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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:244856
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: Bulan apa inflasi sebesar 0,63 persen terjadi pada tahun 2013?
sentences:
- Pada bulan Mei 2013 terjadi inflasi sebesar 0,2 persen
- Nilai Tukar Petani (NTP) April 2024 sebesar 116,79 atau turun 2,18 persen.
- Posisi Kredit Perbankan<sup>1</sup>dalam Rupiah dan Valuta Asing Menurut Sektor
Ekonomi (miliar rupiah), 2016-2018
- source_sentence: Berapa persen penurunan Nilai Tukar Petani NTP Februari 2017
sentences:
- Produksi Tanaman Pangan Angka Ramalan II Tahun 2015
- Nilai Tukar Petani (NTP) Februari 2017 Sebesar 100,33 Atau Turun 0,58 Persen
- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Juni 2024
- source_sentence: analisis industri pariwisata indonesia tahun 2013
sentences:
- Ringkasan Neraca Arus Dana, Triwulan IV, 2012), (Miliar Rupiah)
- Pengeluaran Untuk Konsumsi Penduduk Indonesia September 2014
- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
Negara, Desember 2020
- source_sentence: Sosial ekonomi Indonesia bulan November 2020
sentences:
- Pos Kesehatan Desa
- Jumlah Wisman Pada Januari 2011 Naik 11,14 Persen dan Penumpang Angkutan Udara
Domestik Pada Januari 2011 Turun 6,88 Persen
- Laporan Bulanan Data Sosial Ekonomi September 2017
- source_sentence: Tahun berapa Rupiah terdepresiasi 0,23 persen terhadap Dolar Amerika?
sentences:
- 'Nilai Impor Menurut Negara Asal Utama (Nilai CIF: juta US$), 2000-2023'
- Ringkasan Neraca Arus Dana Triwulan Pertama, 2002, (Miliar Rupiah)
- Depresiasi Rupiah terhadap Dolar Amerika pada tahun 2016 sebesar 0,5 persen.
datasets:
- yahyaabd/allstats-semantic-search-synthetic-dataset-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
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: 0.9838643974678674
name: Pearson Cosine
- type: spearman_cosine
value: 0.8951406685580494
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: 0.98307083670705
name: Pearson Cosine
- type: spearman_cosine
value: 0.8922084062478435
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](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2) 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](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2)
<!-- - **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: 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")
# Run inference
sentences = [
'Tahun berapa Rupiah terdepresiasi 0,23 persen terhadap Dolar Amerika?',
'Depresiasi Rupiah terhadap Dolar Amerika pada tahun 2016 sebesar 0,5 persen.',
'Ringkasan Neraca Arus Dana Triwulan Pertama, 2002, (Miliar Rupiah)',
]
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
#### 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 | 0.9839 | 0.9831 |
| **spearman_cosine** | **0.8951** | **0.8922** |
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## 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.*
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## Training Details
### Training Dataset
#### allstats-semantic-search-synthetic-dataset-v2
* Dataset: [allstats-semantic-search-synthetic-dataset-v2](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2) at [c76f31a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2/tree/c76f31abb3f2d3a2edd9895b9f5e896bf7c84f34)
* Size: 244,856 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: 12.75 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.81 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc | label |
|:------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------|
| <code>Dtaa harg konsymen edesaan (non-makann) 201</code> | <code>Statistik Harga Konsumen Perdesaan Kelompok Nonmakanan (Data 2013)</code> | <code>0.95</code> |
| <code>Bagaimna konidsi keuamgan rymah atngga Indonsia 2020-2022?</code> | <code>Statistik Perusahaan Perikanan 2007</code> | <code>0.1</code> |
| <code>Tingkat hunian kamar hotel tahun 2023</code> | <code>Tingkat Penghunian Kamar Hotel 2023</code> | <code>0.99</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
* Dataset: [allstats-semantic-search-synthetic-dataset-v2](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2) at [c76f31a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2/tree/c76f31abb3f2d3a2edd9895b9f5e896bf7c84f34)
