sentence-transformers safetensors modernbert sentence-similarity feature-extraction generated_from_trainer dataset_size:353831 loss:CosineSimilarityLoss en dataset:sentence-transformers/stsb arxiv:1908.10084 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- sentence-transformers/stsb
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:353831
- loss:CosineSimilarityLoss
widget:
- source_sentence: A chef is preparing some food.
  sentences:
  - Five birds stand on the snow.
  - A chef prepared a meal.
  - There is no 'still' that is not relative to some other object.
- source_sentence: A woman is adding oil on fishes.
  sentences:
  - Large cruise ship floating on the water.
  - It refers to the maximum f-stop (which is defined as the ratio of focal length
    to effective aperture diameter).
  - The woman is cutting potatoes.
- source_sentence: The player shoots the winning points.
  sentences:
  - Minimum wage laws hurt the least skilled, least productive the most.
  - The basketball player is about to score points for his team.
  - Three televisions, on on the floor, the other two on a box.
- source_sentence: Stars form in star-formation regions, which itself develop from
    molecular clouds.
  sentences:
  - Although I believe Searle is mistaken, I don't think you have found the problem.
  - It may be possible for a solar system like ours to exist outside of a galaxy.
  - A blond-haired child performing on the trumpet in front of a house while his younger
    brother watches.
- source_sentence: While Queen may refer to both Queen regent (sovereign) or Queen
    consort, the King has always been the sovereign.
  sentences:
  - At first, I thought this is a bit of a tricky question.
  - A man plays the guitar.
  - There is a very good reason not to refer to the Queen's spouse as "King" - because
    they aren't the King.
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.7982244251277283
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8130492542348773
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.7554305375132837
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7644057551801444
      name: Spearman Cosine
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("cahya/last-sts")
# Run inference
sentences = [
    'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.',
    'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.',
    'A man plays the guitar.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### 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

#### Semantic Similarity

* Datasets: `sts-dev` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | sts-dev   | sts-test   |
|:--------------------|:----------|:-----------|
| pearson_cosine      | 0.7982    | 0.7554     |
| **spearman_cosine** | **0.813** | **0.7644** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 353,831 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                          | score                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 38.53 tokens</li><li>max: 151 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 38.78 tokens</li><li>max: 145 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.91</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                     | sentence2                                                                                                                                                                                                 | score                          |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|
  | <code>A long-term researcher into diabetes, he achieved significant notability with his 1988 Banting Lecture (organized annually by the American Diabetes Association in memory of Frederick Banting).</code> | <code>A renowned expert on diabetes, he gained widespread acclaim for his 1988 Banting Lecture, which is presented annually by the American Diabetes Association to commemorate Frederick Banting.</code> | <code>0.926345705986023</code> |
  | <code>investigators claim the british company was a cia cover. </code>                                                                                                                                        | <code>russian investigators stated that the british company was a cia cover.</code>                                                                                                                       | <code>0.88</code>              |
