SentenceTransformer based on denaya/indoSBERT-large

This is a sentence-transformers model finetuned from denaya/indoSBERT-large on the statictable-triplets-all dataset. It maps sentences & paragraphs to a 256-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 Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

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/indoSBERT-Large-mnrl-2")
# Run inference
sentences = [
    'Upah bulanan rata-rata: Hubungan pendidikan tertinggi dan sektor pekerjaan utama, data 2021',
    'Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Lapangan Pekerjaan Utama, 2021',
    'Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Jenis Pekerjaan Utama, 2021',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.9218
cosine_accuracy@5 0.9902
cosine_accuracy@10 0.9967
cosine_precision@1 0.9218
cosine_precision@5 0.2248
cosine_precision@10 0.1316
cosine_recall@1 0.7225
cosine_recall@5 0.793
cosine_recall@10 0.8182
cosine_ndcg@1 0.9218
cosine_ndcg@5 0.8341
cosine_ndcg@10 0.8332
cosine_mrr@1 0.9218
cosine_mrr@5 0.9522
cosine_mrr@10 0.9532
cosine_map@1 0.9218
cosine_map@5 0.792
cosine_map@10 0.7848

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: 3 tokens
    • mean: 16.97 tokens
    • max: 30 tokens
    • min: 3 tokens
    • mean: 20.79 tokens
    • max: 48 tokens
    • min: 4 tokens
    • mean: 20.94 tokens
    • max: 48 tokens
  • Samples:
    query pos neg
    Data input-output antar daerah, 34 provinsi: Transaksi domestik (52 industri, harga produsen, 2016) Tabel Inter Regional Input-Output Indonesia Transaksi Domestik Atas Dasar Harga Produsen Menurut 34 Provinsi dan 52 Industri, 2016 (Juta Rupiah) Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Golongan Umur dan Jumlah Jam Kerja Seluruhnya, 2008 - 2024
    Data total penghasilan berbagai golongan rumah tangga setelah dipotong pajak, tahun 2000 (dalam miliar rupiah) Jumlah Pendapatan Setelah Pajak Menurut Golongan Rumah Tangga (miliar rupiah), 2000, 2005, dan 2008 Institusi Korporasi Non Finansial Neraca Institusi Terintegrasi ( triliun rupiah), 2016 2022
    Rumah tangga dengan area resapan, data per provinsi, 2014 Persentase Rumah Tangga Menurut Provinsi dan Keberadaan Area Resapan Air, 2013-2014 Nilai Produksi dan Biaya Produksi per Musim Tanam per Hektar Budidaya Tanaman Padi Sawah, Padi Ladang, Jagung, dan Kedelai, 2017
  • 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: 3 tokens
    • mean: 16.69 tokens
    • max: 32 tokens
    • min: 3 tokens
    • mean: 20.85 tokens
    • max: 48 tokens
    • min: 3 tokens
    • mean: 20.9 tokens
    • max: 48 tokens
  • Samples:
    query pos neg
    Kredit UMKM bank umum (miliar rupiah), 2012-2016 Posisi Kredit Usaha Mikro, Kecil, dan Menengah (UMKM) 1 pada Bank Umum (miliar rupiah), 2012-2016 Jumlah Penghuni Lapas per Kanwil
    Infant Mortality Rate di Indonesia per provinsi, 1971 Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi, 1971-2020 Jumlah Sekolah, Guru, dan Murid Sekolah Menengah Kejuruan (SMK) di Bawah Kementrian Pendidikan dan Kebudayaan Menurut Provinsi tahun ajaran 2011/2012-2015/2016
    Partisipasi sekolah anak dan remaja: Data persentase usia 7-24 tahun per gender dan kelompok umur, 2021 Persentase Penduduk Usia 7-24 Tahun Menurut Jenis Kelamin, Kelompok Umur, dan Partisipasi Sekolah, 2002-2023 Tabel Input-Output Indonesia Transaksi Total Atas Dasar Harga Pembeli (17 Produk), 2016 (Juta Rupiah)
  • 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: 64
  • per_device_eval_batch_size: 64
  • 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

Click to expand
  • 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: 1
  • 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: False
  • 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: 0
  • 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

Training Logs

Epoch Step Training Loss Validation Loss bps-statictable-ir_cosine_ndcg@10
0 0 - 0.7678 0.7378
0.1391 100 0.2164 0.0292 0.8324
0.2782 200 0.032 0.0143 0.8383
0.4172 300 0.0221 0.0077 0.8392
0.5563 400 0.0088 0.0055 0.8391
0.6954 500 0.0058 0.0033 0.8301
0.8345 600 0.0039 0.0016 0.8331
0.9736 700 0.0027 0.0019 0.8332
  • 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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