sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:212940 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:212940
- loss:CosineSimilarityLoss
widget:
- source_sentence: Ringkasan data strategis BPS 2012
  sentences:
  - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan
    Jenis Pekerjaan Utama, 2021
  - Laporan Perekonomian Indonesia 2007
  - Statistik Potensi Desa Provinsi Banten 2008
- source_sentence: tahun berapa ekspor naik 2,37% dan impor naik 30,30%?
  sentences:
  - Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %
  - Indeks Harga Konsumen per Kelompok di 82 Kota <sup>1</sup> (2012=100)
  - 'Februari 2022: Tingkat Pengangguran Terbuka (TPT) sebesar 5,83 persen dan Rata-rata
    upah buruh sebesar 2,89 juta rupiah per bulan'
- source_sentence: akses air bersih di indonesia (2005-2009)
  sentences:
  - Desember 2016, Rupiah Terapresiasi 0,74 Persen Terhadap Dolar Amerika
  - Statistik Air Bersih 2005-2009
  - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
    yang Ditamatkan dan Lapangan Pekerjaan Utama di 17 Sektor (rupiah), 2018
- source_sentence: Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2014-2018, Buku
    2 Pulau Jawa dan Bali
  sentences:
  - Profil Migran Hasil Susenas 2011-2012
  - Statistik Gas Kota 2004-2008
  - Jumlah kunjungan wisman ke Indonesia melalui pintu masuk utama pada Juni 2022
    mencapai 345,44 ribu kunjungan dan Jumlah penumpang angkutan udara internasional
    pada Juni 2022 naik 23,28 persen
- source_sentence: perubahan nilai tukar petani bulan mei 2017
  sentences:
  - Perkembangan Nilai Tukar Petani Mei 2017
  - NTP Naik 0,15%, Harga Gabah Kualitas GKG Naik 0,98%
  - Statistik Restoran/Rumah Makan Tahun 2014
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 3 dev
      type: allstats-semantic-search-v1-3-dev
    metrics:
    - type: pearson_cosine
      value: 0.9955935469233214
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9588270212992008
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstat semantic search v1 3 test
      type: allstat-semantic-search-v1-3-test
    metrics:
    - type: pearson_cosine
      value: 0.9955194411367296
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.958337873285875
      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-v1-3")
# Run inference
sentences = [
    'perubahan nilai tukar petani bulan mei 2017',
    'Perkembangan Nilai Tukar Petani Mei 2017',
    'Statistik Restoran/Rumah Makan Tahun 2014',
]
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: `allstats-semantic-search-v1-3-dev` and `allstat-semantic-search-v1-3-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | allstats-semantic-search-v1-3-dev | allstat-semantic-search-v1-3-test |
|:--------------------|:----------------------------------|:----------------------------------|
| pearson_cosine      | 0.9956                            | 0.9955                            |
| **spearman_cosine** | **0.9588**                        | **0.9583**                        |

<!--
## 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

#### 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 [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e)
* Size: 212,940 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: 11.46 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.47 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.05</li></ul> |
* Samples:
  | query                                                          | doc                                                                    | label             |
  |:---------------------------------------------------------------|:-----------------------------------------------------------------------|:------------------|
  | <code>aDta industri besar dan sedang Indonesia 2008</code>     | <code>Statistik Industri Besar dan Sedang Indonesia 2008</code>        | <code>0.9</code>  |
  | <code>profil bisnis konstruksi individu jawa barat 2022</code> | <code>Statistik Industri Manufaktur Indonesia 2015 - Bahan Baku</code> | <code>0.15</code> |
  | <code>data statistik ekonomi indonesia</code>                  | <code>Nilai Tukar Valuta Asing di Indonesia 2014</code>                | <code>0.08</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 [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e)
* Size: 26,618 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: 11.38 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.63 tokens</li><li>max: 55 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>tahun berapa ekspor naik 2,37% dan impor naik 30,30%?</code> | <code>Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %</code> | <code>1.0</code>  |
  | <code>Berapa produksi padi pada tahun 2023?</code>                 | <code>Produksi padi tahun lainnya</code>                                   | <code>0.0</code>  |
  | <code>data statistik solus per aqua 2015</code>                    | <code>Statistik Solus Per Aqua (SPA) 2015</code>                           | <code>0.97</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`: 12
- `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`: 12
- `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-v1-3-dev_spearman_cosine | allstat-semantic-search-v1-3-test_spearman_cosine |
|:-------:|:-----:|:-------------:|:---------------:|:-------------------------------------------------:|:-------------------------------------------------:|
| 0.0751  | 500   | 0.0653        | 0.0400          | 0.7035                                            | -                                                 |
| 0.1503  | 1000  | 0.0361        | 0.0296          | 0.7310                                            | -                                                 |
| 0.2254  | 1500  | 0.0278        | 0.0226          | 0.7669                                            | -                                                 |
| 0.3005  | 2000  | 0.0226        | 0.0195          | 0.7748                                            | -                                                 |
| 0.3757  | 2500  | 0.0208        | 0.0183          | 0.7769                                            | -                                                 |