* Size: 52,469 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: 3 tokens</li><li>mean: 13.04 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.01 tokens</li><li>max: 54 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>Bulan apa NTP mengalami kenaikan 0,25 persen?</code> | <code>Jumlah Wisatawan Mancanegara Bulan Agustus 2009 Turun 4,49 Persen Dibandingkan Bulan Sebelumnya.</code> | <code>0.0</code> |
| <code>Sebutksn keempa komositi tang disebutkn besert persentae mrajin persagangannya.</code> | <code>Marjin Perdagangan Minyak Goreng 3,86 Persen, Terigu 5,92 Persen, Garam 23,74 Persen, Dan Susu Bubuk 13,02 Persen</code> | <code>1.0</code> |
| <code>Data kemiskinan per kabupaten/kota tahun 2007</code> | <code>Data dan Informasi Kemiskinan 2007 Buku 2: Kabupaten/Kota</code> | <code>0.87</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`: 8
- `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`: 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`: 8
- `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
| Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-mini-v2-eval_spearman_cosine | allstat-semantic-search-mini-v2-test_spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|:-----------------------------------------------------:|:----------------------------------------------------:|
| 0.1307 | 500 | 0.0963 | 0.0657 | 0.6836 | - |
| 0.2614 | 1000 | 0.0558 | 0.0428 | 0.7480 | - |
| 0.3921 | 1500 | 0.0403 | 0.0335 | 0.7665 | - |
| 0.5227 | 2000 | 0.0324 | 0.0285 | 0.7744 | - |
| 0.6534 | 2500 | 0.0284 | 0.0255 | 0.7987 | - |
| 0.7841 | 3000 | 0.0246 | 0.0225 | 0.7883 | - |
| 0.9148 | 3500 | 0.0217 | 0.0217 | 0.7964 | - |
| 1.0455 | 4000 | 0.0193 | 0.0187 | 0.8111 | - |
| 1.1762 | 4500 | 0.017 | 0.0174 | 0.8086 | - |
| 1.3068 | 5000 | 0.0163 | 0.0170 | 0.8157 | - |
| 1.4375 | 5500 | 0.0157 | 0.0161 | 0.8000 | - |
| 1.5682 | 6000 | 0.015 | 0.0156 | 0.8133 | - |
| 1.6989 | 6500 | 0.0146 | 0.0146 | 0.8194 | - |
| 1.8296 | 7000 | 0.014 | 0.0140 | 0.8103 | - |
| 1.9603 | 7500 | 0.013 | 0.0132 | 0.8205 | - |
| 2.0910 | 8000 | 0.0111 | 0.0126 | 0.8353 | - |
| 2.2216 | 8500 | 0.0102 | 0.0123 | 0.8407 | - |
| 2.3523 | 9000 | 0.0101 | 0.0118 | 0.8389 | - |
| 2.4830 | 9500 | 0.01 | 0.0115 | 0.8444 | - |
| 2.6137 | 10000 | 0.0097 | 0.0111 | 0.8456 | - |
| 2.7444 | 10500 | 0.0097 | 0.0105 | 0.8524 | - |
| 2.8751 | 11000 | 0.0091 | 0.0102 | 0.8526 | - |
| 3.0058 | 11500 | 0.0088 | 0.0100 | 0.8561 | - |
| 3.1364 | 12000 | 0.0069 | 0.0095 | 0.8619 | - |
| 3.2671 | 12500 | 0.0071 | 0.0094 | 0.8534 | - |
| 3.3978 | 13000 | 0.0068 | 0.0092 | 0.8648 | - |
| 3.5285 | 13500 | 0.0069 | 0.0093 | 0.8638 | - |
| 3.6592 | 14000 | 0.0071 | 0.0091 | 0.8548 | - |
| 3.7899 | 14500 | 0.0065 | 0.0085 | 0.8711 | - |
| 3.9205 | 15000 | 0.0064 | 0.0084 | 0.8622 | - |
| 4.0512 | 15500 | 0.0061 | 0.0080 | 0.8675 | - |
| 4.1819 | 16000 | 0.0051 | 0.0082 | 0.8673 | - |
| 4.3126 | 16500 | 0.0052 | 0.0080 | 0.8659 | - |
| 4.4433 | 17000 | 0.0053 | 0.0078 | 0.8669 | - |
| 4.5740 | 17500 | 0.0053 | 0.0077 | 0.8690 | - |
| 4.7047 | 18000 | 0.005 | 0.0076 | 0.8758 | - |
| 4.8353 | 18500 | 0.0048 | 0.0074 | 0.8700 | - |
| 4.9660 | 19000 | 0.0049 | 0.0072 | 0.8785 | - |
| 5.0967 | 19500 | 0.0041 | 0.0070 | 0.8795 | - |
| 5.2274 | 20000 | 0.0039 | 0.0071 | 0.8803 | - |
| 5.3581 | 20500 | 0.0039 | 0.0071 | 0.8843 | - |
| 5.4888 | 21000 | 0.0041 | 0.0070 | 0.8818 | - |
| 5.6194 | 21500 | 0.0039 | 0.0069 | 0.8812 | - |
| 5.7501 | 22000 | 0.0038 | 0.0068 | 0.8868 | - |
| 5.8808 | 22500 | 0.0038 | 0.0067 | 0.8831 | - |
| 6.0115 | 23000 | 0.0037 | 0.0066 | 0.8869 | - |
| 6.1422 | 23500 | 0.003 | 0.0065 | 0.8888 | - |
| 6.2729 | 24000 | 0.0031 | 0.0064 | 0.8879 | - |
| 6.4036 | 24500 | 0.0032 | 0.0064 | 0.8881 | - |
| 6.5342 | 25000 | 0.003 | 0.0062 | 0.8919 | - |
| 6.6649 | 25500 | 0.0031 | 0.0062 | 0.8919 | - |
| 6.7956 | 26000 | 0.0031 | 0.0061 | 0.8910 | - |
| 6.9263 | 26500 | 0.003 | 0.0061 | 0.8911 | - |
| 7.0570 | 27000 | 0.0028 | 0.0061 | 0.8925 | - |
| 7.1877 | 27500 | 0.0025 | 0.0061 | 0.8922 | - |
| 7.3183 | 28000 | 0.0026 | 0.0060 | 0.8944 | - |
| 7.4490 | 28500 | 0.0026 | 0.0061 | 0.8953 | - |
| 7.5797 | 29000 | 0.0026 | 0.0060 | 0.8948 | - |
| 7.7104 | 29500 | 0.0025 | 0.0060 | 0.8941 | - |
| 7.8411 | 30000 | 0.0025 | 0.0059 | 0.8950 | - |
| 7.9718 | 30500 | 0.0025 | 0.0059 | 0.8951 | - |
| 8.0 | 30608 | - | - | - | 0.8922 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- 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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