metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - cross-encoder
  - generated_from_trainer
  - dataset_size:578402
  - loss:BinaryCrossEntropyLoss
base_model: answerdotai/ModernBERT-large
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
  - map
  - mrr@10
  - ndcg@10
model-index:
  - name: ModernBERT-large trained on GooAQ
    results:
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: gooaq dev
          type: gooaq-dev
        metrics:
          - type: map
            value: 0.7586
            name: Map
          - type: mrr@10
            value: 0.7576
            name: Mrr@10
          - type: ndcg@10
            value: 0.7946
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoMSMARCO R100
          type: NanoMSMARCO_R100
        metrics:
          - type: map
            value: 0.5488
            name: Map
          - type: mrr@10
            value: 0.5443
            name: Mrr@10
          - type: ndcg@10
            value: 0.6323
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNFCorpus R100
          type: NanoNFCorpus_R100
        metrics:
          - type: map
            value: 0.3682
            name: Map
          - type: mrr@10
            value: 0.5677
            name: Mrr@10
          - type: ndcg@10
            value: 0.4136
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNQ R100
          type: NanoNQ_R100
        metrics:
          - type: map
            value: 0.6103
            name: Map
          - type: mrr@10
            value: 0.6108
            name: Mrr@10
          - type: ndcg@10
            value: 0.657
            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: 0.5091
            name: Map
          - type: mrr@10
            value: 0.5743
            name: Mrr@10
          - type: ndcg@10
            value: 0.5676
            name: Ndcg@10

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",
}