sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:212930 loss:CosineSimilarityLoss dataset:yahyaabd/allstats-semantic-search-synthetic-dataset-v1 arxiv:1908.10084 base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
datasets:
- yahyaabd/allstats-semantic-search-synthetic-dataset-v1
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:212930
- loss:CosineSimilarityLoss
widget:
- source_sentence: analisis perekonomian indonesia triwulan i 2007
  sentences:
  - Indikator Ekonomi Februari 2017
  - Perkembangan Harga Produsen Gabah Maret 2021
  - Hasil Survei Komoditas Perikanan Potensi 2021 Profil Rumah Tangga Usaha Budidaya
    Rumput Laut
- source_sentence: Analisis indikator ekonomi Indonesia September 2023
  sentences:
  - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
    Negara Oktober 2011
  - April 2015 Harga Grosir Naik 0,17%
  - Direktori Eksportir Indonesia 2014
- source_sentence: Ekonomi Indonesia awal 2009
  sentences:
  - Statistik Hotel dan Akomodasi Lainnya di Indonesia 2005
  - Neraca Pemerintahan Pusat Indonesia Triwulanan 2007-2013:2
  - Persentase Rumah Tangga menurut Provinsi dan Sumber Penerangan Listrik PLN, 1993-2022
- source_sentence: Berapa persen kenaikan ekspor Indonesia pada Oktober 2024 dibandingkan
    September 2024?
  sentences:
  - Impor Indonesia mengalami penurunan sebesar 5% pada bulan Oktober 2024.
  - Statistik Pendidikan 2009
  - Penduduk Jambi Hasil Sensus Penduduk SP2000
- source_sentence: Berapa persen deflasi ysng terjadi paa Maret 2010?
  sentences:
  - Pada Bulan Maret 2010 Terjadi Deflasi Sebesar 0,14 Persen.
  - Analisis Rumah Tangga Usaha Hortikultura di Indonesia Hasil Sensus Pertanian 2013
  - Inflasi September 2008 sebesar 0,97 persen.
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstats semantic search v1 2 dev
      type: allstats-semantic-search-v1-2-dev
    metrics:
    - type: pearson_cosine
      value: 0.9923210175659568
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9293313388011538
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstat semantic search v1 test
      type: allstat-semantic-search-v1-test
    metrics:
    - type: pearson_cosine
      value: 0.9929329799075536
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9283010769773397
      name: Spearman Cosine
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) dataset. 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:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1)
<!-- - **Language:** Unknown -->
<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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("yahyaabd/allstats-semantic-search-model-v1-2")
# Run inference
sentences = [
    'Berapa persen deflasi ysng terjadi paa Maret 2010?',
    'Pada Bulan Maret 2010 Terjadi Deflasi Sebesar 0,14 Persen.',
    'Inflasi September 2008 sebesar 0,97 persen.',
]
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>
-->

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### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Semantic Similarity

* Datasets: `allstats-semantic-search-v1-2-dev` and `allstat-semantic-search-v1-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | allstats-semantic-search-v1-2-dev | allstat-semantic-search-v1-test |
|:--------------------|:----------------------------------|:--------------------------------|
| pearson_cosine      | 0.9923                            | 0.9929                          |
| **spearman_cosine** | **0.9293**                        | **0.9283**                      |

<!--
## 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.*
-->

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### Recommendations

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

## Training Details

### Training Dataset

#### allstats-semantic-search-synthetic-dataset-v1

* Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [c477abf](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/c477abfd5da1a6968bad673a50c71e17b62f39b4)
* Size: 212,930 training samples
* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | doc                                                                               | label                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 11.52 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.85 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
* Samples:
  | query                                                                       | doc                                                                                                                             | label             |
  |:----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|:------------------|
  | <code>studi tentang kemiskinan urban</code>                                 | <code>Perkembangan Mingguan Harga Eceran Beberapa Bahan Pokok di Ibukota Provinsi Seluruh Indonesia (Juli-Desember 2018)</code> | <code>0.1</code>  |
  | <code>Harga gabah di tingkat produsen bulan September</code>                | <code>Upah Buruh Juli 2020</code>                                                                                               | <code>0.1</code>  |
  | <code>Data perusahaan konstruksi di wilayah timur Indonesia thn 2013</code> | <code>Direktori Perusahaan Konstruksi 2013 Buku 6 Maluku dan Papua</code>                                                       | <code>0.92</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

