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---
language:
- en
tags:
- sentence-transformers
- cross-encoder
- generated_from_trainer
- dataset_size:399282
- loss:LambdaLoss
base_model: answerdotai/ModernBERT-base
datasets:
- sentence-transformers/msmarco
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
model-index:
- name: CrossEncoder based on answerdotai/ModernBERT-base
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R100
type: NanoMSMARCO_R100
metrics:
- type: map
value: 0.6768
name: Map
- type: mrr@10
value: 0.669
name: Mrr@10
- type: ndcg@10
value: 0.7251
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNFCorpus R100
type: NanoNFCorpus_R100
metrics:
- type: map
value: 0.3576
name: Map
- type: mrr@10
value: 0.5819
name: Mrr@10
- type: ndcg@10
value: 0.4143
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R100
type: NanoNQ_R100
metrics:
- type: map
value: 0.7134
name: Map
- type: mrr@10
value: 0.7402
name: Mrr@10
- type: ndcg@10
value: 0.7594
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: 0.5826
name: Map
- type: mrr@10
value: 0.6637
name: Mrr@10
- type: ndcg@10
value: 0.6329
name: Ndcg@10
license: apache-2.0
---
# 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 [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset 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:**
- [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco)
- **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("tomaarsen/reranker-msmarco-ModernBERT-base-lambdaloss")
# 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_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 | 0.6768 (+0.1872) | 0.3576 (+0.0966) | 0.7134 (+0.2938) |
| mrr@10 | 0.6690 (+0.1915) | 0.5819 (+0.0820) | 0.7402 (+0.3135) |
| **ndcg@10** | **0.7251 (+0.1847)** | **0.4143 (+0.0892)** | **0.7594 (+0.2587)** |
#### 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 | 0.5826 (+0.1925) |
| mrr@10 | 0.6637 (+0.1957) |
| **ndcg@10** | **0.6329 (+0.1776)** |
<!--
## 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
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [a0537b6](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/a0537b6c8669051b215b020183c276a1eb2027d5)
* Size: 399,282 training samples
* Columns: <code>query_id</code>, <code>doc_ids</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | doc_ids | labels |
|:--------|:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 6 characters</li><li>mean: 33.0 characters</li><li>max: 154 characters</li></ul> | <ul><li>min: 6 elements</li><li>mean: 13.23 elements</li><li>max: 20 elements</li></ul> | <ul><li>min: 6 elements</li><li>mean: 13.23 elements</li><li>max: 20 elements</li></ul> |
* Samples:
| query_id | doc_ids | labels |
|:-----------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
| <code>intel current gen core processors</code> | <code>["Identical or more capable versions of Core processors are also sold as Xeon processors for the server and workstation markets. As of 2017 the current lineup of Core processors included the Intel Core i7, Intel Core i5, and Intel Core i3, along with the Y - Series Intel Core CPU's.", "Most noticeably that Panasonic switched from Intel Core 2 Duo power to the latest Intel Core i3 and i5 processors. The three processors available in the new Toughbook 31, together with the new Mobile Intel QM57 Express chipset, are all part of Intel's Calpella platform.", 'The new 7th Gen Intel Core i7-7700HQ processor gives the 14-inch Razer Blade 2.8GHz of quad-core processing power and Turbo Boost speeds, which automatically increases the speed of active cores â\x80\x93 up to 3.8GHz.', 'Key difference: Intel Core i3 is a type of dual-core processor. i5 processors have 2 to 4 cores. A dual-core processor is a type of a central processing unit (CPU) that has two complete execution cores. Hence, it has t...