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---
language:
- en
tags:
- sentence-transformers
- cross-encoder
- text-classification
- generated_from_trainer
- dataset_size:82326
- loss:BinaryCrossEntropyLoss
base_model: answerdotai/ModernBERT-base
datasets:
- microsoft/ms_marco
pipeline_tag: text-classification
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
model-index:
- name: CrossEncoder based on answerdotai/ModernBERT-base
results: []
---
# CrossEncoder based on answerdotai/ModernBERT-base
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) on the [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## 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:**
- [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco)
- **Language:** en
<!-- - **License:** Unknown -->
### 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("sentence_transformers_model_id")
# Get scores for pairs...
pairs = [
['what popular gemstone sounds like peridotite', 'Peridot (pronounced pair-a-doe) is the gem variety of olivine. Olivine, which is actually not an official mineral, is composed of two minerals: fayalite and forsterite. Fayalite is the iron rich member with a pure formula of Fe2SiO4. Forsterite is the magnesium rich member with a pure formula of Mg2SiO4.'],
['how much does it cost to keep an inmate in jail', 'The cost of each prison varies, depending on the types of inmates who are housed there. For example, it costs $99.12 a day to house an inmate at a reception center, because the inmates residing there are being evaluated and tested medically, psychologically, academically, vocationally, etc. In contrast, a typical adult male facility costs just $44.96 per day to house an inmate (excluding private prisons). When you average all types of state prison facilities together like those listed in the chart below, the daily cost to house an inmate is $55.09.'],
['what process is found in both cellular respiration and photosynthesis', 'Cellular respiration takes place in the same way in both plants and animals. Living cells obtain the products of photosynthesis (sugar molecules) and undergo cellular respiration to produce ATP molecules. Some cells respire aerobically, using oxygen, while others undergo anaerobic respiration, without using oxygen. ☞ Cellular respiration takes place in the cytoplasm and mitochondria of the cell. ☛ Photosynthesis uses water, sunlight, and CO 2 from the atmosphere to create glucose molecules, and releases oxygen as a by-product.'],
["how to keep someone at arm's length", "keep at arm's length from someone or something. Fig. to retain a degree of physical or social remoteness from someone or something. I try to keep at arm's length from Larry, since our disagreement. I keep Tom at arm's length because we don't get along. 1 keep someone or something around. 2 keep someone or something at arm's length. 3 keep someone or something at bay. 4 keep someone or something hanging. 5 keep someone or something off. 6 keep someone or something on track. 7 keep someone or something under control"],
['which is a characteristic of something in the domain bacteria', 'Archaea were only shown to be a separate domain—through analysis of their RNA—in 1977. Many archaea thrive under the extreme conditions of hot sulfur pools or in minerals and rock deep inside the Earth. '],
]
scores = model.predict(pairs)
print(scores.shape)
# [5]
# ... or rank different texts based on similarity to a single text
ranks = model.rank(
'what popular gemstone sounds like peridotite',
[
'Peridot (pronounced pair-a-doe) is the gem variety of olivine. Olivine, which is actually not an official mineral, is composed of two minerals: fayalite and forsterite. Fayalite is the iron rich member with a pure formula of Fe2SiO4. Forsterite is the magnesium rich member with a pure formula of Mg2SiO4.',
'The cost of each prison varies, depending on the types of inmates who are housed there. For example, it costs $99.12 a day to house an inmate at a reception center, because the inmates residing there are being evaluated and tested medically, psychologically, academically, vocationally, etc. In contrast, a typical adult male facility costs just $44.96 per day to house an inmate (excluding private prisons). When you average all types of state prison facilities together like those listed in the chart below, the daily cost to house an inmate is $55.09.',
