sentence-transformers safetensors modernbert cross-encoder text-classification generated_from_trainer dataset_size:82326 loss:ListNetLoss en dataset:microsoft/ms_marco arxiv:1908.10084 base_model:answerdotai/ModernBERT-base base_model:finetune:answerdotai/ModernBERT-base region:us
---
base_model: answerdotai/ModernBERT-base
datasets:
- microsoft/ms_marco
language:
- en
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
pipeline_tag: text-classification
tags:
- sentence-transformers
- cross-encoder
- text-classification
- generated_from_trainer
- dataset_size:82326
- loss:ListNetLoss
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("Studeni/reranker-msmarco-v1.1-ModernBERT-base-listnet")
# Get scores for pairs of texts
pairs = [
    ['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
    ['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
    ['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (3,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'How many calories in an egg',
    [
        'There are on average between 55 and 80 calories in an egg depending on its size.',
        'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
        'Most of the calories in an egg come from the yellow yolk in the center.',
    ]
)
# [{'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

* 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         | 0.4674 (-0.0222)     | 0.3153 (+0.0449)     | 0.5727 (+0.1520)     |
| mrr@10      | 0.4580 (-0.0195)     | 0.4976 (-0.0023)     | 0.5714 (+0.1447)     |
| **ndcg@10** | **0.5335 (-0.0069)** | **0.3530 (+0.0280)** | **0.6278 (+0.1272)** |

#### 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         | 0.4518 (+0.0582)     |
| mrr@10      | 0.5090 (+0.0410)     |
| **ndcg@10** | **0.5048 (+0.0494)** |

<!--
## 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

#### 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>docs</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                                          | docs                                | labels                              |
  |:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
  | type    | string                                                                                         | list                                | list                                |
  | details | <ul><li>min: 11 characters</li><li>mean: 34.16 characters</li><li>max: 96 characters</li></ul> | <ul><li>size: 10 elements</li></ul> | <ul><li>size: 10 elements</li></ul> |
* Samples:
  | query                             | docs                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | labels                            |
  |:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
  | <code>what does a bursa do</code> | <code>['Bursae (plural for bursa) are flattened fluid-filled sacs that function as cushions between your bones and the muscles (deep bursae) or bones and tendons (superficial bursae). Your bursae play an important role in leading a healthy, active life. When the bursae are not irritated and working properly, your joints move smoothly and painlessly. However, when a bursa becomes swollen and inflamed, the condition is known as bursitis.', 'A bursa is a small, fluid-filled sac that acts as a cushion between a bone and other moving parts: muscles, tendons, or skin. Bursae are found throughout the body. Bursitis occurs when a bursa becomes inflamed (redness and increased fluid in the bursa). A tendon is a flexible band of fibrous tissue that connects muscles to bones. Tendinitis is inflammation of a tendon. Tendons transmit the pull of the muscle to the bone to cause movement.', 'A bursa (plural bursae or bursas) is a small fluid-filled sac lined by synovial membrane with an inner capillary laye...</code> | <code>[1, 1, 0, 0, 0, ...]</code> |
  | <code>what is gluten in</code>    | <code>['Gluten is a general name for the proteins found in wheat (durum, emmer, spelt, farina, farro, KAMUT® khorasan wheat and einkorn), rye, barley and triticale. Gluten helps foods maintain their shape, acting as a glue that holds food together.', 'Definition. A gluten-free diet is a diet that excludes the protein gluten. Gluten is found in grains such as wheat, barley, rye, and a cross between wheat and rye called triticale. A gluten-free diet is primarily used to treat celiac disease. Gluten causes inflammation in the small intestines of people with', 'A gluten-free diet is a diet that excludes the protein gluten. Gluten is found in grains such as wheat, barley, rye, and a cross between wheat and rye called triticale. A gluten-free diet is primarily used to treat celiac disease. Gluten causes inflammation in the small intestines of people with', 'Gluten is found in wheat, rye, barley and any foods made with these grains. Avoiding wheat can be especially hard because this means you shoul...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
  | <code>what is a payaway</code>    | <code>['Playaway is the name of a solid-state prerecorded audio player introduced in 2005 by Findaway World, LLC, based in Solon, Ohio. About the size of a deck of playing cards and weighing 2 ounces, it can store up to 80 hours of audio. As of March 2010, the audiobooks are all produced in high definition audio. The digital content (audiobook or music compilation) is preloaded at the factory and cannot be changed or copied by the end user. A 3.5 mm stereo jack provides output to earphones or an external amplifier. Playaway was specifically designed to use most commonly available cassette adaptors and FM transmitters. Power is provided by a changeable 1.5V AAA cell, which the manufacturer claims allows it to operate approximately 20 hours before battery depletion, 30 hours for the newer versions.', "Playaway Audio Books. Playaway® is the simplest way to listen to audio on the go. Each Playaway is a self-contained audiobook, weighs only two ounces, and comes with a battery. Just plug in your ...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