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>renovation definition</code> | <code>['Renovation is the act of renewing or restoring something. If your kitchen is undergoing a renovation, thereâ\x80\x99s probably plaster and paint all over the place and you should probably get take-out.', 'NEW GALLERY SPACES OPENING IN 2017. In early 2017, our fourth floor will be transformed into a new destination for historical education and innovation. During the current renovation, objects from our permanent collection are on view throughout the Museum.', 'A same level house extension in Australia will cost approximately $60,000 to $200,000+. Adding a room or extending your living area on the ground floor are affordable ways of creating more space.Here are some key points to consider that will help you keep your renovation costs in check.RTICLE Stephanie Matheson. A same level house extension in Australia will cost approximately $60,000 to $200,000+. Adding a room or extending your living area on the ground floor are affordable ways of creating more space. Here are some key points...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>what is a girasol</code> | <code>['Girasol definition, an opal that reflects light in a bright luminous glow. See more.', 'Also, a type of opal from Mexico, referred to as Mexican water opal, is a colorless opal which exhibits either a bluish or golden internal sheen. Girasol opal is a term sometimes mistakenly and improperly used to refer to fire opals, as well as a type of transparent to semitransparent type milky quartz from Madagascar which displays an asterism, or star effect, when cut properly.', 'What is the meaning of Girasol? How popular is the baby name Girasol? Learn the origin and popularity plus how to pronounce Girasol', 'There are 5 basic types of opal. These types are Peruvian Opal, Fire Opal, Girasol Opal, Common opal and Precious Opal. There are 5 basic types of opal. These types are Peruvian Opal, Fire Opal, Girasol Opal, Common opal and Precious Opal.', 'girasol (Ë\x88dÊ\x92ɪrÉ\x99Ë\x8csÉ\x92l; -Ë\x8csÉ\x99Ê\x8al) , girosol or girasole n (Jewellery) a type of opal that has a red or pink glow in br...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
* Loss: [<code>LambdaLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#lambdaloss) with these parameters:
```json
{
"weighting_scheme": "sentence_transformers.cross_encoder.losses.LambdaLoss.NDCGLoss2PPScheme",
"k": null,
"sigma": 1.0,
"eps": 1e-10,
"reduction_log": "binary",
"activation_fct": "torch.nn.modules.linear.Identity",
"mini_batch_size": 8
}
```
### Evaluation Dataset
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [a0537b6](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/a0537b6c8669051b215b020183c276a1eb2027d5)
* Size: 1,000 evaluation samples
* Columns: <code>query_id</code>, <code>doc_ids</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | doc_ids | labels |
