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reranker-ModernBERT-base-gooaq-bce-soft-negs

Text Ranking
sentence-transformers
Safetensors
English
modernbert
cross-encoder
Generated from Trainer
dataset_size:482388
loss:BinaryCrossEntropyLoss
Eval Results
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 ---
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](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-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': ...}, ...]
```
<!--
### 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 | 0.7089 (+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 [<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 | 0.5540 (+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 [<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 | 0.5091 (+0.1190) |
| mrr@10 | 0.5712 (+0.1032) |
| **ndcg@10** | **0.5645 (+0.1092)** |
<!--
## 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: 482,388 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: 17 characters</li><li>mean: 43.7 characters</li><li>max: 91 characters</li></ul> | <ul><li>min: 53 characters</li><li>mean: 250.44 characters</li><li>max: 393 characters</li></ul> | <ul><li>0: ~79.30%</li><li>1: ~20.70%</li></ul> |
* Samples:
| question | answer | label |
|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>what is the difference between ground level ozone and the ozone layer?</code> | <code>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.</code> | <code>1</code> |
| <code>what is the difference between ground level ozone and the ozone layer?</code> | <code>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.</code> | <code>0</code> |
| <code>what is the difference between ground level ozone and the ozone layer?</code> | <code>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.</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 | - | 0.1488 (-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
```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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