sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:2602 loss:ContrastiveLoss dataset:yahyaabd/bps-statictable-query-title-pairs 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/bps-statictable-query-title-pairs
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:2602
- loss:ContrastiveLoss
widget:
- source_sentence: Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah
    (triliun) 2010
  sentences:
  - 'Nilai Ekspor Menurut Pelabuhan Utama (Nilai FOB: juta US$) 2000-2023'
  - Suhu Minimum, Rata-Rata, dan Maksimum di Stasiun Pengamatan BMKG (oC), 2011-2015
  - 'Nilai Ekspor Menurut Negara Tujuan Utama (Nilai FOB: juta US$), 2000-2023'
- source_sentence: Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah
    (triliun) 2010
  sentences:
  - Tabungan Bruto, Investasi Nonfinansial, dan Pinjaman Neto Triwulanan Sektor Pemerintahan
    Umum (triliun rupiah), 2009-2015
  - Produksi Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2020
  - Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur
    (ribu rupiah), 2017
- source_sentence: Gaji bersih vs kelompok umur dan lapangan pekerjaan, 2023
  sentences:
  - Investasi Nonfinansial Menurut Sektor (triliun rupiah), 2008-2014
  - Posisi Kredit Usaha Mikro, Kecil, dan Menengah (UMKM) 1 pada Bank Umum (miliar
    rupiah), 2012-2016
  - Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah)
- source_sentence: Data utang luar negeri Indonesia (pemerintah dan BI), detail kreditor
    dan syarat, tahun 2010
  sentences:
  - Angka Partisipasi Sekolah (APS) Penduduk Umur 7-18 Tahun Menurut Klasifikasi Desa,
    Jenis Kelamin, dan Kelompok Umur, 2009-2023
  - Indeks Integritas Ujian Nasional
  - Rekapitulasi Luas Penutupan Lahan Hutan dan Non Hutan Menurut Provinsi Tahun 2014-2022
    (Ribu Ha)
- source_sentence: Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015
  sentences:
  - Indeks Harga Konsumen Menurut Kelompok Pengeluaran, 2020-2023
  - Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah)
  - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan
    dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023
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 base v1 eval
      type: allstats-semantic-base-v1-eval
    metrics:
    - type: pearson_cosine
      value: 0.8898188833771716
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.779923841631983
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstat semantic base v1 test
      type: allstat-semantic-base-v1-test
    metrics:
    - type: pearson_cosine
      value: 0.9039024076661341
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8077065435723709
      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 [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) 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:**
    - [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs)
<!-- - **Language:** Unknown -->
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### 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-ir-mpnet-base-v1")
# Run inference
sentences = [
    'Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015',
    'Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah)',
    'Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023',
]
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]
```

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## Evaluation

### Metrics

#### Semantic Similarity

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

| Metric              | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test |
|:--------------------|:-------------------------------|:------------------------------|
| pearson_cosine      | 0.8898                         | 0.9039                        |
| **spearman_cosine** | **0.7799**                     | **0.8077**                    |

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## Training Details

### Training Dataset

#### bps-statictable-query-title-pairs

* Dataset: [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) at [c7df38f](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs/tree/c7df38f8b228efe13b1589b94c78fc7b57f02b58)
* Size: 2,602 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: 5 tokens</li><li>mean: 18.35 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.83 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>0: ~66.50%</li><li>1: ~33.50%</li></ul> |
* Samples:
  | query                                                                                                            | doc                                                                                                        | label          |
  |:-----------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Pertumbuhan populasi provinsi di Indonesia 1971-2024</code>                                                | <code>Kecepatan Angin dan Kelembaban di Stasiun Pengamatan BMKG, 2000-2010</code>                          | <code>0</code> |
  | <code>Perbandingan upah nominal dan riil pekerja pertanian di Indonesia (tahun dasar 2012), periode 2017.</code> | <code>Upah Nominal dan Riil Buruh Tani di Indonesia (Rupiah), 2009-2019 (2012=100)</code>                  | <code>1</code> |
  | <code>Laporan singkat cash flow statement Q4/2005</code>                                                         | <code>Nilai Produksi dan Biaya Produksi per Hektar Usaha Tanaman Bawang Merah dan Cabai Merah, 2014</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### Evaluation Dataset

#### bps-statictable-query-title-pairs

* Dataset: [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) at [c7df38f](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs/tree/c7df38f8b228efe13b1589b94c78fc7b57f02b58)
* Size: 558 evaluation samples
* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
* Approximate statistics based on the first 558 samples:
  |         | query                                                                             | doc                                                                               | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 18.45 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.04 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>0: ~70.97%</li><li>1: ~29.03%</li></ul> |
* Samples:
  | query                                                                                                                   | doc                                                                                                                                                                                 | label          |