* Loss: [<code>ListNetLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listnetloss) with these parameters:
  ```json
  {
      "eps": 1e-10,
      "pad_value": -1
  }
  ```

### 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>docs</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                                          | docs                                | labels                              |
  |:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
  | type    | string                                                                                         | list                                | list                                |
  | details | <ul><li>min: 10 characters</li><li>mean: 34.53 characters</li><li>max: 93 characters</li></ul> | <ul><li>size: 10 elements</li></ul> | <ul><li>size: 10 elements</li></ul> |
* Samples:
  | query                                                     | docs                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | labels                            |
  |:----------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
  | <code>how long does ehic card take to arrive</code>       | <code>['The quickest way is to apply online. Your EHIC will normally arrive within seven days and will usually be valid for five years. This means at a reduced cost or sometimes free of charge. Even with an EHIC, you may have to pay towards your treatment, depending on the rules of the country you’re visiting. You may be able to claim the money back – always try to apply for a refund before you return home. Find out how to do this in the country-by-country guide for the EHIC', "What is the EHIC? The European Health Insurance Card or EHIC was introduced in 2004 across the European Union. It allows Irish residents to access health services in any EU country and in Switzerland, Iceland, Liechtenstein and Norway, if they become ill or injured while on a temporary stay in that country. No. Your card will be valid for 4 to 5 years. Check that you and your family's cards are valid before you travel, and if they have expired, it's easy to renew them online at www.ehic.ie or at your Local Health Offi...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
  | <code>what are the muscles that dorsiflex the foot</code> | <code>['Dorsiflexion of the foot uses four muscles. These are the tibialis  anterior, extensor digitorum longus, extensor hallucis longus, and  the peroneus tertius. ', 'There are four muscles in the anterior compartment of the leg; tibialis anterior, extensor digitorun longus, extensor hallucis longus and fibularis tertius. Collectively, they act to dorsiflex and invert the foot at the ankle joint. The extensor digitorum longus and extensor hallucis longus also extend the toes. The muscles in this compartment are innervated by the deep fibular nerve (L4-L5), and blood is supplied via the anterior tibial artery.', 'Many muscles do the work of moving the ankle and foot. Some of the muscles that move the foot start higher up in the leg, and smaller muscles work right in the foot itself. The leg is divided into compartments: the anterior, lateral, and posterior compartments. The muscles in these compartments help move the ankle and the foot: Anterior compartment: This compartment lies in front ...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
  | <code>What does the thailand flag mean</code>             | <code>["Thai Flag Meaning: The red stripes mean Thailand's nation. The white stands for the country's main religion. Blue is Thailand's national color and it represents the Thai monarchy/Royal Family. Hoped This helped Brought to you by: Firescream66 and the post starter. B … lue is Thailand's national color and it represents the Thai monarchy. The blue is also used to honor Thailand's World War I allies, Great Britain, France, United States and Russia, who all had red, white and blue flags.", "The red represents the blood spilled to maintain Thailand's independence. The white stands for purity and is the color of Buddhism. And the Blue represents the Thai monarchy. The pattern repeats so that the flag can be flown without ever appearing upside down. The old Siam (former name of Thailand) flag was a solid red with a white elephant in the middle.", 'The flag of the Kingdom of Thailand (Thai: ธงไตรรงค์, Thong Trairong, meaning  tricolour flag”) shows five horizontal stripes in the colours red,...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
* Loss: [<code>ListNetLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listnetloss) with these parameters:
  ```json
  {
      "eps": 1e-10,
      "pad_value": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 6
- `per_device_eval_batch_size`: 16
- `torch_empty_cache_steps`: 2000
- `learning_rate`: 4e-06
- `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`: 6
- `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`: 2000
- `learning_rate`: 4e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `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        | -             | -               | 0.0264 (-0.5140)     | 0.2585 (-0.0665)     | 0.0606 (-0.4401)     | 0.1152 (-0.3402)      |
| 0.0001     | 1         | 1.8458        | -               | -                    | -                    | -                    | -                     |
