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: 0.9878048780487805
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7687987089157104
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9813318473112288
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7652501463890076
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9788771539744302
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9837988826815642
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9973707172812245
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.9722833961709166
            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: 0.9819310093082679
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.776313841342926
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9723540910360235
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.776313841342926
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9640088593576965
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9808450704225352
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9918988791388367
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.959014781948805
            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-v1-64-1")
# 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	0.9878	0.9819
cosine_accuracy_threshold	0.7688	0.7763
cosine_f1	0.9813	0.9724
cosine_f1_threshold	0.7653	0.7763
cosine_precision	0.9789	0.964
cosine_recall	0.9838	0.9808
cosine_ap	0.9974	0.9919
cosine_mcc	0.9723	0.959

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: 64
per_device_eval_batch_size: 64
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
dataloader_num_workers: 4
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: 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: 4
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	-	0.5420	-	0.9766
0.05	20	0.4283	0.3152	-	0.9864
0.1	40	0.2681	0.3588	-	0.9828
0.15	60	0.1538	0.2478	-	0.9866
0.2	80	0.1336	0.1804	-	0.9918
0.25	100	0.0763	0.2175	-	0.9906
0.3	120	0.1878	0.2453	-	0.9862
0.35	140	0.0609	0.2112	-	0.9892
0.4	160	0.0933	0.1774	-	0.9896
0.45	180	0.0471	0.1552	-	0.9933
0.5	200	0.0516	0.1933	-	0.9942
0.55	220	0.0421	0.1992	-	0.9910
0.6	240	0.0233	0.1728	-	0.9933
0.65	260	0.0445	0.1640	-	0.9930
0.7	280	0.0157	0.1709	-	0.9894
0.75	300	0.022	0.1653	-	0.9889
0.8	320	0.0192	0.1655	-	0.9893
0.85	340	0.0417	0.1509	-	0.9913
0.9	360	0.0	0.1622	-	0.9916
0.95	380	0.0242	0.1543	-	0.9919
1.0	400	0.0	0.1530	-	0.9919
-1	-1	-	-	0.9974	-

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",
}