This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the statictable-triplets-all 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
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 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': 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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/allstats-search-mini-v1-1-mnrl")
# Run inference
sentences = [
    'Informasi lengkap dan terbaru mengenai statistik edukasi',
    'Statistik Pendidikan Tahunan',
    'Struktur Ongkos Riil Usaha Ternak dan Unggas di Rumah Tangga dengan Pola Pemeliharaan Dikandangkan, 2017',
]
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]

Evaluation

Metrics

Information Retrieval

Dataset: - yahyaabd/bps-statictable-qrels
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8925
cosine_accuracy@3	0.9772
cosine_accuracy@5	0.987
cosine_accuracy@10	0.987
cosine_precision@1	0.8925
cosine_precision@3	0.3616
cosine_precision@5	0.2397
cosine_precision@10	0.1404
cosine_recall@1	0.6962
cosine_recall@3	0.7776
cosine_recall@5	0.7978
cosine_recall@10	0.8202
cosine_ndcg@1	0.8925
cosine_ndcg@3	0.839
cosine_ndcg@5	0.8325
cosine_ndcg@10	0.8287
cosine_mrr@1	0.8925
cosine_mrr@3	0.9321
cosine_mrr@5	0.9344
cosine_mrr@10	0.9344
cosine_map@1	0.8925
cosine_map@3	0.7985
cosine_map@5	0.7862
cosine_map@10	0.7774

Information Retrieval

Dataset: - yahyaabd/bps-statictable-qrels
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8893
cosine_accuracy@3	0.9707
cosine_accuracy@5	0.987
cosine_accuracy@10	0.9967
cosine_precision@1	0.8893
cosine_precision@3	0.3822
cosine_precision@5	0.2697
cosine_precision@10	0.1684
cosine_recall@1	0.6681
cosine_recall@3	0.7612
cosine_recall@5	0.7949
cosine_recall@10	0.8141
cosine_ndcg@1	0.8893
cosine_ndcg@3	0.8449
cosine_ndcg@5	0.8453
cosine_ndcg@10	0.8374
cosine_mrr@1	0.8893
cosine_mrr@3	0.9273
cosine_mrr@5	0.931
cosine_mrr@10	0.9322
cosine_map@1	0.8893
cosine_map@3	0.8074
cosine_map@5	0.7989
cosine_map@10	0.7836

Training Details

Training Dataset

statictable-triplets-all

Dataset: statictable-triplets-all at 0ef226c
Size: 10,998 training samples
Columns: query, positive, and negative
Approximate statistics based on the first 1000 samples:
query positive negative
type string string string
details
min: 6 tokens
mean: 17.69 tokens
max: 33 tokens
min: 5 tokens
mean: 25.79 tokens
max: 58 tokens
min: 4 tokens
mean: 25.72 tokens
max: 58 tokens

Samples:

query	positive	negative
`Neraca arus kas triwulan II 2005 (ringkasan, )`	`Ringkasan Neraca Arus Dana, Triwulan Kedua, 2005, (Miliar Rupiah)`	`Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan Jenis Pekerjaan Utama (Rupiah), 2017`
`Hasil tangkapan ikan per provinsi, bedakan jenis penangkapan, 2013`	`Produksi Perikanan Tangkap Menurut Provinsi dan Jenis Penangkapan, 2000-2020`	`Ringkasan Neraca Arus Dana, Triwulan II, 2006, (Miliar Rupiah)`
`Bagaimana perubahan distribusi pengeluaran?`	`Persentase Perkembangan Distribusi Pengeluaran`	`Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi, 1971-2020`

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 0ef226c
Size: 10,998 evaluation samples
Columns: query, positive, and negative
Approximate statistics based on the first 1000 samples:
query positive negative
type string string string
details
min: 5 tokens
mean: 17.61 tokens
max: 34 tokens
min: 4 tokens
mean: 25.58 tokens
max: 58 tokens
min: 5 tokens
mean: 25.33 tokens
max: 58 tokens