  | <code>Albert Weber (21 November 1888, in Berlin – 17 September 1940) was a German amateur football (soccer) player who competed in the 1912 Summer Olympics.</code>                                           | <code>Albert Weber (21 November 1888, in Berlin – 17 September 1940) was a German amateur footballer who participated in the 1912 Summer Olympics.</code>                                                 | <code>0.904914379119873</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Dataset

#### stsb

* Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 15.44 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.43 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                             | score             |
  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `bf16`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 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`: 10
- `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`: 42
- `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`: True
- `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`: False
- `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 | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
| 0.0362 | 100   | 0.0019        | 0.1114          | 0.8115                  | -                        |
| 0.0724 | 200   | 0.0021        | 0.0882          | 0.8177                  | -                        |
| 0.1085 | 300   | 0.0015        | 0.0748          | 0.8125                  | -                        |
| 0.1447 | 400   | 0.0012        | 0.0679          | 0.8086                  | -                        |
| 0.1809 | 500   | 0.0012        | 0.0608          | 0.8069                  | -                        |
| 0.2171 | 600   | 0.001         | 0.0596          | 0.7986                  | -                        |
| 0.2533 | 700   | 0.0011        | 0.0547          | 0.7946                  | -                        |
| 0.2894 | 800   | 0.0011        | 0.0492          | 0.7870                  | -                        |
| 0.3256 | 900   | 0.0009        | 0.0522          | 0.7862                  | -                        |
| 0.3618 | 1000  | 0.0008        | 0.0519          | 0.7880                  | -                        |
| 0.3980 | 1100  | 0.0009        | 0.0529          | 0.7962                  | -                        |
| 0.4342 | 1200  | 0.0008        | 0.0469          | 0.7954                  | -                        |
| 0.4703 | 1300  | 0.0009        | 0.0506          | 0.7928                  | -                        |
| 0.5065 | 1400  | 0.0009        | 0.0466          | 0.7873                  | -                        |
| 0.5427 | 1500  | 0.001         | 0.0495          | 0.7999                  | -                        |
| 0.5789 | 1600  | 0.0008        | 0.0506          | 0.7861                  | -                        |
| 0.6151 | 1700  | 0.0008        | 0.0522          | 0.7873                  | -                        |
| 0.6512 | 1800  | 0.0009        | 0.0582          | 0.7843                  | -                        |
| 0.6874 | 1900  | 0.0009        | 0.0585          | 0.7888                  | -                        |
| 0.7236 | 2000  | 0.001         | 0.0508          | 0.8040                  | -                        |
| 0.7598 | 2100  | 0.001         | 0.0483          | 0.8018                  | -                        |
| 0.7959 | 2200  | 0.0008        | 0.0520          | 0.7841                  | -                        |
| 0.8321 | 2300  | 0.0009        | 0.0519          | 0.7896                  | -                        |
| 0.8683 | 2400  | 0.001         | 0.0514          | 0.7906                  | -                        |
| 0.9045 | 2500  | 0.0009        | 0.0521          | 0.7946                  | -                        |
| 0.9407 | 2600  | 0.0009        | 0.0496          | 0.7920                  | -                        |
| 0.9768 | 2700  | 0.001         | 0.0566          | 0.7956                  | -                        |
| 1.0130 | 2800  | 0.0009        | 0.0511          | 0.8044                  | -                        |
| 1.0492 | 2900  | 0.0009        | 0.0622          | 0.8197                  | -                        |
| 1.0854 | 3000  | 0.001         | 0.0504          | 0.8113                  | -                        |
