tomaarsen / reranker-ModernBERT-large-gooaq-bce

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Model's Last Updated: May 17 2025
text-ranking

Introduction of reranker-ModernBERT-large-gooaq-bce

Model Details of reranker-ModernBERT-large-gooaq-bce

ModernBERT-large trained on GooAQ

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

See training_gooaq_bce.py for the training script - only the base model was updated from answerdotai/ModernBERT-base to answerdotai/ModernBERT-large . This script is also described in the Cross Encoder > Training Overview documentation and the Training and Finetuning Reranker Models with Sentence Transformers v4 blogpost.

Model size vs NDCG for Rerankers on GooAQ

Model Details
Model Description
  • Model Type: Cross Encoder
  • Base model: answerdotai/ModernBERT-large
  • Maximum Sequence Length: 8192 tokens
  • Number of Output Labels: 1 label
  • Language: en
  • License: apache-2.0
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/reranker-ModernBERT-large-gooaq-bce")
# Get scores for pairs of texts
pairs = [
    ['what are the characteristics and elements of poetry?', 'The elements of poetry include meter, rhyme, form, sound, and rhythm (timing). Different poets use these elements in many different ways.'],
    ['what are the characteristics and elements of poetry?', "What's the first rule of writing poetry? That there are no rules — it's all up to you! Of course there are different poetic forms and devices, and free verse poems are one of the many poetic styles; they have no structure when it comes to format or even rhyming."],
    ['what are the characteristics and elements of poetry?', "['Blank verse. Blank verse is poetry written with a precise meter—almost always iambic pentameter—that does not rhyme. ... ', 'Rhymed poetry. In contrast to blank verse, rhymed poems rhyme by definition, although their scheme varies. ... ', 'Free verse. ... ', 'Epics. ... ', 'Narrative poetry. ... ', 'Haiku. ... ', 'Pastoral poetry. ... ', 'Sonnet.']"],
    ['what are the characteristics and elements of poetry?', 'The main component of poetry is its meter (the regular pattern of strong and weak stress). When a poem has a recognizable but varying pattern of stressed and unstressed syllables, the poetry is written in verse. ... There are many possible patterns of verse, and the basic pattern of each unit is called a foot.'],
    ['what are the characteristics and elements of poetry?', "Some poetry may not make sense to you. But that's because poets don't write to be understood by others. They write because they must. The feelings and emotions that reside within them need to be expressed."],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what are the characteristics and elements of poetry?',
    [
        'The elements of poetry include meter, rhyme, form, sound, and rhythm (timing). Different poets use these elements in many different ways.',
        "What's the first rule of writing poetry? That there are no rules — it's all up to you! Of course there are different poetic forms and devices, and free verse poems are one of the many poetic styles; they have no structure when it comes to format or even rhyming.",
        "['Blank verse. Blank verse is poetry written with a precise meter—almost always iambic pentameter—that does not rhyme. ... ', 'Rhymed poetry. In contrast to blank verse, rhymed poems rhyme by definition, although their scheme varies. ... ', 'Free verse. ... ', 'Epics. ... ', 'Narrative poetry. ... ', 'Haiku. ... ', 'Pastoral poetry. ... ', 'Sonnet.']",
        'The main component of poetry is its meter (the regular pattern of strong and weak stress). When a poem has a recognizable but varying pattern of stressed and unstressed syllables, the poetry is written in verse. ... There are many possible patterns of verse, and the basic pattern of each unit is called a foot.',
        "Some poetry may not make sense to you. But that's because poets don't write to be understood by others. They write because they must. The feelings and emotions that reside within them need to be expressed.",
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
Evaluation
Metrics
Cross Encoder Reranking
Metric Value
map 0.7586 (+0.2275)
mrr@10 0.7576 (+0.2336)
ndcg@10 0.7946 (+0.2034)
Cross Encoder Reranking
Metric Value
map 0.8176 (+0.2865)
mrr@10 0.8166 (+0.2926)
ndcg@10 0.8581 (+0.2669)
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 0.5488 (+0.0592) 0.3682 (+0.1072) 0.6103 (+0.1907)
mrr@10 0.5443 (+0.0668) 0.5677 (+0.0678) 0.6108 (+0.1841)
ndcg@10 0.6323 (+0.0918) 0.4136 (+0.0886) 0.6570 (+0.1564)
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 0.5091 (+0.1190)
