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 Type: Sentence Transformer
Base model: denaya/indoSBERT-large
Maximum Sequence Length: 256 tokens
Output Dimensionality: 256 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- statictable-triplets-all

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/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

Dataset: bps-statictable-ir
Evaluated with InformationRetrievalEvaluator

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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

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

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",
}

MultipleNegativesRankingLoss

bibtex

@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}
}

	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