sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:25551 loss:OnlineContrastiveLoss dataset:yahyaabd/query-hard-pos-neg-doc-pairs-statictable arxiv:1908.10084 base_model:sentence-transformers/paraphrase-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-MiniLM-L12-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-MiniLM-L12-v2
datasets:
- yahyaabd/query-hard-pos-neg-doc-pairs-statictable
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:25551
- loss:OnlineContrastiveLoss
widget:
- source_sentence: Berapa gaji ratarata buruhkaryawan di Indonesia lihat dari umur
    dan lapangan pekerjaannya 2019
  sentences:
  - Rasio laju peningkatan konsumsi tanah dengan laju pertumbuhan penduduk
  - Rata-rata UpahGaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Kelompok Umur
    dan lapangan pekerjaan utama, 2019
  - Ringkasan Neraca Arus Dana, Triwulan Pertama, 2005, (Miliar Rupiah)
- source_sentence: Average monthly net wage/salary of employees by age group and type
    of work (Rupiah), 2018
  sentences:
  - Ringkasan Neraca Arus Dana, Triwulan III, 2014**), (Miliar Rupiah)
  - Nilai Produksi dan Biaya Produksi Rumah Tangga Usaha Peternakan Menurut Jenis
    Ternak, 2014
  - Rekapitulasi Laporan Posisi Keuangan Perusahaan Penyelenggara Program Asuransi
    Wajib dan BPJS Per 31 Desember (miliar rupiah) 2000-2021
- source_sentence: jumlah pembangunan fasilitas sekolah baru
  sentences:
  - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
    yang Ditamatkan dan Lapangan Pekerjaan Utama di 9 Sektor (rupiah), 2017
  - Posisi Kredit Perbankan1dalam Rupiah dan Valuta Asing Menurut Sektor Ekonomi (miliar
    rupiah), 2016-2018
  - Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Hasil Long Form SP2020 Menurut
    Provinsi/Kabupaten/Kota, 2020
- source_sentence: Data Pendapatan Rata-rata Orang Yang Berusaha Sendiri Per Provinsi,
    Berdasarkan Lapangan Pekerjaan Utama (2020)
  sentences:
  - Nilai Pendapatan Disposabel Menurut Golongan Rumah Tangga (miliar rupiah), 2000,
    2005, dan 2008
  - IHK dan Rata-rata Upah per Bulan Buruh Pertambangan di Bawah Mandor (Supervisor),
    1996-2014 (1996=100)
  - Ringkasan Neraca Arus Dana Tahun 2004 (Miliar Rupiah)
- source_sentence: Bagaimana perkembangan koperasi di Indonesia, khususnya sekitar
    tayun 2000?
  sentences:
  - Rata-Rata Harian Aliran Sungai, Tinggi Aliran, dan Volume Air di Beberapa Sungai
    yang Daerah Pengalirannya Lebih dari 1.000 km2, 2000-2011
  - Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut
    Golongan Umur dan Jumlah Jam Kerja Seluruhnya, 2008 - 2024
  - IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor),
    1996-2014 (1996=100)
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L12-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstats semantic mini v1 eval
      type: allstats-semantic-mini-v1-eval
    metrics:
    - type: pearson_cosine
      value: 0.8479971660039509
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7745638757528484
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstat search mini v1 test
      type: allstat-search-mini-v1-test
    metrics:
    - type: pearson_cosine
      value: 0.8538445733470035
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7767623851780713
      name: Spearman Cosine
---

# SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2) on the [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) dataset. It maps sentences & paragraphs to a 384-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2) <!-- at revision 3f21b01a41e265ecb43cef6afeef20b7e578b637 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable)
<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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-search-miniLM-v1")
# Run inference
sentences = [
    'Bagaimana perkembangan koperasi di Indonesia, khususnya sekitar tayun 2000?',
    'IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100)',
    'Rata-Rata Harian Aliran Sungai, Tinggi Aliran, dan Volume Air di Beberapa Sungai yang Daerah Pengalirannya Lebih dari 1.000 km2, 2000-2011',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

| Metric              | allstats-semantic-mini-v1-eval | allstat-search-mini-v1-test |
|:--------------------|:-------------------------------|:----------------------------|
| pearson_cosine      | 0.848                          | 0.8538                      |
| **spearman_cosine** | **0.7746**                     | **0.7768**                  |

<!--
## 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

#### query-hard-pos-neg-doc-pairs-statictable

* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [25756d3](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/25756d36046bf92b56bce1b450fd080853688667)
* Size: 25,551 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                                                                             | int                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 28.64 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 36.67 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>0: ~65.80%</li><li>1: ~34.20%</li></ul> |
* Samples:
  | query                                                                                                      | doc                                                                        | label          |
  |:-----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:---------------|
  | <code>Gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014</code> | <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> | <code>0</code> |
  | <code>gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014</code> | <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> | <code>0</code> |
  | <code>GAJI NOMINAL, INDEKS UPAH: NOMINAL & RIIL PEKERJA MANUFAKTUR NON-MANDOR (2012=100), 2013-2014</code> | <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### query-hard-pos-neg-doc-pairs-statictable

* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [25756d3](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/25756d36046bf92b56bce1b450fd080853688667)
* Size: 5,463 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                                                                            | int                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 29.3 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 37.1 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>0: ~73.20%</li><li>1: ~26.80%</li></ul> |
* Samples:
  | query                                                                                                                         | doc                                                                                                                      | label          |
  |:------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Bagaimana penghasilan wirausahawan di Indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?</code> | <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> | <code>0</code> |
  | <code>bagaimana penghasilan wirausahawan di indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?</code> | <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> | <code>0</code> |
  | <code>BAGAIMANA PENGHASILAN WIRAUSAHAWAN DI INDONESIA BERVARIASI PER PROVINSI DAN JENIS PEKERJAAN UTAMA DI TAHUN 2016?</code> | <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `eval_on_start`: 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`: 4
- `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`: 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`: True
- `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-mini-v1-eval_spearman_cosine | allstat-search-mini-v1-test_spearman_cosine |
|:----------:|:--------:|:-------------:|:---------------:|:----------------------------------------------:|:-------------------------------------------:|
| 0          | 0        | -             | 1.0797          | 0.5314                                         | -                                           |
| 0.0250     | 20       | 1.2823        | 0.9331          | 0.5510                                         | -                                           |
| 0.0501     | 40       | 0.9562        | 0.6159          | 0.6492                                         | -                                           |
| 0.0751     | 60       | 0.5872        | 0.4629          | 0.6913                                         | -                                           |
| 0.1001     | 80       | 0.4101        | 0.3605          | 0.7221                                         | -                                           |
| 0.1252     | 100      | 0.419         | 0.3919          | 0.7301                                         | -                                           |
| 0.1502     | 120      | 0.1517        | 0.2565          | 0.7457                                         | -                                           |
| 0.1752     | 140      | 0.2678        | 0.2503          | 0.7484                                         | -                                           |
| 0.2003     | 160      | 0.225         | 0.2010          | 0.7546                                         | -                                           |
| 0.2253     | 180      | 0.2846        | 0.3203          | 0.7420                                         | -                                           |
| 0.2503     | 200      | 0.2086        | 0.1981          | 0.7589                                         | -                                           |
| 0.2753     | 220      | 0.1255        | 0.1982          | 0.7610                                         | -                                           |
| 0.3004     | 240      | 0.1182        | 0.2328          | 0.7583                                         | -                                           |
| 0.3254     | 260      | 0.1328        | 0.2218          | 0.7561                                         | -                                           |
| 0.3504     | 280      | 0.1228        | 0.4583          | 0.7343                                         | -                                           |
| 0.3755     | 300      | 0.1394        | 0.1785          | 0.7705                                         | -                                           |
| 0.4005     | 320      | 0.2577        | 0.1800          | 0.7650                                         | -                                           |
| 0.4255     | 340      | 0.1903        | 0.2680          | 0.7557                                         | -                                           |
| 0.4506     | 360      | 0.1164        | 0.1761          | 0.7616                                         | -                                           |
| 0.4756     | 380      | 0.0779        | 0.3318          | 0.7453                                         | -                                           |
| 0.5006     | 400      | 0.1563        | 0.2209          | 0.7582                                         | -                                           |
| 0.5257     | 420      | 0.1835        | 0.1683          | 0.7662                                         | -                                           |
| 0.5507     | 440      | 0.1171        | 0.1537          | 0.7658                                         | -                                           |
| 0.5757     | 460      | 0.0973        | 0.1381          | 0.7710                                         | -                                           |
| 0.6008     | 480      | 0.0578        | 0.2303          | 0.7618                                         | -                                           |
| 0.6258     | 500      | 0.1343        | 0.1431          | 0.7710                                         | -                                           |
| 0.6508     | 520      | 0.1274        | 0.1646          | 0.7695                                         | -                                           |
| 0.6758     | 540      | 0.057         | 0.1775          | 0.7606                                         | -                                           |
| 0.7009     | 560      | 0.0392        | 0.1425          | 0.7689                                         | -                                           |
| 0.7259     | 580      | 0.0434        | 0.1399          | 0.7712                                         | -                                           |
| 0.7509     | 600      | 0.1311        | 0.1747          | 0.7670                                         | -                                           |
| 0.7760     | 620      | 0.0475        | 0.1375          | 0.7709                                         | -                                           |
| 0.8010     | 640      | 0.0183        | 0.1465          | 0.7685                                         | -                                           |
| 0.8260     | 660      | 0.024         | 0.1666          | 0.7669                                         | -                                           |
