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reranker-ModernBERT-base-gooaq-bce

Text Ranking
sentence-transformers
Safetensors
English
modernbert
cross-encoder
Generated from Trainer
dataset_size:578402
loss:BinaryCrossEntropyLoss
Eval Results
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 ---
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: 0.7283
name: Map
- type: mrr@10
value: 0.7272
name: Mrr@10
- type: ndcg@10
value: 0.7716
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R100
type: NanoMSMARCO_R100
metrics:
- type: map
value: 0.4489
name: Map
- type: mrr@10
value: 0.4376
name: Mrr@10
- type: ndcg@10
value: 0.5096
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNFCorpus R100
type: NanoNFCorpus_R100
metrics:
- type: map
value: 0.3159
name: Map
- type: mrr@10
value: 0.4737
name: Mrr@10
- type: ndcg@10
value: 0.3176
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R100
type: NanoNQ_R100
metrics:
- type: map
value: 0.4904
name: Map
- type: mrr@10
value: 0.5075
name: Mrr@10
- type: ndcg@10
value: 0.5388
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: 0.4184
name: Map
- type: mrr@10
value: 0.4729
name: Mrr@10
- type: ndcg@10
value: 0.4553
name: Ndcg@10
---
# ModernBERT-base trained on GooAQ
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) using the [sentence-transformers](https://www.SBERT.net) 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](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
- **Maximum Sequence Length:** 8192 tokens
- **Number of Output Labels:** 1 label
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("Oysiyl/reranker-ModernBERT-base-gooaq-bce")
# Get scores for pairs of texts
pairs = [
['what do you do with a degree in criminal justice?', "['Police Patrol Officer. Job Description. ... ', 'Criminal Investigators & Special Agents. Job Description. ... ', 'Private Detective or Investigator. ... ', 'First-Line Police Supervisor. ... ', 'Correctional Officer. ... ', 'Probation and Parole Officers. ... ', 'Postsecondary Criminal Justice & Law Enforcement Teachers.']"],
['what do you do with a degree in criminal justice?', "['Prison officer. ... ', 'Police officer. ... ', 'Detective. ... ', 'Criminologist. ... ', 'Probation officer. ... ', 'Forensic scientist. ... ', 'Crime Scene investigator. ... ', 'Court reporter.']"],
['what do you do with a degree in criminal justice?', "A high school diploma is required to work as a detective. In some cases a bachelor's degree in criminal justice or law enforcement may be needed. Experience in law enforcement is usually required, but the amount varies by employer."],
['what do you do with a degree in criminal justice?', "['Court Reporter. ... ', 'Criminal Intelligence Analyst. ... ', 'Forensic Accountant. ... ', 'Police Officer and Police Support Roles. ... ', 'Immigration, Customs and Border Roles. ... ', 'Prison Officer. ... ', 'Probation Officer. ... ', 'Scene of Crime Officer.']"],
['what do you do with a degree in criminal justice?', "Crime Scene Investigator Education and Training Although a high school diploma or equivalent is a minimum requirement for some positions, many police departments and law enforcement agencies prefer a minimum of an associate's (two-year) or a bachelor's (four-year) degree in criminal justice or a natural science."],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'what do you do with a degree in criminal justice?',
[
