This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-MiniLM-L12-v2 on the query-hard-pos-neg-doc-pairs-statictable 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: sentence-transformers/paraphrase-MiniLM-L12-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 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': 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-miniLM-v1")
# Run inference
sentences = [
    'Bagaimana perkembangan koperasi di Indonesia, khususnya sekitar tayun 2000?',
    'IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100)',
    'Rata-Rata Harian Aliran Sungai, Tinggi Aliran, dan Volume Air di Beberapa Sungai yang Daerah Pengalirannya Lebih dari 1.000 km2, 2000-2011',
]
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: allstats-semantic-mini-v1-eval and allstat-search-mini-v1-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	allstats-semantic-mini-v1-eval	allstat-search-mini-v1-test
pearson_cosine	0.848	0.8538
spearman_cosine	0.7746	0.7768

Training Details

Training Dataset

query-hard-pos-neg-doc-pairs-statictable

Dataset: query-hard-pos-neg-doc-pairs-statictable at 25756d3
Size: 25,551 training samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string int
details
min: 9 tokens
mean: 28.64 tokens
max: 53 tokens
min: 11 tokens
mean: 36.67 tokens
max: 70 tokens
0: ~65.80%
1: ~34.20%

Samples:

query	doc	label
`Gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014`	`Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)`	`0`
`gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014`	`Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)`	`0`
`GAJI NOMINAL, INDEKS UPAH: NOMINAL & RIIL PEKERJA MANUFAKTUR NON-MANDOR (2012=100), 2013-2014`	`Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)`	`0`

Loss: OnlineContrastiveLoss

Evaluation Dataset

query-hard-pos-neg-doc-pairs-statictable

Dataset: query-hard-pos-neg-doc-pairs-statictable at 25756d3
Size: 5,463 evaluation samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string int
details
min: 10 tokens
mean: 29.3 tokens
max: 62 tokens
min: 12 tokens
mean: 37.1 tokens
max: 69 tokens
0: ~73.20%
1: ~26.80%

Samples:

query	doc	label
`Bagaimana penghasilan wirausahawan di Indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?`	`Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012`	`0`
`bagaimana penghasilan wirausahawan di indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?`	`Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012`	`0`
`BAGAIMANA PENGHASILAN WIRAUSAHAWAN DI INDONESIA BERVARIASI PER PROVINSI DAN JENIS PEKERJAAN UTAMA DI TAHUN 2016?`	`Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012`	`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: 4
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: 4
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