| 0.8511     | 680      | 0.0249        | 0.1728          | 0.7656                                         | -                                           |
| 0.8761     | 700      | 0.041         | 0.1624          | 0.7711                                         | -                                           |
| 0.9011     | 720      | 0.0835        | 0.1397          | 0.7716                                         | -                                           |
| 0.9262     | 740      | 0.0404        | 0.1507          | 0.7693                                         | -                                           |
| 0.9512     | 760      | 0.0141        | 0.1369          | 0.7723                                         | -                                           |
| 0.9762     | 780      | 0.0513        | 0.1555          | 0.7687                                         | -                                           |
| 1.0013     | 800      | 0.0387        | 0.1306          | 0.7717                                         | -                                           |
| 1.0263     | 820      | 0.0393        | 0.1420          | 0.7707                                         | -                                           |
| 1.0513     | 840      | 0.0153        | 0.1656          | 0.7700                                         | -                                           |
| 1.0763     | 860      | 0.0263        | 0.1525          | 0.7694                                         | -                                           |
| 1.1014     | 880      | 0.0503        | 0.1947          | 0.7638                                         | -                                           |
| 1.1264     | 900      | 0.0215        | 0.2202          | 0.7615                                         | -                                           |
| 1.1514     | 920      | 0.0217        | 0.1542          | 0.7696                                         | -                                           |
| 1.1765     | 940      | 0.007         | 0.1394          | 0.7713                                         | -                                           |
| 1.2015     | 960      | 0.018         | 0.1573          | 0.7706                                         | -                                           |
| 1.2265     | 980      | 0.0446        | 0.1504          | 0.7686                                         | -                                           |
| 1.2516     | 1000     | 0.026         | 0.1573          | 0.7661                                         | -                                           |
| 1.2766     | 1020     | 0.0098        | 0.1429          | 0.7683                                         | -                                           |
| 1.3016     | 1040     | 0.0196        | 0.1374          | 0.7702                                         | -                                           |
| 1.3267     | 1060     | 0.021         | 0.1594          | 0.7685                                         | -                                           |
| 1.3517     | 1080     | 0.0499        | 0.1378          | 0.7724                                         | -                                           |
| 1.3767     | 1100     | 0.0165        | 0.1335          | 0.7729                                         | -                                           |
| 1.4018     | 1120     | 0.0294        | 0.1451          | 0.7713                                         | -                                           |
| 1.4268     | 1140     | 0.0114        | 0.1338          | 0.7717                                         | -                                           |
| 1.4518     | 1160     | 0.0192        | 0.1327          | 0.7719                                         | -                                           |
| 1.4768     | 1180     | 0.0335        | 0.1618          | 0.7646                                         | -                                           |
| 1.5019     | 1200     | 0.0546        | 0.1389          | 0.7711                                         | -                                           |
| 1.5269     | 1220     | 0.0069        | 0.1239          | 0.7738                                         | -                                           |
| 1.5519     | 1240     | 0.0094        | 0.1180          | 0.7739                                         | -                                           |
| 1.5770     | 1260     | 0.0074        | 0.1238          | 0.7733                                         | -                                           |
| 1.6020     | 1280     | 0.0557        | 0.1428          | 0.7720                                         | -                                           |
| 1.6270     | 1300     | 0.056         | 0.1159          | 0.7751                                         | -                                           |
| 1.6521     | 1320     | 0.0           | 0.1244          | 0.7758                                         | -                                           |
| 1.6771     | 1340     | 0.0066        | 0.1185          | 0.7735                                         | -                                           |
| 1.7021     | 1360     | 0.0178        | 0.1016          | 0.7757                                         | -                                           |
| 1.7272     | 1380     | 0.0156        | 0.0939          | 0.7776                                         | -                                           |
| 1.7522     | 1400     | 0.0           | 0.1138          | 0.7761                                         | -                                           |
| 1.7772     | 1420     | 0.0436        | 0.0980          | 0.7775                                         | -                                           |
| 1.8023     | 1440     | 0.0626        | 0.1096          | 0.7763                                         | -                                           |
| 1.8273     | 1460     | 0.0222        | 0.0968          | 0.7774                                         | -                                           |
| 1.8523     | 1480     | 0.0101        | 0.1021          | 0.7762                                         | -                                           |
| 1.8773     | 1500     | 0.0171        | 0.1076          | 0.7754                                         | -                                           |
| 1.9024     | 1520     | 0.0064        | 0.1279          | 0.7730                                         | -                                           |