| 1.1216 | 3100  | 0.001         | 0.0550          | 0.8005                  | -                        |
| 1.1577 | 3200  | 0.001         | 0.0549          | 0.7821                  | -                        |
| 1.1939 | 3300  | 0.0009        | 0.0578          | 0.7758                  | -                        |
| 1.2301 | 3400  | 0.0009        | 0.0543          | 0.7860                  | -                        |
| 1.2663 | 3500  | 0.0008        | 0.0575          | 0.7891                  | -                        |
| 1.3025 | 3600  | 0.0009        | 0.0567          | 0.7995                  | -                        |
| 1.3386 | 3700  | 0.001         | 0.0488          | 0.7985                  | -                        |
| 1.3748 | 3800  | 0.0009        | 0.0514          | 0.7789                  | -                        |
| 1.4110 | 3900  | 0.001         | 0.0584          | 0.7765                  | -                        |
| 1.4472 | 4000  | 0.001         | 0.0554          | 0.7888                  | -                        |
| 1.4834 | 4100  | 0.001         | 0.0659          | 0.7959                  | -                        |
| 1.5195 | 4200  | 0.0009        | 0.0511          | 0.7816                  | -                        |
| 1.5557 | 4300  | 0.0009        | 0.0555          | 0.7826                  | -                        |
| 1.5919 | 4400  | 0.001         | 0.0525          | 0.7944                  | -                        |
| 1.6281 | 4500  | 0.0009        | 0.0553          | 0.7941                  | -                        |
| 1.6643 | 4600  | 0.001         | 0.0588          | 0.7984                  | -                        |
| 1.7004 | 4700  | 0.001         | 0.0579          | 0.8004                  | -                        |
| 1.7366 | 4800  | 0.0009        | 0.0540          | 0.7916                  | -                        |
| 1.7728 | 4900  | 0.0009        | 0.0557          | 0.7963                  | -                        |
| 1.8090 | 5000  | 0.0008        | 0.0536          | 0.8044                  | -                        |
| 1.8452 | 5100  | 0.0009        | 0.0541          | 0.7870                  | -                        |
| 1.8813 | 5200  | 0.0009        | 0.0594          | 0.7989                  | -                        |
| 1.9175 | 5300  | 0.001         | 0.0558          | 0.8000                  | -                        |
| 1.9537 | 5400  | 0.0009        | 0.0538          | 0.7905                  | -                        |
| 1.9899 | 5500  | 0.0008        | 0.0555          | 0.7944                  | -                        |
| 2.0260 | 5600  | 0.0009        | 0.0557          | 0.8127                  | -                        |
| 2.0622 | 5700  | 0.0007        | 0.0542          | 0.8146                  | -                        |
| 2.0984 | 5800  | 0.0008        | 0.0517          | 0.7990                  | -                        |
| 2.1346 | 5900  | 0.0009        | 0.0500          | 0.8051                  | -                        |
| 2.1708 | 6000  | 0.0009        | 0.0521          | 0.8019                  | -                        |
| 2.2069 | 6100  | 0.0009        | 0.0511          | 0.8101                  | -                        |
| 2.2431 | 6200  | 0.0008        | 0.0578          | 0.8087                  | -                        |
| 2.2793 | 6300  | 0.0008        | 0.0585          | 0.8012                  | -                        |
| 2.3155 | 6400  | 0.0008        | 0.0566          | 0.8083                  | -                        |
| 2.3517 | 6500  | 0.0007        | 0.0535          | 0.8036                  | -                        |
| 2.3878 | 6600  | 0.0008        | 0.0531          | 0.7988                  | -                        |
| 2.4240 | 6700  | 0.0007        | 0.0574          | 0.8102                  | -                        |
| 2.4602 | 6800  | 0.0007        | 0.0566          | 0.7944                  | -                        |
| 2.4964 | 6900  | 0.0008        | 0.0528          | 0.8058                  | -                        |
| 2.5326 | 7000  | 0.0007        | 0.0528          | 0.8056                  | -                        |
| 2.5687 | 7100  | 0.0007        | 0.0506          | 0.8002                  | -                        |
