metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:967831
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
  - source_sentence: >-
      Penghasilan rata-rata pelaku usaha mandiri: Analisis berdasarkan lokasi
      dan jenjang pendidikan, 2023
    sentences:
      - >-
        Rata-rata Pendapatan bersih Berusaha Sendiri menurut Provinsi dan
        Pendidikan yang Ditamatkan, 2023
      - >-
        Rata-Rata Pengeluaran per Kapita Sebulan Menurut Kelompok Barang
        (rupiah), 2013-2021
      - Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)
  - source_sentence: Bagaimana traffic penerbangan internasional di Indonesia pada 2008?
    sentences:
      - Tingkat Inflasi Harga Konsumen Nasional Bulanan (M-to-M) 1 (2022=100)
      - Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005
      - Lalu Lintas Penerbangan Luar Negeri Indonesia Tahun 2003-2022
  - source_sentence: >-
      Data indeks daya penyebaran dan derajat kepekaan sektor ekonomi, ambil
      contoh tahun 2005
    sentences:
      - >-
        Indeks Daya Penyebaran dan Indeks Derajat Kepekaan Menurut Sektor
        Ekonomi, 1995, 2000, 2005, dan 2010
      - Ekspor Kopi Menurut Negara Tujuan Utama, 2000-2023
      - >-
        Anggaran Kesehatan dari Direktorat Penyusunan APBN - Direktorat Jenderal
        Anggaran, Kementerian Keuangan
  - source_sentence: >-
      Data aktivitas penduduk 15 tahun ke atas berdasarkan kelompok umur, satu
      minggu ke belakang (periode 2002)
    sentences:
      - Ekspor Lada Putih menurut Negara Tujuan Utama, 2012-2023
      - >-
        Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi
        Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi
        Sulawesi Selatan, 2018-2023
      - >-
        Penduduk Berumur 15 Tahun Ke Atas Menurut Golongan Umur dan Jenis
        Kegiatan Selama Seminggu yang Lalu, 1997 - 2007
  - source_sentence: Laporan singkat arus kas Q2 2005, dalam miliar
    sentences:
      - Ringkasan Neraca Arus Dana, Triwulan Kedua, 2005, (Miliar Rupiah)
      - Indikator Pendidikan, 1994-2023
      - >-
        Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut
        Pendidikan Tertinggi dan Jumlah Jam Kerja Utama, 2020
datasets:
  - yahyaabd/statictable-triplets-all
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@1
  - cosine_ndcg@5
  - cosine_ndcg@10
  - cosine_mrr@1
  - cosine_mrr@5
  - cosine_mrr@10
  - cosine_map@1
  - cosine_map@5
  - cosine_map@10
model-index:
  - name: >-
      SentenceTransformer based on
      sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: bps statictable ir
          type: bps-statictable-ir
        metrics:
          - type: cosine_accuracy@1
            value: 0.8990228013029316
            name: Cosine Accuracy@1
          - type: cosine_accuracy@5
            value: 0.9837133550488599
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8990228013029316
            name: Cosine Precision@1
          - type: cosine_precision@5
            value: 0.21889250814332245
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.12605863192182412
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7029638149674847
            name: Cosine Recall@1
          - type: cosine_recall@5
            value: 0.789022126091837
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8116078533769628
            name: Cosine Recall@10
          - type: cosine_ndcg@1
            value: 0.8990228013029316
            name: Cosine Ndcg@1
          - type: cosine_ndcg@5
            value: 0.8178579787978988
            name: Cosine Ndcg@5
          - type: cosine_ndcg@10
            value: 0.8156444177517035
            name: Cosine Ndcg@10
          - type: cosine_mrr@1
            value: 0.8990228013029316
            name: Cosine Mrr@1
          - type: cosine_mrr@5
            value: 0.9347991313789358
            name: Cosine Mrr@5
          - type: cosine_mrr@10
            value: 0.9368827878599865
            name: Cosine Mrr@10
          - type: cosine_map@1
            value: 0.8990228013029316
            name: Cosine Map@1
          - type: cosine_map@5
            value: 0.772128121606949
            name: Cosine Map@5
          - type: cosine_map@10
            value: 0.7635855701310564
            name: Cosine Map@10

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/paraphrase-multilingual-miniLM-L12-v2-mnrl-beir-2")
# Run inference
sentences = [
    'Laporan singkat arus kas Q2 2005, dalam miliar',
    'Ringkasan Neraca Arus Dana, Triwulan Kedua, 2005, (Miliar Rupiah)',
    'Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Jumlah Jam Kerja Utama, 2020',
]
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	0.899
cosine_accuracy@5	0.9837
cosine_accuracy@10	1.0
cosine_precision@1	0.899
cosine_precision@5	0.2189
cosine_precision@10	0.1261
cosine_recall@1	0.703
cosine_recall@5	0.789
cosine_recall@10	0.8116
cosine_ndcg@1	0.899
cosine_ndcg@5	0.8179
cosine_ndcg@10	0.8156
cosine_mrr@1	0.899
cosine_mrr@5	0.9348
cosine_mrr@10	0.9369
cosine_map@1	0.899
cosine_map@5	0.7721
cosine_map@10	0.7636

