yahyaabd/allstats-ir-indoSBERT-large-v1

SentenceTransformer based on denaya/indoSBERT-large

This is a sentence-transformers model finetuned from denaya/indoSBERT-large on the bps-statictable-query-title-pairs 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 Type: Sentence Transformer
• Base model: denaya/indoSBERT-large
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 256 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • bps-statictable-query-title-pairs

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': 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/allstats-ir-indoSBERT-large-v1")
# Run inference
sentences = [
    'Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015',
    'Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah)',
    'Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023',
]
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

Semantic Similarity

• Datasets: allstats-semantic-base-v1-eval and allstat-semantic-base-v1-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	allstats-semantic-base-v1-eval	allstat-semantic-base-v1-test
pearson_cosine	0.9027	0.9166
spearman_cosine	0.7797	0.809

Training Details

Training Dataset

bps-statictable-query-title-pairs

• Dataset: bps-statictable-query-title-pairs at c7df38f
• Size: 2,602 training samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	int
  details	min: 4 tokens mean: 16.78 tokens max: 28 tokens	min: 3 tokens mean: 21.01 tokens max: 48 tokens	0: ~66.50% 1: ~33.50%

• Samples:

  query	doc	label
  Pertumbuhan populasi provinsi di Indonesia 1971-2024	Kecepatan Angin dan Kelembaban di Stasiun Pengamatan BMKG, 2000-2010	0
  Perbandingan upah nominal dan riil pekerja pertanian di Indonesia (tahun dasar 2012), periode 2017.	Upah Nominal dan Riil Buruh Tani di Indonesia (Rupiah), 2009-2019 (2012=100)	1
  Laporan singkat cash flow statement Q4/2005	Nilai Produksi dan Biaya Produksi per Hektar Usaha Tanaman Bawang Merah dan Cabai Merah, 2014	0

• Loss: ContrastiveLoss with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Evaluation Dataset

bps-statictable-query-title-pairs

• Dataset: bps-statictable-query-title-pairs at c7df38f
• Size: 558 evaluation samples
• Columns: query, doc, and label
• Approximate statistics based on the first 558 samples:

  	query	doc	label
  type	string	string	int
  details	min: 3 tokens mean: 16.82 tokens max: 30 tokens	min: 3 tokens mean: 21.13 tokens max: 48 tokens	0: ~70.97% 1: ~29.03%

• Samples:

  query	doc	label
  Data pengeluaran makanan rata-rata warga Sulteng per minggu di tahun 2022, berdasarkan kelompok pendapatan	Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (84 x 84)	0
  Konsumsi & belanja makanan per orang di NTB, beda kelompok pengeluaran, 2021	Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Nusa Tenggara Barat, 2018-2023	1
  Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?	Penduduk Berumur 15 Tahun Ke Atas Menurut Provinsi dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008 - 2024	0

• Loss: ContrastiveLoss with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• num_train_epochs: 4
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True
• eval_on_start: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• 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: 4
• 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: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	allstats-semantic-base-v1-eval_spearman_cosine	allstat-semantic-base-v1-test_spearman_cosine
0	0	-	0.0086	0.7549	-
0.1220	10	0.0082	0.0069	0.7610	-
0.2439	20	0.0058	0.0049	0.7688	-
0.3659	30	0.0047	0.0041	0.7686	-
0.4878	40	0.0034	0.0036	0.7682	-
0.6098	50	0.003	0.0034	0.7696	-
0.7317	60	0.0031	0.0027	0.7728	-
0.8537	70	0.0031	0.0029	0.7713	-
0.9756	80	0.003	0.0031	0.7731	-
1.0976	90	0.0011	0.0025	0.7746	-
1.2195	100	0.001	0.0023	0.7759	-
1.3415	110	0.0013	0.0021	0.7767	-
1.4634	120	0.0011	0.0021	0.7773	-
1.5854	130	0.0008	0.0021	0.7786	-
1.7073	140	0.0006	0.0021	0.7789	-
1.8293	150	0.0007	0.0020	0.7788	-
1.9512	160	0.0018	0.002	0.7799	-
2.0732	170	0.0006	0.0020	0.7800	-
2.1951	180	0.0004	0.0021	0.7795	-
2.3171	190	0.0006	0.0021	0.7796	-
2.4390	200	0.0004	0.0021	0.7798	-
2.5610	210	0.0003	0.0021	0.7799	-
2.6829	220	0.0003	0.0021	0.7798	-
2.8049	230	0.0004	0.0021	0.7797	-
2.9268	240	0.0007	0.0021	0.7798	-
3.0488	250	0.0003	0.0021	0.7798	-
3.1707	260	0.0002	0.0021	0.7796	-
3.2927	270	0.0003	0.0021	0.7797	-
3.4146	280	0.0002	0.0021	0.7797	-
3.5366	290	0.0002	0.0021	0.7797	-
3.6585	300	0.0002	0.0021	0.7797	-
3.7805	310	0.0004	0.0021	0.7797	-
3.9024	320	0.0003	0.0021	0.7797	-
-1	-1	-	-	-	0.8090

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.4.0
• Transformers: 4.48.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.2.0
• 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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}