Samples:

query	positive	negative
`Data total penghasilan berbagai golongan rumah tangga setelah dipotong pajak, tahun 2000 (dalam )`	`Jumlah Pendapatan Setelah Pajak Menurut Golongan Rumah Tangga (miliar rupiah), 2000, 2005, dan 2008`	`Indeks Harga Konsumen per Kelompok di 82 Kota 1 (2012=100)`
`Bagaimana perkembangan impor barang modal pada tahun 2020`	`Impor Barang Modal, 1996-2023`	`Indeks Harga yang Diterima Petani (It), Indes Harga yang Dibayar Petani (Ib), dan Nilai Tukar Petani Subsektor Hortikultura (NTPH) di Indonesia (2007=100), 2008-2016`
`Konsumsi makanan per orang di Kalut: data mingguan, beda kelompok pengeluaran (2018)`	`Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Kalimantan Utara, 2018-2023`	`Ekspor Kimia Dasar Organik yang Bersumber dari Hasil Pertanian menurut Negara Tujuan Utama, 2012-2023`

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: 32
per_device_eval_batch_size: 32
weight_decay: 0.01
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

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: 0.01
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

Training Logs

Epoch	Step	Training Loss	Validation Loss	bps-statictable-ir_cosine_ndcg@10
0	0	-	1.6770	0.4644
0.0645	20	1.2483	0.7232	0.6311
0.1290	40	0.5223	0.3231	0.7470
0.1935	60	0.2825	0.2193	0.7888
0.2581	80	0.1617	0.1823	0.7956
0.3226	100	0.2261	0.1141	0.8087
0.3871	120	0.1326	0.0731	0.8114
0.4516	140	0.0782	0.0611	0.8259
0.5161	160	0.0948	0.0605	0.8227
0.5806	180	0.1009	0.0534	0.8315
0.6452	200	0.0696	0.0511	0.8280
0.7097	220	0.0698	0.0431	0.8312
0.7742	240	0.0424	0.0405	0.8369
0.8387	260	0.0521	0.0378	0.8243
0.9032	280	0.0393	0.0339	0.8261
0.9677	300	0.0309	0.0315	0.8339
1.0323	320	0.0226	0.0325	0.8322
1.0968	340	0.0249	0.0323	0.8299
1.1613	360	0.0318	0.0320	0.8297
1.2258	380	0.0192	0.0278	0.8270
1.2903	400	0.025	0.0247	0.8279
1.3548	420	0.0253	0.0219	0.8280
1.4194	440	0.0359	0.0216	0.8294
1.4839	460	0.0252	0.0225	0.8227
1.5484	480	0.0217	0.0209	0.8347
1.6129	500	0.0233	0.0210	0.8346
1.6774	520	0.0131	0.0225	0.8343
1.7419	540	0.0135	0.0211	0.8330
1.8065	560	0.0133	0.0207	0.8308
1.8710	580	0.0235	0.0208	0.8352
1.9355	600	0.02	0.0190	0.8317
2.0	620	0.0142	0.0182	0.8305
2.0645	640	0.0088	0.0174	0.8330
2.1290	660	0.0103	0.0171	0.8324
2.1935	680	0.0141	0.0174	0.8281
2.2581	700	0.0209	0.0169	0.8329
2.3226	720	0.0137	0.0159	0.8316
2.3871	740	0.0173	0.0159	0.8271
2.4516	760	0.0115	0.0167	0.8230
2.5161	780	0.0155	0.0167	0.8278
2.5806	800	0.0136	0.0164	0.8280
2.6452	820	0.0085	0.0156	0.8297
2.7097	840	0.0113	0.0154	0.8293
2.7742	860	0.0094	0.0156	0.8280
2.8387	880	0.0108	0.0150	0.8280
2.9032	900	0.0081	0.0149	0.8275
2.9677	920	0.0179	0.0147	0.8287
-1	-1	-	-	0.8374

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",
}

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	positive	negative
type	string	string	string
details	min: 6 tokens mean: 17.69 tokens max: 33 tokens	min: 5 tokens mean: 25.79 tokens max: 58 tokens	min: 4 tokens mean: 25.72 tokens max: 58 tokens