metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:25580
  - loss:OnlineContrastiveLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
  - source_sentence: ikhtisar arus kas triwulan 1, 2004 (miliar)
    sentences:
      - Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005
      - >-
        Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar
        Pulau Jawa dan Sumatera dengan Nasional (2018=100)
      - >-
        Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi
        Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi
        Sulawesi Tengah, 2018-2023
  - source_sentence: >-
      BaIgaimana gambaran neraca arus dana dUi Indonesia pada kuartal kedua
      tahun 2015?
    sentences:
      - >-
        Jumlah Sekolah, Guru, dan Murid Sekolah Menengah Pertama (SMP) di Bawah
        Kementrian Pendidikan dan Kebudayaan Menurut Provinsi
        2011/2012-2015/2016
      - Ringkasan Neraca Arus Dana Triwulan III Tahun 2003 (Miliar Rupiah)
      - >-
        Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi
        Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi
        Sulawesi Tenggara, 2018-2023
  - source_sentence: >-
      Berapa persen pengeluaran orang di kotaa untuk makanan vs non-makanan, per
      provinsi, 2018?
    sentences:
      - >-
        Ekspor Tanaman Obat, Aromatik, dan Rempah-Rempah menurut Negara Tujuan
        Utama, 2012-2023
      - >-
        Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan
        Pendidikan Tertinggi yang Ditamatkan (ribu rupiah), 2017
      - >-
        IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor
        (Supervisor), 1996-2014 (1996=100)
  - source_sentence: Negara-negara asal impor crude oil dan produk turunannya tahun 2002-2023
    sentences:
      - >-
        Persentase Pengeluaran Rata-rata per Kapita Sebulan Menurut Kelompok
        Barang, Indonesia, 1999, 2002-2023
      - >-
        Rata-rata Pendapatan Bersih Berusaha Sendiri menurut Provinsi dan
        Pendidikan yang Ditamatkan (ribu rupiah), 2016
      - >-
        Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas
        Dasar Harga Berlaku, 2010-2016
  - source_sentence: Arus dana Q3 2006
    sentences:
      - >-
        Posisi Simpanan Berjangka Rupiah pada Bank Umum dan BPR Menurut Golongan
        Pemilik (miliar rupiah), 2005-2018
      - Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)
      - >-
        Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut
        Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012
datasets:
  - yahyaabd/query-hard-pos-neg-doc-pairs-statictable
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: >-
      SentenceTransformer based on
      sentence-transformers/paraphrase-multilingual-mpnet-base-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: allstats search multilingual base v1 eval
          type: allstats-search-multilingual-base-v1-eval
        metrics:
          - type: pearson_cosine
            value: 0.8700002079644513
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8061513951134361
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: allstats search multilingual base v1 test
          type: allstats-search-multilingual-base-v1-test
        metrics:
          - type: pearson_cosine
            value: 0.9023194252531408
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8092675333588865
            name: Spearman Cosine

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on the query-hard-pos-neg-doc-pairs-statictable 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:
- query-hard-pos-neg-doc-pairs-statictable

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/allstats-search-multilingual-base-v1")
# Run inference
sentences = [
    'Arus dana Q3 2006',
    'Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)',
    'Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012',
]
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

Semantic Similarity

Datasets: allstats-search-multilingual-base-v1-eval and allstats-search-multilingual-base-v1-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	allstats-search-multilingual-base-v1-eval	allstats-search-multilingual-base-v1-test
pearson_cosine	0.87	0.9023
spearman_cosine	0.8062	0.8093

Training Details

Training Dataset

query-hard-pos-neg-doc-pairs-statictable

Dataset: query-hard-pos-neg-doc-pairs-statictable at 7b28b96
Size: 25,580 training samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string int
details
min: 7 tokens
mean: 20.14 tokens
max: 55 tokens

min: 5 tokens
mean: 24.9 tokens
max: 47 tokens

0: ~70.80%
1: ~29.20%

	query	doc	label
type	string	string	int
details	min: 7 tokens mean: 20.14 tokens max: 55 tokens	min: 5 tokens mean: 24.9 tokens max: 47 tokens	0: ~70.80% 1: ~29.20%

Samples:

query	doc	label
`Status pekerjaan utama penduduk usia 15+ yang bekerja, 2020`	`Jumlah Penghuni Lapas per Kanwil`	`0`
`status pekerjaan utama penduduk usia 15+ yang bekerja, 2020`	`Jumlah Penghuni Lapas per Kanwil`	`0`
`STATUS PEKERJAAN UTAMA PENDUDUK USIA 15+ YANG BEKERJA, 2020`	`Jumlah Penghuni Lapas per Kanwil`	`0`

