sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:70280 loss:CosineSimilarityLoss dataset:yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini arxiv:1908.10084 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets:
- yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini
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:70280
- loss:CosineSimilarityLoss
widget:
- source_sentence: Data SBH tahun 2012 di Mamuju
  sentences:
  - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Harmonized System November
    2013
  - SBH 2012 - Mamuju
  - IHK di 66 Kota di Indonesia 2013
- source_sentence: Statistik konstruksi tahun 2020
  sentences:
  - Indeks Ketimpangan Gender 2022
  - Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi, 1971-2020
  - Perkembangan Beberapa Indikator Utama sosial-Ekonomi Indonesia Edisi Februari
    2016
- source_sentence: Berapa besar inflasi pada bulan Oktober 2008?
  sentences:
  - Tinjauan Ekonomi Regional Indonesia Berdasarkan Data PDRB 2004-2008 Buku 2
  - Statistik Sumber Daya Laut dan Pesisir 2020
  - Inflasi September 2008 sebesar 0,97 persen.
- source_sentence: 'Sektor konstruksi Indonesia: data statistik 1990-2013'
  sentences:
  - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan
    Lapangan Pekerjaan Utama, 2023
  - Direktori Perusahaan Kehutanan 2019
  - Sensus Ekonomi 2006 Hasil Pendaftaran Perusahaan Sumatera Selatan
- source_sentence: Perdagangan luar negeri, impor, Oktober 2020
  sentences:
  - Indikator Ekonomi September 2005
  - Statistik Potensi Desa Provinsi DI Yogyakarta 2005
  - Indikator Ekonomi November 1999
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstats semantic search mini v2 eval
      type: allstats-semantic-search-mini-v2-eval
    metrics:
    - type: pearson_cosine
      value: 0.9617082550278393
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8518022238549516
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstat semantic search mini v2 test
      type: allstat-semantic-search-mini-v2-test
    metrics:
    - type: pearson_cosine
      value: 0.9604638064122318
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8480797444308495
      name: Spearman Cosine
---

# 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 [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) 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 8d6b950845285729817bf8e1af1861502c2fed0c -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini)
<!-- - **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("yahyaabd/allstats-semantic-search-mini-model-v2-2")
# Run inference
sentences = [
    'Perdagangan luar negeri, impor, Oktober 2020',
    'Indikator Ekonomi November 1999',
    'Indikator Ekonomi September 2005',
]
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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## Evaluation

### Metrics

#### Semantic Similarity

* Datasets: `allstats-semantic-search-mini-v2-eval` and `allstat-semantic-search-mini-v2-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | allstats-semantic-search-mini-v2-eval | allstat-semantic-search-mini-v2-test |
|:--------------------|:--------------------------------------|:-------------------------------------|
| pearson_cosine      | 0.9617                                | 0.9605                               |
| **spearman_cosine** | **0.8518**                            | **0.8481**                           |

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## Training Details

### Training Dataset

#### allstats-semantic-search-synthetic-dataset-v2-mini

* Dataset: [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) at [8222b01](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini/tree/8222b01e37490603bc838a6368bc2946a6455a7c)
* Size: 70,280 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: 3 tokens</li><li>mean: 10.92 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.68 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
* Samples:
  | query                                                                              | doc                                                                      | label             |
  |:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:------------------|
  | <code>Statistik perusahaan pembudidaya tanaman kehutanan 2018</code>               | <code>Statistik Perusahaan Pembudidaya Tanaman Kehutanan 2018</code>     | <code>0.97</code> |
  | <code>Berapa persen pertumbuhan PDB Indonesia pada Triwulan III Tahun 2002?</code> | <code>Inflasi Bulan November 2002 Sebesar 1,85 %</code>                  | <code>0.0</code>  |
  | <code>Perdagangan luar negeri Indonesia, impor 2019, jilid 2</code>                | <code>Pendataan Sapi Potong Sapi Perah (PSPK 2011) Sulawesi Barat</code> | <code>0.06</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-v2-mini

