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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2602
- loss:ContrastiveLoss
base_model: denaya/indoSBERT-large
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
datasets:
- yahyaabd/bps-statictable-query-title-pairs
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on denaya/indoSBERT-large
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: 0.902671671573215
name: Pearson Cosine
- type: spearman_cosine
value: 0.7797277576994545
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: 0.9166324050239434
name: Pearson Cosine
- type: spearman_cosine
value: 0.8089661156615633
name: Spearman Cosine
---
# SentenceTransformer based on denaya/indoSBERT-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) on the [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) dataset. It maps sentences & paragraphs to a 256-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:** [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) <!-- at revision 5c64d43f07f7054dfbf33d226b3066414b6ebc4a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 256 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
(2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## 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-indoSBERT-large-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, 256]
# 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 | 0.9027 | 0.9166 |
| **spearman_cosine** | **0.7797** | **0.809** |
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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: 4 tokens</li><li>mean: 16.78 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 21.01 tokens</li><li>max: 48 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: 3 tokens</li><li>mean: 16.82 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 21.13 tokens</li><li>max: 48 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 | - | 0.0086 | 0.7549 | - |
| 0.1220 | 10 | 0.0082 | 0.0069 | 0.7610 | - |
| 0.2439 | 20 | 0.0058 | 0.0049 | 0.7688 | - |
| 0.3659 | 30 | 0.0047 | 0.0041 | 0.7686 | - |
| 0.4878 | 40 | 0.0034 | 0.0036 | 0.7682 | - |
| 0.6098 | 50 | 0.003 | 0.0034 | 0.7696 | - |
| 0.7317 | 60 | 0.0031 | 0.0027 | 0.7728 | - |
| 0.8537 | 70 | 0.0031 | 0.0029 | 0.7713 | - |
| 0.9756 | 80 | 0.003 | 0.0031 | 0.7731 | - |
| 1.0976 | 90 | 0.0011 | 0.0025 | 0.7746 | - |
| 1.2195 | 100 | 0.001 | 0.0023 | 0.7759 | - |
| 1.3415 | 110 | 0.0013 | 0.0021 | 0.7767 | - |
| 1.4634 | 120 | 0.0011 | 0.0021 | 0.7773 | - |
| 1.5854 | 130 | 0.0008 | 0.0021 | 0.7786 | - |
| 1.7073 | 140 | 0.0006 | 0.0021 | 0.7789 | - |
| 1.8293 | 150 | 0.0007 | 0.0020 | 0.7788 | - |
| **1.9512** | **160** | **0.0018** | **0.002** | **0.7799** | **-** |
| 2.0732 | 170 | 0.0006 | 0.0020 | 0.7800 | - |
| 2.1951 | 180 | 0.0004 | 0.0021 | 0.7795 | - |
| 2.3171 | 190 | 0.0006 | 0.0021 | 0.7796 | - |
| 2.4390 | 200 | 0.0004 | 0.0021 | 0.7798 | - |
| 2.5610 | 210 | 0.0003 | 0.0021 | 0.7799 | - |
| 2.6829 | 220 | 0.0003 | 0.0021 | 0.7798 | - |
| 2.8049 | 230 | 0.0004 | 0.0021 | 0.7797 | - |
| 2.9268 | 240 | 0.0007 | 0.0021 | 0.7798 | - |
| 3.0488 | 250 | 0.0003 | 0.0021 | 0.7798 | - |
| 3.1707 | 260 | 0.0002 | 0.0021 | 0.7796 | - |
| 3.2927 | 270 | 0.0003 | 0.0021 | 0.7797 | - |
| 3.4146 | 280 | 0.0002 | 0.0021 | 0.7797 | - |
| 3.5366 | 290 | 0.0002 | 0.0021 | 0.7797 | - |
| 3.6585 | 300 | 0.0002 | 0.0021 | 0.7797 | - |
| 3.7805 | 310 | 0.0004 | 0.0021 | 0.7797 | - |
| 3.9024 | 320 | 0.0003 | 0.0021 | 0.7797 | - |
| -1 | -1 | - | - | - | 0.8090 |
* 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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