear when the stressful event is over.hronic (constant) stress causes our bodies to go into high gear on and off for days or weeks at a time. The links between chronic stress and blood pressure are not clear. Although stress does not clearly cause heart disease, it can play a role in general wellness. 8.200530961155891
    what country is the name astrid from Comment by silkfire. Astrid, Estrid, Æstriðr æstriðr Ástríðr astriðr ástríðr is a given Name Of north. Germanic origin it Comes From Ásfríðr (Norse Asfriðr), ásfríðr divine (beauty) + from (ass). áss god friðr Fríðr beautiful names derived From, astrid include the name astrida which is a somewhat common name for Girls. in the country of latviat comes from Old Norse Ásfríðr (ásfríðr Divine), beauty from (ass) + áss (god). Friðr fríðr beautiful Names derived from astrid Include, the name astrida which is a somewhat common name for girls in The. country of latvia » Follow author. » Share. Astrid Ayrianto was on omeegle one day on the video chat, little did she know, she had almost everything in common with the lead singer of her favorite band Get Scared, Nicholas Matthews. 10.449922064940136
  • 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 - - 0.0694 (-0.4711) 0.2964 (-0.0286) 0.0281 (-0.4725) 0.1313 (-0.3241)
0.0001 1 209.9035 - - - - -
0.0020 39 207.8315 - - - - -
0.0040 78 204.3864 - - - - -
0.0060 117 197.3435 - - - - -
0.0080 156 188.2296 - - - - -
0.0100 195 176.2927 169.8326 0.1222 (-0.4182) 0.2790 (-0.0460) 0.0840 (-0.4166) 0.1618 (-0.2936)
0.0121 234 161.1845 - - - - -
0.0141 273 145.5738 - - - - -
0.0161 312 122.6948 - - - - -
0.0181 351 81.3822 - - - - -
0.0201 390 43.1117 35.8988 0.5569 (+0.0164) 0.3702 (+0.0452) 0.4567 (-0.0439) 0.4613 (+0.0059)
0.0221 429 32.5851 - - - - -
0.0241 468 27.7726 - - - - -
0.0261 507 24.6193 - - - - -
0.0281 546 22.2331 - - - - -
0.0301 585 20.8158 19.3642 0.6007 (+0.0603) 0.3694 (+0.0444) 0.5467 (+0.0460) 0.5056 (+0.0502)
0.0321 624 19.5039 - - - - -
0.0341 663 18.151 - - - - -
0.0362 702 17.1317 - - - - -
0.0382 741 16.6977 - - - - -
0.0402 780 15.6749 15.1013 0.6167 (+0.0763) 0.3794 (+0.0544) 0.5682 (+0.0675) 0.5214 (+0.0661)
0.0422 819 15.2219 - - - - -
0.0442 858 14.7046 - - - - -
0.0462 897 14.1086 - - - - -
0.0482 936 13.5811 - - - - -
0.0502 975 13.2202 12.5601 0.6616 (+0.1212) 0.3763 (+0.0513) 0.5839 (+0.0833) 0.5406 (+0.0852)
0.0522 1014 12.7417 - - - - -
0.0542 1053 12.4341 - - - - -
0.0562 1092 12.1748 - - - - -
0.0582 1131 11.8355 - - - - -
0.0603 1170 11.3288 11.0595 0.6582 (+0.1178) 0.3847 (+0.0596) 0.6125 (+0.1118) 0.5518 (+0.0964)
0.0623 1209 11.2907 - - - - -
0.0643 1248 10.9028 - - - - -
0.0663 1287 10.627 - - - - -
0.0683 1326 10.4322 - - - - -
0.0703 1365 10.0831 9.7591 0.6565 (+0.1161) 0.3910 (+0.0659) 0.6118 (+0.1111) 0.5531 (+0.0977)
0.0723 1404 10.0484 - - - - -
0.0743 1443 9.7691 - - - - -
0.0763 1482 9.5597 - - - - -
0.0783 1521 9.47 - - - - -
