metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:8159
  - loss:CosineSimilarityLoss
base_model: yahyaabd/allstats-search-mini-v1-1-mnrl
widget:
  - source_sentence: Bagaimana prevalensi stunting pada anak di bawah lima tahun?
    sentences:
      - >-
        Statistik Penduduk Lanjut Usia Provinsi Sulawesi Barat 2010-Hasil Sensus
        Penduduk 2010
      - Keadaan Angkatan Kerja di Indonesia Februari 2024
      - Perkembangan Indeks Produksi Industri Besar dan Sedang 2006 - 2009
  - source_sentence: Statistik Transportasi Darat Penumpang
    sentences:
      - >-
        Direktori Perusahaan Industri Pengolahan Skala Kecil Buku II Hasil Se
        2006
      - Benchmark Statistik Konstruksi 2010-2015
      - Laporan Bulanan Data Sosial Ekonomi Maret 2021
  - source_sentence: Bagaimana tren penggunaan bibit unggul di sektor perkebunan?
    sentences:
      - Perjanjian Kinerja Badan Pusat Statistik Provinsi Tahun 2016
      - Buletin Statistik Perdagangan Luar Negeri Impor Januari 2009
      - Indikator Ekonomi Juni 2014
  - source_sentence: produksi perikanan tangkap laut 2020-2023
    sentences:
      - Profil dan Tren Pendapatan Pekerja Bebas di Indonesia 2009-2011
      - Neraca Arus Dana Indonesia Triwulanan 2020-2023:2
      - Penghitungan dan Analisis Kemiskinan Makro 2014
  - source_sentence: Berapa produksi sampah perkotaan per kapita per hari?
    sentences:
      - >-
        Harga Konsumen Beberapa Barang dan Jasa Kelompok Sandang di 66 Kota di
        Indonesia 2013
      - Direktori Perusahaan/Usaha Hotel dan Akomodasi Lainnya 2013
      - Indikator Ekonomi Juni 2003
datasets:
  - yahyaabd/bps-pub-cosine-pairs
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on yahyaabd/allstats-search-mini-v1-1-mnrl
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.9668033133268101
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8567762792511281
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.9692220034852868
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8589381360001556
            name: Spearman Cosine

SentenceTransformer based on yahyaabd/allstats-search-mini-v1-1-mnrl

This is a sentence-transformers model finetuned from yahyaabd/allstats-search-mini-v1-1-mnrl on the bps-pub-cosine-pairs 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: yahyaabd/allstats-search-mini-v1-1-mnrl
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- bps-pub-cosine-pairs

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/allstats-search-mini-v1-1-mnrl-special-token-v4")
# Run inference
sentences = [
    'Berapa produksi sampah perkotaan per kapita per hari?',
    'Harga Konsumen Beberapa Barang dan Jasa Kelompok Sandang di 66 Kota di Indonesia 2013',
    'Indikator Ekonomi Juni 2003',
]
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

Semantic Similarity

Datasets: sts-dev and sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev	sts-test
pearson_cosine	0.9668	0.9692
spearman_cosine	0.8568	0.8589

Training Details

Training Dataset

bps-pub-cosine-pairs

Dataset: bps-pub-cosine-pairs at d624648
Size: 8,159 training samples
Columns: query, title, and score
Approximate statistics based on the first 1000 samples:
query title score
type string string float
details
min: 4 tokens
mean: 11.04 tokens
max: 30 tokens

min: 5 tokens
mean: 13.02 tokens
max: 43 tokens

min: 0.1
mean: 0.55
max: 0.9

	query	title	score
type	string	string	float
details	min: 4 tokens mean: 11.04 tokens max: 30 tokens	min: 5 tokens mean: 13.02 tokens max: 43 tokens	min: 0.1 mean: 0.55 max: 0.9

Samples:

query	title	score
`Nilai Tukar Nelayan`	`Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013`	`0.1`
`Berapa angka statistik pertambangan non migas Indonesia periode 2012?`	`Statistik Pertambangan Non Minyak dan Gas Bumi 2011-2015`	`0.9`
`Bagaimana situasi angkatan kerja Indonesia di bulan Februari 2021?`	`Keadaan Angkatan Kerja di Indonesia Februari 2021`	`0.9`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Evaluation Dataset

bps-pub-cosine-pairs

Dataset: bps-pub-cosine-pairs at d624648
Size: 1,022 evaluation samples
Columns: query, title, and score
Approximate statistics based on the first 1000 samples:
query title score
type string string float
details
min: 4 tokens
mean: 11.19 tokens
max: 31 tokens

min: 5 tokens
mean: 13.24 tokens
max: 44 tokens

min: 0.1
mean: 0.56
max: 0.9

	query	title	score
type	string	string	float
details	min: 4 tokens mean: 11.19 tokens max: 31 tokens	min: 5 tokens mean: 13.24 tokens max: 44 tokens	min: 0.1 mean: 0.56 max: 0.9