| 0.0430     | 500       | 2.1043        | -               | -                    | -                    | -                    | -                     |
| 0.0861     | 1000      | 2.0906        | -               | -                    | -                    | -                    | -                     |
| 0.1291     | 1500      | 2.0873        | -               | -                    | -                    | -                    | -                     |
| 0.1721     | 2000      | 2.0848        | 2.0847          | 0.0655 (-0.4749)     | 0.2309 (-0.0941)     | 0.1177 (-0.3830)     | 0.1380 (-0.3174)      |
| 0.2152     | 2500      | 2.0864        | -               | -                    | -                    | -                    | -                     |
| 0.2582     | 3000      | 2.0884        | -               | -                    | -                    | -                    | -                     |
| 0.3013     | 3500      | 2.0783        | -               | -                    | -                    | -                    | -                     |
| 0.3443     | 4000      | 2.0792        | 2.0791          | 0.3223 (-0.2181)     | 0.3229 (-0.0021)     | 0.2919 (-0.2088)     | 0.3124 (-0.1430)      |
| 0.3873     | 4500      | 2.0817        | -               | -                    | -                    | -                    | -                     |
| 0.4304     | 5000      | 2.0828        | -               | -                    | -                    | -                    | -                     |
| 0.4734     | 5500      | 2.0785        | -               | -                    | -                    | -                    | -                     |
| 0.5164     | 6000      | 2.0751        | 2.0740          | 0.4743 (-0.0661)     | 0.3450 (+0.0200)     | 0.5233 (+0.0226)     | 0.4475 (-0.0078)      |
| 0.5595     | 6500      | 2.0719        | -               | -                    | -                    | -                    | -                     |
| 0.6025     | 7000      | 2.0726        | -               | -                    | -                    | -                    | -                     |
| 0.6456     | 7500      | 2.0734        | -               | -                    | -                    | -                    | -                     |
| 0.6886     | 8000      | 2.0769        | 2.0722          | 0.5006 (-0.0398)     | 0.3449 (+0.0198)     | 0.4920 (-0.0087)     | 0.4458 (-0.0095)      |
| 0.7316     | 8500      | 2.0722        | -               | -                    | -                    | -                    | -                     |
| 0.7747     | 9000      | 2.0669        | -               | -                    | -                    | -                    | -                     |
| 0.8177     | 9500      | 2.0787        | -               | -                    | -                    | -                    | -                     |
| 0.8607     | 10000     | 2.0661        | 2.0710          | 0.5646 (+0.0242)     | 0.3363 (+0.0113)     | 0.5672 (+0.0666)     | 0.4894 (+0.0340)      |
| 0.9038     | 10500     | 2.0754        | -               | -                    | -                    | -                    | -                     |
| 0.9468     | 11000     | 2.0717        | -               | -                    | -                    | -                    | -                     |
| 0.9898     | 11500     | 2.0779        | -               | -                    | -                    | -                    | -                     |
| 1.0329     | 12000     | 2.0703        | 2.0706          | 0.5609 (+0.0205)     | 0.3107 (-0.0144)     | 0.5817 (+0.0811)     | 0.4844 (+0.0291)      |
| 1.0759     | 12500     | 2.0692        | -               | -                    | -                    | -                    | -                     |
| 1.1190     | 13000     | 2.0665        | -               | -                    | -                    | -                    | -                     |
| 1.1620     | 13500     | 2.0801        | -               | -                    | -                    | -                    | -                     |
| 1.2050     | 14000     | 2.0723        | 2.0702          | 0.5413 (+0.0009)     | 0.3249 (-0.0001)     | 0.5961 (+0.0954)     | 0.4874 (+0.0321)      |
| 1.2481     | 14500     | 2.0707        | -               | -                    | -                    | -                    | -                     |
| 1.2911     | 15000     | 2.0715        | -               | -                    | -                    | -                    | -                     |
| 1.3341     | 15500     | 2.0664        | -               | -                    | -                    | -                    | -                     |
| 1.3772     | 16000     | 2.0736        | 2.0700          | 0.5234 (-0.0171)     | 0.3314 (+0.0064)     | 0.6068 (+0.1061)     | 0.4872 (+0.0318)      |
| 1.4202     | 16500     | 2.0733        | -               | -                    | -                    | -                    | -                     |
| 1.4632     | 17000     | 2.0728        | -               | -                    | -                    | -                    | -                     |
| 1.5063     | 17500     | 2.068         | -               | -                    | -                    | -                    | -                     |
| **1.5493** | **18000** | **2.0669**    | **2.0699**      | **0.5335 (-0.0069)** | **0.3530 (+0.0280)** | **0.6278 (+0.1272)** | **0.5048 (+0.0494)**  |
| 1.5924     | 18500     | 2.0713        | -               | -                    | -                    | -                    | -                     |