0.0803 1560 9.2528 8.9910 0.6667 (+0.1263) 0.3815 (+0.0564) 0.6247 (+0.1240) 0.5576 (+0.1023)
0.0823 1599 9.1445 - - - - -
0.0844 1638 9.0069 - - - - -
0.0864 1677 8.6523 - - - - -
0.0884 1716 8.5901 - - - - -
0.0904 1755 8.715 8.3166 0.6402 (+0.0997) 0.3853 (+0.0603) 0.6448 (+0.1442) 0.5568 (+0.1014)
0.0924 1794 8.457 - - - - -
0.0944 1833 8.3209 - - - - -
0.0964 1872 8.1242 - - - - -
0.0984 1911 8.1453 - - - - -
0.1004 1950 8.0561 7.6634 0.6806 (+0.1401) 0.3843 (+0.0592) 0.6495 (+0.1489) 0.5715 (+0.1161)
0.1024 1989 7.855 - - - - -
0.1044 2028 7.7573 - - - - -
0.1064 2067 7.5976 - - - - -
0.1085 2106 7.4529 - - - - -
0.1105 2145 7.372 7.0798 0.6667 (+0.1263) 0.3952 (+0.0702) 0.6386 (+0.1379) 0.5668 (+0.1115)
0.1125 2184 7.1673 - - - - -
0.1145 2223 7.2075 - - - - -
0.1165 2262 7.1261 - - - - -
0.1185 2301 7.0716 - - - - -
0.1205 2340 7.0182 6.7560 0.6557 (+0.1153) 0.3876 (+0.0626) 0.6544 (+0.1537) 0.5659 (+0.1105)
0.1225 2379 6.861 - - - - -
0.1245 2418 6.8366 - - - - -
0.1265 2457 6.6857 - - - - -
0.1285 2496 6.665 - - - - -
0.1305 2535 6.6564 6.2824 0.6796 (+0.1392) 0.3834 (+0.0584) 0.6637 (+0.1630) 0.5756 (+0.1202)
0.1326 2574 6.5436 - - - - -
0.1346 2613 6.4091 - - - - -
0.1366 2652 6.2974 - - - - -
0.1386 2691 6.3251 - - - - -
0.1406 2730 6.2799 5.9852 0.6719 (+0.1314) 0.3829 (+0.0579) 0.6711 (+0.1704) 0.5753 (+0.1199)
0.1426 2769 6.2637 - - - - -
0.1446 2808 6.1648 - - - - -
0.1466 2847 6.1382 - - - - -
0.1486 2886 6.1291 - - - - -
0.1506 2925 5.9413 5.8402 0.6665 (+0.1261) 0.3950 (+0.0700) 0.6448 (+0.1441) 0.5688 (+0.1134)
0.1526 2964 5.9748 - - - - -
0.1546 3003 5.8973 - - - - -
0.1567 3042 5.8654 - - - - -
0.1587 3081 5.6654 - - - - -
0.1607 3120 5.8549 5.6918 0.6765 (+0.1361) 0.3948 (+0.0697) 0.6639 (+0.1632) 0.5784 (+0.1230)
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0.8234 15990 2.6695 2.7110 0.6821 (+0.1417) 0.4104 (+0.0853) 0.7304 (+0.2298) 0.6076 (+0.1523)
0.8254 16029 2.6974 - - - - -
0.8274 16068 2.7043 - - - - -
0.8294 16107 2.6929 - - - - -
0.8315 16146 2.667 - - - - -
0.8335 16185 2.7035 2.6746 0.6891 (+0.1487) 0.4084 (+0.0834) 0.7262 (+0.2256) 0.6079 (+0.1526)
0.8355 16224 2.6472 - - - - -
0.8375 16263 2.6956 - - - - -
0.8395 16302 2.6823 - - - - -
0.8415 16341 2.695 - - - - -
0.8435 16380 2.6439 2.6841 0.6835 (+0.1430) 0.4015 (+0.0764) 0.7288 (+0.2282) 0.6046 (+0.1492)
0.8455 16419 2.6767 - - - - -
0.8475 16458 2.6556 - - - - -
0.8495 16497 2.6431 - - - - -
0.8515 16536 2.6594 - - - - -
0.8535 16575 2.6403 2.6657 0.6709 (+0.1304) 0.4044 (+0.0794) 0.7323 (+0.2316) 0.6025 (+0.1471)
0.8556 16614 2.6687 - - - - -
0.8576 16653 2.6887 - - - - -
0.8596 16692 2.6761 - - - - -
0.8616 16731 2.6371 - - - - -
0.8636 16770 2.6368 2.6791 0.6768 (+0.1364) 0.4068 (+0.0818) 0.7314 (+0.2308) 0.6050 (+0.1497)
0.8656 16809 2.6325 - - - - -
0.8676 16848 2.641 - - - - -
0.8696 16887 2.6614 - - - - -
0.8716 16926 2.7 - - - - -
0.8736 16965 2.678 2.6354 0.6938 (+0.1533) 0.4108 (+0.0857) 0.7274 (+0.2268) 0.6107 (+0.1553)
