yahyaabd / allstats-search-mini-v1-1-mnrl

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-v1-1-mnrl")
# Run inference
sentences = [
    'Informasi lengkap dan terbaru mengenai statistik edukasi',
    'Statistik Pendidikan Tahunan',
    'Struktur Ongkos Riil Usaha Ternak dan Unggas di Rumah Tangga dengan Pola Pemeliharaan Dikandangkan, 2017',
]
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

Information Retrieval

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

Training Details

Training Dataset

statictable-triplets-all

• Dataset: statictable-triplets-all at 0ef226c
• Size: 10,998 training samples
• Columns: query , positive , and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 17.69 tokens max: 33 tokens	min: 5 tokens mean: 25.79 tokens max: 58 tokens	min: 4 tokens mean: 25.72 tokens max: 58 tokens

• Samples:

  query	positive	negative
  Neraca arus kas triwulan II 2005 (ringkasan, )	Ringkasan Neraca Arus Dana, Triwulan Kedua, 2005, (Miliar Rupiah)	Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan Jenis Pekerjaan Utama (Rupiah), 2017
  Hasil tangkapan ikan per provinsi, bedakan jenis penangkapan, 2013	Produksi Perikanan Tangkap Menurut Provinsi dan Jenis Penangkapan, 2000-2020	Ringkasan Neraca Arus Dana, Triwulan II, 2006, (Miliar Rupiah)
  Bagaimana perubahan distribusi pengeluaran?	Persentase Perkembangan Distribusi Pengeluaran	Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi, 1971-2020

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

statictable-triplets-all

• Dataset: statictable-triplets-all at 0ef226c
• Size: 10,998 evaluation samples
• Columns: query , positive , and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 17.61 tokens max: 34 tokens	min: 4 tokens mean: 25.58 tokens max: 58 tokens	min: 5 tokens mean: 25.33 tokens max: 58 tokens

• Samples:

  query	positive	negative
  Data total penghasilan berbagai golongan rumah tangga setelah dipotong pajak, tahun 2000 (dalam )	Jumlah Pendapatan Setelah Pajak Menurut Golongan Rumah Tangga (miliar rupiah), 2000, 2005, dan 2008	Indeks Harga Konsumen per Kelompok di 82 Kota 1 (2012=100)
  Bagaimana perkembangan impor barang modal pada tahun 2020	Impor Barang Modal, 1996-2023	Indeks Harga yang Diterima Petani (It), Indes Harga yang Dibayar Petani (Ib), dan Nilai Tukar Petani Subsektor Hortikultura (NTPH) di Indonesia (2007=100), 2008-2016
  Konsumsi makanan per orang di Kalut: data mingguan, beda kelompok pengeluaran (2018)	Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Kalimantan Utara, 2018-2023	Ekspor Kimia Dasar Organik yang Bersumber dari Hasil Pertanian menurut Negara Tujuan Utama, 2012-2023

• 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 : 32
• per_device_eval_batch_size : 32
• weight_decay : 0.01
• warmup_ratio : 0.1
• save_on_each_node : True
• fp16 : True
• dataloader_num_workers : 2
• load_best_model_at_end : True
• eval_on_start : True
• batch_sampler : no_duplicates

All Hyperparameters

Training Logs

• The bold row denotes the saved checkpoint.

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}
}