metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - cross-encoder
  - generated_from_trainer
  - dataset_size:482388
  - 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: 0.7089
            name: Map
          - type: mrr@10
            value: 0.7076
            name: Mrr@10
          - type: ndcg@10
            value: 0.755
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoMSMARCO R100
          type: NanoMSMARCO_R100
        metrics:
          - type: map
            value: 0.554
            name: Map
          - type: mrr@10
            value: 0.5472
            name: Mrr@10
          - type: ndcg@10
            value: 0.6229
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNFCorpus R100
          type: NanoNFCorpus_R100
        metrics:
          - type: map
            value: 0.3421
            name: Map
          - type: mrr@10
            value: 0.5284
            name: Mrr@10
          - type: ndcg@10
            value: 0.3792
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNQ R100
          type: NanoNQ_R100
        metrics:
          - type: map
            value: 0.6312
            name: Map
          - type: mrr@10
            value: 0.638
            name: Mrr@10
          - type: ndcg@10
            value: 0.6915
            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: 0.5712
            name: Mrr@10
          - type: ndcg@10
            value: 0.5645
            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.

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-soft-negs")
# Get scores for pairs of texts
pairs = [
    ['what is the difference between ground level ozone and the ozone layer?', 'Here, ground-level or "bad" ozone is an air pollutant that is harmful to breathe and it damages crops, trees and other vegetation. ... The stratosphere or "good" ozone layer extends upward from about 6 to 30 miles and protects life on Earth from the sun\'s harmful ultraviolet (UV) rays.'],
    ['what is the difference between ground level ozone and the ozone layer?', 'In the stratosphere, temperature increases with altitude. The reason is that the direct heat source for the stratosphere is the Sun. A layer of ozone molecules absorbs solar radiation, which heats the stratosphere.'],
    ['what is the difference between ground level ozone and the ozone layer?', "Atmosphere layers. Earth's atmosphere is divided into five main layers: the exosphere, the thermosphere, the mesosphere, the stratosphere and the troposphere. ... Ozone is abundant here and it heats the atmosphere while also absorbing harmful radiation from the sun."],
    ['what is the difference between ground level ozone and the ozone layer?', "['Water vapor (H. 2O)', 'Carbon dioxide (CO. ... ', 'Methane (CH. ... ', 'Nitrous oxide (N. 2O)', 'Ozone (O. ... ', 'Chlorofluorocarbons (CFCs)', 'Hydrofluorocarbons (includes HCFCs and HFCs)']"],
    ['what is the difference between ground level ozone and the ozone layer?', "Gases in the atmosphere, such as carbon dioxide, trap heat just like the glass roof of a greenhouse. These heat-trapping gases are called greenhouse gases. During the day, the Sun shines through the atmosphere. Earth's surface warms up in the sunlight."],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what is the difference between ground level ozone and the ozone layer?',
    [
        'Here, ground-level or "bad" ozone is an air pollutant that is harmful to breathe and it damages crops, trees and other vegetation. ... The stratosphere or "good" ozone layer extends upward from about 6 to 30 miles and protects life on Earth from the sun\'s harmful ultraviolet (UV) rays.',
        'In the stratosphere, temperature increases with altitude. The reason is that the direct heat source for the stratosphere is the Sun. A layer of ozone molecules absorbs solar radiation, which heats the stratosphere.',
        "Atmosphere layers. Earth's atmosphere is divided into five main layers: the exosphere, the thermosphere, the mesosphere, the stratosphere and the troposphere. ... Ozone is abundant here and it heats the atmosphere while also absorbing harmful radiation from the sun.",
        "['Water vapor (H. 2O)', 'Carbon dioxide (CO. ... ', 'Methane (CH. ... ', 'Nitrous oxide (N. 2O)', 'Ozone (O. ... ', 'Chlorofluorocarbons (CFCs)', 'Hydrofluorocarbons (includes HCFCs and HFCs)']",
        "Gases in the atmosphere, such as carbon dioxide, trap heat just like the glass roof of a greenhouse. These heat-trapping gases are called greenhouse gases. During the day, the Sun shines through the atmosphere. Earth's surface warms up in the sunlight.",
    ]
)
# [{'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	0.7089 (+0.1778)
mrr@10	0.7076 (+0.1836)
ndcg@10	0.7550 (+0.1637)

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	0.5540 (+0.0644)	0.3421 (+0.0811)	0.6312 (+0.2116)
mrr@10	0.5472 (+0.0697)	0.5284 (+0.0286)	0.6380 (+0.2113)
ndcg@10	0.6229 (+0.0825)	0.3792 (+0.0541)	0.6915 (+0.1908)

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	0.5712 (+0.1032)
ndcg@10	0.5645 (+0.1092)

