metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - cross-encoder
  - generated_from_trainer
  - dataset_size:578402
  - loss:BinaryCrossEntropyLoss
base_model: answerdotai/ModernBERT-base
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
  - map
  - mrr@10
  - ndcg@10
model-index:
  - name: ModernBERT-base trained on GooAQ
    results:
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: gooaq dev
          type: gooaq-dev
        metrics:
          - type: map
            value: 0.7308
            name: Map
          - type: mrr@10
            value: 0.7292
            name: Mrr@10
          - type: ndcg@10
            value: 0.7713
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoMSMARCO R100
          type: NanoMSMARCO_R100
        metrics:
          - type: map
            value: 0.4579
            name: Map
          - type: mrr@10
            value: 0.4479
            name: Mrr@10
          - type: ndcg@10
            value: 0.5275
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNFCorpus R100
          type: NanoNFCorpus_R100
        metrics:
          - type: map
            value: 0.3414
            name: Map
          - type: mrr@10
            value: 0.534
            name: Mrr@10
          - type: ndcg@10
            value: 0.3821
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNQ R100
          type: NanoNQ_R100
        metrics:
          - type: map
            value: 0.3932
            name: Map
          - type: mrr@10
            value: 0.3918
            name: Mrr@10
          - type: ndcg@10
            value: 0.463
            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: 0.3975
            name: Map
          - type: mrr@10
            value: 0.4579
            name: Mrr@10
          - type: ndcg@10
            value: 0.4575
            name: Ndcg@10

ModernBERT-base trained on GooAQ

This is a Cross Encoder model finetuned from answerdotai/ModernBERT-base 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. 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-base
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-base-gooaq-bce")
# Get scores for pairs of texts
pairs = [
    ['why are rye chips so good?', "It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips."],
    ['why are rye chips so good?', 'There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.'],
    ['why are rye chips so good?', 'Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.'],
    ['why are rye chips so good?', 'KFC Chips – The salt mix on the seasoned chips and the actual chips do not contain any animal products. Our supplier/s of chips and seasoning have confirmed they are suitable for vegans.'],
    ['why are rye chips so good?', 'A study in the American Journal of Clinical Nutrition found that eating rye leads to better blood-sugar control compared to wheat. Rye bread is packed with magnesium, which helps control blood pressure and optimize heart health. Its high levels of soluble fibre can also reduce cholesterol.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'why are rye chips so good?',
    [
        "It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips.",
        'There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.',
        'Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.',
        'KFC Chips – The salt mix on the seasoned chips and the actual chips do not contain any animal products. Our supplier/s of chips and seasoning have confirmed they are suitable for vegans.',
        'A study in the American Journal of Clinical Nutrition found that eating rye leads to better blood-sugar control compared to wheat. Rye bread is packed with magnesium, which helps control blood pressure and optimize heart health. Its high levels of soluble fibre can also reduce cholesterol.',
    ]
)
# [{'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	0.7308 (+0.1997)
mrr@10	0.7292 (+0.2052)
ndcg@10	0.7713 (+0.1801)

Cross Encoder Reranking

Dataset: gooaq-dev

Evaluated with CrossEncoderRerankingEvaluator with these parameters:

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

Metric	Value
map	0.7908 (+0.2597)
mrr@10	0.7890 (+0.2650)
ndcg@10	0.8351 (+0.2439)

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	0.4579 (-0.0317)	0.3414 (+0.0804)	0.3932 (-0.0264)
mrr@10	0.4479 (-0.0296)	0.5340 (+0.0342)	0.3918 (-0.0349)
ndcg@10	0.5275 (-0.0130)	0.3821 (+0.0571)	0.4630 (-0.0377)

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	0.3975 (+0.0074)
mrr@10	0.4579 (-0.0101)
ndcg@10	0.4575 (+0.0022)

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: 19 characters mean: 45.14 characters max: 85 characters	min: 65 characters mean: 254.8 characters max: 379 characters	0: ~82.90% 1: ~17.10%

Samples:

question	answer	label
`why are rye chips so good?`	`It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips.`	`1`
`why are rye chips so good?`	`There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.`	`0`
`why are rye chips so good?`	`Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.`	`0`

Loss: BinaryCrossEntropyLoss with these parameters:

{
    "activation_fct": "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	-	0.1288 (-0.4624)	0.0149 (-0.5255)	0.2278 (-0.0972)	0.0229 (-0.4777)	0.0885 (-0.3668)
0.0001	1	1.0435	-	-	-	-	-
0.0221	200	1.1924	-	-	-	-	-
0.0443	400	1.1531	-	-	-	-	-
0.0664	600	0.9371	-	-	-	-	-
0.0885	800	0.6993	-	-	-	-	-
0.1106	1000	0.669	0.7042 (+0.1130)	0.4353 (-0.1051)	0.3289 (+0.0039)	0.4250 (-0.0757)	0.3964 (-0.0590)
0.1328	1200	0.6257	-	-	-	-	-
0.1549	1400	0.6283	-	-	-	-	-
0.1770	1600	0.6014	-	-	-	-	-
0.1992	1800	0.5888	-	-	-	-	-
0.2213	2000	0.5493	0.7425 (+0.1513)	0.4947 (-0.0457)	0.3568 (+0.0318)	0.4634 (-0.0373)	0.4383 (-0.0171)
0.2434	2200	0.5479	-	-	-	-	-
0.2655	2400	0.5329	-	-	-	-	-
0.2877	2600	0.5208	-	-	-	-	-
0.3098	2800	0.5259	-	-	-	-	-
0.3319	3000	0.5221	0.7479 (+0.1567)	0.5146 (-0.0258)	0.3710 (+0.0460)	0.4846 (-0.0160)	0.4568 (+0.0014)
0.3541	3200	0.4977	-	-	-	-	-
0.3762	3400	0.4965	-	-	-	-	-
0.3983	3600	0.4985	-	-	-	-	-
0.4204	3800	0.4907	-	-	-	-	-
0.4426	4000	0.5058	0.7624 (+0.1712)	0.5166 (-0.0238)	0.3665 (+0.0415)	0.4868 (-0.0138)	0.4567 (+0.0013)
0.4647	4200	0.4885	-	-	-	-	-
0.4868	4400	0.495	-	-	-	-	-
0.5090	4600	0.4839	-	-	-	-	-
0.5311	4800	0.4983	-	-	-	-	-
0.5532	5000	0.4778	0.7603 (+0.1691)	0.5110 (-0.0294)	0.3540 (+0.0290)	0.4809 (-0.0197)	0.4487 (-0.0067)
0.5753	5200	0.4726	-	-	-	-	-
0.5975	5400	0.477	-	-	-	-	-
0.6196	5600	0.4613	-	-	-	-	-
0.6417	5800	0.4492	-	-	-	-	-
0.6639	6000	0.4506	0.7643 (+0.1731)	0.5275 (-0.0129)	0.3639 (+0.0389)	0.4913 (-0.0094)	0.4609 (+0.0055)
0.6860	6200	0.4618	-	-	-	-	-
0.7081	6400	0.463	-	-	-	-	-
0.7303	6600	0.4585	-	-	-	-	-
0.7524	6800	0.4612	-	-	-	-	-
0.7745	7000	0.4621	0.7649 (+0.1736)	0.5105 (-0.0299)	0.3688 (+0.0437)	0.4552 (-0.0454)	0.4448 (-0.0105)
0.7966	7200	0.4536	-	-	-	-	-
0.8188	7400	0.4515	-	-	-	-	-
0.8409	7600	0.4396	-	-	-	-	-
0.8630	7800	0.4542	-	-	-	-	-
0.8852	8000	0.4332	0.7669 (+0.1757)	0.5247 (-0.0157)	0.3794 (+0.0544)	0.4370 (-0.0637)	0.4470 (-0.0083)
0.9073	8200	0.447	-	-	-	-	-
0.9294	8400	0.4335	-	-	-	-	-
0.9515	8600	0.4179	-	-	-	-	-
0.9737	8800	0.4459	-	-	-	-	-
0.9958	9000	0.4196	0.7713 (+0.1801)	0.5275 (-0.0130)	0.3821 (+0.0571)	0.4630 (-0.0377)	0.4575 (+0.0022)
-1	-1	-	0.7713 (+0.1801)	0.5275 (-0.0130)	0.3821 (+0.0571)	0.4630 (-0.0377)	0.4575 (+0.0022)

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",
}