| 2.6049 | 7200  | 0.0007        | 0.0526          | 0.8038                  | -                        |
| 2.6411 | 7300  | 0.0007        | 0.0554          | 0.8054                  | -                        |
| 2.6773 | 7400  | 0.0007        | 0.0505          | 0.7928                  | -                        |
| 2.7135 | 7500  | 0.0007        | 0.0505          | 0.8070                  | -                        |
| 2.7496 | 7600  | 0.0007        | 0.0535          | 0.7977                  | -                        |
| 2.7858 | 7700  | 0.0007        | 0.0536          | 0.8019                  | -                        |
| 2.8220 | 7800  | 0.0006        | 0.0546          | 0.7989                  | -                        |
| 2.8582 | 7900  | 0.0007        | 0.0543          | 0.8042                  | -                        |
| 2.8944 | 8000  | 0.0007        | 0.0542          | 0.8105                  | -                        |
| 2.9305 | 8100  | 0.0007        | 0.0541          | 0.8053                  | -                        |
| 2.9667 | 8200  | 0.0007        | 0.0545          | 0.8135                  | -                        |
| 3.0029 | 8300  | 0.0007        | 0.0598          | 0.8201                  | -                        |
| 3.0391 | 8400  | 0.0008        | 0.0558          | 0.8050                  | -                        |
| 3.0753 | 8500  | 0.0007        | 0.0510          | 0.7965                  | -                        |
| 3.1114 | 8600  | 0.0006        | 0.0564          | 0.8042                  | -                        |
| 3.1476 | 8700  | 0.0006        | 0.0559          | 0.7932                  | -                        |
| 3.1838 | 8800  | 0.0006        | 0.0529          | 0.8028                  | -                        |
| 3.2200 | 8900  | 0.0006        | 0.0542          | 0.8142                  | -                        |
| 3.2562 | 9000  | 0.0006        | 0.0532          | 0.8055                  | -                        |
| 3.2923 | 9100  | 0.0006        | 0.0506          | 0.7930                  | -                        |
| 3.3285 | 9200  | 0.0007        | 0.0542          | 0.7927                  | -                        |
| 3.3647 | 9300  | 0.0006        | 0.0523          | 0.8033                  | -                        |
| 3.4009 | 9400  | 0.0006        | 0.0530          | 0.8079                  | -                        |
| 3.4370 | 9500  | 0.0006        | 0.0544          | 0.7977                  | -                        |
| 3.4732 | 9600  | 0.0005        | 0.0515          | 0.8019                  | -                        |
| 3.5094 | 9700  | 0.0006        | 0.0481          | 0.8037                  | -                        |
| 3.5456 | 9800  | 0.0005        | 0.0557          | 0.8007                  | -                        |
| 3.5818 | 9900  | 0.0006        | 0.0495          | 0.8087                  | -                        |
| 3.6179 | 10000 | 0.0006        | 0.0555          | 0.7991                  | -                        |
| 3.6541 | 10100 | 0.0005        | 0.0560          | 0.7973                  | -                        |
| 3.6903 | 10200 | 0.0007        | 0.0581          | 0.7945                  | -                        |
| 3.7265 | 10300 | 0.0006        | 0.0546          | 0.8098                  | -                        |
| 3.7627 | 10400 | 0.0006        | 0.0539          | 0.8074                  | -                        |
| 3.7988 | 10500 | 0.0005        | 0.0501          | 0.8051                  | -                        |
| 3.8350 | 10600 | 0.0005        | 0.0531          | 0.8032                  | -                        |
| 3.8712 | 10700 | 0.0005        | 0.0502          | 0.8077                  | -                        |
| 3.9074 | 10800 | 0.0006        | 0.0537          | 0.8131                  | -                        |
| 3.9436 | 10900 | 0.0005        | 0.0510          | 0.8115                  | -                        |
| 3.9797 | 11000 | 0.0006        | 0.0525          | 0.8173                  | -                        |
| 4.0159 | 11100 | 0.0005        | 0.0513          | 0.8106                  | -                        |
| 4.0521 | 11200 | 0.0006        | 0.0594          | 0.8061                  | -                        |
| 4.0883 | 11300 | 0.0005        | 0.0514          | 0.8150                  | -                        |
