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.
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
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
model = CrossEncoder("chingackgook/reranker-ModernBERT-base-gooaq-bce")
pairs = [
['how many days can i drive my car without mot?', "If your car fails its MOT you can only continue to drive it if the previous year's MOT is still valid - which might occur if you submitted the car for its test two weeks early. You can still drive it away from the testing centre or garage if no 'dangerous' problems were identified during the MOT."],
['how many days can i drive my car without mot?', '["Open File Explorer\', go to \'This PC\'. Check the status of the network drive. [00:11]", \'An error occurred while reconnecting or mapping Drive letter Z: to Network folder. [00:36]\', \'You can do either connect the drive (Map network drive) or Disconnect Network drive. [01:01]\', \'Disconnect Network Drive by this way. [01:28]\']'],
['what xbox 360 games are compatible with xbox 1?', "['0 day Attack on Earth.', '3D Ultra Minigolf.', 'A Kingdom for Keflings.', 'A World of Keflings.', 'Ace Combat 6: Fires of Liberation.', 'Aegis Wing.', 'Age of Booty.', 'Alan Wake (Tested by Digital Foundry)']"],
['what xbox 360 games are compatible with xbox 1?', "['1) Computer and Information Systems Managers.', '2) Computer and Information Research Scientists.', '3) Computer Network Architects.', '4) Software Development Engineer.', '5) Software Developers.', '6) Information Security Analysts.', '8) Computer Systems Analysts.']"],
['what does it mean when a guy asks for a picture of you?', 'He wants to confirm if he is talking to Priya or Angel Priya (I.e., if he is really talking to a girl or just a guy with fake profile) They are talking to you and want to see how you look. I found it normal but would say, be careful about whom do you share your picture with as they might misuse it. I hate this one.'],
]
scores = model.predict(pairs)
print(scores.shape)
ranks = model.rank(
'how many days can i drive my car without mot?',
[
"If your car fails its MOT you can only continue to drive it if the previous year's MOT is still valid - which might occur if you submitted the car for its test two weeks early. You can still drive it away from the testing centre or garage if no 'dangerous' problems were identified during the MOT.",
'["Open File Explorer\', go to \'This PC\'. Check the status of the network drive. [00:11]", \'An error occurred while reconnecting or mapping Drive letter Z: to Network folder. [00:36]\', \'You can do either connect the drive (Map network drive) or Disconnect Network drive. [01:01]\', \'Disconnect Network Drive by this way. [01:28]\']',
"['0 day Attack on Earth.', '3D Ultra Minigolf.', 'A Kingdom for Keflings.', 'A World of Keflings.', 'Ace Combat 6: Fires of Liberation.', 'Aegis Wing.', 'Age of Booty.', 'Alan Wake (Tested by Digital Foundry)']",
"['1) Computer and Information Systems Managers.', '2) Computer and Information Research Scientists.', '3) Computer Network Architects.', '4) Software Development Engineer.', '5) Software Developers.', '6) Information Security Analysts.', '8) Computer Systems Analysts.']",
'He wants to confirm if he is talking to Priya or Angel Priya (I.e., if he is really talking to a girl or just a guy with fake profile) They are talking to you and want to see how you look. I found it normal but would say, be careful about whom do you share your picture with as they might misuse it. I hate this one.',
]
)
Evaluation
Metrics
Cross Encoder Reranking
| Metric |
Value |
| map |
0.2500 (-0.7500) |
| mrr@10 |
0.2500 (-0.7500) |
| ndcg@10 |
0.4307 (-0.5693) |
Cross Encoder Reranking
| Metric |
NanoMSMARCO_R100 |
NanoNFCorpus_R100 |
NanoNQ_R100 |
| map |
0.0358 (-0.4538) |
0.2903 (+0.0293) |
0.0379 (-0.3817) |
| mrr@10 |
0.0109 (-0.4666) |
0.5069 (+0.0070) |
0.0140 (-0.4127) |
| ndcg@10 |
0.0260 (-0.5145) |
0.2900 (-0.0350) |
0.0174 (-0.4833) |
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 |
0.1213 (-0.2688) |
| mrr@10 |
0.1772 (-0.2908) |
| ndcg@10 |
0.1111 (-0.3443) |
Training Details
Training Dataset
Unnamed Dataset
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 16
per_device_eval_batch_size: 16
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: 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}
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
| 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 |
- |
0.4307 (-0.5693) |
0.0220 (-0.5184) |
0.2802 (-0.0448) |
0.0200 (-0.4806) |
0.1074 (-0.3479) |
| 0.25 |
1 |
0.7171 |
- |
- |
- |
- |
- |
| -1 |
-1 |
- |
0.4307 (-0.5693) |
0.0260 (-0.5145) |
0.2900 (-0.0350) |
0.0174 (-0.4833) |
0.1111 (-0.3443) |
Framework Versions
- Python: 3.10.19
- Sentence Transformers: 5.2.0.dev0
- Transformers: 4.57.3
- PyTorch: 2.9.1+cu128
- 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",
}