Click to expand

Epoch	Step	Training Loss	Validation Loss	allstats-semantic-mini-v1-eval_spearman_cosine	allstat-search-mini-v1-test_spearman_cosine
0	0	-	1.0797	0.5314	-
0.0250	20	1.2823	0.9331	0.5510	-
0.0501	40	0.9562	0.6159	0.6492	-
0.0751	60	0.5872	0.4629	0.6913	-
0.1001	80	0.4101	0.3605	0.7221	-
0.1252	100	0.419	0.3919	0.7301	-
0.1502	120	0.1517	0.2565	0.7457	-
0.1752	140	0.2678	0.2503	0.7484	-
0.2003	160	0.225	0.2010	0.7546	-
0.2253	180	0.2846	0.3203	0.7420	-
0.2503	200	0.2086	0.1981	0.7589	-
0.2753	220	0.1255	0.1982	0.7610	-
0.3004	240	0.1182	0.2328	0.7583	-
0.3254	260	0.1328	0.2218	0.7561	-
0.3504	280	0.1228	0.4583	0.7343	-
0.3755	300	0.1394	0.1785	0.7705	-
0.4005	320	0.2577	0.1800	0.7650	-
0.4255	340	0.1903	0.2680	0.7557	-
0.4506	360	0.1164	0.1761	0.7616	-
0.4756	380	0.0779	0.3318	0.7453	-
0.5006	400	0.1563	0.2209	0.7582	-
0.5257	420	0.1835	0.1683	0.7662	-
0.5507	440	0.1171	0.1537	0.7658	-
0.5757	460	0.0973	0.1381	0.7710	-
0.6008	480	0.0578	0.2303	0.7618	-
0.6258	500	0.1343	0.1431	0.7710	-
0.6508	520	0.1274	0.1646	0.7695	-
0.6758	540	0.057	0.1775	0.7606	-
0.7009	560	0.0392	0.1425	0.7689	-
0.7259	580	0.0434	0.1399	0.7712	-
0.7509	600	0.1311	0.1747	0.7670	-
0.7760	620	0.0475	0.1375	0.7709	-
0.8010	640	0.0183	0.1465	0.7685	-
0.8260	660	0.024	0.1666	0.7669	-
0.8511	680	0.0249	0.1728	0.7656	-
0.8761	700	0.041	0.1624	0.7711	-
0.9011	720	0.0835	0.1397	0.7716	-
0.9262	740	0.0404	0.1507	0.7693	-
0.9512	760	0.0141	0.1369	0.7723	-
0.9762	780	0.0513	0.1555	0.7687	-
1.0013	800	0.0387	0.1306	0.7717	-
1.0263	820	0.0393	0.1420	0.7707	-
1.0513	840	0.0153	0.1656	0.7700	-
1.0763	860	0.0263	0.1525	0.7694	-
1.1014	880	0.0503	0.1947	0.7638	-
1.1264	900	0.0215	0.2202	0.7615	-
1.1514	920	0.0217	0.1542	0.7696	-
1.1765	940	0.007	0.1394	0.7713	-
1.2015	960	0.018	0.1573	0.7706	-
1.2265	980	0.0446	0.1504	0.7686	-
1.2516	1000	0.026	0.1573	0.7661	-
1.2766	1020	0.0098	0.1429	0.7683	-
1.3016	1040	0.0196	0.1374	0.7702	-
1.3267	1060	0.021	0.1594	0.7685	-
1.3517	1080	0.0499	0.1378	0.7724	-
1.3767	1100	0.0165	0.1335	0.7729	-
1.4018	1120	0.0294	0.1451	0.7713	-
1.4268	1140	0.0114	0.1338	0.7717	-
1.4518	1160	0.0192	0.1327	0.7719	-
1.4768	1180	0.0335	0.1618	0.7646	-
1.5019	1200	0.0546	0.1389	0.7711	-
1.5269	1220	0.0069	0.1239	0.7738	-
1.5519	1240	0.0094	0.1180	0.7739	-
1.5770	1260	0.0074	0.1238	0.7733	-
1.6020	1280	0.0557	0.1428	0.7720	-
1.6270	1300	0.056	0.1159	0.7751	-
1.6521	1320	0.0	0.1244	0.7758	-
1.6771	1340	0.0066	0.1185	0.7735	-
1.7021	1360	0.0178	0.1016	0.7757	-
1.7272	1380	0.0156	0.0939	0.7776	-
1.7522	1400	0.0	0.1138	0.7761	-
1.7772	1420	0.0436	0.0980	0.7775	-
1.8023	1440	0.0626	0.1096	0.7763	-
1.8273	1460	0.0222	0.0968	0.7774	-
1.8523	1480	0.0101	0.1021	0.7762	-
1.8773	1500	0.0171	0.1076	0.7754	-
1.9024	1520	0.0064	0.1279	0.7730	-
1.9274	1540	0.0068	0.1237	0.7729	-
1.9524	1560	0.0066	0.1229	0.7733	-
1.9775	1580	0.0	0.1263	0.7731	-
2.0025	1600	0.0065	0.1152	0.7746	-