| 4.1245 | 11400 | 0.0005        | 0.0537          | 0.8168                  | -                        |
| 4.1606 | 11500 | 0.0005        | 0.0571          | 0.8176                  | -                        |
| 4.1968 | 11600 | 0.0005        | 0.0546          | 0.8159                  | -                        |
| 4.2330 | 11700 | 0.0005        | 0.0496          | 0.8115                  | -                        |
| 4.2692 | 11800 | 0.0005        | 0.0526          | 0.8072                  | -                        |
| 4.3054 | 11900 | 0.0005        | 0.0512          | 0.8081                  | -                        |
| 4.3415 | 12000 | 0.0005        | 0.0517          | 0.8025                  | -                        |
| 4.3777 | 12100 | 0.0005        | 0.0533          | 0.8128                  | -                        |
| 4.4139 | 12200 | 0.0005        | 0.0501          | 0.8121                  | -                        |
| 4.4501 | 12300 | 0.0005        | 0.0507          | 0.8079                  | -                        |
| 4.4863 | 12400 | 0.0005        | 0.0501          | 0.8070                  | -                        |
| 4.5224 | 12500 | 0.0004        | 0.0537          | 0.8019                  | -                        |
| 4.5586 | 12600 | 0.0004        | 0.0541          | 0.8005                  | -                        |
| 4.5948 | 12700 | 0.0005        | 0.0525          | 0.8117                  | -                        |
| 4.6310 | 12800 | 0.0004        | 0.0523          | 0.8070                  | -                        |
| 4.6671 | 12900 | 0.0005        | 0.0526          | 0.8099                  | -                        |
| 4.7033 | 13000 | 0.0004        | 0.0518          | 0.8166                  | -                        |
| 4.7395 | 13100 | 0.0004        | 0.0547          | 0.8129                  | -                        |
| 4.7757 | 13200 | 0.0005        | 0.0523          | 0.8130                  | -                        |
| 4.8119 | 13300 | 0.0004        | 0.0504          | 0.8129                  | -                        |
| 4.8480 | 13400 | 0.0005        | 0.0539          | 0.8113                  | -                        |
| 4.8842 | 13500 | 0.0004        | 0.0523          | 0.8169                  | -                        |
| 4.9204 | 13600 | 0.0005        | 0.0521          | 0.8164                  | -                        |
| 4.9566 | 13700 | 0.0004        | 0.0575          | 0.8115                  | -                        |
| 4.9928 | 13800 | 0.0004        | 0.0538          | 0.8186                  | -                        |
| 5.0289 | 13900 | 0.0004        | 0.0530          | 0.8095                  | -                        |
| 5.0651 | 14000 | 0.0003        | 0.0537          | 0.8162                  | -                        |
| 5.1013 | 14100 | 0.0004        | 0.0560          | 0.8112                  | -                        |
| 5.1375 | 14200 | 0.0004        | 0.0528          | 0.8125                  | -                        |
| 5.1737 | 14300 | 0.0004        | 0.0533          | 0.8137                  | -                        |
| 5.2098 | 14400 | 0.0003        | 0.0537          | 0.8198                  | -                        |
| 5.2460 | 14500 | 0.0004        | 0.0530          | 0.8102                  | -                        |
| 5.2822 | 14600 | 0.0004        | 0.0562          | 0.8099                  | -                        |
| 5.3184 | 14700 | 0.0004        | 0.0522          | 0.8084                  | -                        |
| 5.3546 | 14800 | 0.0004        | 0.0515          | 0.8128                  | -                        |
| 5.3907 | 14900 | 0.0004        | 0.0555          | 0.8107                  | -                        |
| 5.4269 | 15000 | 0.0004        | 0.0533          | 0.8113                  | -                        |
| 5.4631 | 15100 | 0.0003        | 0.0538          | 0.8135                  | -                        |
| 5.4993 | 15200 | 0.0004        | 0.0552          | 0.8139                  | -                        |
| 5.5355 | 15300 | 0.0003        | 0.0513          | 0.8102                  | -                        |
| 5.5716 | 15400 | 0.0004        | 0.0542          | 0.8108                  | -                        |