| 0.4508  | 3000  | 0.0184        | 0.0172          | 0.7994                                            | -                                                 |
| 0.5259  | 3500  | 0.0179        | 0.0159          | 0.7931                                            | -                                                 |
| 0.6011  | 4000  | 0.0159        | 0.0155          | 0.7966                                            | -                                                 |
| 0.6762  | 4500  | 0.0161        | 0.0150          | 0.8047                                            | -                                                 |
| 0.7513  | 5000  | 0.0163        | 0.0153          | 0.7910                                            | -                                                 |
| 0.8264  | 5500  | 0.0158        | 0.0155          | 0.7956                                            | -                                                 |
| 0.9016  | 6000  | 0.0149        | 0.0141          | 0.8148                                            | -                                                 |
| 0.9767  | 6500  | 0.0149        | 0.0145          | 0.8287                                            | -                                                 |
| 1.0518  | 7000  | 0.0148        | 0.0150          | 0.7933                                            | -                                                 |
| 1.1270  | 7500  | 0.0131        | 0.0136          | 0.8083                                            | -                                                 |
| 1.2021  | 8000  | 0.0124        | 0.0131          | 0.8173                                            | -                                                 |
| 1.2772  | 8500  | 0.0133        | 0.0130          | 0.8117                                            | -                                                 |
| 1.3524  | 9000  | 0.012         | 0.0126          | 0.8259                                            | -                                                 |
| 1.4275  | 9500  | 0.0119        | 0.0120          | 0.8178                                            | -                                                 |
| 1.5026  | 10000 | 0.0116        | 0.0118          | 0.8332                                            | -                                                 |
| 1.5778  | 10500 | 0.0132        | 0.0123          | 0.8108                                            | -                                                 |
| 1.6529  | 11000 | 0.0114        | 0.0111          | 0.8365                                            | -                                                 |
| 1.7280  | 11500 | 0.0105        | 0.0109          | 0.8235                                            | -                                                 |
| 1.8032  | 12000 | 0.0107        | 0.0105          | 0.8445                                            | -                                                 |
| 1.8783  | 12500 | 0.0106        | 0.0101          | 0.8330                                            | -                                                 |
| 1.9534  | 13000 | 0.0095        | 0.0096          | 0.8437                                            | -                                                 |
| 2.0285  | 13500 | 0.0093        | 0.0094          | 0.8417                                            | -                                                 |
| 2.1037  | 14000 | 0.0079        | 0.0093          | 0.8485                                            | -                                                 |
| 2.1788  | 14500 | 0.008         | 0.0089          | 0.8422                                            | -                                                 |
| 2.2539  | 15000 | 0.0081        | 0.0086          | 0.8485                                            | -                                                 |
| 2.3291  | 15500 | 0.008         | 0.0084          | 0.8530                                            | -                                                 |
| 2.4042  | 16000 | 0.007         | 0.0084          | 0.8597                                            | -                                                 |
| 2.4793  | 16500 | 0.0081        | 0.0087          | 0.8499                                            | -                                                 |
| 2.5545  | 17000 | 0.0078        | 0.0078          | 0.8577                                            | -                                                 |
| 2.6296  | 17500 | 0.007         | 0.0080          | 0.8559                                            | -                                                 |
| 2.7047  | 18000 | 0.0072        | 0.0078          | 0.8569                                            | -                                                 |
| 2.7799  | 18500 | 0.0069        | 0.0079          | 0.8579                                            | -                                                 |
| 2.8550  | 19000 | 0.0064        | 0.0072          | 0.8693                                            | -                                                 |
| 2.9301  | 19500 | 0.0064        | 0.0070          | 0.8747                                            | -                                                 |
| 3.0053  | 20000 | 0.0061        | 0.0068          | 0.8757                                            | -                                                 |
| 3.0804  | 20500 | 0.0052        | 0.0069          | 0.8727                                            | -                                                 |
| 3.1555  | 21000 | 0.005         | 0.0067          | 0.8734                                            | -                                                 |
| 3.2307  | 21500 | 0.0054        | 0.0065          | 0.8727                                            | -                                                 |
| 3.3058  | 22000 | 0.0058        | 0.0070          | 0.8715                                            | -                                                 |