|:--------|:------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 10 characters</li><li>mean: 33.63 characters</li><li>max: 137 characters</li></ul> | <ul><li>min: 3 elements</li><li>mean: 12.50 elements</li><li>max: 20 elements</li></ul> | <ul><li>min: 3 elements</li><li>mean: 12.50 elements</li><li>max: 20 elements</li></ul> |
* Samples:
| query_id | doc_ids | labels |
|:----------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
| <code>can marijuana help dementia</code> | <code>["Cannabis 'could stop dementia in its tracks'. Cannabis may help keep Alzheimer's disease at bay. In experiments, a marijuana-based medicine triggered the formation of new brain cells and cut inflammation linked to dementia. The researchers say that using the information to create a pill suitable for people could help prevent or delay the onset of Alzheimer's.", 'Marijuana (cannabis): Marijuana in any form is not allowed on aircraft and is not allowed in the secure part of the airport (beyond the TSA screening areas). In addition it is illegal to import marijuana or marijuana-related items into the US.', 'Depakote and dementia - Can dementia be cured? Unfortunately, no. Dementia is a progressive disease. Even available treatments only slow progression or tame symptoms.', 'Marijuana Prices. The price of marijuana listed below is the typical price to buy marijuana on the black market in U.S. dollars. How much marijuana cost and the sale price of marijuana are based upon the United Natio...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>what are carcinogen</code> | <code>['Written By: Carcinogen, any of a number of agents that can cause cancer in humans. They can be divided into three major categories: chemical carcinogens (including those from biological sources), physical carcinogens, and oncogenic (cancer-causing) viruses. 1 Most carcinogens, singly or in combination, produce cancer by interacting with DNA in cells and thereby interfering with normal cellular function.', 'Tarragon (Artemisia dracunculus) is a species of perennial herb in the sunflower family. It is widespread in the wild across much of Eurasia and North America, and is cultivated for culinary and medicinal purposes in many lands.One sub-species, Artemisia dracunculus var. sativa, is cultivated for use of the leaves as an aromatic culinary herb.arragon has an aromatic property reminiscent of anise, due to the presence of estragole, a known carcinogen and teratogen in mice. The European Union investigation revealed that the danger of estragole is minimal even at 100â\x80\x931,000 tim...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>who played ben geller in friends</code> | <code>["Noelle and Cali aren't the only twins to have played one child character in Friends. Double vision: Ross' cheeky son Ben (pictured), from his first marriage to Carol, was also played by twins, Dylan and Cole Sprouse, who are now 22.", 'Update 7/29/06: There are now three â\x80\x9cTeaching Pastorsâ\x80\x9d at Applegate Christian Fellowship, according to their web site. Jon Courson is now back at Applegate. The other two listed as Teaching Pastors are Jonâ\x80\x99s two sons: Peter John and Ben Courson.on Courson has been appreciated over the years by many people who are my friends and whom I respect. I believe that he preaches the real Jesus and the true Gospel, for which I rejoice. I also believe that his ministry and church organization is a reasonable example with which to examine important issues together.', 'Ben 10 (Reboot) Ben 10: Omniverse is the fourth iteration of the Ben 10 franchise, and it is the sequel of Ben 10: Ultimate Alien. Ben was all set to be a solo hero with his n...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