'Cellular respiration takes place in the same way in both plants and animals. Living cells obtain the products of photosynthesis (sugar molecules) and undergo cellular respiration to produce ATP molecules. Some cells respire aerobically, using oxygen, while others undergo anaerobic respiration, without using oxygen. ☞ Cellular respiration takes place in the cytoplasm and mitochondria of the cell. ☛ Photosynthesis uses water, sunlight, and CO 2 from the atmosphere to create glucose molecules, and releases oxygen as a by-product.',
"keep at arm's length from someone or something. Fig. to retain a degree of physical or social remoteness from someone or something. I try to keep at arm's length from Larry, since our disagreement. I keep Tom at arm's length because we don't get along. 1 keep someone or something around. 2 keep someone or something at arm's length. 3 keep someone or something at bay. 4 keep someone or something hanging. 5 keep someone or something off. 6 keep someone or something on track. 7 keep someone or something under control",
'Archaea were only shown to be a separate domain—through analysis of their RNA—in 1977. Many archaea thrive under the extreme conditions of hot sulfur pools or in minerals and rock deep inside the Earth. ',
]
)
# [{'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
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## Evaluation
### Metrics
#### Cross Encoder Reranking
* Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ`
* Evaluated with [<code>CERerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CERerankingEvaluator)
| Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ |
|:------------|:---------------------|:---------------------|:---------------------|
| map | 0.4939 (+0.0044) | 0.3115 (+0.0411) | 0.5612 (+0.1405) |
| mrr@10 | 0.4840 (+0.0065) | 0.4840 (-0.0158) | 0.5607 (+0.1340) |
| **ndcg@10** | **0.5549 (+0.0145)** | **0.3503 (+0.0252)** | **0.6091 (+0.1085)** |
#### Cross Encoder Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>CENanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CENanoBEIREvaluator)
| Metric | Value |
|:------------|:---------------------|
| map | 0.4555 (+0.0620) |
| mrr@10 | 0.5096 (+0.0416) |
| **ndcg@10** | **0.5048 (+0.0494)** |
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*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
#### ms_marco
* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
* Size: 82,326 training samples
* Columns: <code>query</code>, <code>passage</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | passage | label |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 11 characters</li><li>mean: 34.37 characters</li><li>max: 99 characters</li></ul> | <ul><li>min: 61 characters</li><li>mean: 415.74 characters</li><li>max: 949 characters</li></ul> | <ul><li>0: ~87.50%</li><li>1: ~12.50%</li></ul> |
* Samples:
| query | passage | label |
|:-------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>honor meaning</code> | <code>Honor is also employed to signify integrity in a judge, courage in a soldier, and chastity in a woman. To deprive a woman of her honor is, in some cases, punished as a public wrong, and by an action for the recovery of damages done to the relative rights of a husband or a father. Vide Criminal conversation. 2.</code> | <code>0</code> |
| <code>what happens if xmas day falls on weekend</code> | <code>1 If the holiday falls on a Saturday or Sunday and that day would not otherwise be a working day for the employee, the holiday is transferred to the following Monday or Tuesday so that the employee still gets a paid day off if the employee would usually work on these days.</code> | <code>0</code> |
| <code>what is the longest recorded flight time of a chicken</code> | <code>According to several chicken trivia sites like Feathersite, Poultryhelp and also including the Guinness World Book of World records, the longest verified flight of a domestic … chicken is 13 seconds and 301.5 feet distance.</code> | <code>0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss)
### Evaluation Dataset
#### ms_marco
* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
* Size: 82,326 evaluation samples