#### allstats-semantic-search-synthetic-dataset-v1

* Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [c477abf](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/c477abfd5da1a6968bad673a50c71e17b62f39b4)
* Size: 26,616 evaluation samples
* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | doc                                                                              | label                                                         |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | float                                                         |
  | details | <ul><li>min: 5 tokens</li><li>mean: 11.33 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.6 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
  | query                                                                                                                           | doc                                                      | label             |
  |:--------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|:------------------|
  | <code>Informasi potensi deas di Maluku 011</code>                                                                               | <code>Statistik Potensi Desa Provinsi Maluku 2011</code> | <code>0.87</code> |
  | <code>Berapa persen kenaikan jumlah penumpang angkutan udara internasional pada Januari 2024 dibandingkan Desember 2023?</code> | <code>Kenaikan jumlah penumpang bulan lainnya</code>     | <code>0.0</code>  |
  | <code>informasi tentang potensi desa jambi tahun 2005</code>                                                                    | <code>Statistik Potensi Desa Provinsi Jambi 2005</code>  | <code>0.85</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`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 6
- `warmup_ratio`: 0.1
- `fp16`: 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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 6
- `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`: False
- `fp16`: True
- `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`: 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 | allstats-semantic-search-v1-2-dev_spearman_cosine | allstat-semantic-search-v1-test_spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|:-------------------------------------------------:|:-----------------------------------------------:|
| 0.0376 | 250   | 0.0734        | 0.0464          | 0.6927                                            | -                                               |
| 0.0751 | 500   | 0.043         | 0.0362          | 0.7146                                            | -                                               |
| 0.1127 | 750   | 0.0353        | 0.0288          | 0.7364                                            | -                                               |
| 0.1503 | 1000  | 0.0271        | 0.0274          | 0.7571                                            | -                                               |
| 0.1878 | 1250  | 0.0241        | 0.0225          | 0.7738                                            | -                                               |
| 0.2254 | 1500  | 0.0228        | 0.0203          | 0.7699                                            | -                                               |
| 0.2630 | 1750  | 0.0207        | 0.0197          | 0.7881                                            | -                                               |
| 0.3005 | 2000  | 0.0187        | 0.0191          | 0.7900                                            | -                                               |
| 0.3381 | 2250  | 0.0194        | 0.0183          | 0.7794                                            | -                                               |
| 0.3757 | 2500  | 0.0182        | 0.0178          | 0.7870                                            | -                                               |
| 0.4132 | 2750  | 0.0198        | 0.0183          | 0.8009                                            | -                                               |
| 0.4508 | 3000  | 0.0189        | 0.0182          | 0.7912                                            | -                                               |
| 0.4884 | 3250  | 0.0177        | 0.0168          | 0.7963                                            | -                                               |
| 0.5259 | 3500  | 0.0178        | 0.0173          | 0.7920                                            | -                                               |
| 0.5635 | 3750  | 0.017         | 0.0183          | 0.8014                                            | -                                               |
| 0.6011 | 4000  | 0.0186        | 0.0180          | 0.7777                                            | -                                               |
| 0.6386 | 4250  | 0.0187        | 0.0167          | 0.7976                                            | -                                               |
| 0.6762 | 4500  | 0.015         | 0.0154          | 0.8194                                            | -                                               |
| 0.7137 | 4750  | 0.0158        | 0.0157          | 0.8062                                            | -                                               |
| 0.7513 | 5000  | 0.0152        | 0.0148          | 0.8117                                            | -                                               |
| 0.7889 | 5250  | 0.0148        | 0.0149          | 0.8115                                            | -                                               |
| 0.8264 | 5500  | 0.0146        | 0.0141          | 0.8175                                            | -                                               |
| 0.8640 | 5750  | 0.0154        | 0.0144          | 0.7951                                            | -                                               |
| 0.9016 | 6000  | 0.0155        | 0.0152          | 0.8163                                            | -                                               |
| 0.9391 | 6250  | 0.0145        | 0.0136          | 0.8216                                            | -                                               |
| 0.9767 | 6500  | 0.0149        | 0.0149          | 0.8140                                            | -                                               |
| 1.0143 | 6750  | 0.0132        | 0.0132          | 0.8179                                            | -                                               |
| 1.0518 | 7000  | 0.0108        | 0.0124          | 0.8232                                            | -                                               |
| 1.0894 | 7250  | 0.0109        | 0.0120          | 0.8330                                            | -                                               |