mrr@10 0.5743 (+0.1063)
ndcg@10 0.5676 (+0.1123)
Training Details
Training Dataset
Unnamed Dataset
  • Size: 578,402 training samples
  • Columns: question , answer , and label
  • Approximate statistics based on the first 1000 samples:
    question answer label
    type string string int
    details
    • min: 22 characters
    • mean: 43.99 characters
    • max: 93 characters
    • min: 51 characters
    • mean: 252.75 characters
    • max: 378 characters
    • 0: ~82.30%
    • 1: ~17.70%
  • Samples:
    question answer label
    what are the characteristics and elements of poetry? The elements of poetry include meter, rhyme, form, sound, and rhythm (timing). Different poets use these elements in many different ways. 1
    what are the characteristics and elements of poetry? What's the first rule of writing poetry? That there are no rules — it's all up to you! Of course there are different poetic forms and devices, and free verse poems are one of the many poetic styles; they have no structure when it comes to format or even rhyming. 0
    what are the characteristics and elements of poetry? ['Blank verse. Blank verse is poetry written with a precise meter—almost always iambic pentameter—that does not rhyme. ... ', 'Rhymed poetry. In contrast to blank verse, rhymed poems rhyme by definition, although their scheme varies. ... ', 'Free verse. ... ', 'Epics. ... ', 'Narrative poetry. ... ', 'Haiku. ... ', 'Pastoral poetry. ... ', 'Sonnet.'] 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 : 64
  • per_device_eval_batch_size : 64
  • learning_rate : 2e-05
  • num_train_epochs : 1
  • warmup_ratio : 0.1
  • seed : 12
  • bf16 : True
  • dataloader_num_workers : 4
  • 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 : 64
  • per_device_eval_batch_size : 64
  • 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
  • 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 : 4
  • 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}
  • 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
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 - 0.1279 (-0.4633) 0.0555 (-0.4849) 0.1735 (-0.1516) 0.0686 (-0.4320) 0.0992 (-0.3562)
0.0001 1 1.2592 - - - - -
0.0221 200 1.1826 - - - - -
0.0443 400 0.7653 - - - - -
0.0664 600 0.6423 - - - - -
0.0885 800 0.6 - - - - -
0.1106 1000 0.5753 0.7444 (+0.1531) 0.5365 (-0.0039) 0.4249 (+0.0998) 0.6111 (+0.1105) 0.5242 (+0.0688)
0.1328 1200 0.5313 - - - - -
0.1549 1400 0.5315 - - - - -
0.1770 1600 0.5195 - - - - -
0.1992 1800 0.5136 - - - - -
0.2213 2000 0.4782 0.7774 (+0.1862) 0.6080 (+0.0676) 0.4371 (+0.1120) 0.6520 (+0.1513) 0.5657 (+0.1103)
0.2434 2200 0.5026 - - - - -
0.2655 2400 0.5011 - - - - -
0.2877 2600 0.4893 - - - - -
0.3098 2800 0.4855 - - - - -
0.3319 3000 0.4687 0.7692 (+0.1779) 0.6181 (+0.0777) 0.4273 (+0.1023) 0.6686 (+0.1679) 0.5713 (+0.1160)
0.3541 3200 0.4619 - - - - -
0.3762 3400 0.4626 - - - - -
0.3983 3600 0.4504 - - - - -
0.4204 3800 0.4435 - - - - -
0.4426 4000 0.4573 0.7776 (+0.1864) 0.6589 (+0.1184) 0.4262 (+0.1012) 0.6634 (+0.1628) 0.5828 (+0.1275)
0.4647 4200 0.4608 - - - - -
0.4868 4400 0.4275 - - - - -
0.5090 4600 0.4317 - - - - -
0.5311 4800 0.4427 - - - - -
0.5532 5000 0.4245 0.7795 (+0.1883) 0.6021 (+0.0617) 0.4387 (+0.1137) 0.6560 (+0.1553) 0.5656 (+0.1102)
0.5753 5200 0.4243 - - - - -
0.5975 5400 0.4295 - - - - -
0.6196 5600 0.422 - - - - -
0.6417 5800 0.4165 - - - - -
0.6639 6000 0.4281 0.7859 (+0.1946) 0.6404 (+0.1000) 0.4449 (+0.1199) 0.6458 (+0.1451) 0.5770 (+0.1217)
0.6860 6200 0.4155 - - - - -
0.7081 6400 0.4189 - - - - -
0.7303 6600 0.4066 - - - - -
0.7524 6800 0.4114 - - - - -
0.7745 7000 0.4111 0.7875 (+0.1963) 0.6358 (+0.0954) 0.4289 (+0.1038) 0.6358 (+0.1351) 0.5668 (+0.1114)
0.7966 7200 0.3949 - - - - -
0.8188 7400 0.4019 - - - - -
0.8409 7600 0.395 - - - - -
0.8630 7800 0.3885 - - - - -
0.8852 8000 0.3991 0.7946 (+0.2034) 0.6323 (+0.0918) 0.4136 (+0.0886) 0.6570 (+0.1564) 0.5676 (+0.1123)
0.9073 8200 0.3894 - - - - -
0.9294 8400 0.392 - - - - -
0.9515 8600 0.3853 - - - - -
0.9737 8800 0.3691 - - - - -
0.9958 9000 0.3784 0.7936 (+0.2024) 0.6481 (+0.1077) 0.4211 (+0.0961) 0.6439 (+0.1433) 0.5711 (+0.1157)
-1 -1 - 0.7946 (+0.2034) 0.6323 (+0.0918) 0.4136 (+0.0886) 0.6570 (+0.1564) 0.5676 (+0.1123)
  • The bold row denotes the saved checkpoint.
Framework Versions
  • Python: 3.11.10
  • Sentence Transformers: 3.5.0.dev0
  • Transformers: 4.49.0
  • PyTorch: 2.5.1+cu124
  • Accelerate: 1.5.2
  • Datasets: 2.21.0
  • Tokenizers: 0.21.0
Citation
BibTeX
Sentence Transformers
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

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