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: 18.55 tokens
max: 37 tokens

min: 4 tokens
mean: 25.6 tokens
max: 58 tokens

min: 4 tokens
mean: 25.7 tokens
max: 58 tokens

Samples:

query	pos	neg
`Indeks harga petani (diterima & dibayar) dan NTP per provinsi, 2012`	`Indeks Harga yang Diterima Petani (It), Indeks Harga yang Dibayar Petani (Ib), dan Nilai Tukar Petani (NTP) Menurut Provinsi, 2008-2016`	`Persentase Rumah Tangga Menurut Provinsi dan KebiasaanMemanfaatkan Air Bekas untuk Keperluan Lain, 2013, 2014, 2017, 2021`
`Data rumah tangga perikanan budidaya Indonesia, detail per provinsi dan jenis budidaya, di tahun 2008`	`Jumlah Rumah Tangga Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2016`	`Ringkasan Neraca Arus Dana, 2005, (Miliar Rupiah)`
`Lapangan pekerjaan vs pendidikan pekerja (15 tahun ke atas), 1986 hingga 1996`	`Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Lapangan Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan, 1986 -1996`	`Tabel Input-Output Indonesia Transaksi Domestik Atas Dasar Harga Produsen (17 Lapangan Usaha), 2016 (Juta Rupiah)`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

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: 18.38 tokens max: 37 tokens	min: 4 tokens mean: 25.28 tokens max: 58 tokens	min: 5 tokens mean: 25.65 tokens max: 58 tokens

Samples:

query	pos	neg
`Bagaimana hubungan IHK dan rata-rata upah buruh industri (bukan supervisor) bulanan tahun 2010, acuan 1996?`	`IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100)`	`Rata-rata Harga Valuta Asing Terpilih menurut Provinsi, 2014`
`Berapa rata-rata gaji bulanan pekerja Indonesia berdasarkan ijazah terakhir dan sektor pekerjaannya (2017)?`	`Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi yang Ditamatkan dan Lapangan Pekerjaan Utama di 9 Sektor (rupiah), 2017`	`Rata-Rata Pengeluaran per Kapita Sebulan Menurut Kelompok Barang (rupiah), 2013-2021`
`Data luas lahan (hektar) yang dipakai untuk jenis budidaya perikanan di tiap provinsi tahun 2009`	`Luas Area Usaha Budidaya Perikanan Menurut Provinsi dan Jenis Budidaya (ha), 2005-2016`	`Ringkasan Neraca Arus Dana, Triwulan I, 2008, (Miliar Rupiah)`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
weight_decay: 0.01
num_train_epochs: 2
lr_scheduler_type: reduce_lr_on_plateau
lr_scheduler_kwargs: {'factor': 0.5, 'patience': 2}
warmup_steps: 10000
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: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: reduce_lr_on_plateau
lr_scheduler_kwargs: {'factor': 0.5, 'patience': 2}
warmup_ratio: 0.0
warmup_steps: 10000
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	-	1.0819	0.4643
0.0334	200	0.1373	-	-
0.0668	400	0.0354	-	-
0.0835	500	-	0.0132	0.8252
0.1002	600	0.028	-	-
0.1336	800	0.018	-	-
0.1670	1000	0.0145	0.0096	0.8286
0.2004	1200	0.0089	-	-
0.2338	1400	0.0103	-	-
0.2505	1500	-	0.0067	0.8312
0.2672	1600	0.0098	-	-
0.3006	1800	0.0086	-	-
0.3339	2000	0.0086	0.0044	0.8246
0.3673	2200	0.0088	-	-
0.4007	2400	0.0075	-	-
0.4174	2500	-	0.0051	0.8295
0.4341	2600	0.0066	-	-
0.4675	2800	0.0054	-	-
0.5009	3000	0.0051	0.0059	0.8294
0.5343	3200	0.0052	-	-
0.5677	3400	0.0037	-	-
0.5844	3500	-	0.0041	0.8126
0.6011	3600	0.0078	-	-
0.6345	3800	0.005	-	-
0.6679	4000	0.0045	0.0050	0.8308
0.7013	4200	0.0047	-	-
0.7347	4400	0.0066	-	-
0.7514	4500	-	0.0033	0.8233
0.7681	4600	0.0043	-	-
0.8015	4800	0.003	-	-
0.8349	5000	0.0029	0.0036	0.8224
0.8683	5200	0.0014	-	-
0.9017	5400	0.0058	-	-
0.9184	5500	-	0.0020	0.8169
0.9350	5600	0.0045	-	-
0.9684	5800	0.0036	-	-
1.0018	6000	0.0053	0.0018	0.8152
1.0352	6200	0.0035	-	-
1.0686	6400	0.0017	-	-
1.0853	6500	-	0.0024	0.8231
1.1020	6600	0.0037	-	-
1.1354	6800	0.0044	-	-
1.1688	7000	0.0011	0.0113	0.8153
1.2022	7200	0.0042	-	-
1.2356	7400	0.0028	-	-
1.2523	7500	-	0.0046	0.8253
1.2690	7600	0.0005	-	-
1.3024	7800	0.001	-	-
1.3358	8000	0.0011	0.0017	0.8216
1.3692	8200	0.0007	-	-
1.4026	8400	0.0014	-	-
1.4193	8500	-	0.0014	0.8253
1.4360	8600	0.0003	-	-
1.4694	8800	0.0005	-	-
1.5028	9000	0.002	0.0012	0.8250
1.5361	9200	0.0013	-	-
1.5695	9400	0.0009	-	-
1.5862	9500	-	0.0003	0.8162
1.6029	9600	0.0021	-	-
1.6363	9800	0.0013	-	-
1.6697	10000	0.0005	0.0003	0.8234
1.7031	10200	0.0004	-	-
1.7365	10400	0.0004	-	-
1.7532	10500	-	0.0001	0.8225
1.7699	10600	0.0011	-	-
1.8033	10800	0.0004	-	-
1.8367	11000	0.0009	0.0008	0.8259
1.8701	11200	0.0024	-	-
1.9035	11400	0.0002	-	-
1.9202	11500	-	0.0008	0.8156
1.9369	11600	0.0007	-	-
1.9703	11800	0.0007	-	-

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

@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

@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}
}