README.md

---

tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:350
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: Data pengeluaran bulanan rumah tangga pedesaan untuk konsumsi makanan
    dan non-makanan per provinsi, tahun berapa saja tersedia?
  sentences:
  - Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (84 x 84)
  - Persentase RataRata Pengeluaran per Kapita Sebulan Untuk Makanan dan Bukan Makanan
    di Daerah Perdesaan Menurut Provinsi, 2007-2024
  - Nilai Impor Jawa Madura Menurut Pelabuhan Impor di Pulau Jawa Madura Tahun 2009
    - 2013 (Juta US $) 1)
- source_sentence: Asal impor gula Indonesia periode 2017 hingga 2023
  sentences:
  - Banyaknya Anggota Kadinda Menurut Kabupaten/Kota di Provinsi Jawa Tengah, 2019
  - Impor Gula menurut Negara Asal Utama, 2017-2023
  - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Kelompok Umur,
    2023
- source_sentence: Laju kehilangan hutan Indonesia dalam dan luar kawasan hutan 2013-2022.
  sentences:
  - Institusi Pemerintah Neraca Institusi Terintegrasi (Triliun Rupiah), 2016 2023
  - Angka Deforestasi (Netto) Indonesia di Dalam dan di Luar Kawasan Hutan Tahun 2013-2022
    (Ha/Th)
  - Produksi Perkebunan Menurut Kabupaten/Kota dan Jenis Tanaman di Provinsi Jawa
    Tengah (ton), 2021 dan 2022
- source_sentence: Kemana saja lada putih Indonesia diekspor pada periode 2012 sampai
    2023?
  sentences:
  - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Kelompok Umur,
    2022-2023
  - Ekspor Lada Putih menurut Negara Tujuan Utama, 2012-2023
  - Angka Kelahiran Kasar (Crude Birth Rate) Hasil Long Form SP2020 Menurut Provinsi/Kabupaten/Kota,
    2020
- source_sentence: data gaji bersih pegawai per bulan tahun 2023 berdasarkan pendidikan
    dan jenis pekerjaan utama
  sentences:
  - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
    yang Ditamatkan dan Jenis Pekerjaan Utama, 2023
  - Banyaknya Kunjungan Kapal Melalui Pelabuhan Jepara Menurut Jenis Pelayaran Tahun
    2009 - 2013
  - Ekspor Sarang Burung menurut Negara Tujuan Utama, 2012-2023
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: bps val mfd all
      type: bps-val-mfd-all
    metrics:
    - type: cosine_accuracy@1
      value: 0.9861111111111112
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9861111111111112
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9861111111111112
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9861111111111112
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9861111111111112
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.9351851851851851
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.9055555555555554
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.8333333333333334
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.016151592322246593
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.0425075387306992
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.06836160354671791
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.11202747994449548
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8706665539282586
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9861111111111112
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.44673547368787836
      name: Cosine Map@100
---


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

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the csv 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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **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: 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("sentence_transformers_model_id")

# Run inference

sentences = [

    'data gaji bersih pegawai per bulan tahun 2023 berdasarkan pendidikan dan jenis pekerjaan utama',

    'Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Pekerjaan Utama, 2023',

    'Banyaknya Kunjungan Kapal Melalui Pelabuhan Jepara Menurut Jenis Pelayaran Tahun 2009 - 2013',

]

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]

```

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You can finetune this model on your own dataset.

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## Evaluation

### Metrics

#### Information Retrieval

* Dataset: `bps-val-mfd-all`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9861     |

| cosine_accuracy@3   | 0.9861     |
| cosine_accuracy@5   | 0.9861     |

| cosine_accuracy@10  | 0.9861     |
| cosine_precision@1  | 0.9861     |

| cosine_precision@3  | 0.9352     |
| cosine_precision@5  | 0.9056     |

| cosine_precision@10 | 0.8333     |
| cosine_recall@1     | 0.0162     |

| cosine_recall@3     | 0.0425     |
| cosine_recall@5     | 0.0684     |

| cosine_recall@10    | 0.112      |
| **cosine_ndcg@10**  | **0.8707** |

| cosine_mrr@10       | 0.9861     |

| cosine_map@100      | 0.4467     |



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## Training Details



### Training Dataset



#### csv



* Dataset: csv

* Size: 350 training samples

* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>

* Approximate statistics based on the first 350 samples:

  |         | query                                                                             | positive                                                                         | negative                                                                          |

  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|

  | type    | string                                                                            | string                                                                           | string                                                                            |

  | details | <ul><li>min: 7 tokens</li><li>mean: 16.16 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 23.2 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 27.02 tokens</li><li>max: 59 tokens</li></ul> |

* Samples:

  | query                                                                                                         | positive                                                                                            | negative                                                                                                      |

  |:--------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|

  | <code>Bagaimana pengeluaran rumah tangga per orang di Indonesia berubah dari 2010 sampai 2024?</code>         | <code>Distribusi Pembagian Pengeluaran per Kapita dan Indeks Gini, 2010-2024</code>                 | <code>Proyeksi Beban Pencemaran Udara Menurut Industri di Jawa Tengah Tahun 2020 (Ton/Tahun)</code>           |

  | <code>Data kesenjangan pendapatan di Indonesia tahun 2010-2024: indeks Gini dan pengeluaran rata-rata.</code> | <code>Distribusi Pembagian Pengeluaran per Kapita dan Indeks Gini, 2010-2024</code>                 | <code>Banyaknya Mahasiswa dan Dosen Pada Perguruan Tinggi Agama Islam Swasta di Jawa Tengah, 2018/2019</code> |

  | <code>Berapa konsumsi makanan pokok per orang per minggu di Indonesia tahun 2007-2024?</code>                 | <code>Rata-Rata Konsumsi per Kapita Seminggu Beberapa Macam Bahan Makanan Penting, 2007-2024</code> | <code>Rekapitulasi Industri Non Formal Yang Baru Menurut Kabupaten/kota 2012</code>                           |

* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:

  ```json

  {

      "scale": 20.0,

      "similarity_fct": "cos_sim"

  }

  ```



### Training Hyperparameters

#### Non-Default Hyperparameters



- `eval_strategy`: steps

- `per_device_train_batch_size`: 32

- `per_device_eval_batch_size`: 32

- `weight_decay`: 0.01

- `warmup_ratio`: 0.1

- `fp16`: True

- `load_best_model_at_end`: 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`: 0.01

- `adam_beta1`: 0.9

- `adam_beta2`: 0.999

- `adam_epsilon`: 1e-08

- `max_grad_norm`: 1.0

- `num_train_epochs`: 3

- `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

- `hub_revision`: None

- `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

- `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

- `liger_kernel_config`: None

- `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   | bps-val-mfd-all_cosine_ndcg@10 |

|:----------:|:------:|:------------------------------:|

| 0.9091     | 10     | 0.8300                         |

| **1.8182** | **20** | **0.8736**                     |

| 2.7273     | 30     | 0.8707                         |



* The bold row denotes the saved checkpoint.



### Framework Versions

- Python: 3.10.11

- Sentence Transformers: 3.4.0

- Transformers: 4.53.1

- PyTorch: 2.7.1+cpu

- Accelerate: 1.8.1

- Datasets: 3.6.0

- Tokenizers: 0.21.2



## 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",

}

```



#### MultipleNegativesRankingLoss

```bibtex

@misc{henderson2017efficient,

    title={Efficient Natural Language Response Suggestion for Smart Reply},

    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},

    year={2017},

    eprint={1705.00652},

    archivePrefix={arXiv},

    primaryClass={cs.CL}

}

```



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