* Loss: [<code>LambdaLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#lambdaloss) with these parameters:
```json
{
"weighting_scheme": "sentence_transformers.cross_encoder.losses.LambdaLoss.NDCGLoss2PPScheme",
"k": null,
"sigma": 1.0,
"eps": 1e-10,
"reduction_log": "binary",
"activation_fct": "torch.nn.modules.linear.Identity",
"mini_batch_size": 8
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `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`: 8
- `per_device_eval_batch_size`: 8
- `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`: 5e-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
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
|:----------:|:---------:|:-------------:|:---------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
| -1 | -1 | - | - | 0.0234 (-0.5170) | 0.3412 (+0.0161) | 0.0321 (-0.4686) | 0.1322 (-0.3231) |
| 0.0000 | 1 | 0.8349 | - | - | - | - | - |
| 0.0040 | 200 | 0.8417 | - | - | - | - | - |
| 0.0080 | 400 | 0.8371 | - | - | - | - | - |
| 0.0120 | 600 | 0.8288 | - | - | - | - | - |
| 0.0160 | 800 | 0.8076 | - | - | - | - | - |
| 0.0200 | 1000 | 0.7802 | 0.7316 | 0.2004 (-0.3400) | 0.3110 (-0.0140) | 0.2594 (-0.2413) | 0.2569 (-0.1984) |
| 0.0240 | 1200 | 0.6988 | - | - | - | - | - |
| 0.0280 | 1400 | 0.4688 | - | - | - | - | - |
| 0.0321 | 1600 | 0.3742 | - | - | - | - | - |
| 0.0361 | 1800 | 0.3441 | - | - | - | - | - |
| 0.0401 | 2000 | 0.3058 | 0.1975 | 0.6091 (+0.0687) | 0.3978 (+0.0727) | 0.6645 (+0.1639) | 0.5571 (+0.1018) |
| 0.0441 | 2200 | 0.2812 | - | - | - | - | - |
| 0.0481 | 2400 | 0.2748 | - | - | - | - | - |
| 0.0521 | 2600 | 0.2518 | - | - | - | - | - |
| 0.0561 | 2800 | 0.2591 | - | - | - | - | - |
| 0.0601 | 3000 | 0.2508 | 0.1673 | 0.7137 (+0.1733) | 0.3980 (+0.0730) | 0.7471 (+0.2464) | 0.6196 (+0.1642) |
| 0.0641 | 3200 | 0.2446 | - | - | - | - | - |
| 0.0681 | 3400 | 0.2385 | - | - | - | - | - |
| 0.0721 | 3600 | 0.2381 | - | - | - | - | - |
| 0.0761 | 3800 | 0.2204 | - | - | - | - | - |
| 0.0801 | 4000 | 0.221 | 0.1757 | 0.6321 (+0.0916) | 0.3937 (+0.0687) | 0.7029 (+0.2023) | 0.5762 (+0.1209) |
| 0.0841 | 4200 | 0.2131 | - | - | - | - | - |
| 0.0882 | 4400 | 0.2222 | - | - | - | - | - |
| 0.0922 | 4600 | 0.2307 | - | - | - | - | - |
| 0.0962 | 4800 | 0.2104 | - | - | - | - | - |
| 0.1002 | 5000 | 0.2151 | 0.1697 | 0.6388 (+0.0984) | 0.3846 (+0.0595) | 0.6659 (+0.1653) | 0.5631 (+0.1077) |
| 0.1042 | 5200 | 0.208 | - | - | - | - | - |
| 0.1082 | 5400 | 0.2147 | - | - | - | - | - |
| 0.1122 | 5600 | 0.2114 | - | - | - | - | - |
| 0.1162 | 5800 | 0.2224 | - | - | - | - | - |
| 0.1202 | 6000 | 0.2094 | 0.1583 | 0.6165 (+0.0761) | 0.3969 (+0.0718) | 0.6968 (+0.1961) | 0.5700 (+0.1147) |
| 0.1242 | 6200 | 0.2065 | - | - | - | - | - |
| 0.1282 | 6400 | 0.2191 | - | - | - | - | - |
| 0.1322 | 6600 | 0.2108 | - | - | - | - | - |
| 0.1362 | 6800 | 0.2067 | - | - | - | - | - |
| 0.1402 | 7000 | 0.2055 | 0.1554 | 0.6295 (+0.0891) | 0.3968 (+0.0718) | 0.6862 (+0.1855) | 0.5708 (+0.1155) |
| 0.1443 | 7200 | 0.1994 | - | - | - | - | - |
