yahyaabd / allstats-search-mini-v1-2

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

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the statictable-triplets-all 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
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • statictable-triplets-all

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/allstats-search-mini-v1-2")
# Run inference
sentences = [
    'Status pernikahan penduduk (10+) tiap provinsi, data 2012',
    'Persentase Penduduk Berumur 10 Tahun ke Atas menurut Provinsi, Jenis Kelamin, dan Status Perkawinan, 2009-2018',
    'Ekspor Batu Bara Menurut Negara Tujuan Utama, 2012-2023',
]
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]

Evaluation

Metrics

Information Retrieval

• Dataset: bps-statictable-ir
• Evaluated with InformationRetrievalEvaluator

Training Details

Training Dataset

statictable-triplets-all

• Dataset: statictable-triplets-all at 24979b4
• Size: 967,831 training samples
• Columns: query , pos , and neg
• Approximate statistics based on the first 1000 samples:

  	query	pos	neg
  type	string	string	string
  details	min: 5 tokens mean: 18.35 tokens max: 37 tokens	min: 4 tokens mean: 25.22 tokens max: 58 tokens	min: 4 tokens mean: 25.78 tokens max: 58 tokens

• Samples:

  query	pos	neg
  Jumlah bank dan kantor bank di Indonesia, 2010-2017	Bank dan Kantor Bank, 2010-2017	Rata-Rata Pengeluaran per Kapita Sebulan Menurut Kelompok Barang (rupiah), 1998-2012
  Konsumsi makanan mingguan per orang di Sulteng: beda tingkat pengeluaran (2021)	Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Selatan, 2018-2023	IHK, Upah Nominal, Indeks Upah Nominal dan Riil Buruh Industri Berstatus di bawah Mandor Menurut Wilayah, 2008-2014 (2007=100)
  Impor semen Indonesia, negara asal utama, 2021	Impor Semen Menurut Negara Asal Utama, 2017-2023	Penerimaan dari Wisatawan Mancanegara Menurut Negara Tempat Tinggal (juta US$), 2000-2014

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

statictable-triplets-all

• Dataset: statictable-triplets-all at 24979b4
• Size: 967,831 evaluation samples
• Columns: query , pos , and neg
• Approximate statistics based on the first 1000 samples:

  	query	pos	neg
  type	string	string	string
  details	min: 5 tokens mean: 18.39 tokens max: 37 tokens	min: 4 tokens mean: 25.22 tokens max: 50 tokens	min: 4 tokens mean: 25.33 tokens max: 58 tokens

• Samples:

  query	pos	neg
  Bagaimana hubungan antara bidang pekerjaan utama dan pendidikan pekerja 15+ di minggu lalu (tahun 2016)?	Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Lapangan Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan, 2008 - 2024	Bank dan Kantor Bank, 2010-2017
  Tren indikator kondisi perumahan, 2001	Indikator Perumahan 1993-2023	Banyaknya Desa/Kelurahan Menurut Keberadaan Kelompok Pertokoan, Pasar, dan Kios Sarana Produksi Pertanian (Saprotan), 2014 & 2018
  Gaji bersih rata-rata: Per pendidikan & lapangan kerja utama, Indonesia, 2021	Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Lapangan Pekerjaan Utama, 2021	[Seri 2000] Laju Pertumbuhan Kumulatif PDB Menurut Lapangan Usaha (Persen), 2001-2014

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy : steps
• per_device_train_batch_size : 16
• per_device_eval_batch_size : 16
• num_train_epochs : 1
• warmup_ratio : 0.1
• fp16 : True
• load_best_model_at_end : True
• eval_on_start : True
• batch_sampler : no_duplicates

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.4.0
• Transformers: 4.48.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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

@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}
}