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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:123640
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: data perempuan dan laki-laki di indonesia 2022
sentences:
- Statistik Telekomunikasi Indonesia 2012
- Perkembangan Indeks Produksi Triwulanan Industri Mikro dan Kecil 2023
- Pada Agustus 2014, Jumlah wisman mencapai 826,8 ribu
- source_sentence: hasil survei kebutuhan data 2011 di indonesia
sentences:
- Analisis Survei Kebutuhan Data 2011
- Produk Domestik Bruto Indonesia Triwulanan 2007-2011
- Direktori Perusahaan Air Bersih, Listrik, dan Gas 2022
- source_sentence: komoditas apa yang produksinya naik 3,24 persen pada tahun 2013?
sentences:
- Indikator Ekonomi Juni 2017
- Produksi jagung naik pada tahun 2013.
- Statistik Keuangan Pemerintah Desa 2018
- source_sentence: buku-buku statistik tahun 2007
sentences:
- Statistik Keuangan Badan Usaha Milik Negara dan Badan Usaha Milik Daerah 2019
- Statistik Harga Konsumen Perdesaan Kelompok Makanan 2011
- Buletin Statistik Perdagangan Luar Negeri Impor Mei 2019
- source_sentence: analisis kinerja ekspor indonesia feb 2014
sentences:
- Kajian Big Data Sinyal Pemulihan Indonesia dari Pandemi Covid-19
- Laporan Bulanan Data Sosial Ekonomi Januari 2019
- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
Negara Februari 2014
datasets:
- yahyaabd/allstats-semantic-synthetic-dataset-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
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: 0.9866451272402678
name: Pearson Cosine
- type: spearman_cosine
value: 0.9032950863870964
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: 0.9876833290128094
name: Pearson Cosine
- type: spearman_cosine
value: 0.9063327700749637
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 [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) 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:**
- [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1)
<!-- - **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: 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-semantic-base-v1")
# Run inference
sentences = [
'analisis kinerja ekspor indonesia feb 2014',
'Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara Februari 2014',
'Laporan Bulanan Data Sosial Ekonomi Januari 2019',
]
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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You can finetune this model on your own dataset.
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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 | 0.9866 | 0.9877 |
| **spearman_cosine** | **0.9033** | **0.9063** |
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## Training Details
### Training Dataset
#### allstats-semantic-synthetic-dataset-v1
* Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [d59a245](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/d59a24585b2ee30e806569dc6a091becd5fcac0c)
* Size: 123,640 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: 6 tokens</li><li>mean: 10.64 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.06 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc | label |
|:----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>Gambaran umum karakteristik usaha di Indonesia</code> | <code>Statistik Karakteristik Usaha 2022/2023</code> | <code>0.9</code> |
| <code>Tabel data jumlah sekolah, guru, dan murid MA di bawah Kementerian Agama per provinsi.</code> | <code>Jumlah Sekolah, Guru, dan Murid Madrasah Aliyah (MA) di Bawah Kementerian Agama Menurut Provinsi, tahun ajaran 2005/2006-2015/2016</code> | <code>0.96</code> |
| <code>bagaimana kinerja sektor konstruksi indonesia di triwulan ketiga tahun 2008?</code> | <code>Statistik Restoran/Rumah Makan 2007</code> | <code>0.09</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-synthetic-dataset-v1
* Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [d59a245](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/d59a24585b2ee30e806569dc6a091becd5fcac0c)
* Size: 26,494 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: 5 tokens</li><li>mean: 10.48 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.86 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc | label |
|:-----------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|:------------------|
| <code>Harga barang konsumsi Indonesia 2022: data per kota</code> | <code>Harga Konsumen Beberapa Barang Kelompok Makanan, Minuman, dan Tembakau 90 Kota di Indonesia 2022</code> | <code>0.92</code> |
| <code>data biaya hidup bali 2018</code> | <code>Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, Maret 2018</code> | <code>0.1</code> |
