paraphrase-multilingual-miniLM-L12-V2-ocl

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-pair-class 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-pair-class

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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-ocl")
# Run inference
sentences = [
    'DATA HARIAN DEBIT, KETINGGIAN, DAN VOLUME AIR SUNGAI DENGAN DAERAH ALIRAN SUNGAI DI ATAS 100 KM2, TAHUN 2015',
    'Ringkasan Neraca Arus Dana, Triwulan IV, 2009, (Miliar Rupiah)',
    'Ringkasan Neraca Arus Dana, Triwulan III, 2014**), (Miliar Rupiah)',
]
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>

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Training Details

Training Dataset

statictable-pair-class

• Dataset: statictable-pair-class at 62bf40d
• Size: 37,302 training samples
• Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	int
  details	<ul><li>min: 5 tokens</li><li>mean: 20.23 tokens</li><li>max: 50 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 25.61 tokens</li><li>max: 50 tokens</li></ul>	<ul><li>0: ~78.10%</li><li>1: ~21.90%</li></ul>

• Samples:

  query	doc	label
  <code>BERAPA JUMLAH PENDUDUK 15 TAHUN KE ATAS YANG BEKERJA DI TIAP PROVINSI, MENURUT STATUS PEKERJAAN (2022)?</code>	<code>Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Menurut Provinsi dan Status Pekerjaan Utama, 2022</code>	<code>1</code>
  <code>Budget kesehatan Kemenkeu Ditjen Anggaran</code>	<code>Persentase Rumah Tangga yang Menempati Rumah dengan Dinding Terluas Bukan Bambu/lainnya, 1993-2021</code>	<code>0</code>
  <code>Cek pengeluaran makanan mingguan rata-rata warga Kalsel (2000-2021), bedakan per kelompok pengeluaran</code>	<code>Persentase Rumah Tangga Menurut Provinsi dan Fasilitas Tempat Buang Air Besar, 2000-2021</code>	<code>0</code>

• Loss: <code>OnlineContrastiveLoss</code>

Evaluation Dataset

statictable-pair-class

• Dataset: statictable-pair-class at 62bf40d
• Size: 37,302 evaluation samples
• Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	int
  details	<ul><li>min: 8 tokens</li><li>mean: 20.43 tokens</li><li>max: 48 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 25.59 tokens</li><li>max: 58 tokens</li></ul>	<ul><li>0: ~75.40%</li><li>1: ~24.60%</li></ul>

• Samples:

  query	doc	label
  <code>Negara tujuan ekspor jewelry 2012-2023</code>	<code>Ekspor Barang Perhiasan dan Barang Berharga Menurut Negara Tujuan Utama, 2012-2023</code>	<code>1</code>
  <code>Jumlah Pns Indonesia Per Masa Kerja Dan Gender 2005</code>	<code>Jumlah Pegawai Negeri Sipil Menurut Masa Kerja dan Jenis Kelamin, 2004 - 2023</code>	<code>1</code>
  <code>Berapa rata-rata pendapatan per orang (setelah pajak) menurut golongan rumah tangga pada tahun 2000?</code>	<code>Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Hasil Long Form SP2020 Menurut Provinsi/Kabupaten/Kota, 2020</code>	<code>0</code>

• Loss: <code>OnlineContrastiveLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• 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

<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: 64
• per_device_eval_batch_size: 64
• 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: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• 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",
}

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