Training Details

Training Dataset

Unnamed Dataset

Size: 482,388 training samples
Columns: question, answer, and label

Approximate statistics based on the first 1000 samples:

	question	answer	label
type	string	string	int
details	min: 17 characters mean: 43.7 characters max: 91 characters	min: 53 characters mean: 250.44 characters max: 393 characters	0: ~79.30% 1: ~20.70%

Samples:

question	answer	label
`what is the difference between ground level ozone and the ozone layer?`	`Here, ground-level or "bad" ozone is an air pollutant that is harmful to breathe and it damages crops, trees and other vegetation. ... The stratosphere or "good" ozone layer extends upward from about 6 to 30 miles and protects life on Earth from the sun's harmful ultraviolet (UV) rays.`	`1`
`what is the difference between ground level ozone and the ozone layer?`	`In the stratosphere, temperature increases with altitude. The reason is that the direct heat source for the stratosphere is the Sun. A layer of ozone molecules absorbs solar radiation, which heats the stratosphere.`	`0`
`what is the difference between ground level ozone and the ozone layer?`	`Atmosphere layers. Earth's atmosphere is divided into five main layers: the exosphere, the thermosphere, the mesosphere, the stratosphere and the troposphere. ... Ozone is abundant here and it heats the atmosphere while also absorbing harmful radiation from the sun.`	`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	-	0.1488 (-0.4424)	0.0573 (-0.4832)	0.2647 (-0.0604)	0.0388 (-0.4619)	0.1202 (-0.3351)
0.0001	1	1.3143	-	-	-	-	-
0.0265	200	1.2539	-	-	-	-	-
0.0531	400	0.9497	-	-	-	-	-
0.0796	600	0.5613	-	-	-	-	-
0.1061	800	0.4687	-	-	-	-	-
0.1327	1000	0.4042	0.7103 (+0.1191)	0.5262 (-0.0142)	0.3298 (+0.0048)	0.5589 (+0.0583)	0.4717 (+0.0163)
0.1592	1200	0.3562	-	-	-	-	-
0.1857	1400	0.3543	-	-	-	-	-
0.2123	1600	0.3467	-	-	-	-	-
0.2388	1800	0.3153	-	-	-	-	-
0.2653	2000	0.3033	0.7317 (+0.1405)	0.5662 (+0.0258)	0.3859 (+0.0609)	0.6828 (+0.1822)	0.5450 (+0.0896)
0.2919	2200	0.2986	-	-	-	-	-
0.3184	2400	0.3016	-	-	-	-	-
0.3449	2600	0.2984	-	-	-	-	-
0.3715	2800	0.2646	-	-	-	-	-
0.3980	3000	0.3048	0.7359 (+0.1447)	0.5713 (+0.0309)	0.3987 (+0.0736)	0.6960 (+0.1953)	0.5553 (+0.1000)
0.4245	3200	0.2714	-	-	-	-	-
0.4510	3400	0.2773	-	-	-	-	-
0.4776	3600	0.2621	-	-	-	-	-
0.5041	3800	0.2529	-	-	-	-	-
0.5306	4000	0.2533	0.7459 (+0.1546)	0.5893 (+0.0489)	0.3887 (+0.0637)	0.6749 (+0.1743)	0.5510 (+0.0956)
0.5572	4200	0.2822	-	-	-	-	-
0.5837	4400	0.2299	-	-	-	-	-
0.6102	4600	0.2554	-	-	-	-	-
0.6368	4800	0.2373	-	-	-	-	-
0.6633	5000	0.2248	0.7497 (+0.1584)	0.6110 (+0.0706)	0.3782 (+0.0531)	0.6885 (+0.1878)	0.5592 (+0.1038)
0.6898	5200	0.2315	-	-	-	-	-
0.7164	5400	0.2313	-	-	-	-	-
0.7429	5600	0.2294	-	-	-	-	-
0.7694	5800	0.2384	-	-	-	-	-
0.7960	6000	0.2195	0.7530 (+0.1617)	0.6249 (+0.0845)	0.3873 (+0.0623)	0.6773 (+0.1766)	0.5632 (+0.1078)
0.8225	6200	0.2047	-	-	-	-	-
0.8490	6400	0.2192	-	-	-	-	-
0.8756	6600	0.1926	-	-	-	-	-
0.9021	6800	0.2185	-	-	-	-	-
0.9286	7000	0.2365	0.7550 (+0.1637)	0.6229 (+0.0825)	0.3792 (+0.0541)	0.6915 (+0.1908)	0.5645 (+0.1092)
0.9552	7200	0.2173	-	-	-	-	-
0.9817	7400	0.2249	-	-	-	-	-
-1	-1	-	0.7550 (+0.1637)	0.6229 (+0.0825)	0.3792 (+0.0541)	0.6915 (+0.1908)	0.5645 (+0.1092)

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",
}