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/paraphrase-multilingual-miniLM-L12-V2-mnrl-2")
# Run inference
sentences = [
    'Perbandingan Indeks Harga Konsumen antar negara, data tahun 2011',
    'Indeks Harga Konsumen Beberapa Negara (2005=100), 2009-2012',
    'Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Kepulauan Riau, 2018-2023',
]
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: bps-statictable-ir
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9121
cosine_accuracy@5	0.9837
cosine_accuracy@10	0.9837
cosine_precision@1	0.9121
cosine_precision@5	0.2254
cosine_precision@10	0.1235
cosine_recall@1	0.7068
cosine_recall@5	0.7895
cosine_recall@10	0.7967
cosine_ndcg@1	0.9121
cosine_ndcg@5	0.825
cosine_ndcg@10	0.8137
cosine_mrr@1	0.9121
cosine_mrr@5	0.9421
cosine_mrr@10	0.9421
cosine_map@1	0.9121
cosine_map@5	0.7806
cosine_map@10	0.7663

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: 4 tokens
mean: 18.52 tokens
max: 37 tokens
min: 4 tokens
mean: 25.18 tokens
max: 49 tokens
min: 4 tokens
mean: 25.87 tokens
max: 58 tokens

Samples:

query	pos	neg
`Pekerja 15+ berdasarkan status pekerjaan & pendidikan, 1997-2007`	`Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Status Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan, 1997 - 2007`	`Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur, 2023`
`Output kilang minyak dan gas Indonesia berdasarkan jenis produk, dalam barel, sekitar tahun 2008`	`Produksi Beberapa Hasil Kilang Minyak dan Gas Menurut Jenis Hasil Kilang (barel), 2000-2018`	`Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Lapangan Pekerjaan Utama, 2021`
`Berapa rata-rata pendapatan (ribu rupiah) pekerja bebas di Indonesia tahun 2018 berdasarkan provinsi dan pendidikan terakhirnya?`	`Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi yang Ditamatkan (ribu rupiah), 2018`	`Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan Lapangan Pekerjaan Utama di 17 Sektor (rupiah), 2018`

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: 5 tokens
mean: 18.46 tokens
max: 37 tokens
min: 4 tokens
mean: 25.44 tokens
max: 58 tokens
min: 4 tokens
mean: 25.54 tokens
max: 50 tokens

Samples:

query	pos	neg
`Data pendapatan rata-rata pengusaha di tiap provinsi berdasarkan tingkat pendidikan (tahun 2022)`	`Rata-rata Pendapatan bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang Ditamatkan, 2022`	`Kecepatan Angin dan Kelembaban di Stasiun Pengamatan BMKG, 2011-2015`
`Berapa harga rata-rata valuta asing di berbagai provinsi Indonesia pada tahun 2017?`	`Rata-Rata Harga Valuta Asing Terpilih menurut Provinsi 2017`	`Kumulatif Kasus AIDS, Kasus Meninggal, Rate Kumulatif, dan Jumlah Kasus Baru AIDS Menurut Provinsi di Indonesia, 2008-2012`
`Ikhtisar arus kas triwulan 1, 2004 (miliar)`	`Ringkasan Neraca Arus Dana Triwulan I 2004 (Miliar Rupiah)`	`Ekspor Bijih Tembaga 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: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
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: 16
per_device_eval_batch_size: 16
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: 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	-	1.1582	0.4643
0.0209	250	0.4825	0.0930	0.7864
0.0417	500	0.0746	0.0339	0.8081
0.0626	750	0.0337	0.0239	0.8064
0.0835	1000	0.0226	0.0149	0.8140
0.1044	1250	0.0174	0.0168	0.8267
0.1252	1500	0.0152	0.0185	0.8015
0.1461	1750	0.0127	0.0142	0.8269
0.1670	2000	0.0091	0.0104	0.8394
0.1879	2250	0.0089	0.0126	0.8255
0.2087	2500	0.0055	0.0133	0.8299
0.2296	2750	0.008	0.0086	0.8293
0.2505	3000	0.0087	0.0090	0.8317
0.2714	3250	0.0056	0.0061	0.8221
0.2922	3500	0.0057	0.0064	0.8220
0.3131	3750	0.0074	0.0056	0.8174
0.3340	4000	0.0033	0.0048	0.8263
0.3548	4250	0.0028	0.0054	0.8263
0.3757	4500	0.0036	0.0052	0.8055
0.3966	4750	0.0037	0.0037	0.8017
0.4175	5000	0.0045	0.0039	0.8242
0.4383	5250	0.0018	0.0035	0.8206
0.4592	5500	0.0013	0.0051	0.8202
0.4801	5750	0.0033	0.0033	0.8239
0.5010	6000	0.0017	0.0021	0.8210
0.5218	6250	0.0055	0.0119	0.8222
0.5427	6500	0.0019	0.0016	0.8070
0.5636	6750	0.001	0.0015	0.8128
0.5845	7000	0.0022	0.0013	0.8236
0.6053	7250	0.001	0.0005	0.8201
0.6262	7500	0.0005	0.0004	0.8226
0.6471	7750	0.001	0.0010	0.8251
0.6679	8000	0.0009	0.0011	0.8182
0.6888	8250	0.0015	0.0008	0.8185
0.7097	8500	0.0006	0.0005	0.8153
0.7306	8750	0.0012	0.0012	0.8133
0.7514	9000	0.0016	0.0007	0.8134
0.7723	9250	0.0011	0.0001	0.8152
0.7932	9500	0.0006	0.0002	0.8149
0.8141	9750	0.0011	0.0002	0.8141
0.8349	10000	0.0006	0.0002	0.8153
0.8558	10250	0.0002	0.0002	0.8174
0.8767	10500	0.0009	0.0001	0.8117
0.8976	10750	0.0006	0.0003	0.8117
0.9184	11000	0.0006	0.0003	0.8122
0.9393	11250	0.0008	0.0003	0.8161
0.9602	11500	0.0005	0.0003	0.8151
0.9810	11750	0.0004	0.0003	0.8137

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.4.1
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	pos	neg
type	string	string	string
details	min: 4 tokens mean: 18.52 tokens max: 37 tokens	min: 4 tokens mean: 25.18 tokens max: 49 tokens	min: 4 tokens mean: 25.87 tokens max: 58 tokens