yahyaabd / paraphrase-multilingual-mpnet-base-v2-mnrl

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on the statictable-triplets-all 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
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 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: 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:

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/paraphrase-multilingual-mpnet-base-v2-mnrl")
# Run inference
sentences = [
    'Seperti apa rincian inflasi di Indonesia, termasuk inflasi inti dan harga diatur, pada 2020?',
    'Inflasi Umum, Inti, Harga Yang Diatur Pemerintah, dan Barang Bergejolak Inflasi Indonesia, 2009-2024',
    'Angka Kematian Ibu/AKI (Maternal Mortality Rate/MMR) Hasil Long Form SP2020 Menurut Provinsi, 2020',
]
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]

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: 4 tokens mean: 18.38 tokens max: 35 tokens	min: 4 tokens mean: 25.39 tokens max: 50 tokens	min: 4 tokens mean: 25.6 tokens max: 58 tokens

• Samples:

  query	pos	neg
  Neraca arus dana triwulan I tahun 2004 (ringkasan, miliar)	Ringkasan Neraca Arus Dana Triwulan I 2004 (Miliar Rupiah)	Proporsi Penduduk Berumur 10 Tahun ke Atas yang Membaca Surat Kabar/Majalah Selama Seminggu Terakhir menurut Provinsi, Tipe Daerah dan Jenis Kelamin, 2012
  Kumpulan dokumen Rencana Pengurangan Bencana level kabupaten dan kota	Rekap Dokumen RPB Kabupaten/Kota	Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Golongan Umur dan Jumlah Jam Kerja Seluruhnya, 1986-1996
  IHK dan gaji bulanan buruh hotel, di bawah supervisor, 2007=100, tahun 2009	IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 2007-2014 (2007=100)	Rata-Rata Bulanan Konsentrasi Partikel Terlarut di Udara Beberapa Kota Menurut Bulan dan Kota (μgr/m3), 2006-2015

• 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: 4 tokens mean: 18.39 tokens max: 35 tokens	min: 4 tokens mean: 25.42 tokens max: 58 tokens	min: 4 tokens mean: 25.6 tokens max: 50 tokens

• Samples:

  query	pos	neg
  Bagaimana hubungan IHK dan rata-rata upah buruh hotel (bukan supervisor), acuan 2012, sekitar tahun 2012	IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 2012-2014 (2012=100)	Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas Dasar Harga Berlaku, 2010-2016
  Kegiatan mingguan penduduk 15+ (berdasarkan pendidikan terakhir), 1990	Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Kegiatan Selama Seminggu yang Lalu, 1986-1996	Transaksi Total Atas Dasar Harga Dasar, 2010
  Bandingkan indeks harga konsumen (inflasi) di kota-kota Sumatera vs nasional, Desember 2023	Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Pulau Sumatera dengan Nasional (2018=100)	Persentase Penduduk Berumur 15 tahun Ke Atas menurut Jenis Kegiatan Seminggu Yang Lalu, 2009-2012

• 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

• The bold row denotes the saved checkpoint.

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: 3.4.0
• 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}
}