This is a sentence-transformers model finetuned from yahyaabd/allstats-search-mini-v1-1-mnrl on the bps-sts-dataset-v1 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-sts-dataset-v1

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/allstats-search-mini-v1-1-mnrl-sts")
# Run inference
sentences = [
    'PDRB per kapita Provinsi Riau sangat dipengaruhi oleh harga minyak bumi dunia.',
    'The Riau Islands province is known for its beautiful beaches and marine tourism.',
    'Di wilayah perkotaan, angka kemiskinan pada Maret 2023 adalah 7,29%.',
]
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	0.8384	0.8686
spearman_cosine	0.8363	0.8631

Training Details

Training Dataset

bps-sts-dataset-v1

Dataset: bps-sts-dataset-v1 at 5c8f96e
Size: 2,436 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 20.49 tokens
max: 36 tokens
min: 9 tokens
mean: 20.71 tokens
max: 45 tokens
min: 0.0
mean: 0.51
max: 1.0

Samples:

sentence1	sentence2	score
`bagaimana capaian Tujuan Pembangunan Berkelanjutan di Indonesia?`	`Laporan Pencapaian Indikator Tujuan Pembangunan Berkelanjutan (TPB/SDGs) Indonesia, Edisi 2024`	`0.8`
`Jumlah perpustakaan umum di Indonesia tahun 2022 sebanyak 170.000 unit.`	`Minat baca masyarakat Indonesia masih perlu ditingkatkan melalui berbagai program literasi.`	`0.4`
`Jumlah sekolah negeri jenjang SMP di Kota Bandar Lampung adalah 30 sekolah.`	`Laju deforestasi di Provinsi Kalimantan Tengah masih mengkhawatirkan.`	`0.0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Evaluation Dataset

bps-sts-dataset-v1

Dataset: bps-sts-dataset-v1 at 5c8f96e
Size: 522 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 522 samples:
sentence1 sentence2 score
type string string float
details
min: 9 tokens
mean: 20.83 tokens
max: 39 tokens
min: 8 tokens
mean: 20.84 tokens
max: 44 tokens
min: 0.0
mean: 0.5
max: 1.0

Samples:

sentence1	sentence2	score
`Persentase desa yang memiliki fasilitas internet di Provinsi Y pada tahun 2021 adalah 85%.`	`Luas perkebunan kelapa sawit di Provinsi Y pada tahun 2021 adalah 500.000 hektar.`	`0.2`
`Kontribusi sektor UMKM terhadap PDRB Kota Malang pada tahun 2023 sebesar 60%.`	`Usaha Mikro, Kecil, dan Menengah menyumbang 60 persen terhadap total Produk Domestik Regional Bruto di kota pendidikan Malang pada tahun 2023.`	`1.0`
`Jumlah Industri Kecil dan Menengah (IKM) di Kabupaten Tegal, Jawa Tengah, bertambah 200 unit pada tahun 2024.`	`Di Tegal, sebuah kabupaten di Jateng, terjadi penambahan 200 unit IKM sepanjang tahun 2024.`	`1.0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

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}
tp_size: 0
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
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	-	0.0588	0.7404	-
0.1299	10	0.0535	0.0577	0.7454	-
0.2597	20	0.046	0.0539	0.7614	-
0.3896	30	0.0552	0.0497	0.7796	-
0.5195	40	0.0442	0.0470	0.7947	-
0.6494	50	0.0437	0.0450	0.8057	-
0.7792	60	0.0425	0.0438	0.8123	-
0.9091	70	0.0465	0.0423	0.8183	-
1.0390	80	0.0384	0.0414	0.8223	-
1.1688	90	0.0362	0.0405	0.8260	-
1.2987	100	0.0309	0.0401	0.8276	-
1.4286	110	0.0335	0.0397	0.8289	-
1.5584	120	0.033	0.0394	0.8307	-
1.6883	130	0.0272	0.0392	0.8317	-
1.8182	140	0.032	0.0390	0.8324	-
1.9481	150	0.0317	0.0387	0.8331	-
2.0779	160	0.0322	0.0385	0.8338	-
2.2078	170	0.0285	0.0383	0.8345	-
2.3377	180	0.0299	0.0382	0.8349	-
2.4675	190	0.0326	0.0381	0.8351	-
2.5974	200	0.0258	0.0380	0.8356	-
2.7273	210	0.0282	0.0379	0.8361	-
2.8571	220	0.0286	0.0379	0.8363	-
2.987	230	0.025	0.0379	0.8363	-
-1	-1	-	-	-	0.8631

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.12
Sentence Transformers: 3.4.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.6.0
Datasets: 3.2.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 20.49 tokens max: 36 tokens	min: 9 tokens mean: 20.71 tokens max: 45 tokens	min: 0.0 mean: 0.51 max: 1.0