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.

python

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:
json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 0.5, 4 "size_average": true 5}

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:
json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 0.5, 4 "size_average": true 5}

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

bibtex

@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

bibtex

@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}
}

	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%