tomaarsen/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 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

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("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

• Dataset: gooaq-dev
• Evaluated with CrossEncoderRerankingEvaluator with these parameters:

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

Metric	Value
map	0.7586 (+0.2275)
mrr@10	0.7576 (+0.2336)
ndcg@10	0.7946 (+0.2034)

Cross Encoder Reranking

• Dataset: gooaq-dev
• Evaluated with CrossEncoderRerankingEvaluator with these parameters:

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

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",
}