This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the statictable-pair-class 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-pair-class

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-ocl")
# Run inference
sentences = [
    'DATA HARIAN DEBIT, KETINGGIAN, DAN VOLUME AIR SUNGAI DENGAN DAERAH ALIRAN SUNGAI DI ATAS 100 KM2, TAHUN 2015',
    'Ringkasan Neraca Arus Dana, Triwulan IV, 2009, (Miliar Rupiah)',
    'Ringkasan Neraca Arus Dana, Triwulan III, 2014**), (Miliar Rupiah)',
]
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	0.7948
cosine_accuracy@3	0.9381
cosine_accuracy@5	0.9739
cosine_accuracy@10	0.9935
cosine_precision@1	0.7948
cosine_precision@3	0.3518
cosine_precision@5	0.2404
cosine_precision@10	0.1485
cosine_recall@1	0.6249
cosine_recall@3	0.7494
cosine_recall@5	0.7882
cosine_recall@10	0.8352
cosine_ndcg@1	0.7948
cosine_ndcg@3	0.7878
cosine_ndcg@5	0.7923
cosine_ndcg@10	0.7999
cosine_mrr@1	0.7948
cosine_mrr@3	0.861
cosine_mrr@5	0.8692
cosine_mrr@10	0.8716
cosine_map@1	0.7948
cosine_map@3	0.7436
cosine_map@5	0.7386
cosine_map@10	0.7372

Training Details

Training Dataset

statictable-pair-class

Dataset: statictable-pair-class at 62bf40d
Size: 37,302 training samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string int
details
min: 5 tokens
mean: 20.23 tokens
max: 50 tokens
min: 5 tokens
mean: 25.61 tokens
max: 50 tokens
0: ~78.10%
1: ~21.90%

Samples:

query	doc	label
`BERAPA JUMLAH PENDUDUK 15 TAHUN KE ATAS YANG BEKERJA DI TIAP PROVINSI, MENURUT STATUS PEKERJAAN (2022)?`	`Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Menurut Provinsi dan Status Pekerjaan Utama, 2022`	`1`
`Budget kesehatan Kemenkeu Ditjen Anggaran`	`Persentase Rumah Tangga yang Menempati Rumah dengan Dinding Terluas Bukan Bambu/lainnya, 1993-2021`	`0`
`Cek pengeluaran makanan mingguan rata-rata warga Kalsel (2000-2021), bedakan per kelompok pengeluaran`	`Persentase Rumah Tangga Menurut Provinsi dan Fasilitas Tempat Buang Air Besar, 2000-2021`	`0`

Loss: OnlineContrastiveLoss

Evaluation Dataset

statictable-pair-class

Dataset: statictable-pair-class at 62bf40d
Size: 37,302 evaluation samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string int
details
min: 8 tokens
mean: 20.43 tokens
max: 48 tokens
min: 5 tokens
mean: 25.59 tokens
max: 58 tokens
0: ~75.40%
1: ~24.60%

Samples:

query	doc	label
`Negara tujuan ekspor jewelry 2012-2023`	`Ekspor Barang Perhiasan dan Barang Berharga Menurut Negara Tujuan Utama, 2012-2023`	`1`
`Jumlah Pns Indonesia Per Masa Kerja Dan Gender 2005`	`Jumlah Pegawai Negeri Sipil Menurut Masa Kerja dan Jenis Kelamin, 2004 - 2023`	`1`
`Berapa rata-rata pendapatan per orang (setelah pajak) menurut golongan rumah tangga pada tahun 2000?`	`Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Hasil Long Form SP2020 Menurut Provinsi/Kabupaten/Kota, 2020`	`0`

Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 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: 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	-	1.5101	0.4643
0.1905	100	0.7674	0.2281	0.7461
0.3810	200	0.2224	0.1618	0.7612
0.5714	300	0.1341	0.0648	0.7620
0.7619	400	0.0865	0.0533	0.7732
0.9524	500	0.0804	0.0303	0.7596
1.1410	600	0.0398	0.0115	0.7931
1.3314	700	0.0173	0.0163	0.7919
1.5219	800	0.0204	0.0376	0.7876
1.7124	900	0.0231	0.0111	0.7887
1.9029	1000	0.0055	0.0085	0.7841
2.0914	1100	0.0136	0.0115	0.7931
2.2819	1200	0.0091	0.0041	0.7923
2.4724	1300	0.0086	0.0045	0.7977
2.6629	1400	0.0	0.0045	0.7980
2.8533	1500	0.0074	0.0051	0.7999

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",
}

	query	doc	label
type	string	string	int
details	min: 5 tokens mean: 20.23 tokens max: 50 tokens	min: 5 tokens mean: 25.61 tokens max: 50 tokens	0: ~78.10% 1: ~21.90%