Loss: OnlineContrastiveLoss

Evaluation Dataset

query-hard-pos-neg-doc-pairs-statictable

Dataset: query-hard-pos-neg-doc-pairs-statictable at 7b28b96
Size: 5,479 evaluation samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string int
details
min: 7 tokens
mean: 20.78 tokens
max: 52 tokens

min: 4 tokens
mean: 26.28 tokens
max: 43 tokens

0: ~71.50%
1: ~28.50%

	query	doc	label
type	string	string	int
details	min: 7 tokens mean: 20.78 tokens max: 52 tokens	min: 4 tokens mean: 26.28 tokens max: 43 tokens	0: ~71.50% 1: ~28.50%

Samples:

query	doc	label
`Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?`	`Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017`	`0`
`bagaimana perbandingan pns pria dan wanita di berbagai golongan tahun 2014?`	`Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017`	`0`
`BAGAIMANA PERBANDINGAN PNS PRIA DAN WANITA DI BERBAGAI GOLONGAN TAHUN 2014?`	`Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017`	`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: 0.05
fp16: True
load_best_model_at_end: True
eval_on_start: True

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: 0.05
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	allstats-search-multilingual-base-v1-eval_spearman_cosine	allstats-search-multilingual-base-v1-test_spearman_cosine
0	0	-	1.3012	0.7447	-
0.05	20	0.9548	0.3980	0.7961	-
0.1	40	0.3959	0.3512	0.7993	-
0.15	60	0.1949	0.3102	0.8016	-
0.2	80	0.2126	0.4306	0.7967	-
0.25	100	0.2228	0.2865	0.8026	-
0.3	120	0.1306	0.2476	0.8035	-
0.35	140	0.172	0.2592	0.8014	-
0.4	160	0.1619	0.2495	0.8037	-
0.45	180	0.1416	0.1890	0.8046	-
0.5	200	0.1041	0.1717	0.8059	-
0.55	220	0.2145	0.2165	0.8049	-
0.6	240	0.0459	0.2176	0.8036	-
0.65	260	0.0627	0.2670	0.8023	-
0.7	280	0.1132	0.2309	0.8041	-
0.75	300	0.1048	0.2623	0.8028	-
0.8	320	0.0524	0.2328	0.8031	-
0.85	340	0.034	0.2580	0.8024	-
0.9	360	0.0664	0.2309	0.8034	-
0.95	380	0.0623	0.1746	0.8053	-
1.0	400	0.0402	0.2126	0.8041	-
1.05	420	0.0459	0.1660	0.8062	-
1.1	440	0.0739	0.1487	0.8068	-
1.15	460	0.0191	0.1595	0.8066	-
1.2	480	0.0073	0.1509	0.8066	-
1.25	500	0.0265	0.1779	0.8062	-
1.3	520	0.0325	0.2646	0.8032	-
1.35	540	0.0536	0.2818	0.8030	-
1.4	560	0.0076	0.1768	0.8057	-
1.45	580	0.011	0.1866	0.8054	-
1.5	600	0.0181	0.1726	0.8057	-
1.55	620	0.032	0.1881	0.8052	-
1.6	640	0.0459	0.1482	0.8066	-
1.65	660	0.041	0.1571	0.8065	-
1.7	680	0.0228	0.1298	0.807	-
1.75	700	0.0275	0.1571	0.8067	-
1.8	720	0.0	0.1624	0.8066	-
1.85	740	0.0218	0.1537	0.8068	-
1.9	760	0.0241	0.1699	0.8062	-
1.95	780	0.0065	0.1841	0.8059	-
2.0	800	0.0073	0.1805	0.8061	-
2.05	820	0.0	0.1703	0.8064	-
2.1	840	0.0	0.1702	0.8064	-
2.15	860	0.0	0.1717	0.8064	-
2.2	880	0.0	0.1717	0.8064	-
2.25	900	0.0	0.1717	0.8064	-
2.3	920	0.0097	0.1875	0.8059	-
2.35	940	0.0148	0.1868	0.8060	-
2.4	960	0.0067	0.2205	0.8051	-
2.45	980	0.0	0.2295	0.8049	-
2.5	1000	0.0154	0.2238	0.8052	-
2.55	1020	0.0063	0.2125	0.8055	-
2.6	1040	0.0	0.2183	0.8053	-
2.65	1060	0.0	0.2188	0.8053	-
2.7	1080	0.0068	0.2082	0.8056	-
2.75	1100	0.0384	0.1770	0.8060	-
2.8	1120	0.0	0.1645	0.8061	-
2.85	1140	0.0105	0.1613	0.8061	-
2.9	1160	0.0	0.1601	0.8061	-
2.95	1180	0.0	0.1601	0.8062	-
3.0	1200	0.0	0.1601	0.8062	-
-1	-1	-	-	-	0.8093

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

@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",
}