This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on the statictable-triplets-all dataset. It maps sentences & paragraphs to a 768-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-mpnet-base-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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-mpnet-base-v2-mnrl")
# Run inference
sentences = [
    'Seperti apa rincian inflasi di Indonesia, termasuk inflasi inti dan harga diatur, pada 2020?',
    'Inflasi Umum, Inti, Harga Yang Diatur Pemerintah, dan Barang Bergejolak Inflasi Indonesia, 2009-2024',
    'Angka Kematian Ibu/AKI (Maternal Mortality Rate/MMR) Hasil Long Form SP2020 Menurut Provinsi, 2020',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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	0.9609
cosine_accuracy@5	0.9967
cosine_accuracy@10	1.0
cosine_precision@1	0.9609
cosine_precision@5	0.2339
cosine_precision@10	0.1349
cosine_recall@1	0.7591
cosine_recall@5	0.8053
cosine_recall@10	0.825
cosine_ndcg@1	0.9609
cosine_ndcg@5	0.8586
cosine_ndcg@10	0.854
cosine_mrr@1	0.9609
cosine_mrr@5	0.9769
cosine_mrr@10	0.9773
cosine_map@1	0.9609
cosine_map@5	0.8187
cosine_map@10	0.8083

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: 18.38 tokens
max: 35 tokens
min: 4 tokens
mean: 25.39 tokens
max: 50 tokens
min: 4 tokens
mean: 25.6 tokens
max: 58 tokens

Samples:

query	pos	neg
`Neraca arus dana triwulan I tahun 2004 (ringkasan, miliar)`	`Ringkasan Neraca Arus Dana Triwulan I 2004 (Miliar Rupiah)`	`Proporsi Penduduk Berumur 10 Tahun ke Atas yang Membaca Surat Kabar/Majalah Selama Seminggu Terakhir menurut Provinsi, Tipe Daerah dan Jenis Kelamin, 2012`
`Kumpulan dokumen Rencana Pengurangan Bencana level kabupaten dan kota`	`Rekap Dokumen RPB Kabupaten/Kota`	`Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Golongan Umur dan Jumlah Jam Kerja Seluruhnya, 1986-1996`
`IHK dan gaji bulanan buruh hotel, di bawah supervisor, 2007=100, tahun 2009`	`IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 2007-2014 (2007=100)`	`Rata-Rata Bulanan Konsentrasi Partikel Terlarut di Udara Beberapa Kota Menurut Bulan dan Kota (μgr/m3), 2006-2015`

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: 4 tokens
mean: 18.39 tokens
max: 35 tokens
min: 4 tokens
mean: 25.42 tokens
max: 58 tokens
min: 4 tokens
mean: 25.6 tokens
max: 50 tokens

Samples:

query	pos	neg
`Bagaimana hubungan IHK dan rata-rata upah buruh hotel (bukan supervisor), acuan 2012, sekitar tahun 2012`	`IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 2012-2014 (2012=100)`	`Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas Dasar Harga Berlaku, 2010-2016`
`Kegiatan mingguan penduduk 15+ (berdasarkan pendidikan terakhir), 1990`	`Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Kegiatan Selama Seminggu yang Lalu, 1986-1996`	`Transaksi Total Atas Dasar Harga Dasar, 2010`
`Bandingkan indeks harga konsumen (inflasi) di kota-kota Sumatera vs nasional, Desember 2023`	`Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Pulau Sumatera dengan Nasional (2018=100)`	`Persentase Penduduk Berumur 15 tahun Ke Atas menurut Jenis Kegiatan Seminggu Yang Lalu, 2009-2012`

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
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: 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: 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	-	0.7057	0.5598
0.0348	100	0.2794	0.0554	0.8089
0.0696	200	0.0545	0.0389	0.8189
0.1044	300	0.041	0.0407	0.8194
0.1392	400	0.0381	0.0366	0.8186
0.1740	500	0.0441	0.0283	0.8250
0.2088	600	0.0235	0.0212	0.8405
0.2436	700	0.0216	0.0175	0.8256
0.2784	800	0.0175	0.0119	0.8269
0.3132	900	0.0144	0.0131	0.8086
0.3479	1000	0.008	0.0111	0.8269
0.3827	1100	0.01	0.0106	0.8251
0.4175	1200	0.0238	0.0138	0.8296
0.4523	1300	0.0218	0.0074	0.8360
0.4871	1400	0.0126	0.0077	0.8257
0.5219	1500	0.0082	0.0101	0.8447
0.5567	1600	0.01	0.0057	0.8513
0.5915	1700	0.0057	0.0060	0.8500
0.6263	1800	0.0069	0.0051	0.8522
0.6611	1900	0.0062	0.0053	0.8477
0.6959	2000	0.0056	0.0057	0.8541
0.7307	2100	0.0081	0.0051	0.8492
0.7655	2200	0.0048	0.0049	0.8455
0.8003	2300	0.004	0.0047	0.8493
0.8351	2400	0.0068	0.0041	0.8522
0.8699	2500	0.003	0.0036	0.8530
0.9047	2600	0.0029	0.0035	0.8509
0.9395	2700	0.0031	0.0035	0.8518
0.9743	2800	0.002	0.0034	0.854

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: 3.4.0
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	pos	neg
type	string	string	string
details	min: 4 tokens mean: 18.38 tokens max: 35 tokens	min: 4 tokens mean: 25.39 tokens max: 50 tokens	min: 4 tokens mean: 25.6 tokens max: 58 tokens