  |:------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Data pengeluaran makanan rata-rata warga Sulteng per minggu di tahun 2022, berdasarkan kelompok pendapatan</code> | <code>Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (84 x 84)</code>                                                                                                            | <code>0</code> |
  | <code>Konsumsi & belanja makanan per orang di NTB, beda kelompok pengeluaran, 2021</code>                               | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Nusa Tenggara Barat, 2018-2023</code> | <code>1</code> |
  | <code>Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?</code>                                | <code>Penduduk Berumur 15 Tahun Ke Atas Menurut Provinsi dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008 - 2024</code>                                                           | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### 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
| Epoch      | Step    | Training Loss | Validation Loss | allstats-semantic-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine |
|:----------:|:-------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:|
| 0          | 0       | -             | 0.0099          | 0.7449                                         | -                                             |
| 0.1220     | 10      | 0.0091        | 0.0065          | 0.7640                                         | -                                             |
| 0.2439     | 20      | 0.0059        | 0.0040          | 0.7743                                         | -                                             |
| 0.3659     | 30      | 0.0045        | 0.0036          | 0.7688                                         | -                                             |
| 0.4878     | 40      | 0.0045        | 0.0036          | 0.7694                                         | -                                             |
| 0.6098     | 50      | 0.0032        | 0.0037          | 0.7758                                         | -                                             |
| 0.7317     | 60      | 0.003         | 0.0025          | 0.7753                                         | -                                             |
| 0.8537     | 70      | 0.0035        | 0.0029          | 0.7710                                         | -                                             |
| 0.9756     | 80      | 0.0028        | 0.0026          | 0.7745                                         | -                                             |
| 1.0976     | 90      | 0.0015        | 0.0023          | 0.7754                                         | -                                             |
| 1.2195     | 100     | 0.0013        | 0.0021          | 0.7760                                         | -                                             |
| 1.3415     | 110     | 0.0013        | 0.0022          | 0.7751                                         | -                                             |
| 1.4634     | 120     | 0.002         | 0.0021          | 0.7746                                         | -                                             |
| 1.5854     | 130     | 0.0012        | 0.0020          | 0.7750                                         | -                                             |
| 1.7073     | 140     | 0.0007        | 0.0019          | 0.7740                                         | -                                             |
| 1.8293     | 150     | 0.0008        | 0.0019          | 0.7738                                         | -                                             |
| 1.9512     | 160     | 0.0026        | 0.0018          | 0.7772                                         | -                                             |
| 2.0732     | 170     | 0.0009        | 0.0019          | 0.7785                                         | -                                             |
| 2.1951     | 180     | 0.0005        | 0.0020          | 0.7781                                         | -                                             |
| 2.3171     | 190     | 0.0009        | 0.0017          | 0.7777                                         | -                                             |
| 2.4390     | 200     | 0.0005        | 0.0017          | 0.7773                                         | -                                             |
| 2.5610     | 210     | 0.0004        | 0.0018          | 0.7766                                         | -                                             |
| 2.6829     | 220     | 0.0006        | 0.0018          | 0.7762                                         | -                                             |
| 2.8049     | 230     | 0.0006        | 0.0019          | 0.7756                                         | -                                             |
| 2.9268     | 240     | 0.0016        | 0.0019          | 0.7777                                         | -                                             |
| 3.0488     | 250     | 0.0008        | 0.0018          | 0.7796                                         | -                                             |
| 3.1707     | 260     | 0.0005        | 0.0017          | 0.7802                                         | -                                             |
| **3.2927** | **270** | **0.0006**    | **0.0017**      | **0.7802**                                     | **-**                                         |
| 3.4146     | 280     | 0.0004        | 0.0017          | 0.7805                                         | -                                             |
| 3.5366     | 290     | 0.0004        | 0.0017          | 0.7805                                         | -                                             |
| 3.6585     | 300     | 0.0003        | 0.0018          | 0.7802                                         | -                                             |
| 3.7805     | 310     | 0.0006        | 0.0018          | 0.7800                                         | -                                             |
| 3.9024     | 320     | 0.0003        | 0.0018          | 0.7799                                         | -                                             |
| -1         | -1      | -             | -               | -                                              | 0.8077                                        |

* The bold row denotes the saved checkpoint.

### 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",
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

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