This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on the allstats-semantic-search-synthetic-dataset-v1 dataset. It maps sentences & paragraphs to a 768-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-multilingual-mpnet-base-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- allstats-semantic-search-synthetic-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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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-semantic-search-model-v1-2")
# Run inference
sentences = [
    'Berapa persen deflasi ysng terjadi paa Maret 2010?',
    'Pada Bulan Maret 2010 Terjadi Deflasi Sebesar 0,14 Persen.',
    'Inflasi September 2008 sebesar 0,97 persen.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Datasets: allstats-semantic-search-v1-2-dev and allstat-semantic-search-v1-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	allstats-semantic-search-v1-2-dev	allstat-semantic-search-v1-test
pearson_cosine	0.9923	0.9929
spearman_cosine	0.9293	0.9283

Training Details

Training Dataset

allstats-semantic-search-synthetic-dataset-v1

Dataset: allstats-semantic-search-synthetic-dataset-v1 at c477abf
Size: 212,930 training samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string float
details
min: 5 tokens
mean: 11.52 tokens
max: 32 tokens
min: 4 tokens
mean: 14.85 tokens
max: 70 tokens
min: 0.0
mean: 0.51
max: 1.0

Samples:

query	doc	label
`studi tentang kemiskinan urban`	`Perkembangan Mingguan Harga Eceran Beberapa Bahan Pokok di Ibukota Provinsi Seluruh Indonesia (Juli-Desember 2018)`	`0.1`
`Harga gabah di tingkat produsen bulan September`	`Upah Buruh Juli 2020`	`0.1`
`Data perusahaan konstruksi di wilayah timur Indonesia thn 2013`	`Direktori Perusahaan Konstruksi 2013 Buku 6 Maluku dan Papua`	`0.92`

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

Evaluation Dataset

allstats-semantic-search-synthetic-dataset-v1

Dataset: allstats-semantic-search-synthetic-dataset-v1 at c477abf
Size: 26,616 evaluation samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string float
details
min: 5 tokens
mean: 11.33 tokens
max: 53 tokens
min: 4 tokens
mean: 14.6 tokens
max: 69 tokens
min: 0.0
mean: 0.5
max: 1.0

Samples:

query	doc	label
`Informasi potensi deas di Maluku 011`	`Statistik Potensi Desa Provinsi Maluku 2011`	`0.87`
`Berapa persen kenaikan jumlah penumpang angkutan udara internasional pada Januari 2024 dibandingkan Desember 2023?`	`Kenaikan jumlah penumpang bulan lainnya`	`0.0`
`informasi tentang potensi desa jambi tahun 2005`	`Statistik Potensi Desa Provinsi Jambi 2005`	`0.85`