Samples:

query	title	score
`Sosek Desember 2021`	`Laporan Bulanan Data Sosial Ekonomi Desember 2021`	`0.9`
`Ekspor Indonesia menurut SITC 2019-2020`	`Statistik Perdagangan Luar Negeri Indonesia Ekspor Menurut Kode SITC, 2019-2020`	`0.9`
`Pengeluaran konsumsi penduduk Indonesia Maret 2018`	`Pengeluaran untuk Konsumsi Penduduk Indonesia, Maret 2018`	`0.9`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 1e-05
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
label_smoothing_factor: 0.01
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: 1e-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: 0.1
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: 0.01
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	sts-dev_spearman_cosine	sts-test_spearman_cosine
0	0	-	0.0371	0.8433	-
0.0392	10	0.0365	0.0366	0.8434	-
0.0784	20	0.0514	0.0351	0.8440	-
0.1176	30	0.036	0.0330	0.8448	-
0.1569	40	0.0299	0.0310	0.8456	-
0.1961	50	0.0371	0.0293	0.8465	-
0.2353	60	0.037	0.0276	0.8479	-
0.2745	70	0.0307	0.0260	0.8495	-
0.3137	80	0.0283	0.0243	0.8508	-
0.3529	90	0.0251	0.0230	0.8517	-
0.3922	100	0.0213	0.0220	0.8520	-
0.4314	110	0.0244	0.0215	0.8522	-
0.4706	120	0.0234	0.0208	0.8526	-
0.5098	130	0.0219	0.0200	0.8530	-
0.5490	140	0.0187	0.0195	0.8536	-
0.5882	150	0.0188	0.0189	0.8538	-
0.6275	160	0.0189	0.0184	0.8540	-
0.6667	170	0.0193	0.0178	0.8543	-
0.7059	180	0.0171	0.0173	0.8545	-
0.7451	190	0.017	0.0171	0.8546	-
0.7843	200	0.0206	0.0168	0.8548	-
0.8235	210	0.016	0.0163	0.8549	-
0.8627	220	0.0173	0.0161	0.8552	-
0.9020	230	0.0161	0.0158	0.8553	-
0.9412	240	0.0173	0.0156	0.8553	-
0.9804	250	0.0131	0.0155	0.8552	-
1.0196	260	0.0175	0.0152	0.8554	-
1.0588	270	0.015	0.0149	0.8555	-
1.0980	280	0.0119	0.0145	0.8556	-
1.1373	290	0.0126	0.0143	0.8557	-
1.1765	300	0.0133	0.0141	0.8557	-
1.2157	310	0.0134	0.0138	0.8557	-
1.2549	320	0.0123	0.0136	0.8558	-
1.2941	330	0.0118	0.0135	0.8558	-
1.3333	340	0.0117	0.0134	0.8558	-
1.3725	350	0.0143	0.0133	0.8559	-
1.4118	360	0.0118	0.0131	0.8559	-
1.4510	370	0.0119	0.0129	0.8563	-
1.4902	380	0.0117	0.0126	0.8565	-
1.5294	390	0.0132	0.0125	0.8566	-
1.5686	400	0.0112	0.0124	0.8566	-
1.6078	410	0.0117	0.0125	0.8566	-
1.6471	420	0.013	0.0125	0.8566	-
1.6863	430	0.0109	0.0123	0.8566	-
1.7255	440	0.0135	0.0123	0.8566	-
1.7647	450	0.0116	0.0123	0.8566	-
1.8039	460	0.0115	0.0121	0.8566	-
1.8431	470	0.0116	0.0119	0.8566	-
1.8824	480	0.013	0.0118	0.8567	-
1.9216	490	0.0114	0.0117	0.8567	-
1.9608	500	0.0111	0.0117	0.8567	-
2.0	510	0.0114	0.0115	0.8567	-
2.0392	520	0.0098	0.0113	0.8567	-
2.0784	530	0.0075	0.0112	0.8567	-
2.1176	540	0.0089	0.0112	0.8567	-
2.1569	550	0.0083	0.0111	0.8567	-
2.1961	560	0.0077	0.0110	0.8567	-
2.2353	570	0.0128	0.0110	0.8567	-
2.2745	580	0.0092	0.0109	0.8567	-
2.3137	590	0.0103	0.0109	0.8567	-
2.3529	600	0.009	0.0108	0.8567	-
2.3922	610	0.0086	0.0108	0.8567	-
2.4314	620	0.0076	0.0108	0.8567	-
2.4706	630	0.0101	0.0107	0.8568	-
2.5098	640	0.0094	0.0107	0.8568	-
2.5490	650	0.0102	0.0107	0.8568	-
2.5882	660	0.008	0.0106	0.8568	-
2.6275	670	0.0091	0.0106	0.8568	-
2.6667	680	0.0101	0.0106	0.8568	-
2.7059	690	0.0119	0.0105	0.8568	-
2.7451	700	0.0081	0.0105	0.8568	-
2.7843	710	0.0098	0.0105	0.8568	-
2.8235	720	0.0076	0.0105	0.8568	-
2.8627	730	0.009	0.0105	0.8568	-
2.9020	740	0.0091	0.0105	0.8568	-
2.9412	750	0.0106	0.0105	0.8568	-
2.9804	760	0.0097	0.0105	0.8568	-
-1	-1	-	-	-	0.8589

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",
}