| 3.3809  | 22500 | 0.0056        | 0.0066          | 0.8724                                            | -                                                 |
| 3.4560  | 23000 | 0.0056        | 0.0070          | 0.8740                                            | -                                                 |
| 3.5312  | 23500 | 0.0054        | 0.0060          | 0.8775                                            | -                                                 |
| 3.6063  | 24000 | 0.0051        | 0.0062          | 0.8746                                            | -                                                 |
| 3.6814  | 24500 | 0.0047        | 0.0060          | 0.8765                                            | -                                                 |
| 3.7566  | 25000 | 0.0051        | 0.0067          | 0.8783                                            | -                                                 |
| 3.8317  | 25500 | 0.0048        | 0.0058          | 0.8824                                            | -                                                 |
| 3.9068  | 26000 | 0.0048        | 0.0059          | 0.8862                                            | -                                                 |
| 3.9820  | 26500 | 0.005         | 0.0056          | 0.8853                                            | -                                                 |
| 4.0571  | 27000 | 0.0042        | 0.0053          | 0.8868                                            | -                                                 |
| 4.1322  | 27500 | 0.0036        | 0.0056          | 0.8893                                            | -                                                 |
| 4.2074  | 28000 | 0.0041        | 0.0052          | 0.8954                                            | -                                                 |
| 4.2825  | 28500 | 0.0041        | 0.0050          | 0.8943                                            | -                                                 |
| 4.3576  | 29000 | 0.0036        | 0.0050          | 0.8890                                            | -                                                 |
| 4.4328  | 29500 | 0.0036        | 0.0046          | 0.8990                                            | -                                                 |
| 4.5079  | 30000 | 0.0038        | 0.0051          | 0.8934                                            | -                                                 |
| 4.5830  | 30500 | 0.0037        | 0.0049          | 0.9011                                            | -                                                 |
| 4.6582  | 31000 | 0.0036        | 0.0049          | 0.9000                                            | -                                                 |
| 4.7333  | 31500 | 0.0041        | 0.0052          | 0.8938                                            | -                                                 |
| 4.8084  | 32000 | 0.004         | 0.0049          | 0.8971                                            | -                                                 |
| 4.8835  | 32500 | 0.0038        | 0.0043          | 0.9023                                            | -                                                 |
| 4.9587  | 33000 | 0.0036        | 0.0044          | 0.9006                                            | -                                                 |
| 5.0338  | 33500 | 0.0032        | 0.0043          | 0.9042                                            | -                                                 |
| 5.1089  | 34000 | 0.0031        | 0.0042          | 0.9054                                            | -                                                 |
| 5.1841  | 34500 | 0.0028        | 0.0042          | 0.9052                                            | -                                                 |
| 5.2592  | 35000 | 0.0028        | 0.0043          | 0.9065                                            | -                                                 |
| 5.3343  | 35500 | 0.003         | 0.0041          | 0.9093                                            | -                                                 |
| 5.4095  | 36000 | 0.0029        | 0.0042          | 0.9084                                            | -                                                 |
| 5.4846  | 36500 | 0.0029        | 0.0044          | 0.9078                                            | -                                                 |
| 5.5597  | 37000 | 0.0027        | 0.0043          | 0.9062                                            | -                                                 |
| 5.6349  | 37500 | 0.003         | 0.0039          | 0.9101                                            | -                                                 |
| 5.7100  | 38000 | 0.0027        | 0.0041          | 0.9092                                            | -                                                 |
| 5.7851  | 38500 | 0.0025        | 0.0039          | 0.9140                                            | -                                                 |
| 5.8603  | 39000 | 0.0027        | 0.0037          | 0.9138                                            | -                                                 |
| 5.9354  | 39500 | 0.0027        | 0.0037          | 0.9137                                            | -                                                 |
| 6.0105  | 40000 | 0.0027        | 0.0036          | 0.9162                                            | -                                                 |
| 6.0856  | 40500 | 0.002         | 0.0035          | 0.9209                                            | -                                                 |
| 6.1608  | 41000 | 0.0021        | 0.0037          | 0.9180                                            | -                                                 |
| 6.2359  | 41500 | 0.0023        | 0.0036          | 0.9183                                            | -                                                 |