| 0.1483 | 7400 | 0.2067 | - | - | - | - | - |
| 0.1523 | 7600 | 0.1933 | - | - | - | - | - |
| 0.1563 | 7800 | 0.1903 | - | - | - | - | - |
| 0.1603 | 8000 | 0.1837 | 0.1569 | 0.6236 (+0.0831) | 0.4196 (+0.0946) | 0.6927 (+0.1920) | 0.5786 (+0.1232) |
| 0.1643 | 8200 | 0.1968 | - | - | - | - | - |
| 0.1683 | 8400 | 0.2037 | - | - | - | - | - |
| 0.1723 | 8600 | 0.2052 | - | - | - | - | - |
| 0.1763 | 8800 | 0.2007 | - | - | - | - | - |
| 0.1803 | 9000 | 0.1771 | 0.1642 | 0.6579 (+0.1175) | 0.3949 (+0.0699) | 0.6931 (+0.1924) | 0.5820 (+0.1266) |
| 0.1843 | 9200 | 0.1828 | - | - | - | - | - |
| 0.1883 | 9400 | 0.195 | - | - | - | - | - |
| 0.1923 | 9600 | 0.1992 | - | - | - | - | - |
| 0.1963 | 9800 | 0.1859 | - | - | - | - | - |
| 0.2004 | 10000 | 0.1934 | 0.1514 | 0.6756 (+0.1351) | 0.4280 (+0.1029) | 0.7235 (+0.2228) | 0.6090 (+0.1536) |
| 0.2044 | 10200 | 0.1828 | - | - | - | - | - |
| 0.2084 | 10400 | 0.1749 | - | - | - | - | - |
| 0.2124 | 10600 | 0.1908 | - | - | - | - | - |
| 0.2164 | 10800 | 0.1837 | - | - | - | - | - |
| 0.2204 | 11000 | 0.1726 | 0.1469 | 0.6427 (+0.1023) | 0.4170 (+0.0920) | 0.7408 (+0.2402) | 0.6002 (+0.1448) |
| 0.2244 | 11200 | 0.1922 | - | - | - | - | - |
| 0.2284 | 11400 | 0.1853 | - | - | - | - | - |
| 0.2324 | 11600 | 0.1856 | - | - | - | - | - |
| 0.2364 | 11800 | 0.1797 | - | - | - | - | - |
| 0.2404 | 12000 | 0.1631 | 0.1508 | 0.6758 (+0.1354) | 0.4076 (+0.0825) | 0.7316 (+0.2310) | 0.6050 (+0.1496) |
| 0.2444 | 12200 | 0.1778 | - | - | - | - | - |
| 0.2484 | 12400 | 0.174 | - | - | - | - | - |
| 0.2524 | 12600 | 0.159 | - | - | - | - | - |
| 0.2565 | 12800 | 0.1744 | - | - | - | - | - |
| 0.2605 | 13000 | 0.1828 | 0.1524 | 0.6696 (+0.1291) | 0.4039 (+0.0788) | 0.7001 (+0.1994) | 0.5912 (+0.1358) |
| 0.2645 | 13200 | 0.1726 | - | - | - | - | - |
| 0.2685 | 13400 | 0.1947 | - | - | - | - | - |
| 0.2725 | 13600 | 0.1697 | - | - | - | - | - |
| 0.2765 | 13800 | 0.1958 | - | - | - | - | - |
| 0.2805 | 14000 | 0.1917 | 0.1442 | 0.6612 (+0.1208) | 0.4091 (+0.0841) | 0.6987 (+0.1980) | 0.5897 (+0.1343) |
| 0.2845 | 14200 | 0.1863 | - | - | - | - | - |
| 0.2885 | 14400 | 0.1844 | - | - | - | - | - |
| 0.2925 | 14600 | 0.1764 | - | - | - | - | - |
| 0.2965 | 14800 | 0.1719 | - | - | - | - | - |
| 0.3005 | 15000 | 0.1844 | 0.1481 | 0.6572 (+0.1168) | 0.3984 (+0.0733) | 0.7382 (+0.2376) | 0.5979 (+0.1426) |
| 0.3045 | 15200 | 0.176 | - | - | - | - | - |
| 0.3085 | 15400 | 0.1724 | - | - | - | - | - |
| 0.3126 | 15600 | 0.1747 | - | - | - | - | - |
| 0.3166 | 15800 | 0.1649 | - | - | - | - | - |
| 0.3206 | 16000 | 0.1779 | 0.1450 | 0.6168 (+0.0763) | 0.4096 (+0.0846) | 0.7118 (+0.2112) | 0.5794 (+0.1240) |
| 0.3246 | 16200 | 0.1755 | - | - | - | - | - |
| 0.3286 | 16400 | 0.1567 | - | - | - | - | - |
| 0.3326 | 16600 | 0.1749 | - | - | - | - | - |
| 0.3366 | 16800 | 0.1827 | - | - | - | - | - |
| 0.3406 | 17000 | 0.1773 | 0.1394 | 0.6868 (+0.1464) | 0.3943 (+0.0693) | 0.7007 (+0.2001) | 0.5940 (+0.1386) |
| 0.3446 | 17200 | 0.1747 | - | - | - | - | - |
| 0.3486 | 17400 | 0.1805 | - | - | - | - | - |
| 0.3526 | 17600 | 0.1688 | - | - | - | - | - |
| 0.3566 | 17800 | 0.1649 | - | - | - | - | - |
| 0.3606 | 18000 | 0.1747 | 0.1405 | 0.6390 (+0.0986) | 0.3952 (+0.0701) | 0.7370 (+0.2364) | 0.5904 (+0.1350) |
| 0.3646 | 18200 | 0.1797 | - | - | - | - | - |
| 0.3687 | 18400 | 0.1557 | - | - | - | - | - |
