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yahyaabd/paraphrase-multilingual-miniLM-L12-v2-mnrl-beir

sourceHugging Faceupdated 2y agoView on Hugging Face

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Model Card

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:

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/paraphrase-multilingual-miniLM-L12-v2-mnrl-beir")
# Run inference
sentences = [
    'Bagaimana tren ekspor teh Indonesia ke berbagai negara tahun 2008?',
    'Ekspor Teh Menurut Negara Tujuan Utama, 2000-2015',
    'Luas Kawasan Hutan dan Kawasan Konservasi Perairan Indonesia Berdasarkan Surat Keputusan Menteri Lingkungan Hidup dan Kehutanan, 2017-2022',
]
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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Evaluation

Metrics

Information Retrieval

—Dataset: bps-statictable-ir
—Evaluated with <code>InformationRetrievalEvaluator</code>

Metric	Value
cosine_accuracy@1	0.3648
cosine_accuracy@5	0.6319
cosine_accuracy@10	0.7199
cosine_precision@1	0.3648
cosine_precision@5	0.159
cosine_precision@10	0.1088
cosine_recall@1	0.2757
cosine_recall@5	0.4928
cosine_recall@10	0.5744
cosine_ndcg@1	0.3648
cosine_ndcg@5	0.4404
cosine_ndcg@10	0.4644
cosine_mrr@1	0.3648
cosine_mrr@5	0.4639
cosine_mrr@10	0.4758
cosine_map@1	0.3648
cosine_map@5	0.3868
cosine_map@10	0.3942

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Training Details

Training Dataset

statictable-triplets-all

—Dataset: statictable-triplets-all at 24979b4
—Size: 967,831 training samples
—Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
—Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 18.53 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.42 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.71 tokens</li><li>max: 58 tokens</li></ul> |
—Samples: | query | pos | neg | |:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------| | <code>Data pendapatan per kapita, bedakan per golongan rumah tangga (ribu rupiah), 2008</code> | <code>Rata-rata Jumlah Pendapatan perkapita Menurut Golongan Rumah Tangga (ribu rupiah), 2000, 2005, 2008</code> | <code>Ringkasan Neraca Arus Dana, Triwulan II, 2007, (Miliar Rupiah)</code> | | <code>Berapa ribu ton impor Indonesia dari negara-negara utama tahun 2018?</code> | <code>Volume Impor Menurut Negara Asal Utama (Berat bersih:ribu ton), 2017-2023</code> | <code>Rata-Rata Pengeluaran Konsumsi per Kapita Menurut Golongan Rumah Tangga (ribu rupiah), 2000, 2005, dan 2008</code> | | <code>Kredit dari lembaga keuangan non-bank 2000-2016</code> | <code>Pemberian Kredit oleh Lembaga-Lembaga Keuangan Lainnya (miliar rupiah), 2000-2016</code> | <code>Angka Partisipasi Sekolah (APS) Penduduk Umur 7-18 Tahun Menurut Klasifikasi Desa, Jenis Kelamin, dan Kelompok Umur, 2009-2023</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Evaluation Dataset

statictable-triplets-all

—Dataset: statictable-triplets-all at 24979b4
—Size: 967,831 evaluation samples
—Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
—Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 18.75 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.35 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.44 tokens</li><li>max: 58 tokens</li></ul> |
—Samples: | query | pos | neg | |:----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Bagaimana kebiasaan rumah tangga memisahkan sampah (mudah busuk vs tidak), per provinsi, 2021?</code> | <code>Persentase Rumah Tangga Menurut Provinsi dan Perlakuan Memilah Sampah Mudah Membusuk dan Tidak Mudah Membusuk, 2013-2014, 2021</code> | <code>Rata-Rata Upah/Gaji Bersih Sebulan (rupiah) Buruh/Karyawan/Pegawai menurut Provinsi dan Lapangan Pekerjaan Utama di 17 Sektor, 2023</code> | | <code>Bagaimana rincian pinjaman investasi (hanya Rupiah) dari bank umum ke berbagai sektor ekonomi pada tahun 2016?</code> | <code>Pinjaman Investasi Bank-Bank Umum dalam Rupiah Menurut Sektor Ekonomi (miliar rupiah), 2000 - 2016</code> | <code>Ringkasan Neraca Arus Dana, Triwulan IV, 2009, (Miliar Rupiah)</code> | | <code>Data rumah tangga per provinsi, adakah area serapan air? Ambil tahun 2013</code> | <code>Persentase Rumah Tangga Menurut Provinsi dan Keberadaan Area Resapan Air, 2013-2014</code> | <code>Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (9 x 9)</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Training Logs

Epoch	Step	bps-statictable-ir_cosine_ndcg@10
0	0	0.4644

Framework Versions

—Python: 3.11.11
—Sentence Transformers: 3.4.1
—Transformers: 4.48.3
—PyTorch: 2.6.0+cu124
—Accelerate: 1.3.0
—Datasets: 3.4.1
—Tokenizers: 0.21.1

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",
}

MultipleNegativesRankingLoss

bibtex

@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}
}

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