* Columns: <code>query</code>, <code>passage</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | passage | label |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 11 characters</li><li>mean: 34.76 characters</li><li>max: 99 characters</li></ul> | <ul><li>min: 92 characters</li><li>mean: 420.95 characters</li><li>max: 980 characters</li></ul> | <ul><li>0: ~85.50%</li><li>1: ~14.50%</li></ul> |
* Samples:
| query | passage | label |
|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>what popular gemstone sounds like peridotite</code> | <code>Peridot (pronounced pair-a-doe) is the gem variety of olivine. Olivine, which is actually not an official mineral, is composed of two minerals: fayalite and forsterite. Fayalite is the iron rich member with a pure formula of Fe2SiO4. Forsterite is the magnesium rich member with a pure formula of Mg2SiO4.</code> | <code>0</code> |
| <code>how much does it cost to keep an inmate in jail</code> | <code>The cost of each prison varies, depending on the types of inmates who are housed there. For example, it costs $99.12 a day to house an inmate at a reception center, because the inmates residing there are being evaluated and tested medically, psychologically, academically, vocationally, etc. In contrast, a typical adult male facility costs just $44.96 per day to house an inmate (excluding private prisons). When you average all types of state prison facilities together like those listed in the chart below, the daily cost to house an inmate is $55.09.</code> | <code>0</code> |
| <code>what process is found in both cellular respiration and photosynthesis</code> | <code>Cellular respiration takes place in the same way in both plants and animals. Living cells obtain the products of photosynthesis (sugar molecules) and undergo cellular respiration to produce ATP molecules. Some cells respire aerobically, using oxygen, while others undergo anaerobic respiration, without using oxygen. ☞ Cellular respiration takes place in the cytoplasm and mitochondria of the cell. ☛ Photosynthesis uses water, sunlight, and CO 2 from the atmosphere to create glucose molecules, and releases oxygen as a by-product.</code> | <code>0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 0
- `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 | Validation Loss | NanoMSMARCO_ndcg@10 | NanoNFCorpus_ndcg@10 | NanoNQ_ndcg@10 | NanoBEIR_mean_ndcg@10 |
|:----------:|:--------:|:-------------:|:---------------:|:--------------------:|:--------------------:|:--------------------:|:---------------------:|
| -1 | -1 | - | - | 0.0381 (-0.5023) | 0.3036 (-0.0214) | 0.0388 (-0.4619) | 0.1268 (-0.3285) |
| 0.0002 | 1 | 0.8716 | - | - | - | - | - |
| 0.0384 | 200 | 0.4492 | - | - | - | - | - |
| 0.0768 | 400 | 0.382 | - | - | - | - | - |
| 0.1153 | 600 | 0.3863 | - | - | - | - | - |
| 0.1537 | 800 | 0.3776 | - | - | - | - | - |
| 0.1921 | 1000 | 0.364 | 0.3614 | 0.4437 (-0.0967) | 0.2752 (-0.0498) | 0.5125 (+0.0118) | 0.4105 (-0.0449) |
| 0.2305 | 1200 | 0.3529 | - | - | - | - | - |
| 0.2690 | 1400 | 0.3491 | - | - | - | - | - |
| 0.3074 | 1600 | 0.346 | - | - | - | - | - |
| 0.3458 | 1800 | 0.3411 | - | - | - | - | - |
| 0.3842 | 2000 | 0.3456 | 0.3465 | 0.5523 (+0.0119) | 0.3359 (+0.0109) | 0.5412 (+0.0406) | 0.4765 (+0.0211) |
| 0.4227 | 2200 | 0.3476 | - | - | - | - | - |
| 0.4611 | 2400 | 0.3391 | - | - | - | - | - |
| 0.4995 | 2600 | 0.3392 | - | - | - | - | - |
| 0.5379 | 2800 | 0.3355 | - | - | - | - | - |
| 0.5764 | 3000 | 0.349 | 0.3417 | 0.5289 (-0.0115) | 0.3129 (-0.0122) | 0.5651 (+0.0644) | 0.4690 (+0.0136) |
| 0.6148 | 3200 | 0.3342 | - | - | - | - | - |
| 0.6532 | 3400 | 0.3352 | - | - | - | - | - |
| 0.6916 | 3600 | 0.341 | - | - | - | - | - |
| 0.7301 | 3800 | 0.3383 | - | - | - | - | - |
| **0.7685** | **4000** | **0.3353** | **0.3364** | **0.5549 (+0.0145)** | **0.3503 (+0.0252)** | **0.6091 (+0.1085)** | **0.5048 (+0.0494)** |
| 0.8069 | 4200 | 0.3339 | - | - | - | - | - |
| 0.8453 | 4400 | 0.3275 | - | - | - | - | - |
| 0.8838 | 4600 | 0.3283 | - | - | - | - | - |
| 0.9222 | 4800 | 0.3293 | - | - | - | - | - |
| 0.9606 | 5000 | 0.3368 | 0.3351 | 0.5314 (-0.0090) | 0.3221 (-0.0030) | 0.5952 (+0.0946) | 0.4829 (+0.0275) |
| 0.9990 | 5200 | 0.3298 | - | - | - | - | - |
| -1 | -1 | - | - | 0.5549 (+0.0145) | 0.3503 (+0.0252) | 0.6091 (+0.1085) | 0.5048 (+0.0494) |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0.dev0
- PyTorch: 2.6.0.dev20241112+cu121
- Accelerate: 1.2.0
- Datasets: 3.2.0
- 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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