"['Police Patrol Officer. Job Description. ... ', 'Criminal Investigators & Special Agents. Job Description. ... ', 'Private Detective or Investigator. ... ', 'First-Line Police Supervisor. ... ', 'Correctional Officer. ... ', 'Probation and Parole Officers. ... ', 'Postsecondary Criminal Justice & Law Enforcement Teachers.']",
"['Prison officer. ... ', 'Police officer. ... ', 'Detective. ... ', 'Criminologist. ... ', 'Probation officer. ... ', 'Forensic scientist. ... ', 'Crime Scene investigator. ... ', 'Court reporter.']",
"A high school diploma is required to work as a detective. In some cases a bachelor's degree in criminal justice or law enforcement may be needed. Experience in law enforcement is usually required, but the amount varies by employer.",
"['Court Reporter. ... ', 'Criminal Intelligence Analyst. ... ', 'Forensic Accountant. ... ', 'Police Officer and Police Support Roles. ... ', 'Immigration, Customs and Border Roles. ... ', 'Prison Officer. ... ', 'Probation Officer. ... ', 'Scene of Crime Officer.']",
"Crime Scene Investigator Education and Training Although a high school diploma or equivalent is a minimum requirement for some positions, many police departments and law enforcement agencies prefer a minimum of an associate's (two-year) or a bachelor's (four-year) degree in criminal justice or a natural science.",
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Cross Encoder Reranking
* Dataset: `gooaq-dev`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": false
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.7283 (+0.1972) |
| mrr@10 | 0.7272 (+0.2033) |
| **ndcg@10** | **0.7716 (+0.1804)** |
#### Cross Encoder Reranking
* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
|:------------|:---------------------|:---------------------|:---------------------|
| map | 0.4489 (-0.0407) | 0.3159 (+0.0549) | 0.4904 (+0.0708) |
| mrr@10 | 0.4376 (-0.0399) | 0.4737 (-0.0262) | 0.5075 (+0.0808) |
| **ndcg@10** | **0.5096 (-0.0309)** | **0.3176 (-0.0074)** | **0.5388 (+0.0382)** |
#### Cross Encoder Nano BEIR
* Dataset: `NanoBEIR_R100_mean`
* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"rerank_k": 100,
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.4184 (+0.0284) |
| mrr@10 | 0.4729 (+0.0049) |
| **ndcg@10** | **0.4553 (-0.0000)** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 578,402 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | label |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 20 characters</li><li>mean: 43.36 characters</li><li>max: 95 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 252.47 characters</li><li>max: 386 characters</li></ul> | <ul><li>0: ~82.80%</li><li>1: ~17.20%</li></ul> |
* Samples:
| question | answer | label |
|:---------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>what do you do with a degree in criminal justice?</code> | <code>['Police Patrol Officer. Job Description. ... ', 'Criminal Investigators & Special Agents. Job Description. ... ', 'Private Detective or Investigator. ... ', 'First-Line Police Supervisor. ... ', 'Correctional Officer. ... ', 'Probation and Parole Officers. ... ', 'Postsecondary Criminal Justice & Law Enforcement Teachers.']</code> | <code>1</code> |
| <code>what do you do with a degree in criminal justice?</code> | <code>['Prison officer. ... ', 'Police officer. ... ', 'Detective. ... ', 'Criminologist. ... ', 'Probation officer. ... ', 'Forensic scientist. ... ', 'Crime Scene investigator. ... ', 'Court reporter.']</code> | <code>0</code> |
| <code>what do you do with a degree in criminal justice?</code> | <code>A high school diploma is required to work as a detective. In some cases a bachelor's degree in criminal justice or law enforcement may be needed. Experience in law enforcement is usually required, but the amount varies by employer.</code> | <code>0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": 5