2.0275	1620	0.0147	0.1021	0.7773	-
2.0526	1640	0.0	0.1021	0.7773	-
2.0776	1660	0.0209	0.1017	0.7774	-
2.1026	1680	0.0	0.0993	0.7773	-
2.1277	1700	0.0067	0.0922	0.7784	-
2.1527	1720	0.0333	0.1158	0.7749	-
2.1777	1740	0.0	0.1397	0.7721	-
2.2028	1760	0.0158	0.1248	0.7751	-
2.2278	1780	0.0201	0.1021	0.7767	-
2.2528	1800	0.0	0.1029	0.7768	-
2.2778	1820	0.0107	0.1007	0.7767	-
2.3029	1840	0.0156	0.0923	0.7767	-
2.3279	1860	0.0	0.1012	0.7754	-
2.3529	1880	0.0131	0.1184	0.7731	-
2.3780	1900	0.0072	0.1113	0.7752	-
2.4030	1920	0.0337	0.0952	0.7775	-
2.4280	1940	0.0068	0.1086	0.7754	-
2.4531	1960	0.0	0.1194	0.7740	-
2.4781	1980	0.0176	0.1184	0.7747	-
2.5031	2000	0.0188	0.1123	0.7745	-
2.5282	2020	0.0	0.1138	0.7742	-
2.5532	2040	0.0	0.1141	0.7742	-
2.5782	2060	0.0269	0.1126	0.7743	-
2.6033	2080	0.0193	0.1470	0.7707	-
2.6283	2100	0.0074	0.1333	0.7726	-
2.6533	2120	0.0253	0.1004	0.7756	-
2.6783	2140	0.0	0.0980	0.7758	-
2.7034	2160	0.0	0.0984	0.7758	-
2.7284	2180	0.0	0.0984	0.7758	-
2.7534	2200	0.0	0.0984	0.7758	-
2.7785	2220	0.007	0.0971	0.7766	-
2.8035	2240	0.0	0.0998	0.7766	-
2.8285	2260	0.015	0.0988	0.7760	-
2.8536	2280	0.0	0.1020	0.7757	-
2.8786	2300	0.0	0.1023	0.7756	-
2.9036	2320	0.0	0.1023	0.7756	-
2.9287	2340	0.0	0.1023	0.7756	-
2.9537	2360	0.0075	0.1043	0.7751	-
2.9787	2380	0.0067	0.1125	0.7749	-
3.0038	2400	0.0	0.1083	0.7752	-
3.0288	2420	0.0	0.1083	0.7752	-
3.0538	2440	0.0	0.1083	0.7752	-
3.0788	2460	0.0063	0.1018	0.7755	-
3.1039	2480	0.0	0.1012	0.7756	-
3.1289	2500	0.0162	0.092	0.7768	-
3.1539	2520	0.01	0.0941	0.7768	-
3.1790	2540	0.0069	0.0946	0.7761	-
3.2040	2560	0.0	0.0956	0.7759	-
3.2290	2580	0.0	0.0956	0.7758	-
3.2541	2600	0.0	0.0956	0.7758	-
3.2791	2620	0.0	0.0956	0.7758	-
3.3041	2640	0.0131	0.0981	0.7756	-
3.3292	2660	0.0195	0.1142	0.7748	-
3.3542	2680	0.0	0.1172	0.7746	-
3.3792	2700	0.0065	0.1186	0.7748	-
3.4043	2720	0.0169	0.1184	0.7750	-
3.4293	2740	0.0	0.1175	0.7749	-
3.4543	2760	0.0	0.1165	0.7748	-
3.4793	2780	0.0105	0.1173	0.7747	-
3.5044	2800	0.0066	0.1123	0.7751	-
3.5294	2820	0.0	0.1103	0.7753	-
3.5544	2840	0.0	0.1106	0.7753	-
3.5795	2860	0.0139	0.1158	0.7745	-
3.6045	2880	0.0	0.1183	0.7741	-
3.6295	2900	0.0	0.1181	0.7741	-
3.6546	2920	0.0	0.1179	0.7741	-
3.6796	2940	0.0	0.1179	0.7741	-
3.7046	2960	0.0119	0.1172	0.7742	-
3.7297	2980	0.0068	0.1183	0.7742	-
3.7547	3000	0.0	0.1193	0.7741	-
3.7797	3020	0.0	0.1193	0.7741	-
3.8048	3040	0.0	0.1193	0.7741	-
3.8298	3060	0.0	0.1191	0.7741	-
3.8548	3080	0.0	0.1193	0.7741	-
3.8798	3100	0.0	0.1193	0.7741	-
3.9049	3120	0.0131	0.1165	0.7745	-
3.9299	3140	0.0	0.1159	0.7745	-
3.9549	3160	0.0	0.1158	0.7746	-
3.9800	3180	0.0	0.1153	0.7746	-
-1	-1	-	-	-	0.7768

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",
}

	query	doc	label
type	string	string	int
details	min: 9 tokens mean: 28.64 tokens max: 53 tokens	min: 11 tokens mean: 36.67 tokens max: 70 tokens	0: ~65.80% 1: ~34.20%