| 1.1270 | 7500  | 0.0112        | 0.0132          | 0.8219                                            | -                                               |
| 1.1645 | 7750  | 0.0116        | 0.0124          | 0.8226                                            | -                                               |
| 1.2021 | 8000  | 0.0121        | 0.0120          | 0.8151                                            | -                                               |
| 1.2397 | 8250  | 0.0109        | 0.0119          | 0.8384                                            | -                                               |
| 1.2772 | 8500  | 0.0103        | 0.0114          | 0.8415                                            | -                                               |
| 1.3148 | 8750  | 0.0105        | 0.0116          | 0.8191                                            | -                                               |
| 1.3524 | 9000  | 0.0104        | 0.0122          | 0.8292                                            | -                                               |
| 1.3899 | 9250  | 0.0108        | 0.0117          | 0.8292                                            | -                                               |
| 1.4275 | 9500  | 0.011         | 0.0118          | 0.8339                                            | -                                               |
| 1.4651 | 9750  | 0.0105        | 0.0106          | 0.8367                                            | -                                               |
| 1.5026 | 10000 | 0.0093        | 0.0098          | 0.8467                                            | -                                               |
| 1.5402 | 10250 | 0.0105        | 0.0101          | 0.8334                                            | -                                               |
| 1.5778 | 10500 | 0.0102        | 0.0106          | 0.8324                                            | -                                               |
| 1.6153 | 10750 | 0.01          | 0.0097          | 0.8472                                            | -                                               |
| 1.6529 | 11000 | 0.0106        | 0.0098          | 0.8378                                            | -                                               |
| 1.6905 | 11250 | 0.0088        | 0.0095          | 0.8531                                            | -                                               |
| 1.7280 | 11500 | 0.0085        | 0.0095          | 0.8409                                            | -                                               |
| 1.7656 | 11750 | 0.0089        | 0.0091          | 0.8431                                            | -                                               |
| 1.8032 | 12000 | 0.0083        | 0.0088          | 0.8524                                            | -                                               |
| 1.8407 | 12250 | 0.0082        | 0.0088          | 0.8591                                            | -                                               |
| 1.8783 | 12500 | 0.0078        | 0.0092          | 0.8478                                            | -                                               |
| 1.9159 | 12750 | 0.009         | 0.0085          | 0.8480                                            | -                                               |
| 1.9534 | 13000 | 0.0082        | 0.0089          | 0.8465                                            | -                                               |
| 1.9910 | 13250 | 0.0076        | 0.0085          | 0.8564                                            | -                                               |
| 2.0285 | 13500 | 0.0059        | 0.0082          | 0.8602                                            | -                                               |
| 2.0661 | 13750 | 0.0073        | 0.0081          | 0.8558                                            | -                                               |
| 2.1037 | 14000 | 0.0075        | 0.0081          | 0.8492                                            | -                                               |
| 2.1412 | 14250 | 0.0066        | 0.0077          | 0.8520                                            | -                                               |
| 2.1788 | 14500 | 0.0066        | 0.0076          | 0.8599                                            | -                                               |
| 2.2164 | 14750 | 0.007         | 0.0080          | 0.8589                                            | -                                               |
| 2.2539 | 15000 | 0.0065        | 0.0076          | 0.8552                                            | -                                               |
| 2.2915 | 15250 | 0.0071        | 0.0075          | 0.8604                                            | -                                               |
| 2.3291 | 15500 | 0.0062        | 0.0073          | 0.8714                                            | -                                               |
| 2.3666 | 15750 | 0.0058        | 0.0069          | 0.8714                                            | -                                               |
| 2.4042 | 16000 | 0.0066        | 0.0072          | 0.8570                                            | -                                               |
| 2.4418 | 16250 | 0.0058        | 0.0069          | 0.8757                                            | -                                               |
| 2.4793 | 16500 | 0.0059        | 0.0067          | 0.8726                                            | -                                               |
| 2.5169 | 16750 | 0.0057        | 0.0067          | 0.8663                                            | -                                               |
| 2.5545 | 17000 | 0.0058        | 0.0068          | 0.8703                                            | -                                               |
| 2.5920 | 17250 | 0.0058        | 0.0068          | 0.8765                                            | -                                               |
| 2.6296 | 17500 | 0.006         | 0.0067          | 0.8729                                            | -                                               |
| 2.6672 | 17750 | 0.0057        | 0.0067          | 0.8689                                            | -                                               |
| 2.7047 | 18000 | 0.0055        | 0.0065          | 0.8750                                            | -                                               |
| 2.7423 | 18250 | 0.0056        | 0.0066          | 0.8734                                            | -                                               |