}
```
### 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
<details><summary>Click to expand</summary>
- `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
- `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}
- `tp_size`: 0
- `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
- `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
</details>
### 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 | - | 0.1374 (-0.4539) | 0.0392 (-0.5012) | 0.2824 (-0.0427) | 0.0371 (-0.4635) | 0.1196 (-0.3358) |
| 0.0000 | 1 | 1.3234 | - | - | - | - | - |
| 0.0277 | 1000 | 1.1925 | - | - | - | - | - |
| 0.0553 | 2000 | 0.9574 | - | - | - | - | - |
| 0.0830 | 3000 | 0.7563 | - | - | - | - | - |
| 0.1106 | 4000 | 0.7247 | 0.7171 (+0.1259) | 0.5045 (-0.0359) | 0.3093 (-0.0158) | 0.5701 (+0.0694) | 0.4613 (+0.0059) |
| 0.1383 | 5000 | 0.6744 | - | - | - | - | - |
| 0.1660 | 6000 | 0.6876 | - | - | - | - | - |
| 0.1936 | 7000 | 0.6446 | - | - | - | - | - |
| 0.2213 | 8000 | 0.6519 | 0.7319 (+0.1407) | 0.5106 (-0.0298) | 0.3346 (+0.0095) | 0.6054 (+0.1048) | 0.4835 (+0.0282) |
| 0.2490 | 9000 | 0.6339 | - | - | - | - | - |
| 0.2766 | 10000 | 0.6168 | - | - | - | - | - |
| 0.3043 | 11000 | 0.6005 | - | - | - | - | - |
| 0.3319 | 12000 | 0.6363 | 0.7463 (+0.1550) | 0.5087 (-0.0317) | 0.3075 (-0.0176) | 0.5757 (+0.0751) | 0.4640 (+0.0086) |
| 0.3596 | 13000 | 0.5882 | - | - | - | - | - |
| 0.3873 | 14000 | 0.5888 | - | - | - | - | - |
| 0.4149 | 15000 | 0.5824 | - | - | - | - | - |
| 0.4426 | 16000 | 0.5882 | 0.7487 (+0.1574) | 0.5277 (-0.0127) | 0.3215 (-0.0036) | 0.5609 (+0.0603) | 0.4701 (+0.0147) |
| 0.4702 | 17000 | 0.5661 | - | - | - | - | - |
| 0.4979 | 18000 | 0.5758 | - | - | - | - | - |
| 0.5256 | 19000 | 0.556 | - | - | - | - | - |
| 0.5532 | 20000 | 0.5524 | 0.7556 (+0.1644) | 0.5419 (+0.0014) | 0.3191 (-0.0059) | 0.6132 (+0.1125) | 0.4914 (+0.0360) |
| 0.5809 | 21000 | 0.5546 | - | - | - | - | - |
| 0.6086 | 22000 | 0.563 | - | - | - | - | - |
| 0.6362 | 23000 | 0.5369 | - | - | - | - | - |
| 0.6639 | 24000 | 0.5492 | 0.7570 (+0.1658) | 0.4779 (-0.0626) | 0.2881 (-0.0369) | 0.5079 (+0.0073) | 0.4246 (-0.0307) |
| 0.6915 | 25000 | 0.5443 | - | - | - | - | - |
| 0.7192 | 26000 | 0.5522 | - | - | - | - | - |
| 0.7469 | 27000 | 0.5323 | - | - | - | - | - |
| 0.7745 | 28000 | 0.5167 | 0.7695 (+0.1782) | 0.4941 (-0.0463) | 0.3167 (-0.0083) | 0.5194 (+0.0188) | 0.4434 (-0.0119) |
| 0.8022 | 29000 | 0.4998 | - | - | - | - | - |
| 0.8299 | 30000 | 0.5326 | - | - | - | - | - |
| 0.8575 | 31000 | 0.5262 | - | - | - | - | - |
| 0.8852 | 32000 | 0.5136 | 0.7682 (+0.1769) | 0.5004 (-0.0400) | 0.3273 (+0.0023) | 0.5318 (+0.0311) | 0.4531 (-0.0022) |
| 0.9128 | 33000 | 0.5145 | - | - | - | - | - |
| 0.9405 | 34000 | 0.5038 | - | - | - | - | - |
| 0.9682 | 35000 | 0.5236 | - | - | - | - | - |
| **0.9958** | **36000** | **0.5347** | **0.7716 (+0.1804)** | **0.5096 (-0.0309)** | **0.3176 (-0.0074)** | **0.5388 (+0.0382)** | **0.4553 (-0.0000)** |
| -1 | -1 | - | 0.7716 (+0.1804) | 0.5096 (-0.0309) | 0.3176 (-0.0074) | 0.5388 (+0.0382) | 0.4553 (-0.0000) |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.10
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.7.0+cu128
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@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",
}
```
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