| <code>ekspor barang indonesia november 2011: data lengkap</code> | <code>Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara Februari 2013</code> | <code>0.12</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`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 10
- `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`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `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`: 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-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine |
|:----------:|:---------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:|
| 0.1294 | 500 | 0.0454 | 0.0267 | 0.7374 | - |
| 0.2588 | 1000 | 0.0243 | 0.0205 | 0.7527 | - |
| 0.3882 | 1500 | 0.0199 | 0.0169 | 0.7720 | - |
| 0.5176 | 2000 | 0.0186 | 0.0164 | 0.7733 | - |
| 0.6470 | 2500 | 0.0179 | 0.0158 | 0.7806 | - |
| 0.7764 | 3000 | 0.0158 | 0.0155 | 0.7826 | - |
| 0.9058 | 3500 | 0.0159 | 0.0155 | 0.7771 | - |
| 1.0352 | 4000 | 0.0155 | 0.0143 | 0.7847 | - |
| 1.1646 | 4500 | 0.0133 | 0.0141 | 0.7935 | - |
| 1.2940 | 5000 | 0.0128 | 0.0132 | 0.7986 | - |
| 1.4234 | 5500 | 0.0121 | 0.0120 | 0.8148 | - |
| 1.5528 | 6000 | 0.012 | 0.0118 | 0.8030 | - |
| 1.6822 | 6500 | 0.0118 | 0.0121 | 0.8132 | - |
| 1.8116 | 7000 | 0.0119 | 0.0109 | 0.8130 | - |
| 1.9410 | 7500 | 0.0107 | 0.0108 | 0.8132 | - |
| 2.0704 | 8000 | 0.009 | 0.0098 | 0.8181 | - |
| 2.1998 | 8500 | 0.0082 | 0.0099 | 0.8221 | - |
| 2.3292 | 9000 | 0.008 | 0.0100 | 0.8221 | - |
| 2.4586 | 9500 | 0.008 | 0.0095 | 0.8302 | - |
| 2.5880 | 10000 | 0.0083 | 0.0090 | 0.8284 | - |
| 2.7174 | 10500 | 0.0084 | 0.0093 | 0.8261 | - |
| 2.8468 | 11000 | 0.0084 | 0.0089 | 0.8283 | - |
| 2.9762 | 11500 | 0.0083 | 0.0093 | 0.8259 | - |
| 3.1056 | 12000 | 0.0056 | 0.0083 | 0.8362 | - |
| 3.2350 | 12500 | 0.006 | 0.0081 | 0.8357 | - |
| 3.3644 | 13000 | 0.0057 | 0.0078 | 0.8381 | - |
| 3.4938 | 13500 | 0.006 | 0.0081 | 0.8399 | - |
| 3.6232 | 14000 | 0.0058 | 0.0078 | 0.8420 | - |
| 3.7526 | 14500 | 0.0068 | 0.0078 | 0.8303 | - |
| 3.8820 | 15000 | 0.0056 | 0.0072 | 0.8502 | - |
| 4.0114 | 15500 | 0.0054 | 0.0073 | 0.8483 | - |
| 4.1408 | 16000 | 0.004 | 0.0068 | 0.8565 | - |
| 4.2702 | 16500 | 0.0042 | 0.0069 | 0.8493 | - |
| 4.3996 | 17000 | 0.0043 | 0.0069 | 0.8507 | - |
| 4.5290 | 17500 | 0.0045 | 0.0069 | 0.8536 | - |
| 4.6584 | 18000 | 0.0042 | 0.0064 | 0.8602 | - |
| 4.7878 | 18500 | 0.0043 | 0.0065 | 0.8537 | - |
| 4.9172 | 19000 | 0.0039 | 0.0062 | 0.8623 | - |
| 5.0466 | 19500 | 0.0041 | 0.0065 | 0.8601 | - |
| 5.1760 | 20000 | 0.0032 | 0.0060 | 0.8643 | - |
| 5.3054 | 20500 | 0.0032 | 0.0064 | 0.8657 | - |
| 5.4348 | 21000 | 0.0032 | 0.0062 | 0.8669 | - |
| 5.5642 | 21500 | 0.0031 | 0.0065 | 0.8633 | - |
| 5.6936 | 22000 | 0.003 | 0.0059 | 0.8682 | - |
| 5.8230 | 22500 | 0.0032 | 0.0057 | 0.8713 | - |
| 5.9524 | 23000 | 0.0032 | 0.0057 | 0.8688 | - |
| 6.0818 | 23500 | 0.0026 | 0.0055 | 0.8772 | - |
| 6.2112 | 24000 | 0.0023 | 0.0056 | 0.8708 | - |
| 6.3406 | 24500 | 0.0029 | 0.0056 | 0.8734 | - |
| 6.4700 | 25000 | 0.0027 | 0.0054 | 0.8748 | - |
| 6.5994 | 25500 | 0.0022 | 0.0054 | 0.8827 | - |
| 6.7288 | 26000 | 0.0021 | 0.0053 | 0.8823 | - |
| 6.8582 | 26500 | 0.0021 | 0.0053 | 0.8832 | - |
| 6.9876 | 27000 | 0.0025 | 0.0052 | 0.8839 | - |
| 7.1170 | 27500 | 0.002 | 0.0051 | 0.8887 | - |
| 7.2464 | 28000 | 0.0017 | 0.0050 | 0.8869 | - |
| 7.3758 | 28500 | 0.0019 | 0.0052 | 0.8845 | - |
| 7.5052 | 29000 | 0.0017 | 0.0051 | 0.8897 | - |
| 7.6346 | 29500 | 0.0017 | 0.0051 | 0.8920 | - |
| 7.7640 | 30000 | 0.0018 | 0.0050 | 0.8889 | - |
| 7.8934 | 30500 | 0.0019 | 0.0050 | 0.8931 | - |
| 8.0228 | 31000 | 0.002 | 0.0049 | 0.8889 | - |
| 8.1522 | 31500 | 0.0014 | 0.0049 | 0.8912 | - |
| 8.2816 | 32000 | 0.0013 | 0.0049 | 0.8922 | - |
| 8.4110 | 32500 | 0.0014 | 0.0049 | 0.8947 | - |
| 8.5404 | 33000 | 0.0014 | 0.0049 | 0.8960 | - |
| 8.6698 | 33500 | 0.0014 | 0.0049 | 0.8972 | - |
| 8.7992 | 34000 | 0.0014 | 0.0048 | 0.8982 | - |
| 8.9286 | 34500 | 0.0013 | 0.0048 | 0.9003 | - |
| 9.0580 | 35000 | 0.0014 | 0.0048 | 0.9001 | - |
| 9.1874 | 35500 | 0.0012 | 0.0048 | 0.8995 | - |
| 9.3168 | 36000 | 0.0011 | 0.0048 | 0.9008 | - |
| 9.4462 | 36500 | 0.001 | 0.0047 | 0.9015 | - |
| 9.5756 | 37000 | 0.0011 | 0.0047 | 0.9026 | - |
| 9.7050 | 37500 | 0.0011 | 0.0047 | 0.9027 | - |
| 9.8344 | 38000 | 0.001 | 0.0047 | 0.9035 | - |
| **9.9638** | **38500** | **0.0011** | **0.0047** | **0.9033** | **-** |
| 10.0 | 38640 | - | - | - | 0.9063 |
* The bold row denotes the saved checkpoint.
### 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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