| 1.6354     | 19000     | 2.0689        | -               | -                    | -                    | -                    | -                     |
| 1.6784     | 19500     | 2.07          | -               | -                    | -                    | -                    | -                     |
| 1.7215     | 20000     | 2.0731        | 2.0696          | 0.5365 (-0.0039)     | 0.3497 (+0.0247)     | 0.5845 (+0.0838)     | 0.4902 (+0.0349)      |
| 1.7645     | 20500     | 2.0678        | -               | -                    | -                    | -                    | -                     |
| 1.8075     | 21000     | 2.0646        | -               | -                    | -                    | -                    | -                     |
| 1.8506     | 21500     | 2.0631        | -               | -                    | -                    | -                    | -                     |
| 1.8936     | 22000     | 2.0714        | 2.0694          | 0.5340 (-0.0064)     | 0.3490 (+0.0239)     | 0.5653 (+0.0646)     | 0.4828 (+0.0274)      |
| 1.9367     | 22500     | 2.059         | -               | -                    | -                    | -                    | -                     |
| 1.9797     | 23000     | 2.068         | -               | -                    | -                    | -                    | -                     |
| 2.0227     | 23500     | 2.0664        | -               | -                    | -                    | -                    | -                     |
| 2.0658     | 24000     | 2.0719        | 2.0699          | 0.5442 (+0.0038)     | 0.3531 (+0.0281)     | 0.5879 (+0.0873)     | 0.4951 (+0.0397)      |
| 2.1088     | 24500     | 2.0621        | -               | -                    | -                    | -                    | -                     |
| 2.1518     | 25000     | 2.0669        | -               | -                    | -                    | -                    | -                     |
| 2.1949     | 25500     | 2.067         | -               | -                    | -                    | -                    | -                     |
| 2.2379     | 26000     | 2.0676        | 2.0700          | 0.5449 (+0.0044)     | 0.3334 (+0.0084)     | 0.5656 (+0.0649)     | 0.4813 (+0.0259)      |
| 2.2809     | 26500     | 2.0621        | -               | -                    | -                    | -                    | -                     |
| 2.3240     | 27000     | 2.0634        | -               | -                    | -                    | -                    | -                     |
| 2.3670     | 27500     | 2.065         | -               | -                    | -                    | -                    | -                     |
| 2.4101     | 28000     | 2.0669        | 2.0704          | 0.5128 (-0.0276)     | 0.3495 (+0.0244)     | 0.5751 (+0.0744)     | 0.4791 (+0.0237)      |
| 2.4531     | 28500     | 2.0636        | -               | -                    | -                    | -                    | -                     |
| 2.4961     | 29000     | 2.0623        | -               | -                    | -                    | -                    | -                     |
| 2.5392     | 29500     | 2.0669        | -               | -                    | -                    | -                    | -                     |
| 2.5822     | 30000     | 2.0615        | 2.0698          | 0.5448 (+0.0044)     | 0.3406 (+0.0156)     | 0.5768 (+0.0762)     | 0.4874 (+0.0321)      |
| 2.6252     | 30500     | 2.0708        | -               | -                    | -                    | -                    | -                     |
| 2.6683     | 31000     | 2.0655        | -               | -                    | -                    | -                    | -                     |
| 2.7113     | 31500     | 2.0511        | -               | -                    | -                    | -                    | -                     |
| 2.7543     | 32000     | 2.0623        | 2.0699          | 0.5377 (-0.0027)     | 0.3505 (+0.0255)     | 0.5854 (+0.0847)     | 0.4912 (+0.0358)      |
| 2.7974     | 32500     | 2.0651        | -               | -                    | -                    | -                    | -                     |
| 2.8404     | 33000     | 2.0675        | -               | -                    | -                    | -                    | -                     |
| 2.8835     | 33500     | 2.0689        | -               | -                    | -                    | -                    | -                     |
| 2.9265     | 34000     | 2.067         | 2.0699          | 0.5221 (-0.0184)     | 0.3605 (+0.0354)     | 0.5695 (+0.0688)     | 0.4840 (+0.0286)      |
| 2.9695     | 34500     | 2.0634        | -               | -                    | -                    | -                    | -                     |
| -1         | -1        | -             | -               | 0.5335 (-0.0069)     | 0.3530 (+0.0280)     | 0.6278 (+0.1272)     | 0.5048 (+0.0494)      |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- 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",
}
```

#### ListNetLoss
```bibtex
@inproceedings{cao2007learning,
    title={Learning to rank: from pairwise approach to listwise approach},
    author={Cao, Zhe and Qin, Tao and Liu, Tie-Yan and Tsai, Ming-Feng and Li, Hang},
    booktitle={Proceedings of the 24th international conference on Machine learning},
    pages={129--136},
    year={2007}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->