| 2.7799 | 18500 | 0.0053        | 0.0062          | 0.8745                                            | -                                               |
| 2.8174 | 18750 | 0.0053        | 0.0062          | 0.8814                                            | -                                               |
| 2.8550 | 19000 | 0.0048        | 0.0063          | 0.8839                                            | -                                               |
| 2.8926 | 19250 | 0.005         | 0.0063          | 0.8741                                            | -                                               |
| 2.9301 | 19500 | 0.0063        | 0.0061          | 0.8752                                            | -                                               |
| 2.9677 | 19750 | 0.0052        | 0.0059          | 0.8790                                            | -                                               |
| 3.0053 | 20000 | 0.0049        | 0.0058          | 0.8825                                            | -                                               |
| 3.0428 | 20250 | 0.0042        | 0.0059          | 0.8787                                            | -                                               |
| 3.0804 | 20500 | 0.0043        | 0.0056          | 0.8839                                            | -                                               |
| 3.1180 | 20750 | 0.0036        | 0.0058          | 0.8870                                            | -                                               |
| 3.1555 | 21000 | 0.004         | 0.0056          | 0.8825                                            | -                                               |
| 3.1931 | 21250 | 0.0041        | 0.0056          | 0.8884                                            | -                                               |
| 3.2307 | 21500 | 0.004         | 0.0054          | 0.8872                                            | -                                               |
| 3.2682 | 21750 | 0.0044        | 0.0052          | 0.8838                                            | -                                               |
| 3.3058 | 22000 | 0.0036        | 0.0053          | 0.8904                                            | -                                               |
| 3.3434 | 22250 | 0.0036        | 0.0054          | 0.8898                                            | -                                               |
| 3.3809 | 22500 | 0.0037        | 0.0051          | 0.8938                                            | -                                               |
| 3.4185 | 22750 | 0.0036        | 0.0051          | 0.8953                                            | -                                               |
| 3.4560 | 23000 | 0.0036        | 0.0051          | 0.8935                                            | -                                               |
| 3.4936 | 23250 | 0.004         | 0.0049          | 0.8955                                            | -                                               |
| 3.5312 | 23500 | 0.0033        | 0.0051          | 0.8912                                            | -                                               |
| 3.5687 | 23750 | 0.0037        | 0.0048          | 0.8995                                            | -                                               |
| 3.6063 | 24000 | 0.0037        | 0.0048          | 0.8887                                            | -                                               |
| 3.6439 | 24250 | 0.0037        | 0.0048          | 0.8921                                            | -                                               |
| 3.6814 | 24500 | 0.0034        | 0.0046          | 0.9001                                            | -                                               |
| 3.7190 | 24750 | 0.0041        | 0.0048          | 0.9008                                            | -                                               |
| 3.7566 | 25000 | 0.0037        | 0.0048          | 0.8928                                            | -                                               |
| 3.7941 | 25250 | 0.0038        | 0.0049          | 0.8949                                            | -                                               |
| 3.8317 | 25500 | 0.0037        | 0.0045          | 0.9029                                            | -                                               |
| 3.8693 | 25750 | 0.0034        | 0.0057          | 0.8962                                            | -                                               |
| 3.9068 | 26000 | 0.0035        | 0.0047          | 0.8963                                            | -                                               |
| 3.9444 | 26250 | 0.0039        | 0.0044          | 0.9026                                            | -                                               |
| 3.9820 | 26500 | 0.0034        | 0.0044          | 0.8994                                            | -                                               |
| 4.0195 | 26750 | 0.0029        | 0.0042          | 0.9039                                            | -                                               |
| 4.0571 | 27000 | 0.0025        | 0.0040          | 0.9047                                            | -                                               |
| 4.0947 | 27250 | 0.0027        | 0.0041          | 0.9033                                            | -                                               |
| 4.1322 | 27500 | 0.0027        | 0.0041          | 0.9034                                            | -                                               |
| 4.1698 | 27750 | 0.0025        | 0.0040          | 0.9040                                            | -                                               |
| 4.2074 | 28000 | 0.0033        | 0.0041          | 0.9079                                            | -                                               |
| 4.2449 | 28250 | 0.0027        | 0.0040          | 0.9078                                            | -                                               |
| 4.2825 | 28500 | 0.0024        | 0.0040          | 0.9059                                            | -                                               |
| 4.3201 | 28750 | 0.0026        | 0.0040          | 0.9084                                            | -                                               |
| 4.3576 | 29000 | 0.0021        | 0.0039          | 0.9101                                            | -                                               |
| 4.3952 | 29250 | 0.0024        | 0.0040          | 0.9081                                            | -                                               |