| 0.3727 | 18600 | 0.1644 | - | - | - | - | - |
| 0.3767 | 18800 | 0.1701 | - | - | - | - | - |
| 0.3807 | 19000 | 0.1673 | 0.1433 | 0.6799 (+0.1395) | 0.4012 (+0.0762) | 0.7286 (+0.2279) | 0.6032 (+0.1479) |
| 0.3847 | 19200 | 0.1736 | - | - | - | - | - |
| 0.3887 | 19400 | 0.1767 | - | - | - | - | - |
| 0.3927 | 19600 | 0.1735 | - | - | - | - | - |
| 0.3967 | 19800 | 0.1758 | - | - | - | - | - |
| 0.4007 | 20000 | 0.1711 | 0.1380 | 0.6773 (+0.1369) | 0.4149 (+0.0898) | 0.7166 (+0.2159) | 0.6029 (+0.1476) |
| 0.4047 | 20200 | 0.1704 | - | - | - | - | - |
| 0.4087 | 20400 | 0.1637 | - | - | - | - | - |
| 0.4127 | 20600 | 0.1783 | - | - | - | - | - |
| 0.4167 | 20800 | 0.1585 | - | - | - | - | - |
| 0.4207 | 21000 | 0.1769 | 0.1399 | 0.6832 (+0.1428) | 0.4254 (+0.1003) | 0.6977 (+0.1970) | 0.6021 (+0.1467) |
| 0.4248 | 21200 | 0.1644 | - | - | - | - | - |
| 0.4288 | 21400 | 0.1693 | - | - | - | - | - |
| 0.4328 | 21600 | 0.1604 | - | - | - | - | - |
| 0.4368 | 21800 | 0.1714 | - | - | - | - | - |
| 0.4408 | 22000 | 0.1577 | 0.1392 | 0.6715 (+0.1311) | 0.4199 (+0.0948) | 0.7038 (+0.2032) | 0.5984 (+0.1430) |
| 0.4448 | 22200 | 0.1742 | - | - | - | - | - |
| 0.4488 | 22400 | 0.1744 | - | - | - | - | - |
| 0.4528 | 22600 | 0.1682 | - | - | - | - | - |
| 0.4568 | 22800 | 0.1597 | - | - | - | - | - |
| 0.4608 | 23000 | 0.1626 | 0.1364 | 0.6698 (+0.1294) | 0.4191 (+0.0941) | 0.7255 (+0.2249) | 0.6048 (+0.1494) |
| 0.4648 | 23200 | 0.1543 | - | - | - | - | - |
| 0.4688 | 23400 | 0.1571 | - | - | - | - | - |
| 0.4728 | 23600 | 0.1576 | - | - | - | - | - |
| 0.4768 | 23800 | 0.1644 | - | - | - | - | - |
| 0.4809 | 24000 | 0.1542 | 0.1444 | 0.6618 (+0.1213) | 0.4095 (+0.0844) | 0.7442 (+0.2436) | 0.6052 (+0.1498) |
| 0.4849 | 24200 | 0.1826 | - | - | - | - | - |
| 0.4889 | 24400 | 0.1649 | - | - | - | - | - |
| 0.4929 | 24600 | 0.154 | - | - | - | - | - |
| 0.4969 | 24800 | 0.1779 | - | - | - | - | - |
| 0.5009 | 25000 | 0.1615 | 0.1373 | 0.6506 (+0.1102) | 0.3971 (+0.0721) | 0.7165 (+0.2159) | 0.5881 (+0.1327) |
| 0.5049 | 25200 | 0.1558 | - | - | - | - | - |
| 0.5089 | 25400 | 0.1741 | - | - | - | - | - |
| 0.5129 | 25600 | 0.151 | - | - | - | - | - |
| 0.5169 | 25800 | 0.1654 | - | - | - | - | - |
| 0.5209 | 26000 | 0.1656 | 0.1368 | 0.6631 (+0.1226) | 0.3888 (+0.0638) | 0.7092 (+0.2085) | 0.5870 (+0.1317) |
| 0.5249 | 26200 | 0.1603 | - | - | - | - | - |
| 0.5289 | 26400 | 0.1547 | - | - | - | - | - |
| 0.5329 | 26600 | 0.1782 | - | - | - | - | - |
| 0.5370 | 26800 | 0.1571 | - | - | - | - | - |
| 0.5410 | 27000 | 0.1595 | 0.1376 | 0.6352 (+0.0948) | 0.3960 (+0.0710) | 0.7081 (+0.2074) | 0.5798 (+0.1244) |
| 0.5450 | 27200 | 0.1764 | - | - | - | - | - |
| 0.5490 | 27400 | 0.1672 | - | - | - | - | - |
| 0.5530 | 27600 | 0.1669 | - | - | - | - | - |
| 0.5570 | 27800 | 0.1719 | - | - | - | - | - |
| 0.5610 | 28000 | 0.1759 | 0.1355 | 0.6629 (+0.1225) | 0.4013 (+0.0762) | 0.7671 (+0.2665) | 0.6104 (+0.1551) |
| 0.5650 | 28200 | 0.1595 | - | - | - | - | - |
| 0.5690 | 28400 | 0.1558 | - | - | - | - | - |
| 0.5730 | 28600 | 0.1617 | - | - | - | - | - |
| 0.5770 | 28800 | 0.1669 | - | - | - | - | - |
| 0.5810 | 29000 | 0.1481 | 0.1363 | 0.6613 (+0.1208) | 0.3961 (+0.0710) | 0.7413 (+0.2406) | 0.5995 (+0.1442) |
| 0.5850 | 29200 | 0.1584 | - | - | - | - | - |
| 0.5890 | 29400 | 0.1654 | - | - | - | - | - |
| 0.5931 | 29600 | 0.1659 | - | - | - | - | - |
