sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:73392 loss:CosineSimilarityLoss dataset:yahyaabd/allstats-semantic-dataset-v4 arxiv:1908.10084 base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
datasets:
- yahyaabd/allstats-semantic-dataset-v4
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:73392
- loss:CosineSimilarityLoss
widget:
- source_sentence: Berapa persen kenaikan Indeks Harga Perdagangan Besar (IHPB) Umum
    Nasional pada bulan April 2021?
  sentences:
  - Statistik Kriminal 2023
  - Ekonomi Indonesia Triwulan I-2021 turun 0,74 persen (y-on-y)
  - Survei Biaya Hidup (SBH) 2018 Ambon dan Tual
- source_sentence: Usaha pertanian sampingan di Indonesia tahun 2022
  sentences:
  - Analisis Hasil Survei Dampak Covid-19 Terhadap Pelaku Usaha
  - Direktori Usaha Pertanian Lainnya 2022
  - EksporImpor September 2018
- source_sentence: Pertumbuhan industri Indonesia 2006-2009
  sentences:
  - Pertumbuhan Produksi IBS Triwulan III 2019 Naik 4,35 Persen
  - Indikator Ekonomi April 2000
  - Perkembangan Indeks Produksi Industri Besar dan Sedang 2006 - 2009
- source_sentence: 'Sensus ekonomi Kalbar 2016: data usaha'
  sentences:
  - Pertumbuhan ekonomi Indonesia tahun 2022
  - Buletin Statistik Perdagangan Luar Negeri Impor November 2017
  - Data jumlah wisatawan mancanegara 2019
- source_sentence: Direktori perusahaan pengelola hutan 2015
  sentences:
  - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
    Negara, April 2017
  - Direktori Perusahaan Kehutanan 2015
  - Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat
    0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39.
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 mpnet v2 eval
      type: allstats-semantic-mpnet-v2-eval
    metrics:
    - type: pearson_cosine
      value: 0.9437560461787071
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7866108512073439
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstats semantic mpnet v2 test
      type: allstats-semantic-mpnet-v2-test
    metrics:
    - type: pearson_cosine
      value: 0.9433638771860691
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7869770777792755
      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-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) 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-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4)
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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: 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-mpnet-v2")
# Run inference
sentences = [
    'Direktori perusahaan pengelola hutan 2015',
    'Direktori Perusahaan Kehutanan 2015',
    'Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat 0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39.',
]
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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## Evaluation

### Metrics

#### Semantic Similarity

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

| Metric              | allstats-semantic-mpnet-v2-eval | allstats-semantic-mpnet-v2-test |
|:--------------------|:--------------------------------|:--------------------------------|
| pearson_cosine      | 0.9438                          | 0.9434                          |
| **spearman_cosine** | **0.7866**                      | **0.787**                       |

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## Training Details

### Training Dataset

#### allstats-semantic-dataset-v4

* Dataset: [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) at [0e15dee](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/0e15dee2f2696b45949c1e2256b7cb0b8c87ad8d)
* Size: 73,392 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: 5 tokens</li><li>mean: 11.28 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.71 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
* Samples:
  | query                                                                                                | doc                                                                                                                                                                      | label             |
  |:-----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
  | <code>Data bisnis Kalbar sensus 2016</code>                                                          | <code>Indikator Ekonomi Oktober 2012</code>                                                                                                                              | <code>0.1</code>  |
  | <code>Informasi tentang pola pengeluaran masyarakat Bengkulu berdasarkan kelompok pendapatan?</code> | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Bengkulu, 2018-2023</code> | <code>0.88</code> |
  | <code>Laopran keuagnan lmebaga non proft 20112-013</code>                                            | <code>Neraca Lembaga Non Profit yang Melayani Rumah Tangga 2011-2013</code>                                                                                              | <code>0.93</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-dataset-v4

* Dataset: [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) at [0e15dee](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/0e15dee2f2696b45949c1e2256b7cb0b8c87ad8d)
* Size: 15,726 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: 11.52 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.38 tokens</li><li>max: 61 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>Data transportasi bulan Februari 2021</code>                                                         | <code>Tenaga Kerja Februari 2023</code>                                                       | <code>0.08</code> |
  | <code>Sebear berspa prrsen eknaikan Inseks Hraga Predagangan eBsar (IHB) Umym Nasiona di aMret 202?</code> | <code>Maret 2020, Indeks Harga Perdagangan Besar (IHPB) Umum Nasional naik 0,10 persen</code> | <code>1.0</code>  |
  | <code>Data ekspor dan moda transportasi tahun 2018-2019</code>                                             | <code>Indikator Pasar Tenaga Kerja Indonesia Agustus 2012</code>                              | <code>0.08</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`: 0.5
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_num_workers`: 4
- `load_best_model_at_end`: True
- `label_smoothing_factor`: 0.05
- `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`: 0.5
- `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`: 4
- `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`: 0.05
- `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-mpnet-v2-eval_spearman_cosine | allstats-semantic-mpnet-v2-test_spearman_cosine |
|:----------:|:--------:|:-------------:|:---------------:|:-----------------------------------------------:|:-----------------------------------------------:|
| 0          | 0        | -             | 0.1031          | 0.6244                                          | -                                               |
| 0.1090     | 250      | 0.0442        | 0.0321          | 0.7393                                          | -                                               |
| 0.2180     | 500      | 0.0295        | 0.0248          | 0.7641                                          | -                                               |
| 0.3269     | 750      | 0.0259        | 0.0224          | 0.7733                                          | -                                               |
| **0.4359** | **1000** | **0.0208**    | **0.0199**      | **0.7866**                                      | **-**                                           |
| 0.5        | 1147     | -             | -               | -                                               | 0.7870                                          |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- 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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