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
num_train_epochs: 6
warmup_ratio: 0.1
fp16: 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: 6
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: False
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: False
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-search-v1-2-dev_spearman_cosine	allstat-semantic-search-v1-test_spearman_cosine
0.0376	250	0.0734	0.0464	0.6927	-
0.0751	500	0.043	0.0362	0.7146	-
0.1127	750	0.0353	0.0288	0.7364	-
0.1503	1000	0.0271	0.0274	0.7571	-
0.1878	1250	0.0241	0.0225	0.7738	-
0.2254	1500	0.0228	0.0203	0.7699	-
0.2630	1750	0.0207	0.0197	0.7881	-
0.3005	2000	0.0187	0.0191	0.7900	-
0.3381	2250	0.0194	0.0183	0.7794	-
0.3757	2500	0.0182	0.0178	0.7870	-
0.4132	2750	0.0198	0.0183	0.8009	-
0.4508	3000	0.0189	0.0182	0.7912	-
0.4884	3250	0.0177	0.0168	0.7963	-
0.5259	3500	0.0178	0.0173	0.7920	-
0.5635	3750	0.017	0.0183	0.8014	-
0.6011	4000	0.0186	0.0180	0.7777	-
0.6386	4250	0.0187	0.0167	0.7976	-
0.6762	4500	0.015	0.0154	0.8194	-
0.7137	4750	0.0158	0.0157	0.8062	-
0.7513	5000	0.0152	0.0148	0.8117	-
0.7889	5250	0.0148	0.0149	0.8115	-
0.8264	5500	0.0146	0.0141	0.8175	-
0.8640	5750	0.0154	0.0144	0.7951	-
0.9016	6000	0.0155	0.0152	0.8163	-
0.9391	6250	0.0145	0.0136	0.8216	-
0.9767	6500	0.0149	0.0149	0.8140	-
1.0143	6750	0.0132	0.0132	0.8179	-
1.0518	7000	0.0108	0.0124	0.8232	-
1.0894	7250	0.0109	0.0120	0.8330	-
1.1270	7500	0.0112	0.0132	0.8219	-
1.1645	7750	0.0116	0.0124	0.8226	-
1.2021	8000	0.0121	0.0120	0.8151	-
1.2397	8250	0.0109	0.0119	0.8384	-
1.2772	8500	0.0103	0.0114	0.8415	-
1.3148	8750	0.0105	0.0116	0.8191	-
1.3524	9000	0.0104	0.0122	0.8292	-
1.3899	9250	0.0108	0.0117	0.8292	-
1.4275	9500	0.011	0.0118	0.8339	-
1.4651	9750	0.0105	0.0106	0.8367	-
1.5026	10000	0.0093	0.0098	0.8467	-
1.5402	10250	0.0105	0.0101	0.8334	-
1.5778	10500	0.0102	0.0106	0.8324	-
1.6153	10750	0.01	0.0097	0.8472	-
1.6529	11000	0.0106	0.0098	0.8378	-
1.6905	11250	0.0088	0.0095	0.8531	-
1.7280	11500	0.0085	0.0095	0.8409	-
1.7656	11750	0.0089	0.0091	0.8431	-
1.8032	12000	0.0083	0.0088	0.8524	-
1.8407	12250	0.0082	0.0088	0.8591	-
1.8783	12500	0.0078	0.0092	0.8478	-
1.9159	12750	0.009	0.0085	0.8480	-
1.9534	13000	0.0082	0.0089	0.8465	-
1.9910	13250	0.0076	0.0085	0.8564	-
2.0285	13500	0.0059	0.0082	0.8602	-
2.0661	13750	0.0073	0.0081	0.8558	-
2.1037	14000	0.0075	0.0081	0.8492	-
2.1412	14250	0.0066	0.0077	0.8520	-
2.1788	14500	0.0066	0.0076	0.8599	-
2.2164	14750	0.007	0.0080	0.8589	-
2.2539	15000	0.0065	0.0076	0.8552	-
2.2915	15250	0.0071	0.0075	0.8604	-
2.3291	15500	0.0062	0.0073	0.8714	-
2.3666	15750	0.0058	0.0069	0.8714	-
2.4042	16000	0.0066	0.0072	0.8570	-
2.4418	16250	0.0058	0.0069	0.8757	-
2.4793	16500	0.0059	0.0067	0.8726	-
2.5169	16750	0.0057	0.0067	0.8663	-
2.5545	17000	0.0058	0.0068	0.8703	-
2.5920	17250	0.0058	0.0068	0.8765	-
2.6296	17500	0.006	0.0067	0.8729	-
2.6672	17750	0.0057	0.0067	0.8689	-
2.7047	18000	0.0055	0.0065	0.8750	-
2.7423	18250	0.0056	0.0066	0.8734	-
2.7799	18500	0.0053	0.0062	0.8745	-
2.8174	18750	0.0053	0.0062	0.8814	-
2.8550	19000	0.0048	0.0063	0.8839	-
2.8926	19250	0.005	0.0063	0.8741	-
2.9301	19500	0.0063	0.0061	0.8752	-
2.9677	19750	0.0052	0.0059	0.8790	-
