paraphrase-multilingual-miniLM-L12-v2-mnrl-beir-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 <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • statictable-triplets-all

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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/paraphrase-multilingual-miniLM-L12-v2-mnrl-beir-2")
# Run inference
sentences = [
    'Laporan singkat arus kas Q2 2005, dalam miliar',
    'Ringkasan Neraca Arus Dana, Triwulan Kedua, 2005, (Miliar Rupiah)',
    'Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Jumlah Jam Kerja Utama, 2020',
]
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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Information Retrieval

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

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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: 4 tokens</li><li>mean: 18.55 tokens</li><li>max: 37 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 25.6 tokens</li><li>max: 58 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 25.7 tokens</li><li>max: 58 tokens</li></ul>

• Samples:

  query	pos	neg
  <code>Indeks harga petani (diterima & dibayar) dan NTP per provinsi, 2012</code>	<code>Indeks Harga yang Diterima Petani (It), Indeks Harga yang Dibayar Petani (Ib), dan Nilai Tukar Petani (NTP) Menurut Provinsi, 2008-2016</code>	<code>Persentase Rumah Tangga Menurut Provinsi dan KebiasaanMemanfaatkan Air Bekas untuk Keperluan Lain, 2013, 2014, 2017, 2021</code>
  <code>Data rumah tangga perikanan budidaya Indonesia, detail per provinsi dan jenis budidaya, di tahun 2008</code>	<code>Jumlah Rumah Tangga Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2016</code>	<code>Ringkasan Neraca Arus Dana, 2005, (Miliar Rupiah)</code>
  <code>Lapangan pekerjaan vs pendidikan pekerja (15 tahun ke atas), 1986 hingga 1996</code>	<code>Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Lapangan Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan, 1986 -1996</code>	<code>Tabel Input-Output Indonesia Transaksi Domestik Atas Dasar Harga Produsen (17 Lapangan Usaha), 2016 (Juta Rupiah)</code>

• Loss: <code>MultipleNegativesRankingLoss</code> 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: <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: 18.38 tokens</li><li>max: 37 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 25.28 tokens</li><li>max: 58 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 25.65 tokens</li><li>max: 58 tokens</li></ul>

• Samples:

  query	pos	neg
  <code>Bagaimana hubungan IHK dan rata-rata upah buruh industri (bukan supervisor) bulanan tahun 2010, acuan 1996?</code>	<code>IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100)</code>	<code>Rata-rata Harga Valuta Asing Terpilih menurut Provinsi, 2014</code>
  <code>Berapa rata-rata gaji bulanan pekerja Indonesia berdasarkan ijazah terakhir dan sektor pekerjaannya (2017)?</code>	<code>Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi yang Ditamatkan dan Lapangan Pekerjaan Utama di 9 Sektor (rupiah), 2017</code>	<code>Rata-Rata Pengeluaran per Kapita Sebulan Menurut Kelompok Barang (rupiah), 2013-2021</code>
  <code>Data luas lahan (hektar) yang dipakai untuk jenis budidaya perikanan di tiap provinsi tahun 2009</code>	<code>Luas Area Usaha Budidaya Perikanan Menurut Provinsi dan Jenis Budidaya (ha), 2005-2016</code>	<code>Ringkasan Neraca Arus Dana, Triwulan I, 2008, (Miliar Rupiah)</code>

• Loss: <code>MultipleNegativesRankingLoss</code> 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
• weight_decay: 0.01
• num_train_epochs: 2
• lr_scheduler_type: reduce_lr_on_plateau
• lr_scheduler_kwargs: {'factor': 0.5, 'patience': 2}
• warmup_steps: 10000
• 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

<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: 16
• per_device_eval_batch_size: 16
• 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: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 2
• max_steps: -1
• lr_scheduler_type: reduce_lr_on_plateau
• lr_scheduler_kwargs: {'factor': 0.5, 'patience': 2}
• warmup_ratio: 0.0
• warmup_steps: 10000
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: True
• 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: 2
• 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: 0.0
• 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: no_duplicates
• multi_dataset_batch_sampler: proportional

</details>

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: 3.4.1
• Tokenizers: 0.21.1

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}
}

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