| 5.6078 | 15500 | 0.0003        | 0.0541          | 0.8041                  | -                        |
| 5.6440 | 15600 | 0.0004        | 0.0512          | 0.8074                  | -                        |
| 5.6802 | 15700 | 0.0003        | 0.0553          | 0.8100                  | -                        |
| 5.7164 | 15800 | 0.0003        | 0.0539          | 0.8088                  | -                        |
| 5.7525 | 15900 | 0.0004        | 0.0527          | 0.8094                  | -                        |
| 5.7887 | 16000 | 0.0004        | 0.0524          | 0.8080                  | -                        |
| 5.8249 | 16100 | 0.0003        | 0.0525          | 0.8112                  | -                        |
| 5.8611 | 16200 | 0.0003        | 0.0537          | 0.8109                  | -                        |
| 5.8973 | 16300 | 0.0003        | 0.0539          | 0.8129                  | -                        |
| 5.9334 | 16400 | 0.0003        | 0.0543          | 0.8052                  | -                        |
| 5.9696 | 16500 | 0.0003        | 0.0544          | 0.8093                  | -                        |
| 6.0058 | 16600 | 0.0004        | 0.0532          | 0.8109                  | -                        |
| 6.0420 | 16700 | 0.0002        | 0.0558          | 0.8108                  | -                        |
| 6.0781 | 16800 | 0.0002        | 0.0529          | 0.8089                  | -                        |
| 6.1143 | 16900 | 0.0003        | 0.0539          | 0.8074                  | -                        |
| 6.1505 | 17000 | 0.0003        | 0.0534          | 0.8118                  | -                        |
| 6.1867 | 17100 | 0.0003        | 0.0539          | 0.8048                  | -                        |
| 6.2229 | 17200 | 0.0003        | 0.0537          | 0.8049                  | -                        |
| 6.2590 | 17300 | 0.0003        | 0.0553          | 0.8102                  | -                        |
| 6.2952 | 17400 | 0.0002        | 0.0533          | 0.8053                  | -                        |
| 6.3314 | 17500 | 0.0003        | 0.0550          | 0.8071                  | -                        |
| 6.3676 | 17600 | 0.0002        | 0.0530          | 0.8128                  | -                        |
| 6.4038 | 17700 | 0.0003        | 0.0547          | 0.8159                  | -                        |
| 6.4399 | 17800 | 0.0002        | 0.0539          | 0.8120                  | -                        |
| 6.4761 | 17900 | 0.0003        | 0.0540          | 0.8107                  | -                        |
| 6.5123 | 18000 | 0.0003        | 0.0535          | 0.8069                  | -                        |
| 6.5485 | 18100 | 0.0003        | 0.0541          | 0.8129                  | -                        |
| 6.5847 | 18200 | 0.0003        | 0.0522          | 0.8132                  | -                        |
| 6.6208 | 18300 | 0.0002        | 0.0539          | 0.8135                  | -                        |
| 6.6570 | 18400 | 0.0002        | 0.0542          | 0.8142                  | -                        |
| 6.6932 | 18500 | 0.0003        | 0.0529          | 0.8101                  | -                        |
| 6.7294 | 18600 | 0.0003        | 0.0533          | 0.8073                  | -                        |
| 6.7656 | 18700 | 0.0003        | 0.0525          | 0.8095                  | -                        |
| 6.8017 | 18800 | 0.0003        | 0.0534          | 0.8089                  | -                        |
| 6.8379 | 18900 | 0.0002        | 0.0519          | 0.8134                  | -                        |
| 6.8741 | 19000 | 0.0002        | 0.0536          | 0.8141                  | -                        |
| 6.9103 | 19100 | 0.0002        | 0.0535          | 0.8115                  | -                        |
| 6.9465 | 19200 | 0.0002        | 0.0519          | 0.8107                  | -                        |
| 6.9826 | 19300 | 0.0002        | 0.0546          | 0.8093                  | -                        |
| 7.0188 | 19400 | 0.0002        | 0.0532          | 0.8112                  | -                        |
| 7.0550 | 19500 | 0.0002        | 0.0526          | 0.8145                  | -                        |
| 7.0912 | 19600 | 0.0002        | 0.0529          | 0.8111                  | -                        |
