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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- cross-encoder
- generated_from_trainer
- dataset_size:577957
- 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: 0.7234
name: Map
- type: mrr@10
value: 0.7223
name: Mrr@10
- type: ndcg@10
value: 0.7676
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R100
type: NanoMSMARCO_R100
metrics:
- type: map
value: 0.4711
name: Map
- type: mrr@10
value: 0.4565
name: Mrr@10
- type: ndcg@10
value: 0.5342
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNFCorpus R100
type: NanoNFCorpus_R100
metrics:
- type: map
value: 0.3601
name: Map
- type: mrr@10
value: 0.5969
name: Mrr@10
- type: ndcg@10
value: 0.425
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R100
type: NanoNQ_R100
metrics:
- type: map
value: 0.6047
name: Map
- type: mrr@10
value: 0.6064
name: Mrr@10
- type: ndcg@10
value: 0.6652
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: 0.4786
name: Map
- type: mrr@10
value: 0.5533
name: Mrr@10
- type: ndcg@10
value: 0.5415
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("tomaarsen/reranker-ModernBERT-base-gooaq-bce-0margin-3min-100max-5top")
# Get scores for pairs of texts
pairs = [
['what is baking powder bicarbonate soda?', 'Baking soda and bicarbonate of soda are actually different names for the same thing. ... Both bicarbonate of soda and baking powder are leavening (raising) agents. When included in a batter, the leavening agent creates air bubbles that expand when cooked, and cause it to rise.'],
['what is baking powder bicarbonate soda?', "What is baking soda? Baking soda is a leavening agent used in baked goods like cakes, muffins, and cookies. Formally known as sodium bicarbonate, it's a white crystalline powder that is naturally alkaline, or basic (1). Baking soda becomes activated when it's combined with both an acidic ingredient and a liquid."],
['what is baking powder bicarbonate soda?', 'The chemical name for baking powder is sodium hydrogencarbonate. You may see it called bicarbonate of soda in the supermarket. This is the old name for the same stuff. It has the chemical formula NaHCO3.'],
['what is baking powder bicarbonate soda?', "Substituting baking soda for baking powder What's more, baking soda has much stronger leavening power than baking powder. As a rule of thumb, about 1 teaspoon of baking powder is equivalent to 1/4 teaspoon of baking soda."],
['what is baking powder bicarbonate soda?', "Baking soda is a leavening agent used in baked goods like cakes, muffins, and cookies. Formally known as sodium bicarbonate, it's a white crystalline powder that is naturally alkaline, or basic (1). Baking soda becomes activated when it's combined with both an acidic ingredient and a liquid."],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'what is baking powder bicarbonate soda?',
[
'Baking soda and bicarbonate of soda are actually different names for the same thing. ... Both bicarbonate of soda and baking powder are leavening (raising) agents. When included in a batter, the leavening agent creates air bubbles that expand when cooked, and cause it to rise.',
"What is baking soda? Baking soda is a leavening agent used in baked goods like cakes, muffins, and cookies. Formally known as sodium bicarbonate, it's a white crystalline powder that is naturally alkaline, or basic (1). Baking soda becomes activated when it's combined with both an acidic ingredient and a liquid.",
'The chemical name for baking powder is sodium hydrogencarbonate. You may see it called bicarbonate of soda in the supermarket. This is the old name for the same stuff. It has the chemical formula NaHCO3.',
"Substituting baking soda for baking powder What's more, baking soda has much stronger leavening power than baking powder. As a rule of thumb, about 1 teaspoon of baking powder is equivalent to 1/4 teaspoon of baking soda.",
"Baking soda is a leavening agent used in baked goods like cakes, muffins, and cookies. Formally known as sodium bicarbonate, it's a white crystalline powder that is naturally alkaline, or basic (1). Baking soda becomes activated when it's combined with both an acidic ingredient and a liquid.",
]
)
# [{'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>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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 | 0.7234 (+0.1923) |
| mrr@10 | 0.7223 (+0.1984) |
| **ndcg@10** | **0.7676 (+0.1764)** |
#### 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 | 0.4711 (-0.0185) | 0.3601 (+0.0992) | 0.6047 (+0.1851) |
| mrr@10 | 0.4565 (-0.0210) | 0.5969 (+0.0971) | 0.6064 (+0.1797) |
| **ndcg@10** | **0.5342 (-0.0062)** | **0.4250 (+0.0999)** | **0.6652 (+0.1646)** |
#### 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 | 0.4786 (+0.0886) |
| mrr@10 | 0.5533 (+0.0853) |
| **ndcg@10** | **0.5415 (+0.0861)** |
<!--