| 0.5971 | 29800 | 0.1653 | - | - | - | - | - |
| 0.6011 | 30000 | 0.1606 | 0.1368 | 0.6554 (+0.1150) | 0.3927 (+0.0676) | 0.7139 (+0.2132) | 0.5873 (+0.1320) |
| 0.6051 | 30200 | 0.1625 | - | - | - | - | - |
| 0.6091 | 30400 | 0.1581 | - | - | - | - | - |
| 0.6131 | 30600 | 0.145 | - | - | - | - | - |
| 0.6171 | 30800 | 0.1584 | - | - | - | - | - |
| 0.6211 | 31000 | 0.1566 | 0.1325 | 0.6680 (+0.1275) | 0.3978 (+0.0728) | 0.7372 (+0.2365) | 0.6010 (+0.1456) |
| 0.6251 | 31200 | 0.1611 | - | - | - | - | - |
| 0.6291 | 31400 | 0.1724 | - | - | - | - | - |
| 0.6331 | 31600 | 0.1609 | - | - | - | - | - |
| 0.6371 | 31800 | 0.1621 | - | - | - | - | - |
| 0.6411 | 32000 | 0.1537 | 0.1300 | 0.6615 (+0.1211) | 0.4063 (+0.0813) | 0.7697 (+0.2691) | 0.6125 (+0.1571) |
| 0.6451 | 32200 | 0.1641 | - | - | - | - | - |
| 0.6492 | 32400 | 0.1487 | - | - | - | - | - |
| 0.6532 | 32600 | 0.1456 | - | - | - | - | - |
| 0.6572 | 32800 | 0.1514 | - | - | - | - | - |
| 0.6612 | 33000 | 0.158 | 0.1309 | 0.6556 (+0.1152) | 0.4125 (+0.0875) | 0.7479 (+0.2473) | 0.6053 (+0.1500) |
| 0.6652 | 33200 | 0.1451 | - | - | - | - | - |
| 0.6692 | 33400 | 0.1495 | - | - | - | - | - |
| 0.6732 | 33600 | 0.1467 | - | - | - | - | - |
| 0.6772 | 33800 | 0.143 | - | - | - | - | - |
| 0.6812 | 34000 | 0.1639 | 0.1334 | 0.6769 (+0.1365) | 0.4002 (+0.0752) | 0.7420 (+0.2414) | 0.6064 (+0.1510) |
| 0.6852 | 34200 | 0.1542 | - | - | - | - | - |
| 0.6892 | 34400 | 0.1592 | - | - | - | - | - |
| 0.6932 | 34600 | 0.1452 | - | - | - | - | - |
| 0.6972 | 34800 | 0.1569 | - | - | - | - | - |
| 0.7012 | 35000 | 0.1502 | 0.1299 | 0.6648 (+0.1243) | 0.3834 (+0.0583) | 0.7684 (+0.2678) | 0.6055 (+0.1501) |
| 0.7053 | 35200 | 0.1564 | - | - | - | - | - |
| 0.7093 | 35400 | 0.1509 | - | - | - | - | - |
| 0.7133 | 35600 | 0.156 | - | - | - | - | - |
| 0.7173 | 35800 | 0.1547 | - | - | - | - | - |
| 0.7213 | 36000 | 0.1595 | 0.1297 | 0.6521 (+0.1117) | 0.3916 (+0.0665) | 0.7318 (+0.2311) | 0.5918 (+0.1364) |
| 0.7253 | 36200 | 0.1457 | - | - | - | - | - |
| 0.7293 | 36400 | 0.1615 | - | - | - | - | - |
| 0.7333 | 36600 | 0.1508 | - | - | - | - | - |
| 0.7373 | 36800 | 0.1478 | - | - | - | - | - |
| 0.7413 | 37000 | 0.1455 | 0.1322 | 0.6614 (+0.1210) | 0.4132 (+0.0882) | 0.7656 (+0.2650) | 0.6134 (+0.1581) |
| 0.7453 | 37200 | 0.1526 | - | - | - | - | - |
| 0.7493 | 37400 | 0.1571 | - | - | - | - | - |
| 0.7533 | 37600 | 0.141 | - | - | - | - | - |
| 0.7573 | 37800 | 0.1418 | - | - | - | - | - |
| 0.7614 | 38000 | 0.1597 | 0.1347 | 0.6707 (+0.1302) | 0.4175 (+0.0925) | 0.7568 (+0.2561) | 0.6150 (+0.1596) |
| 0.7654 | 38200 | 0.1512 | - | - | - | - | - |
| 0.7694 | 38400 | 0.1424 | - | - | - | - | - |
| 0.7734 | 38600 | 0.1601 | - | - | - | - | - |
| 0.7774 | 38800 | 0.13 | - | - | - | - | - |
| 0.7814 | 39000 | 0.1508 | 0.1322 | 0.6960 (+0.1556) | 0.4032 (+0.0781) | 0.7585 (+0.2579) | 0.6192 (+0.1639) |
| 0.7854 | 39200 | 0.1456 | - | - | - | - | - |
| 0.7894 | 39400 | 0.1502 | - | - | - | - | - |
| 0.7934 | 39600 | 0.1507 | - | - | - | - | - |
| 0.7974 | 39800 | 0.1696 | - | - | - | - | - |
| **0.8014** | **40000** | **0.1381** | **0.1289** | **0.7251 (+0.1847)** | **0.4143 (+0.0892)** | **0.7594 (+0.2587)** | **0.6329 (+0.1776)** |
| 0.8054 | 40200 | 0.1544 | - | - | - | - | - |
| 0.8094 | 40400 | 0.1541 | - | - | - | - | - |
| 0.8134 | 40600 | 0.1458 | - | - | - | - | - |
| 0.8175 | 40800 | 0.1411 | - | - | - | - | - |