| 7.1274 | 19700 | 0.0002        | 0.0540          | 0.8090                  | -                        |
| 7.1635 | 19800 | 0.0002        | 0.0525          | 0.8116                  | -                        |
| 7.1997 | 19900 | 0.0002        | 0.0534          | 0.8115                  | -                        |
| 7.2359 | 20000 | 0.0002        | 0.0526          | 0.8123                  | -                        |
| 7.2721 | 20100 | 0.0002        | 0.0524          | 0.8143                  | -                        |
| 7.3082 | 20200 | 0.0002        | 0.0526          | 0.8059                  | -                        |
| 7.3444 | 20300 | 0.0002        | 0.0535          | 0.8091                  | -                        |
| 7.3806 | 20400 | 0.0002        | 0.0532          | 0.8094                  | -                        |
| 7.4168 | 20500 | 0.0002        | 0.0529          | 0.8108                  | -                        |
| 7.4530 | 20600 | 0.0002        | 0.0542          | 0.8108                  | -                        |
| 7.4891 | 20700 | 0.0002        | 0.0525          | 0.8102                  | -                        |
| 7.5253 | 20800 | 0.0002        | 0.0541          | 0.8106                  | -                        |
| 7.5615 | 20900 | 0.0002        | 0.0538          | 0.8095                  | -                        |
| 7.5977 | 21000 | 0.0003        | 0.0523          | 0.8136                  | -                        |
| 7.6339 | 21100 | 0.0002        | 0.0544          | 0.8108                  | -                        |
| 7.6700 | 21200 | 0.0002        | 0.0525          | 0.8090                  | -                        |
| 7.7062 | 21300 | 0.0002        | 0.0528          | 0.8108                  | -                        |
| 7.7424 | 21400 | 0.0002        | 0.0531          | 0.8115                  | -                        |
| 7.7786 | 21500 | 0.0002        | 0.0541          | 0.8107                  | -                        |
| 7.8148 | 21600 | 0.0001        | 0.0525          | 0.8117                  | -                        |
| 7.8509 | 21700 | 0.0002        | 0.0534          | 0.8115                  | -                        |
| 7.8871 | 21800 | 0.0002        | 0.0541          | 0.8105                  | -                        |
| 7.9233 | 21900 | 0.0002        | 0.0538          | 0.8094                  | -                        |
| 7.9595 | 22000 | 0.0002        | 0.0530          | 0.8106                  | -                        |
| 7.9957 | 22100 | 0.0002        | 0.0527          | 0.8104                  | -                        |
| 8.0318 | 22200 | 0.0001        | 0.0534          | 0.8098                  | -                        |
| 8.0680 | 22300 | 0.0002        | 0.0537          | 0.8090                  | -                        |
| 8.1042 | 22400 | 0.0001        | 0.0533          | 0.8103                  | -                        |
| 8.1404 | 22500 | 0.0002        | 0.0528          | 0.8099                  | -                        |
| 8.1766 | 22600 | 0.0001        | 0.0531          | 0.8106                  | -                        |
| 8.2127 | 22700 | 0.0001        | 0.0534          | 0.8116                  | -                        |
| 8.2489 | 22800 | 0.0001        | 0.0538          | 0.8102                  | -                        |
| 8.2851 | 22900 | 0.0001        | 0.0530          | 0.8108                  | -                        |
| 8.3213 | 23000 | 0.0002        | 0.0529          | 0.8112                  | -                        |
| 8.3575 | 23100 | 0.0001        | 0.0533          | 0.8099                  | -                        |
| 8.3936 | 23200 | 0.0001        | 0.0534          | 0.8107                  | -                        |
| 8.4298 | 23300 | 0.0002        | 0.0535          | 0.8110                  | -                        |
| 8.4660 | 23400 | 0.0001        | 0.0543          | 0.8108                  | -                        |
| 8.5022 | 23500 | 0.0001        | 0.0530          | 0.8119                  | -                        |
| 8.5384 | 23600 | 0.0001        | 0.0530          | 0.8132                  | -                        |
| 8.5745 | 23700 | 0.0001        | 0.0531          | 0.8128                  | -                        |
| 8.6107 | 23800 | 0.0002        | 0.0532          | 0.8119                  | -                        |
