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Add new SentenceTransformer model

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:2436
  - loss:CosineSimilarityLoss
base_model: yahyaabd/allstats-search-mini-v1-1-mnrl
widget:
  - source_sentence: >-
      Persentase penduduk usia 15-24 tahun di Kota Bandar Lampung yang tidak
      sekolah dan tidak bekerja (NEET) adalah 10%.
    sentences:
      - >-
        Lembaga layanan menerima sekitar sepuluh ribu pengaduan kekerasan
        terhadap perempuan pada 2023.
      - >-
        Volume sampah plastik yang dihasilkan Kota Bandar Lampung setiap hari
        mencapai 100 ton.
      - >-
        Komponen volatile foods mengalami deflasi 0,5 persen secara bulanan pada
        Mei 2025.
  - source_sentence: >-
      Jumlah pengaduan kasus pencemaran lingkungan yang diterima KLHK pada tahun
      2023 sebanyak 1.500 kasus.
    sentences:
      - >-
        Kualitas air laut di Teluk Jakarta tercemar berat akibat limbah industri
        dan domestik dari daratan.
      - >-
        Statistik Pengaduan Lingkungan Hidup: Jumlah Kasus Pencemaran Air,
        Udara, dan Limbah B3 Menurut Provinsi dan Status Tindak Lanjut, Tahun
        2023
      - >-
        Sosialisasi peta rawan bencana kepada masyarakat di daerah rentan perlu
        ditingkatkan untuk meningkatkan kesiapsiagaan.
  - source_sentence: >-
      Pulau Lombok di Provinsi Nusa Tenggara Barat (NTB) memiliki Gunung
      Rinjani.
    sentences:
      - >-
        Sektor yang paling diminati investor PMDN tahun 2023 adalah industri
        pengolahan.
      - >-
        Persentase Penduduk Usia 25 Tahun Ke Atas Menurut Tingkat Pendidikan
        Tertinggi yang Ditamatkan (Termasuk S1), Indonesia, 2024
      - Ayam Taliwang adalah kuliner pedas khas NTB.
  - source_sentence: >-
      Luas terumbu karang yang mengalami pemutihan (bleaching) di perairan Raja
      Ampat pada awal tahun 2024 mencapai 5% dari total area.
    sentences:
      - Jumlah Pompa Air dan Kapasitasnya untuk Penanganan Banjir Jakarta
      - >-
        Kenaikan harga tiket pesawat rute Palembang-Jakarta terjadi menjelang
        libur Idul Adha.
      - >-
        Sekitar 5 persen dari total area terumbu karang di Raja Ampat terdampak
        fenomena pemutihan pada awal 2024.
  - source_sentence: >-
      PDRB per kapita Provinsi Riau sangat dipengaruhi oleh harga minyak bumi
      dunia.
    sentences:
      - >-
        Persentase Penduduk Lanjut Usia (60 Tahun Ke Atas) Menurut Provinsi
        (dalam Statistik Penduduk Lanjut Usia Indonesia 2023)
      - Di wilayah perkotaan, angka kemiskinan pada Maret 2023 adalah 7,29%.
      - >-
        The Riau Islands province is known for its beautiful beaches and marine
        tourism.
datasets:
  - yahyaabd/BPS-STS-dataset-v1
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: 0.8598548892892474
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8569191140389504
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.8884601567043606
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8818393243914469
            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-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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/allstats-search-mini-v1-1-mnrl-1")
# 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	0.8599	0.8885
spearman_cosine	0.8569	0.8818

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

	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:

