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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: 0.7308
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](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.
See [training_gooaq_bce.py](https://github.com/UKPLab/sentence-transformers/blob/feat/cross_encoder_trainer/examples/cross_encoder/training/rerankers/training_gooaq_bce.py) for the training script. This script is also described in the [Cross Encoder > Training Overview](https://sbert.net/docs/cross_encoder/training_overview.html) documentation and the [Training and Finetuning Reranker Models with Sentence Transformers v4](https://huggingface.co/blog/train-reranker) blogpost.
## 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")
# 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': ...}, ...]
```
<!--
### 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 | 0.7308 (+0.1997) |
| mrr@10 | 0.7292 (+0.2052) |
| **ndcg@10** | **0.7713 (+0.1801)** |
#### 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": 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 [<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 | 0.4579 (-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 [<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 | 0.3975 (+0.0074) |
| mrr@10 | 0.4579 (-0.0101) |
| **ndcg@10** | **0.4575 (+0.0022)** |
<!--
## 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: 19 characters</li><li>mean: 45.14 characters</li><li>max: 85 characters</li></ul> | <ul><li>min: 65 characters</li><li>mean: 254.8 characters</li><li>max: 379 characters</li></ul> | <ul><li>0: ~82.90%</li><li>1: ~17.10%</li></ul> |
* Samples:
| question | answer | label |
|:----------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>why are rye chips so good?</code> | <code>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.</code> | <code>1</code> |
| <code>why are rye chips so good?</code> | <code>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.</code> | <code>0</code> |
| <code>why are rye chips so good?</code> | <code>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.</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 | - | 0.1288 (-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
```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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