| 6.3110  | 42000 | 0.0024        | 0.0035          | 0.9218                                            | -                                                 |
| 6.3862  | 42500 | 0.002         | 0.0033          | 0.9216                                            | -                                                 |
| 6.4613  | 43000 | 0.0024        | 0.0035          | 0.9220                                            | -                                                 |
| 6.5364  | 43500 | 0.0018        | 0.0034          | 0.9232                                            | -                                                 |
| 6.6116  | 44000 | 0.0021        | 0.0033          | 0.9236                                            | -                                                 |
| 6.6867  | 44500 | 0.0021        | 0.0035          | 0.9225                                            | -                                                 |
| 6.7618  | 45000 | 0.0027        | 0.0031          | 0.9227                                            | -                                                 |
| 6.8370  | 45500 | 0.0019        | 0.0032          | 0.9242                                            | -                                                 |
| 6.9121  | 46000 | 0.0022        | 0.0033          | 0.9224                                            | -                                                 |
| 6.9872  | 46500 | 0.0022        | 0.0030          | 0.9252                                            | -                                                 |
| 7.0624  | 47000 | 0.0017        | 0.0029          | 0.9294                                            | -                                                 |
| 7.1375  | 47500 | 0.0014        | 0.0028          | 0.9304                                            | -                                                 |
| 7.2126  | 48000 | 0.0015        | 0.0028          | 0.9324                                            | -                                                 |
| 7.2878  | 48500 | 0.0014        | 0.0030          | 0.9313                                            | -                                                 |
| 7.3629  | 49000 | 0.0015        | 0.0029          | 0.9333                                            | -                                                 |
| 7.4380  | 49500 | 0.0015        | 0.0028          | 0.9342                                            | -                                                 |
| 7.5131  | 50000 | 0.0018        | 0.0030          | 0.9261                                            | -                                                 |
| 7.5883  | 50500 | 0.0016        | 0.0030          | 0.9329                                            | -                                                 |
| 7.6634  | 51000 | 0.0019        | 0.0026          | 0.9334                                            | -                                                 |
| 7.7385  | 51500 | 0.0018        | 0.0029          | 0.9336                                            | -                                                 |
| 7.8137  | 52000 | 0.0016        | 0.0026          | 0.9353                                            | -                                                 |
| 7.8888  | 52500 | 0.0016        | 0.0026          | 0.9351                                            | -                                                 |
| 7.9639  | 53000 | 0.0017        | 0.0024          | 0.9356                                            | -                                                 |
| 8.0391  | 53500 | 0.0013        | 0.0023          | 0.9396                                            | -                                                 |
| 8.1142  | 54000 | 0.0012        | 0.0024          | 0.9390                                            | -                                                 |
| 8.1893  | 54500 | 0.001         | 0.0024          | 0.9421                                            | -                                                 |
| 8.2645  | 55000 | 0.0012        | 0.0024          | 0.9406                                            | -                                                 |
| 8.3396  | 55500 | 0.0012        | 0.0023          | 0.9407                                            | -                                                 |
| 8.4147  | 56000 | 0.0012        | 0.0024          | 0.9398                                            | -                                                 |
| 8.4899  | 56500 | 0.0012        | 0.0024          | 0.9412                                            | -                                                 |
| 8.5650  | 57000 | 0.0014        | 0.0024          | 0.9397                                            | -                                                 |
| 8.6401  | 57500 | 0.0013        | 0.0023          | 0.9411                                            | -                                                 |
| 8.7153  | 58000 | 0.0013        | 0.0023          | 0.9418                                            | -                                                 |
| 8.7904  | 58500 | 0.0014        | 0.0022          | 0.9432                                            | -                                                 |
| 8.8655  | 59000 | 0.0011        | 0.0022          | 0.9448                                            | -                                                 |
| 8.9406  | 59500 | 0.0012        | 0.0022          | 0.9455                                            | -                                                 |
| 9.0158  | 60000 | 0.0012        | 0.0021          | 0.9453                                            | -                                                 |
| 9.0909  | 60500 | 0.0009        | 0.0021          | 0.9461                                            | -                                                 |
| 9.1660  | 61000 | 0.0009        | 0.0021          | 0.9465                                            | -                                                 |