3.0053	20000	0.0049	0.0058	0.8825	-
3.0428	20250	0.0042	0.0059	0.8787	-
3.0804	20500	0.0043	0.0056	0.8839	-
3.1180	20750	0.0036	0.0058	0.8870	-
3.1555	21000	0.004	0.0056	0.8825	-
3.1931	21250	0.0041	0.0056	0.8884	-
3.2307	21500	0.004	0.0054	0.8872	-
3.2682	21750	0.0044	0.0052	0.8838	-
3.3058	22000	0.0036	0.0053	0.8904	-
3.3434	22250	0.0036	0.0054	0.8898	-
3.3809	22500	0.0037	0.0051	0.8938	-
3.4185	22750	0.0036	0.0051	0.8953	-
3.4560	23000	0.0036	0.0051	0.8935	-
3.4936	23250	0.004	0.0049	0.8955	-
3.5312	23500	0.0033	0.0051	0.8912	-
3.5687	23750	0.0037	0.0048	0.8995	-
3.6063	24000	0.0037	0.0048	0.8887	-
3.6439	24250	0.0037	0.0048	0.8921	-
3.6814	24500	0.0034	0.0046	0.9001	-
3.7190	24750	0.0041	0.0048	0.9008	-
3.7566	25000	0.0037	0.0048	0.8928	-
3.7941	25250	0.0038	0.0049	0.8949	-
3.8317	25500	0.0037	0.0045	0.9029	-
3.8693	25750	0.0034	0.0057	0.8962	-
3.9068	26000	0.0035	0.0047	0.8963	-
3.9444	26250	0.0039	0.0044	0.9026	-
3.9820	26500	0.0034	0.0044	0.8994	-
4.0195	26750	0.0029	0.0042	0.9039	-
4.0571	27000	0.0025	0.0040	0.9047	-
4.0947	27250	0.0027	0.0041	0.9033	-
4.1322	27500	0.0027	0.0041	0.9034	-
4.1698	27750	0.0025	0.0040	0.9040	-
4.2074	28000	0.0033	0.0041	0.9079	-
4.2449	28250	0.0027	0.0040	0.9078	-
4.2825	28500	0.0024	0.0040	0.9059	-
4.3201	28750	0.0026	0.0040	0.9084	-
4.3576	29000	0.0021	0.0039	0.9101	-
4.3952	29250	0.0024	0.0040	0.9081	-
4.4328	29500	0.0024	0.0039	0.9128	-
4.4703	29750	0.0027	0.0039	0.9067	-
4.5079	30000	0.003	0.0038	0.9120	-
4.5455	30250	0.0024	0.0037	0.9140	-
4.5830	30500	0.0025	0.0037	0.9116	-
4.6206	30750	0.0023	0.0037	0.9124	-
4.6582	31000	0.0026	0.0036	0.9161	-
4.6957	31250	0.0021	0.0036	0.9155	-
4.7333	31500	0.0025	0.0035	0.9147	-
4.7708	31750	0.0023	0.0035	0.9171	-
4.8084	32000	0.0024	0.0035	0.9153	-
4.8460	32250	0.002	0.0035	0.9153	-
4.8835	32500	0.0025	0.0034	0.9173	-
4.9211	32750	0.0018	0.0035	0.9180	-
4.9587	33000	0.0021	0.0035	0.9201	-
4.9962	33250	0.0019	0.0035	0.9205	-
5.0338	33500	0.0016	0.0034	0.9223	-
5.0714	33750	0.0016	0.0034	0.9217	-
5.1089	34000	0.0015	0.0033	0.9208	-
5.1465	34250	0.002	0.0034	0.9234	-
5.1841	34500	0.0017	0.0033	0.9212	-
5.2216	34750	0.002	0.0033	0.9212	-
5.2592	35000	0.0015	0.0032	0.9241	-
5.2968	35250	0.002	0.0031	0.9232	-
5.3343	35500	0.0017	0.0031	0.9251	-
5.3719	35750	0.0015	0.0031	0.9256	-
5.4095	36000	0.0018	0.0031	0.9246	-
5.4470	36250	0.0015	0.0030	0.9257	-
5.4846	36500	0.0017	0.0030	0.9261	-
5.5222	36750	0.0018	0.0030	0.9251	-
5.5597	37000	0.0016	0.0030	0.9270	-
5.5973	37250	0.0016	0.0029	0.9275	-
5.6349	37500	0.0017	0.0029	0.9283	-
5.6724	37750	0.0015	0.0029	0.9277	-
5.7100	38000	0.0017	0.0029	0.9286	-
5.7476	38250	0.0015	0.0029	0.9284	-
5.7851	38500	0.0015	0.0029	0.9286	-
5.8227	38750	0.0014	0.0029	0.9287	-
5.8603	39000	0.0015	0.0028	0.9290	-
5.8978	39250	0.0014	0.0028	0.9291	-
5.9354	39500	0.0014	0.0028	0.9293	-
5.9730	39750	0.0015	0.0028	0.9293	-
6.0	39930	-	-	-	0.9283

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.2.2+cu121
Accelerate: 1.2.1
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	float
details	min: 5 tokens mean: 11.52 tokens max: 32 tokens	min: 4 tokens mean: 14.85 tokens max: 70 tokens	min: 0.0 mean: 0.51 max: 1.0