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

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

	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:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 1e-05
num_train_epochs: 6
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: 16
per_device_eval_batch_size: 16
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: 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: 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	-
0.0654	10	0.0541	0.0586	0.7412	-
0.1307	20	0.0546	0.0579	0.7444	-
0.1961	30	0.0441	0.0565	0.7500	-
0.2614	40	0.0503	0.0546	0.7580	-
0.3268	50	0.0546	0.0528	0.7648	-
0.3922	60	0.0538	0.0509	0.7739	-
0.4575	70	0.0455	0.0490	0.7834	-
0.5229	80	0.0471	0.0472	0.7925	-
0.5882	90	0.0417	0.0455	0.8017	-
0.6536	100	0.0427	0.0441	0.8095	-
0.7190	110	0.0445	0.0432	0.8138	-
0.7843	120	0.0382	0.0425	0.8168	-
0.8497	130	0.0443	0.0413	0.8220	-
0.9150	140	0.0449	0.0405	0.8264	-
0.9804	150	0.0407	0.0401	0.8287	-
1.0458	160	0.0377	0.0400	0.8312	-
1.1111	170	0.0285	0.0392	0.8327	-
1.1765	180	0.033	0.0389	0.8329	-
1.2418	190	0.0299	0.0388	0.8331	-
1.3072	200	0.029	0.0387	0.8333	-
1.3725	210	0.031	0.0384	0.8340	-
1.4379	220	0.0274	0.0384	0.8351	-
1.5033	230	0.0312	0.0382	0.8367	-
1.5686	240	0.0301	0.0378	0.8383	-
1.6340	250	0.0304	0.0375	0.8390	-
1.6993	260	0.0226	0.0374	0.8389	-
1.7647	270	0.0264	0.0373	0.8399	-
1.8301	280	0.0295	0.0370	0.8418	-
1.8954	290	0.0298	0.0368	0.8419	-
1.9608	300	0.0291	0.0366	0.8422	-
2.0261	310	0.0279	0.0365	0.8426	-
2.0915	320	0.0231	0.0363	0.8432	-
2.1569	330	0.0249	0.0361	0.8446	-
2.2222	340	0.0253	0.0359	0.8454	-
2.2876	350	0.024	0.0358	0.8463	-
2.3529	360	0.0239	0.0357	0.8471	-
2.4183	370	0.0222	0.0355	0.8473	-
2.4837	380	0.0284	0.0354	0.8476	-
2.5490	390	0.0176	0.0353	0.8486	-
2.6144	400	0.0184	0.0352	0.8489	-
2.6797	410	0.023	0.0351	0.8495	-
2.7451	420	0.0201	0.0351	0.8494	-
2.8105	430	0.0252	0.0351	0.8499	-
2.8758	440	0.0206	0.0350	0.8503	-
2.9412	450	0.0188	0.0350	0.8499	-
3.0065	460	0.017	0.0348	0.8501	-
3.0719	470	0.0174	0.0347	0.8505	-
3.1373	480	0.0171	0.0345	0.8515	-
3.2026	490	0.0226	0.0344	0.8520	-
3.2680	500	0.0233	0.0344	0.8520	-
3.3333	510	0.0177	0.0344	0.8523	-
3.3987	520	0.0155	0.0343	0.8522	-
3.4641	530	0.0155	0.0344	0.8522	-
3.5294	540	0.0249	0.0343	0.8523	-
3.5948	550	0.0177	0.0343	0.8522	-
3.6601	560	0.0149	0.0343	0.8520	-
3.7255	570	0.0178	0.0343	0.8517	-
3.7908	580	0.0181	0.0343	0.8520	-
3.8562	590	0.018	0.0342	0.8525	-
3.9216	600	0.0178	0.0341	0.8525	-
3.9869	610	0.0225	0.0340	0.8530	-
4.0523	620	0.0194	0.0339	0.8541	-
4.1176	630	0.0145	0.0338	0.8548	-
4.1830	640	0.0151	0.0337	0.8554	-
4.2484	650	0.0187	0.0336	0.8560	-
4.3137	660	0.0142	0.0336	0.8561	-
4.3791	670	0.0162	0.0336	0.8557	-
4.4444	680	0.0167	0.0335	0.8558	-
4.5098	690	0.013	0.0335	0.8555	-
4.5752	700	0.0174	0.0336	0.8555	-
4.6405	710	0.0156	0.0336	0.8556	-
4.7059	720	0.0155	0.0336	0.8555	-
4.7712	730	0.0179	0.0336	0.8553	-
4.8366	740	0.0158	0.0335	0.8553	-
4.9020	750	0.0143	0.0335	0.8553	-
4.9673	760	0.019	0.0335	0.8557	-
5.0327	770	0.0143	0.0334	0.8559	-
5.0980	780	0.0136	0.0334	0.8559	-
5.1634	790	0.0138	0.0334	0.8560	-
5.2288	800	0.0134	0.0333	0.8561	-
5.2941	810	0.0173	0.0333	0.8563	-
5.3595	820	0.0128	0.0333	0.8562	-
5.4248	830	0.0145	0.0333	0.8564	-
5.4902	840	0.0153	0.0333	0.8566	-
5.5556	850	0.0166	0.0333	0.8566	-
5.6209	860	0.0179	0.0332	0.8569	-
5.6863	870	0.0151	0.0332	0.8569	-
5.7516	880	0.0168	0.0332	0.8570	-
5.8170	890	0.0129	0.0332	0.8570	-
5.8824	900	0.015	0.0332	0.8569	-
5.9477	910	0.0148	0.0332	0.8569	-
-1	-1	-	-	-	0.8818

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

@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",
}