## 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.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 577,957 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: 21 characters</li><li>mean: 42.64 characters</li><li>max: 76 characters</li></ul> | <ul><li>min: 54 characters</li><li>mean: 250.97 characters</li><li>max: 376 characters</li></ul> | <ul><li>0: ~83.00%</li><li>1: ~17.00%</li></ul> |
* Samples:
| question | answer | label |
|:-----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>what is baking powder bicarbonate soda?</code> | <code>Baking soda and bicarbonate of soda are actually different names for the same thing. ... Both bicarbonate of soda and baking powder are leavening (raising) agents. When included in a batter, the leavening agent creates air bubbles that expand when cooked, and cause it to rise.</code> | <code>1</code> |
| <code>what is baking powder bicarbonate soda?</code> | <code>What is baking soda? Baking soda is a leavening agent used in baked goods like cakes, muffins, and cookies. Formally known as sodium bicarbonate, it's a white crystalline powder that is naturally alkaline, or basic (1). Baking soda becomes activated when it's combined with both an acidic ingredient and a liquid.</code> | <code>0</code> |
| <code>what is baking powder bicarbonate soda?</code> | <code>The chemical name for baking powder is sodium hydrogencarbonate. You may see it called bicarbonate of soda in the supermarket. This is the old name for the same stuff. It has the chemical formula NaHCO3.</code> | <code>0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"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
<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`: 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
</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 | - | 0.1293 (-0.4619) | 0.0284 (-0.5121) | 0.2145 (-0.1105) | 0.0134 (-0.4872) | 0.0854 (-0.3699) |
| 0.0001 | 1 | 1.2576 | - | - | - | - | - |
| 0.0221 | 200 | 1.2027 | - | - | - | - | - |
| 0.0443 | 400 | 1.1352 | - | - | - | - | - |
| 0.0664 | 600 | 0.7686 | - | - | - | - | - |
| 0.0886 | 800 | 0.6163 | - | - | - | - | - |
| 0.1107 | 1000 | 0.5764 | 0.7162 (+0.1250) | 0.4924 (-0.0480) | 0.3647 (+0.0396) | 0.6409 (+0.1403) | 0.4993 (+0.0440) |
| 0.1329 | 1200 | 0.5488 | - | - | - | - | - |
| 0.1550 | 1400 | 0.525 | - | - | - | - | - |
| 0.1772 | 1600 | 0.4987 | - | - | - | - | - |
| 0.1993 | 1800 | 0.4943 | - | - | - | - | - |
| 0.2215 | 2000 | 0.4777 | 0.7508 (+0.1596) | 0.5672 (+0.0268) | 0.3969 (+0.0718) | 0.6236 (+0.1230) | 0.5292 (+0.0739) |
| 0.2436 | 2200 | 0.4487 | - | - | - | - | - |
| 0.2658 | 2400 | 0.4582 | - | - | - | - | - |
| 0.2879 | 2600 | 0.4473 | - | - | - | - | - |
| 0.3100 | 2800 | 0.4266 | - | - | - | - | - |
| 0.3322 | 3000 | 0.4374 | 0.7478 (+0.1565) | 0.5851 (+0.0446) | 0.3863 (+0.0613) | 0.6684 (+0.1678) | 0.5466 (+0.0912) |
| 0.3543 | 3200 | 0.421 | - | - | - | - | - |
| 0.3765 | 3400 | 0.4317 | - | - | - | - | - |
| 0.3986 | 3600 | 0.4206 | - | - | - | - | - |
| 0.4208 | 3800 | 0.417 | - | - | - | - | - |
| 0.4429 | 4000 | 0.4113 | 0.7577 (+0.1665) | 0.5611 (+0.0207) | 0.3973 (+0.0722) | 0.6564 (+0.1557) | 0.5382 (+0.0829) |
| 0.4651 | 4200 | 0.4008 | - | - | - | - | - |
| 0.4872 | 4400 | 0.3884 | - | - | - | - | - |
| 0.5094 | 4600 | 0.4136 | - | - | - | - | - |
| 0.5315 | 4800 | 0.389 | - | - | - | - | - |
| 0.5536 | 5000 | 0.3877 | 0.7609 (+0.1697) | 0.5509 (+0.0104) | 0.3878 (+0.0627) | 0.6807 (+0.1800) | 0.5398 (+0.0844) |
| 0.5758 | 5200 | 0.3901 | - | - | - | - | - |
| 0.5979 | 5400 | 0.389 | - | - | - | - | - |
| 0.6201 | 5600 | 0.3999 | - | - | - | - | - |
| 0.6422 | 5800 | 0.3703 | - | - | - | - | - |
| 0.6644 | 6000 | 0.3854 | 0.7620 (+0.1708) | 0.5444 (+0.0039) | 0.4040 (+0.0790) | 0.6917 (+0.1911) | 0.5467 (+0.0913) |
| 0.6865 | 6200 | 0.3685 | - | - | - | - | - |
| 0.7087 | 6400 | 0.3751 | - | - | - | - | - |
| 0.7308 | 6600 | 0.3709 | - | - | - | - | - |
| 0.7530 | 6800 | 0.3788 | - | - | - | - | - |
| 0.7751 | 7000 | 0.3734 | 0.7672 (+0.1760) | 0.5404 (+0.0000) | 0.4075 (+0.0824) | 0.6638 (+0.1632) | 0.5372 (+0.0819) |
| 0.7973 | 7200 | 0.3629 | - | - | - | - | - |
| 0.8194 | 7400 | 0.3547 | - | - | - | - | - |
| 0.8415 | 7600 | 0.3639 | - | - | - | - | - |
| 0.8637 | 7800 | 0.3597 | - | - | - | - | - |
| **0.8858** | **8000** | **0.3522** | **0.7676 (+0.1764)** | **0.5342 (-0.0062)** | **0.4250 (+0.0999)** | **0.6652 (+0.1646)** | **0.5415 (+0.0861)** |
| 0.9080 | 8200 | 0.327 | - | - | - | - | - |
| 0.9301 | 8400 | 0.344 | - | - | - | - | - |
| 0.9523 | 8600 | 0.3578 | - | - | - | - | - |
| 0.9744 | 8800 | 0.3547 | - | - | - | - | - |
| 0.9966 | 9000 | 0.3491 | 0.7675 (+0.1763) | 0.5423 (+0.0019) | 0.4188 (+0.0937) | 0.6621 (+0.1614) | 0.5411 (+0.0857) |
| -1 | -1 | - | 0.7676 (+0.1764) | 0.5342 (-0.0062) | 0.4250 (+0.0999) | 0.6652 (+0.1646) | 0.5415 (+0.0861) |
* 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: 1.2.0
- Datasets: 2.21.0
- Tokenizers: 0.21.0
## 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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