metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:25580
  - loss:OnlineContrastiveLoss
base_model: denaya/indoSBERT-large
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:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - cosine_mcc
model-index:
  - name: SentenceTransformer based on denaya/indoSBERT-large
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: allstats semantic large v1 test
          type: allstats-semantic-large-v1_test
        metrics:
          - type: cosine_accuracy
            value: 0.9834364761558063
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7773222327232361
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9745739033249511
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7773222327232361
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9748462828395752
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9743016759776536
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9959810762137397
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.9622916280716365
            name: Cosine Mcc
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: allstats semantic large v1 dev
          type: allstats-semantic-large-v1_dev
        metrics:
          - type: cosine_accuracy
            value: 0.9760905274685161
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7572722434997559
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9640997533570841
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7572722434997559
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9386339381003201
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9909859154929578
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9953499585582108
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.9469795586519781
            name: Cosine Mcc

SentenceTransformer based on denaya/indoSBERT-large

This is a sentence-transformers model finetuned from denaya/indoSBERT-large on the query-hard-pos-neg-doc-pairs-statictable dataset. It maps sentences & paragraphs to a 256-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: denaya/indoSBERT-large
Maximum Sequence Length: 256 tokens
Output Dimensionality: 256 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

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-large-v1-32-2")
# 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, 256]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Binary Classification

Datasets: allstats-semantic-large-v1_test and allstats-semantic-large-v1_dev
Evaluated with BinaryClassificationEvaluator

Metric	allstats-semantic-large-v1_test	allstats-semantic-large-v1_dev
cosine_accuracy	0.9834	0.9761
cosine_accuracy_threshold	0.7773	0.7573
cosine_f1	0.9746	0.9641
cosine_f1_threshold	0.7773	0.7573
cosine_precision	0.9748	0.9386
cosine_recall	0.9743	0.991
cosine_ap	0.996	0.9953
cosine_mcc	0.9623	0.947

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: 6 tokens
mean: 17.12 tokens
max: 31 tokens

min: 5 tokens
mean: 20.47 tokens
max: 42 tokens

0: ~70.80%
1: ~29.20%

	query	doc	label
type	string	string	int
details	min: 6 tokens mean: 17.12 tokens max: 31 tokens	min: 5 tokens mean: 20.47 tokens max: 42 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: 17.85 tokens
max: 35 tokens

min: 3 tokens
mean: 21.2 tokens
max: 31 tokens

0: ~71.50%
1: ~28.50%

	query	doc	label
type	string	string	int
details	min: 7 tokens mean: 17.85 tokens max: 35 tokens	min: 3 tokens mean: 21.2 tokens max: 31 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: 32
per_device_eval_batch_size: 32
num_train_epochs: 2
warmup_ratio: 0.1
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: 32
per_device_eval_batch_size: 32
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: 2
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	allstats-semantic-large-v1_test_cosine_ap	allstats-semantic-large-v1_dev_cosine_ap
-1	-1	-	-	0.9750	-
0	0	-	0.1850	-	0.9766
0.025	20	0.1581	0.1538	-	0.9789
0.05	40	0.1898	0.1200	-	0.9848
0.075	60	0.0647	0.1096	-	0.9855
0.1	80	0.118	0.1242	-	0.9831
0.125	100	0.0545	0.1301	-	0.9827
0.15	120	0.0646	0.1114	-	0.9862
0.175	140	0.0775	0.1005	-	0.9865
0.2	160	0.0664	0.1234	-	0.9840
0.225	180	0.067	0.1349	-	0.9850
0.25	200	0.0823	0.1032	-	0.9877
0.275	220	0.0895	0.1432	-	0.9808
0.3	240	0.0666	0.1389	-	0.9809
0.325	260	0.0872	0.1122	-	0.9844
0.35	280	0.0551	0.1435	-	0.9838
0.375	300	0.0919	0.1068	-	0.9886
0.4	320	0.0437	0.0903	-	0.9861
0.425	340	0.0619	0.1065	-	0.9850
0.45	360	0.0469	0.1346	-	0.9844
0.475	380	0.029	0.1351	-	0.9828
0.5	400	0.0511	0.1123	-	0.9843
0.525	420	0.0394	0.1434	-	0.9815
0.55	440	0.0178	0.1577	-	0.9769
0.575	460	0.047	0.1253	-	0.9796
0.6	480	0.0066	0.1262	-	0.9791
0.625	500	0.0383	0.1277	-	0.9814
0.65	520	0.0084	0.1361	-	0.9845
0.675	540	0.0409	0.1202	-	0.9872
0.7	560	0.0372	0.1245	-	0.9854
0.725	580	0.0353	0.1469	-	0.9817
0.75	600	0.0429	0.1225	-	0.9836
0.775	620	0.0595	0.1082	-	0.9862
0.8	640	0.0266	0.0886	-	0.9903
0.825	660	0.0178	0.0712	-	0.9918
0.85	680	0.0567	0.0511	-	0.9936
0.875	700	0.0142	0.0538	-	0.9916
0.9	720	0.0136	0.0726	-	0.9890
0.925	740	0.0192	0.0707	-	0.9884
0.95	760	0.0253	0.0937	-	0.9872
0.975	780	0.0149	0.0792	-	0.9878
1.0	800	0.0231	0.0912	-	0.9879
1.025	820	0.0	0.1030	-	0.9871
1.05	840	0.0096	0.0990	-	0.9876
1.075	860	0.0	0.1032	-	0.9868
1.1	880	0.0	0.1037	-	0.9866
1.125	900	0.0	0.1038	-	0.9866
1.15	920	0.0	0.1038	-	0.9866
1.175	940	0.0	0.1038	-	0.9866
1.2	960	0.0121	0.1030	-	0.9895
1.225	980	0.0	0.1035	-	0.9899
1.25	1000	0.0	0.1040	-	0.9898
1.275	1020	0.0	0.1049	-	0.9898
1.3	1040	0.0	0.1049	-	0.9898
1.325	1060	0.0067	0.1015	-	0.9903
1.35	1080	0.0	0.1048	-	0.9901
1.375	1100	0.0159	0.0956	-	0.9910
1.4	1120	0.0067	0.0818	-	0.9926
1.425	1140	0.0151	0.0838	-	0.9926
1.45	1160	0.0	0.0889	-	0.9920
1.475	1180	0.0	0.0894	-	0.9920
1.5	1200	0.023	0.0696	-	0.9935
1.525	1220	0.0	0.0693	-	0.9935
1.55	1240	0.0	0.0711	-	0.9935
1.575	1260	0.0	0.0711	-	0.9935
1.6	1280	0.0	0.0711	-	0.9935
1.625	1300	0.0176	0.0743	-	0.9936
1.65	1320	0.0	0.0806	-	0.9931
1.675	1340	0.0	0.0817	-	0.9931
1.7	1360	0.007	0.0809	-	0.9929
1.725	1380	0.0209	0.0700	-	0.9941
1.75	1400	0.0068	0.0605	-	0.9949
1.775	1420	0.0069	0.0564	-	0.9951
1.8	1440	0.0097	0.0559	-	0.9953
1.825	1460	0.0	0.0557	-	0.9953
1.85	1480	0.0	0.0557	-	0.9953
1.875	1500	0.0	0.0557	-	0.9953
1.9	1520	0.0	0.0557	-	0.9953
1.925	1540	0.0	0.0557	-	0.9953
1.95	1560	0.0089	0.0544	-	0.9953
1.975	1580	0.0	0.0544	-	0.9953
2.0	1600	0.0	0.0544	-	0.9953
-1	-1	-	-	0.9960	-

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",
}