0.8756 17004 2.6619 - - - - -
0.8776 17043 2.681 - - - - -
0.8797 17082 2.6465 - - - - -
0.8817 17121 2.6977 - - - - -
0.8837 17160 2.6476 2.6324 0.6969 (+0.1565) 0.4118 (+0.0868) 0.7293 (+0.2287) 0.6127 (+0.1573)
0.8857 17199 2.6405 - - - - -
0.8877 17238 2.6645 - - - - -
0.8897 17277 2.6635 - - - - -
0.8917 17316 2.6038 - - - - -
0.8937 17355 2.6807 2.642 0.6951 (+0.1547) 0.4138 (+0.0888) 0.7294 (+0.2287) 0.6128 (+0.1574)
0.8957 17394 2.6679 - - - - -
0.8977 17433 2.6063 - - - - -
0.8997 17472 2.6215 - - - - -
0.9017 17511 2.6141 - - - - -
0.9038 17550 2.6324 2.6330 0.6963 (+0.1559) 0.4117 (+0.0866) 0.7287 (+0.2280) 0.6122 (+0.1568)
0.9058 17589 2.6405 - - - - -
0.9078 17628 2.619 - - - - -
0.9098 17667 2.6153 - - - - -
0.9118 17706 2.6323 - - - - -
0.9138 17745 2.5891 2.6280 0.6886 (+0.1482) 0.4110 (+0.0860) 0.7275 (+0.2268) 0.6090 (+0.1537)
0.9158 17784 2.6385 - - - - -
0.9178 17823 2.6138 - - - - -
0.9198 17862 2.6181 - - - - -
0.9218 17901 2.6414 - - - - -
0.9238 17940 2.6362 2.6224 0.6907 (+0.1502) 0.4085 (+0.0835) 0.7360 (+0.2354) 0.6117 (+0.1564)
0.9258 17979 2.6156 - - - - -
0.9279 18018 2.597 - - - - -
0.9299 18057 2.6254 - - - - -
0.9319 18096 2.6434 - - - - -
0.9339 18135 2.6474 2.6014 0.6884 (+0.1479) 0.4133 (+0.0882) 0.7248 (+0.2242) 0.6088 (+0.1534)
0.9359 18174 2.6214 - - - - -
0.9379 18213 2.6145 - - - - -
0.9399 18252 2.617 - - - - -
0.9419 18291 2.6209 - - - - -
0.9439 18330 2.5976 2.6057 0.6871 (+0.1467) 0.4072 (+0.0821) 0.7290 (+0.2284) 0.6078 (+0.1524)
0.9459 18369 2.61 - - - - -
0.9479 18408 2.614 - - - - -
0.9499 18447 2.6187 - - - - -
0.9520 18486 2.6004 - - - - -
0.9540 18525 2.6515 2.6052 0.6871 (+0.1467) 0.4091 (+0.0841) 0.7270 (+0.2263) 0.6077 (+0.1524)
0.9560 18564 2.6141 - - - - -
0.9580 18603 2.6016 - - - - -
0.9600 18642 2.5918 - - - - -
0.9620 18681 2.5684 - - - - -
0.9640 18720 2.6087 2.5941 0.6797 (+0.1393) 0.4105 (+0.0854) 0.7284 (+0.2277) 0.6062 (+0.1508)
0.9660 18759 2.5961 - - - - -
0.9680 18798 2.6121 - - - - -
0.9700 18837 2.5896 - - - - -
0.9720 18876 2.6101 - - - - -
0.9740 18915 2.6106 2.5921 0.6856 (+0.1452) 0.4093 (+0.0843) 0.7284 (+0.2277) 0.6078 (+0.1524)
0.9761 18954 2.6046 - - - - -
0.9781 18993 2.6155 - - - - -
0.9801 19032 2.6166 - - - - -
0.9821 19071 2.5866 - - - - -
0.9841 19110 2.6369 2.5943 0.6838 (+0.1433) 0.4117 (+0.0867) 0.7284 (+0.2277) 0.6079 (+0.1526)
0.9861 19149 2.6319 - - - - -
0.9881 19188 2.6035 - - - - -
0.9901 19227 2.565 - - - - -
0.9921 19266 2.6071 - - - - -
0.9941 19305 2.5908 2.5866 0.6838 (+0.1433) 0.4116 (+0.0866) 0.7284 (+0.2277) 0.6079 (+0.1525)
0.9961 19344 2.5983 - - - - -
0.9981 19383 2.5995 - - - - -
-1 -1 - - 0.6951 (+0.1547) 0.4138 (+0.0888) 0.7294 (+0.2287) 0.6128 (+0.1574)
  • The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Energy Consumed: 6.535 kWh
  • Carbon Emitted: 2.412 kg of CO2
  • Hours Used: 1.932 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}
}