* Dataset: [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) at [8222b01](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini/tree/8222b01e37490603bc838a6368bc2946a6455a7c)
* Size: 15,060 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: 4 tokens</li><li>mean: 10.96 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.74 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
  | query                                                           | doc                                                              | label             |
  |:----------------------------------------------------------------|:-----------------------------------------------------------------|:------------------|
  | <code>Review PDRB daerah di Pulau Sumatera 2010-2013</code>     | <code>Statistik Pendidikan 2006</code>                           | <code>0.12</code> |
  | <code>Analisis data angkatan kerja Agustus 2021</code>          | <code>Booklet Survei Angkatan Kerja Nasional Agustus 2021</code> | <code>0.9</code>  |
  | <code>Berapa persen inflasi yang terjadi pada Juli 2015?</code> | <code>Inflasi pada bulan lain tidak disebutkan</code>            | <code>0.0</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`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 24
- `warmup_ratio`: 0.1
- `bf16`: 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 24
- `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`: True
- `fp16`: False
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `eval_use_gather_object`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step  | Training Loss | Validation Loss | allstats-semantic-search-mini-v2-eval_spearman_cosine | allstat-semantic-search-mini-v2-test_spearman_cosine |
|:-------:|:-----:|:-------------:|:---------------:|:-----------------------------------------------------:|:----------------------------------------------------:|
| 0.4550  | 500   | 0.0643        | 0.0413          | 0.6996                                                | -                                                    |
| 0.9099  | 1000  | 0.0348        | 0.0280          | 0.7533                                                | -                                                    |
| 1.3649  | 1500  | 0.0254        | 0.0238          | 0.7737                                                | -                                                    |
| 1.8198  | 2000  | 0.0223        | 0.0205          | 0.7831                                                | -                                                    |
| 2.2748  | 2500  | 0.0181        | 0.0197          | 0.7894                                                | -                                                    |
| 2.7298  | 3000  | 0.0173        | 0.0184          | 0.7876                                                | -                                                    |
| 3.1847  | 3500  | 0.0152        | 0.0170          | 0.7954                                                | -                                                    |
| 3.6397  | 4000  | 0.0123        | 0.0175          | 0.7970                                                | -                                                    |
| 4.0946  | 4500  | 0.0125        | 0.0163          | 0.8118                                                | -                                                    |
| 4.5496  | 5000  | 0.01          | 0.0161          | 0.8047                                                | -                                                    |
| 5.0045  | 5500  | 0.0103        | 0.0157          | 0.8126                                                | -                                                    |
| 5.4595  | 6000  | 0.0079        | 0.0150          | 0.8224                                                | -                                                    |
| 5.9145  | 6500  | 0.0087        | 0.0156          | 0.8219                                                | -                                                    |
| 6.3694  | 7000  | 0.0071        | 0.0152          | 0.8145                                                | -                                                    |
| 6.8244  | 7500  | 0.0068        | 0.0153          | 0.8172                                                | -                                                    |
| 7.2793  | 8000  | 0.0061        | 0.0147          | 0.8216                                                | -                                                    |
| 7.7343  | 8500  | 0.0062        | 0.0146          | 0.8267                                                | -                                                    |
| 8.1893  | 9000  | 0.0055        | 0.0145          | 0.8325                                                | -                                                    |
| 8.6442  | 9500  | 0.005         | 0.0146          | 0.8335                                                | -                                                    |
| 9.0992  | 10000 | 0.0052        | 0.0143          | 0.8356                                                | -                                                    |
| 9.5541  | 10500 | 0.0043        | 0.0144          | 0.8313                                                | -                                                    |
| 10.0091 | 11000 | 0.0051        | 0.0144          | 0.8362                                                | -                                                    |
| 10.4641 | 11500 | 0.004         | 0.0145          | 0.8376                                                | -                                                    |
| 10.9190 | 12000 | 0.0039        | 0.0142          | 0.8331                                                | -                                                    |
| 11.3740 | 12500 | 0.0034        | 0.0141          | 0.8397                                                | -                                                    |
| 11.8289 | 13000 | 0.0033        | 0.0140          | 0.8398                                                | -                                                    |
| 12.2839 | 13500 | 0.0032        | 0.0143          | 0.8411                                                | -                                                    |
| 12.7389 | 14000 | 0.003         | 0.0141          | 0.8407                                                | -                                                    |
| 13.1938 | 14500 | 0.0031        | 0.0141          | 0.8379                                                | -                                                    |
| 13.6488 | 15000 | 0.0026        | 0.0141          | 0.8419                                                | -                                                    |
| 14.1037 | 15500 | 0.0028        | 0.0141          | 0.8442                                                | -                                                    |
| 14.5587 | 16000 | 0.0023        | 0.0138          | 0.8455                                                | -                                                    |
| 15.0136 | 16500 | 0.0025        | 0.0147          | 0.8359                                                | -                                                    |
| 15.4686 | 17000 | 0.0021        | 0.0141          | 0.8459                                                | -                                                    |
| 15.9236 | 17500 | 0.0023        | 0.0140          | 0.8433                                                | -                                                    |
| 16.3785 | 18000 | 0.002         | 0.0139          | 0.8465                                                | -                                                    |
| 16.8335 | 18500 | 0.002         | 0.0139          | 0.8461                                                | -                                                    |
| 17.2884 | 19000 | 0.0018        | 0.0139          | 0.8482                                                | -                                                    |
| 17.7434 | 19500 | 0.0018        | 0.0138          | 0.8477                                                | -                                                    |
| 18.1984 | 20000 | 0.0017        | 0.0138          | 0.8503                                                | -                                                    |
| 18.6533 | 20500 | 0.0016        | 0.0136          | 0.8493                                                | -                                                    |
| 19.1083 | 21000 | 0.0016        | 0.0139          | 0.8501                                                | -                                                    |
| 19.5632 | 21500 | 0.0015        | 0.0138          | 0.8478                                                | -                                                    |
| 20.0182 | 22000 | 0.0015        | 0.0139          | 0.8501                                                | -                                                    |
| 20.4732 | 22500 | 0.0013        | 0.0139          | 0.8508                                                | -                                                    |
| 20.9281 | 23000 | 0.0015        | 0.0139          | 0.8511                                                | -                                                    |
| 21.3831 | 23500 | 0.0013        | 0.0139          | 0.8517                                                | -                                                    |
| 21.8380 | 24000 | 0.0013        | 0.0139          | 0.8512                                                | -                                                    |
| 22.2930 | 24500 | 0.0012        | 0.0139          | 0.8512                                                | -                                                    |
| 22.7480 | 25000 | 0.0012        | 0.0138          | 0.8520                                                | -                                                    |
| 23.2029 | 25500 | 0.0012        | 0.0139          | 0.8520                                                | -                                                    |
| 23.6579 | 26000 | 0.0011        | 0.0139          | 0.8518                                                | -                                                    |
| 24.0    | 26376 | -             | -               | -                                                     | 0.8481                                               |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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