| 8.6469 | 23900 | 0.0002        | 0.0531          | 0.8120                  | -                        |
| 8.6831 | 24000 | 0.0001        | 0.0531          | 0.8121                  | -                        |
| 8.7192 | 24100 | 0.0001        | 0.0525          | 0.8134                  | -                        |
| 8.7554 | 24200 | 0.0002        | 0.0524          | 0.8133                  | -                        |
| 8.7916 | 24300 | 0.0001        | 0.0535          | 0.8141                  | -                        |
| 8.8278 | 24400 | 0.0002        | 0.0529          | 0.8118                  | -                        |
| 8.8640 | 24500 | 0.0001        | 0.0529          | 0.8115                  | -                        |
| 8.9001 | 24600 | 0.0001        | 0.0528          | 0.8127                  | -                        |
| 8.9363 | 24700 | 0.0002        | 0.0527          | 0.8111                  | -                        |
| 8.9725 | 24800 | 0.0001        | 0.0536          | 0.8114                  | -                        |
| 9.0087 | 24900 | 0.0001        | 0.0531          | 0.8124                  | -                        |
| 9.0449 | 25000 | 0.0001        | 0.0532          | 0.8123                  | -                        |
| 9.0810 | 25100 | 0.0001        | 0.0534          | 0.8130                  | -                        |
| 9.1172 | 25200 | 0.0001        | 0.0533          | 0.8121                  | -                        |
| 9.1534 | 25300 | 0.0002        | 0.0534          | 0.8119                  | -                        |
| 9.1896 | 25400 | 0.0001        | 0.0532          | 0.8118                  | -                        |
| 9.2258 | 25500 | 0.0001        | 0.0532          | 0.8112                  | -                        |
| 9.2619 | 25600 | 0.0001        | 0.0532          | 0.8121                  | -                        |
| 9.2981 | 25700 | 0.0002        | 0.0537          | 0.8120                  | -                        |
| 9.3343 | 25800 | 0.0001        | 0.0535          | 0.8127                  | -                        |
| 9.3705 | 25900 | 0.0001        | 0.0529          | 0.8133                  | -                        |
| 9.4067 | 26000 | 0.0001        | 0.0529          | 0.8138                  | -                        |
| 9.4428 | 26100 | 0.0001        | 0.0534          | 0.8131                  | -                        |
| 9.4790 | 26200 | 0.0001        | 0.0529          | 0.8137                  | -                        |
| 9.5152 | 26300 | 0.0002        | 0.0529          | 0.8135                  | -                        |
| 9.5514 | 26400 | 0.0001        | 0.0528          | 0.8129                  | -                        |
| 9.5876 | 26500 | 0.0001        | 0.0530          | 0.8124                  | -                        |
| 9.6237 | 26600 | 0.0001        | 0.0529          | 0.8132                  | -                        |
| 9.6599 | 26700 | 0.0001        | 0.0530          | 0.8128                  | -                        |
| 9.6961 | 26800 | 0.0001        | 0.0530          | 0.8132                  | -                        |
| 9.7323 | 26900 | 0.0001        | 0.0529          | 0.8129                  | -                        |
| 9.7685 | 27000 | 0.0002        | 0.0528          | 0.8131                  | -                        |
| 9.8046 | 27100 | 0.0001        | 0.0529          | 0.8131                  | -                        |
| 9.8408 | 27200 | 0.0002        | 0.0531          | 0.8128                  | -                        |
| 9.8770 | 27300 | 0.0001        | 0.0532          | 0.8130                  | -                        |
| 9.9132 | 27400 | 0.0001        | 0.0531          | 0.8129                  | -                        |
| 9.9493 | 27500 | 0.0001        | 0.0531          | 0.8129                  | -                        |
| 9.9855 | 27600 | 0.0001        | 0.0531          | 0.8130                  | -                        |
| -1     | -1    | -             | -               | -                       | 0.7644                   |

</details>

### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.5.1+cu124
- Accelerate: 0.34.2
- Datasets: 2.19.2
- 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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