| 0.8215 | 41000 | 0.1495 | 0.1280 | 0.7051 (+0.1646) | 0.4102 (+0.0851) | 0.7520 (+0.2514) | 0.6224 (+0.1670) |
| 0.8255 | 41200 | 0.1465 | - | - | - | - | - |
| 0.8295 | 41400 | 0.1577 | - | - | - | - | - |
| 0.8335 | 41600 | 0.1489 | - | - | - | - | - |
| 0.8375 | 41800 | 0.1481 | - | - | - | - | - |
| 0.8415 | 42000 | 0.148 | 0.1304 | 0.6944 (+0.1539) | 0.4023 (+0.0772) | 0.7440 (+0.2433) | 0.6135 (+0.1582) |
| 0.8455 | 42200 | 0.1529 | - | - | - | - | - |
| 0.8495 | 42400 | 0.1522 | - | - | - | - | - |
| 0.8535 | 42600 | 0.1455 | - | - | - | - | - |
| 0.8575 | 42800 | 0.1567 | - | - | - | - | - |
| 0.8615 | 43000 | 0.1435 | 0.1304 | 0.6710 (+0.1306) | 0.4130 (+0.0880) | 0.7493 (+0.2486) | 0.6111 (+0.1557) |
| 0.8655 | 43200 | 0.1426 | - | - | - | - | - |
| 0.8695 | 43400 | 0.1527 | - | - | - | - | - |
| 0.8736 | 43600 | 0.1431 | - | - | - | - | - |
| 0.8776 | 43800 | 0.1382 | - | - | - | - | - |
| 0.8816 | 44000 | 0.1554 | 0.1288 | 0.6842 (+0.1437) | 0.3996 (+0.0746) | 0.7535 (+0.2529) | 0.6124 (+0.1571) |
| 0.8856 | 44200 | 0.1491 | - | - | - | - | - |
| 0.8896 | 44400 | 0.1626 | - | - | - | - | - |
| 0.8936 | 44600 | 0.1471 | - | - | - | - | - |
| 0.8976 | 44800 | 0.1459 | - | - | - | - | - |
| 0.9016 | 45000 | 0.1501 | 0.1284 | 0.6995 (+0.1590) | 0.4051 (+0.0801) | 0.7608 (+0.2602) | 0.6218 (+0.1664) |
| 0.9056 | 45200 | 0.1513 | - | - | - | - | - |
| 0.9096 | 45400 | 0.1521 | - | - | - | - | - |
| 0.9136 | 45600 | 0.1417 | - | - | - | - | - |
| 0.9176 | 45800 | 0.1452 | - | - | - | - | - |
| 0.9216 | 46000 | 0.1591 | 0.1254 | 0.7086 (+0.1682) | 0.3940 (+0.0690) | 0.7567 (+0.2561) | 0.6198 (+0.1644) |
| 0.9256 | 46200 | 0.1473 | - | - | - | - | - |
| 0.9297 | 46400 | 0.1329 | - | - | - | - | - |
| 0.9337 | 46600 | 0.1523 | - | - | - | - | - |
| 0.9377 | 46800 | 0.1385 | - | - | - | - | - |
| 0.9417 | 47000 | 0.1393 | 0.1267 | 0.7161 (+0.1756) | 0.3941 (+0.0690) | 0.7662 (+0.2656) | 0.6255 (+0.1701) |
| 0.9457 | 47200 | 0.1421 | - | - | - | - | - |
| 0.9497 | 47400 | 0.1509 | - | - | - | - | - |
| 0.9537 | 47600 | 0.1587 | - | - | - | - | - |
| 0.9577 | 47800 | 0.1402 | - | - | - | - | - |
| 0.9617 | 48000 | 0.1355 | 0.1278 | 0.6976 (+0.1571) | 0.3958 (+0.0708) | 0.7538 (+0.2531) | 0.6157 (+0.1603) |
| 0.9657 | 48200 | 0.1518 | - | - | - | - | - |
| 0.9697 | 48400 | 0.1369 | - | - | - | - | - |
| 0.9737 | 48600 | 0.1475 | - | - | - | - | - |
| 0.9777 | 48800 | 0.1495 | - | - | - | - | - |
| 0.9817 | 49000 | 0.1402 | 0.1275 | 0.6973 (+0.1568) | 0.3990 (+0.0740) | 0.7534 (+0.2528) | 0.6166 (+0.1612) |
| 0.9858 | 49200 | 0.1527 | - | - | - | - | - |
| 0.9898 | 49400 | 0.143 | - | - | - | - | - |
| 0.9938 | 49600 | 0.1619 | - | - | - | - | - |
| 0.9978 | 49800 | 0.1422 | - | - | - | - | - |
| -1 | -1 | - | - | 0.7251 (+0.1847) | 0.4143 (+0.0892) | 0.7594 (+0.2587) | 0.6329 (+0.1776) |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.0
- Datasets: 2.21.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### LambdaLoss
```bibtex
@inproceedings{wang2018lambdaloss,
title={The lambdaloss framework for ranking metric optimization},
author={Wang, Xuanhui and Li, Cheng and Golbandi, Nadav and Bendersky, Michael and Najork, Marc},
booktitle={Proceedings of the 27th ACM international conference on information and knowledge management},
pages={1313--1322},
year={2018}
}
```
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