| 4.4328 | 29500 | 0.0024        | 0.0039          | 0.9128                                            | -                                               |
| 4.4703 | 29750 | 0.0027        | 0.0039          | 0.9067                                            | -                                               |
| 4.5079 | 30000 | 0.003         | 0.0038          | 0.9120                                            | -                                               |
| 4.5455 | 30250 | 0.0024        | 0.0037          | 0.9140                                            | -                                               |
| 4.5830 | 30500 | 0.0025        | 0.0037          | 0.9116                                            | -                                               |
| 4.6206 | 30750 | 0.0023        | 0.0037          | 0.9124                                            | -                                               |
| 4.6582 | 31000 | 0.0026        | 0.0036          | 0.9161                                            | -                                               |
| 4.6957 | 31250 | 0.0021        | 0.0036          | 0.9155                                            | -                                               |
| 4.7333 | 31500 | 0.0025        | 0.0035          | 0.9147                                            | -                                               |
| 4.7708 | 31750 | 0.0023        | 0.0035          | 0.9171                                            | -                                               |
| 4.8084 | 32000 | 0.0024        | 0.0035          | 0.9153                                            | -                                               |
| 4.8460 | 32250 | 0.002         | 0.0035          | 0.9153                                            | -                                               |
| 4.8835 | 32500 | 0.0025        | 0.0034          | 0.9173                                            | -                                               |
| 4.9211 | 32750 | 0.0018        | 0.0035          | 0.9180                                            | -                                               |
| 4.9587 | 33000 | 0.0021        | 0.0035          | 0.9201                                            | -                                               |
| 4.9962 | 33250 | 0.0019        | 0.0035          | 0.9205                                            | -                                               |
| 5.0338 | 33500 | 0.0016        | 0.0034          | 0.9223                                            | -                                               |
| 5.0714 | 33750 | 0.0016        | 0.0034          | 0.9217                                            | -                                               |
| 5.1089 | 34000 | 0.0015        | 0.0033          | 0.9208                                            | -                                               |
| 5.1465 | 34250 | 0.002         | 0.0034          | 0.9234                                            | -                                               |
| 5.1841 | 34500 | 0.0017        | 0.0033          | 0.9212                                            | -                                               |
| 5.2216 | 34750 | 0.002         | 0.0033          | 0.9212                                            | -                                               |
| 5.2592 | 35000 | 0.0015        | 0.0032          | 0.9241                                            | -                                               |
| 5.2968 | 35250 | 0.002         | 0.0031          | 0.9232                                            | -                                               |
| 5.3343 | 35500 | 0.0017        | 0.0031          | 0.9251                                            | -                                               |
| 5.3719 | 35750 | 0.0015        | 0.0031          | 0.9256                                            | -                                               |
| 5.4095 | 36000 | 0.0018        | 0.0031          | 0.9246                                            | -                                               |
| 5.4470 | 36250 | 0.0015        | 0.0030          | 0.9257                                            | -                                               |
| 5.4846 | 36500 | 0.0017        | 0.0030          | 0.9261                                            | -                                               |
| 5.5222 | 36750 | 0.0018        | 0.0030          | 0.9251                                            | -                                               |
| 5.5597 | 37000 | 0.0016        | 0.0030          | 0.9270                                            | -                                               |
| 5.5973 | 37250 | 0.0016        | 0.0029          | 0.9275                                            | -                                               |
| 5.6349 | 37500 | 0.0017        | 0.0029          | 0.9283                                            | -                                               |
| 5.6724 | 37750 | 0.0015        | 0.0029          | 0.9277                                            | -                                               |
| 5.7100 | 38000 | 0.0017        | 0.0029          | 0.9286                                            | -                                               |
| 5.7476 | 38250 | 0.0015        | 0.0029          | 0.9284                                            | -                                               |
| 5.7851 | 38500 | 0.0015        | 0.0029          | 0.9286                                            | -                                               |
| 5.8227 | 38750 | 0.0014        | 0.0029          | 0.9287                                            | -                                               |
| 5.8603 | 39000 | 0.0015        | 0.0028          | 0.9290                                            | -                                               |
| 5.8978 | 39250 | 0.0014        | 0.0028          | 0.9291                                            | -                                               |
| 5.9354 | 39500 | 0.0014        | 0.0028          | 0.9293                                            | -                                               |
| 5.9730 | 39750 | 0.0015        | 0.0028          | 0.9293                                            | -                                               |
| 6.0    | 39930 | -             | -               | -                                                 | 0.9283                                          |

</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.2.2+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

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