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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/allstats-search-mini-v1-1-mnrl-v2")
# Run inference
sentences = [
    'q-4068',
    'Berapa persentase rumah tangga dengan akses sanitasi layak?',
    '43a5856225b1ff1cb95e319a',
]
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	0.9259	0.9299
spearman_cosine	0.8465	0.8497

Training Details

Training Dataset

bps-pub-cosine-pairs

Dataset: bps-pub-cosine-pairs at d58662e
Size: 8,082 training samples
Columns: query_id, query, corpus_id, title, and score

Approximate statistics based on the first 1000 samples:

	query_id	query	corpus_id	title	score
type	string	string	string	string	float
details	min: 4 tokens mean: 5.21 tokens max: 6 tokens	min: 4 tokens mean: 11.04 tokens max: 30 tokens	min: 4 tokens mean: 17.4 tokens max: 23 tokens	min: 5 tokens mean: 13.02 tokens max: 43 tokens	min: 0.1 mean: 0.55 max: 0.9

Samples:

query_id	query	corpus_id	title	score
`q-1599`	`Nilai Tukar Nelayan`	`0b0da8fc2b6af9329a6d9cfe`	`Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013`	`0.1`
`q-3595`	`Berapa angka statistik pertambangan non migas Indonesia periode 2012?`	`3c83610c3e2e5242177e2b11`	`Statistik Pertambangan Non Minyak dan Gas Bumi 2011-2015`	`0.9`
`q-9112`	`Bagaimana situasi angkatan kerja Indonesia di bulan Februari 2021?`	`b547a5642aeb04d071cb83d4`	`Keadaan Angkatan Kerja di Indonesia Februari 2021`	`0.9`

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

Evaluation Dataset

bps-pub-cosine-pairs

Dataset: bps-pub-cosine-pairs at d58662e
Size: 1,010 evaluation samples
Columns: query_id, query, corpus_id, title, and score

Approximate statistics based on the first 1000 samples:

	query_id	query	corpus_id	title	score
type	string	string	string	string	float
details	min: 4 tokens mean: 5.22 tokens max: 6 tokens	min: 4 tokens mean: 11.19 tokens max: 31 tokens	min: 7 tokens mean: 17.25 tokens max: 23 tokens	min: 5 tokens mean: 13.24 tokens max: 44 tokens	min: 0.1 mean: 0.56 max: 0.9

Samples:

query_id	query	corpus_id	title	score
`q-1273`	`Sosek Desember 2021`	`b7890a143bc751d1d84dcf4a`	`Laporan Bulanan Data Sosial Ekonomi Desember 2021`	`0.9`
`q-4882`	`Ekspor Indonesia menurut SITC 2019-2020`	`9f3d9054c2f29bc478d56cd1`	`Statistik Perdagangan Luar Negeri Indonesia Ekspor Menurut Kode SITC, 2019-2020`	`0.9`
`q-7141`	`Pengeluaran konsumsi penduduk Indonesia Maret 2018`	`4194e924ca33f087b68ab2de`	`Pengeluaran untuk Konsumsi Penduduk Indonesia, Maret 2018`	`0.9`

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}
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	-	0.3773	0.8467	-
0.0395	10	0.3676	0.3628	0.8469	-
0.0791	20	0.3166	0.3161	0.8474	-
0.1186	30	0.2743	0.2423	0.8483	-
0.1581	40	0.1933	0.1625	0.8494	-
0.1976	50	0.1473	0.1154	0.8507	-
0.2372	60	0.1046	0.1020	0.8514	-
0.2767	70	0.0839	0.0878	0.8519	-
0.3162	80	0.0839	0.0759	0.8519	-
0.3557	90	0.0756	0.0667	0.8521	-
0.3953	100	0.0611	0.0597	0.8522	-
0.4348	110	0.0562	0.0554	0.8520	-
0.4743	120	0.0557	0.0518	0.8517	-
0.5138	130	0.0411	0.0482	0.8514	-
0.5534	140	0.0481	0.0454	0.8510	-
0.5929	150	0.0474	0.0423	0.8500	-
0.6324	160	0.0433	0.0404	0.8498	-
0.6719	170	0.0389	0.0390	0.8502	-
0.7115	180	0.0423	0.0373	0.8503	-
0.7510	190	0.0348	0.0360	0.8495	-
0.7905	200	0.0404	0.0346	0.8492	-
0.8300	210	0.0285	0.0334	0.8494	-
0.8696	220	0.0322	0.0317	0.8482	-
0.9091	230	0.0311	0.0305	0.8469	-
0.9486	240	0.027	0.0298	0.8462	-
0.9881	250	0.03	0.0292	0.8462	-
1.0277	260	0.0245	0.0292	0.8458	-
1.0672	270	0.026	0.0290	0.8447	-
1.1067	280	0.0325	0.0279	0.8466	-
1.1462	290	0.0208	0.0274	0.8458	-
1.1858	300	0.0249	0.0271	0.8451	-
1.2253	310	0.026	0.0264	0.8444	-
1.2648	320	0.0234	0.0261	0.8469	-
1.3043	330	0.024	0.0267	0.8482	-
1.3439	340	0.0212	0.0254	0.8480	-
1.3834	350	0.033	0.0247	0.8473	-
1.4229	360	0.0246	0.0244	0.8473	-
1.4625	370	0.0241	0.0242	0.8477	-
1.5020	380	0.0187	0.0237	0.8473	-
1.5415	390	0.0228	0.0235	0.8474	-
1.5810	400	0.0169	0.0234	0.8475	-
1.6206	410	0.0249	0.0233	0.8470	-
1.6601	420	0.0223	0.0234	0.8475	-
1.6996	430	0.0174	0.0232	0.8477	-
1.7391	440	0.0249	0.0229	0.8480	-
1.7787	450	0.0243	0.0229	0.8483	-
1.8182	460	0.0203	0.0232	0.8485	-
1.8577	470	0.0198	0.0226	0.8477	-
1.8972	480	0.019	0.0223	0.8464	-
1.9368	490	0.0185	0.0218	0.8465	-
1.9763	500	0.0168	0.0218	0.8468	-
2.0158	510	0.019	0.0217	0.8472	-
2.0553	520	0.0194	0.0217	0.8476	-
2.0949	530	0.0192	0.0216	0.8475	-
2.1344	540	0.0175	0.0215	0.8473	-
2.1739	550	0.013	0.0214	0.8477	-
2.2134	560	0.017	0.0212	0.8478	-
2.2530	570	0.0157	0.0212	0.8478	-
2.2925	580	0.0169	0.0211	0.8473	-
2.3320	590	0.0192	0.0210	0.8475	-
2.3715	600	0.0116	0.0208	0.8472	-
2.4111	610	0.0151	0.0207	0.8473	-
2.4506	620	0.0182	0.0205	0.8472	-
2.4901	630	0.0143	0.0205	0.8471	-
2.5296	640	0.0193	0.0204	0.8470	-
2.5692	650	0.0194	0.0203	0.8469	-
2.6087	660	0.0132	0.0204	0.8469	-
2.6482	670	0.0208	0.0204	0.8464	-
2.6877	680	0.0155	0.0203	0.8461	-
2.7273	690	0.0142	0.0203	0.8461	-
2.7668	700	0.0162	0.0203	0.8460	-
2.8063	710	0.0198	0.0203	0.8461	-
2.8458	720	0.0138	0.0204	0.8465	-
2.8854	730	0.0145	0.0204	0.8465	-
2.9249	740	0.0129	0.0204	0.8466	-
2.9644	750	0.0108	0.0204	0.8465	-
-1	-1	-	-	-	0.8497

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

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",
}