| 1.9274     | 1540     | 0.0068        | 0.1237          | 0.7729                                         | -                                           |
| 1.9524     | 1560     | 0.0066        | 0.1229          | 0.7733                                         | -                                           |
| 1.9775     | 1580     | 0.0           | 0.1263          | 0.7731                                         | -                                           |
| 2.0025     | 1600     | 0.0065        | 0.1152          | 0.7746                                         | -                                           |
| 2.0275     | 1620     | 0.0147        | 0.1021          | 0.7773                                         | -                                           |
| 2.0526     | 1640     | 0.0           | 0.1021          | 0.7773                                         | -                                           |
| 2.0776     | 1660     | 0.0209        | 0.1017          | 0.7774                                         | -                                           |
| 2.1026     | 1680     | 0.0           | 0.0993          | 0.7773                                         | -                                           |
| 2.1277     | 1700     | 0.0067        | 0.0922          | 0.7784                                         | -                                           |
| 2.1527     | 1720     | 0.0333        | 0.1158          | 0.7749                                         | -                                           |
| 2.1777     | 1740     | 0.0           | 0.1397          | 0.7721                                         | -                                           |
| 2.2028     | 1760     | 0.0158        | 0.1248          | 0.7751                                         | -                                           |
| 2.2278     | 1780     | 0.0201        | 0.1021          | 0.7767                                         | -                                           |
| 2.2528     | 1800     | 0.0           | 0.1029          | 0.7768                                         | -                                           |
| 2.2778     | 1820     | 0.0107        | 0.1007          | 0.7767                                         | -                                           |
| 2.3029     | 1840     | 0.0156        | 0.0923          | 0.7767                                         | -                                           |
| 2.3279     | 1860     | 0.0           | 0.1012          | 0.7754                                         | -                                           |
| 2.3529     | 1880     | 0.0131        | 0.1184          | 0.7731                                         | -                                           |
| 2.3780     | 1900     | 0.0072        | 0.1113          | 0.7752                                         | -                                           |
| 2.4030     | 1920     | 0.0337        | 0.0952          | 0.7775                                         | -                                           |
| 2.4280     | 1940     | 0.0068        | 0.1086          | 0.7754                                         | -                                           |
| 2.4531     | 1960     | 0.0           | 0.1194          | 0.7740                                         | -                                           |
| 2.4781     | 1980     | 0.0176        | 0.1184          | 0.7747                                         | -                                           |
| 2.5031     | 2000     | 0.0188        | 0.1123          | 0.7745                                         | -                                           |
| 2.5282     | 2020     | 0.0           | 0.1138          | 0.7742                                         | -                                           |
| 2.5532     | 2040     | 0.0           | 0.1141          | 0.7742                                         | -                                           |
| 2.5782     | 2060     | 0.0269        | 0.1126          | 0.7743                                         | -                                           |
| 2.6033     | 2080     | 0.0193        | 0.1470          | 0.7707                                         | -                                           |
| 2.6283     | 2100     | 0.0074        | 0.1333          | 0.7726                                         | -                                           |
| 2.6533     | 2120     | 0.0253        | 0.1004          | 0.7756                                         | -                                           |
| 2.6783     | 2140     | 0.0           | 0.0980          | 0.7758                                         | -                                           |
| 2.7034     | 2160     | 0.0           | 0.0984          | 0.7758                                         | -                                           |
| 2.7284     | 2180     | 0.0           | 0.0984          | 0.7758                                         | -                                           |
| 2.7534     | 2200     | 0.0           | 0.0984          | 0.7758                                         | -                                           |
| 2.7785     | 2220     | 0.007         | 0.0971          | 0.7766                                         | -                                           |
| 2.8035     | 2240     | 0.0           | 0.0998          | 0.7766                                         | -                                           |
| 2.8285     | 2260     | 0.015         | 0.0988          | 0.7760                                         | -                                           |
| 2.8536     | 2280     | 0.0           | 0.1020          | 0.7757                                         | -                                           |
| 2.8786     | 2300     | 0.0           | 0.1023          | 0.7756                                         | -                                           |
| 2.9036     | 2320     | 0.0           | 0.1023          | 0.7756                                         | -                                           |
| 2.9287     | 2340     | 0.0           | 0.1023          | 0.7756                                         | -                                           |
| 2.9537     | 2360     | 0.0075        | 0.1043          | 0.7751                                         | -                                           |