| 9.2412  | 61500 | 0.0009        | 0.0021          | 0.9471                                            | -                                                 |
| 9.3163  | 62000 | 0.0009        | 0.0021          | 0.9477                                            | -                                                 |
| 9.3914  | 62500 | 0.0008        | 0.0020          | 0.9482                                            | -                                                 |
| 9.4666  | 63000 | 0.0012        | 0.0020          | 0.9478                                            | -                                                 |
| 9.5417  | 63500 | 0.0009        | 0.0020          | 0.9479                                            | -                                                 |
| 9.6168  | 64000 | 0.0009        | 0.0020          | 0.9485                                            | -                                                 |
| 9.6920  | 64500 | 0.0011        | 0.0020          | 0.9492                                            | -                                                 |
| 9.7671  | 65000 | 0.0008        | 0.0019          | 0.9497                                            | -                                                 |
| 9.8422  | 65500 | 0.001         | 0.0019          | 0.9504                                            | -                                                 |
| 9.9174  | 66000 | 0.0009        | 0.0019          | 0.9518                                            | -                                                 |
| 9.9925  | 66500 | 0.0009        | 0.0019          | 0.9510                                            | -                                                 |
| 10.0676 | 67000 | 0.0008        | 0.0018          | 0.9517                                            | -                                                 |
| 10.1427 | 67500 | 0.0007        | 0.0018          | 0.9524                                            | -                                                 |
| 10.2179 | 68000 | 0.0007        | 0.0018          | 0.9521                                            | -                                                 |
| 10.2930 | 68500 | 0.0008        | 0.0019          | 0.9526                                            | -                                                 |
| 10.3681 | 69000 | 0.0007        | 0.0019          | 0.9529                                            | -                                                 |
| 10.4433 | 69500 | 0.0008        | 0.0018          | 0.9541                                            | -                                                 |
| 10.5184 | 70000 | 0.0007        | 0.0017          | 0.9551                                            | -                                                 |
| 10.5935 | 70500 | 0.0007        | 0.0018          | 0.9550                                            | -                                                 |
| 10.6687 | 71000 | 0.0008        | 0.0017          | 0.9554                                            | -                                                 |
| 10.7438 | 71500 | 0.0007        | 0.0017          | 0.9558                                            | -                                                 |
| 10.8189 | 72000 | 0.0007        | 0.0018          | 0.9558                                            | -                                                 |
| 10.8941 | 72500 | 0.0007        | 0.0018          | 0.9562                                            | -                                                 |
| 10.9692 | 73000 | 0.0009        | 0.0017          | 0.9559                                            | -                                                 |
| 11.0443 | 73500 | 0.0005        | 0.0017          | 0.9571                                            | -                                                 |
| 11.1195 | 74000 | 0.0006        | 0.0017          | 0.9570                                            | -                                                 |
| 11.1946 | 74500 | 0.0005        | 0.0017          | 0.9573                                            | -                                                 |
| 11.2697 | 75000 | 0.0005        | 0.0017          | 0.9574                                            | -                                                 |
| 11.3449 | 75500 | 0.0006        | 0.0017          | 0.9576                                            | -                                                 |
| 11.4200 | 76000 | 0.0006        | 0.0017          | 0.9577                                            | -                                                 |
| 11.4951 | 76500 | 0.0006        | 0.0017          | 0.9577                                            | -                                                 |
| 11.5702 | 77000 | 0.0005        | 0.0016          | 0.9582                                            | -                                                 |
| 11.6454 | 77500 | 0.0006        | 0.0017          | 0.9583                                            | -                                                 |
| 11.7205 | 78000 | 0.0005        | 0.0016          | 0.9584                                            | -                                                 |
| 11.7956 | 78500 | 0.0005        | 0.0016          | 0.9587                                            | -                                                 |
| 11.8708 | 79000 | 0.0005        | 0.0016          | 0.9588                                            | -                                                 |
| 11.9459 | 79500 | 0.0005        | 0.0016          | 0.9588                                            | -                                                 |
| 12.0    | 79860 | -             | -               | -                                                 | 0.9583                                            |

</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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