| 2.9787     | 2380     | 0.0067        | 0.1125          | 0.7749                                         | -                                           |
| 3.0038     | 2400     | 0.0           | 0.1083          | 0.7752                                         | -                                           |
| 3.0288     | 2420     | 0.0           | 0.1083          | 0.7752                                         | -                                           |
| 3.0538     | 2440     | 0.0           | 0.1083          | 0.7752                                         | -                                           |
| 3.0788     | 2460     | 0.0063        | 0.1018          | 0.7755                                         | -                                           |
| 3.1039     | 2480     | 0.0           | 0.1012          | 0.7756                                         | -                                           |
| **3.1289** | **2500** | **0.0162**    | **0.092**       | **0.7768**                                     | **-**                                       |
| 3.1539     | 2520     | 0.01          | 0.0941          | 0.7768                                         | -                                           |
| 3.1790     | 2540     | 0.0069        | 0.0946          | 0.7761                                         | -                                           |
| 3.2040     | 2560     | 0.0           | 0.0956          | 0.7759                                         | -                                           |
| 3.2290     | 2580     | 0.0           | 0.0956          | 0.7758                                         | -                                           |
| 3.2541     | 2600     | 0.0           | 0.0956          | 0.7758                                         | -                                           |
| 3.2791     | 2620     | 0.0           | 0.0956          | 0.7758                                         | -                                           |
| 3.3041     | 2640     | 0.0131        | 0.0981          | 0.7756                                         | -                                           |
| 3.3292     | 2660     | 0.0195        | 0.1142          | 0.7748                                         | -                                           |
| 3.3542     | 2680     | 0.0           | 0.1172          | 0.7746                                         | -                                           |
| 3.3792     | 2700     | 0.0065        | 0.1186          | 0.7748                                         | -                                           |
| 3.4043     | 2720     | 0.0169        | 0.1184          | 0.7750                                         | -                                           |
| 3.4293     | 2740     | 0.0           | 0.1175          | 0.7749                                         | -                                           |
| 3.4543     | 2760     | 0.0           | 0.1165          | 0.7748                                         | -                                           |
| 3.4793     | 2780     | 0.0105        | 0.1173          | 0.7747                                         | -                                           |
| 3.5044     | 2800     | 0.0066        | 0.1123          | 0.7751                                         | -                                           |
| 3.5294     | 2820     | 0.0           | 0.1103          | 0.7753                                         | -                                           |
| 3.5544     | 2840     | 0.0           | 0.1106          | 0.7753                                         | -                                           |
| 3.5795     | 2860     | 0.0139        | 0.1158          | 0.7745                                         | -                                           |
| 3.6045     | 2880     | 0.0           | 0.1183          | 0.7741                                         | -                                           |
| 3.6295     | 2900     | 0.0           | 0.1181          | 0.7741                                         | -                                           |
| 3.6546     | 2920     | 0.0           | 0.1179          | 0.7741                                         | -                                           |
| 3.6796     | 2940     | 0.0           | 0.1179          | 0.7741                                         | -                                           |
| 3.7046     | 2960     | 0.0119        | 0.1172          | 0.7742                                         | -                                           |
| 3.7297     | 2980     | 0.0068        | 0.1183          | 0.7742                                         | -                                           |
| 3.7547     | 3000     | 0.0           | 0.1193          | 0.7741                                         | -                                           |
| 3.7797     | 3020     | 0.0           | 0.1193          | 0.7741                                         | -                                           |
| 3.8048     | 3040     | 0.0           | 0.1193          | 0.7741                                         | -                                           |
| 3.8298     | 3060     | 0.0           | 0.1191          | 0.7741                                         | -                                           |
| 3.8548     | 3080     | 0.0           | 0.1193          | 0.7741                                         | -                                           |
| 3.8798     | 3100     | 0.0           | 0.1193          | 0.7741                                         | -                                           |
| 3.9049     | 3120     | 0.0131        | 0.1165          | 0.7745                                         | -                                           |
| 3.9299     | 3140     | 0.0           | 0.1159          | 0.7745                                         | -                                           |
| 3.9549     | 3160     | 0.0           | 0.1158          | 0.7746                                         | -                                           |
| 3.9800     | 3180     | 0.0           | 0.1153          | 0.7746                                         | -                                           |
| -1         | -1       | -             | -               | -                                              | 0.7768                                      |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

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

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