yahyaabd/allstats-search-miniLM-v1-3

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-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-multilingual-MiniLM-L12-v2
• Maximum Sequence Length: 128 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': 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-miniLM-v1-3")
# Run inference
sentences = [
    'Arus dana Q3 2006',
    'Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)',
    'Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012',
]
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

Binary Classification

• Datasets: allstats-semantic-mini-v1_test and allstats-semantic-mini-v1_dev
• Evaluated with BinaryClassificationEvaluator

Metric	allstats-semantic-mini-v1_test	allstats-semantic-mini-v1_dev
cosine_accuracy	0.965	0.9651
cosine_accuracy_threshold	0.6882	0.6834
cosine_f1	0.9462	0.9465
cosine_f1_threshold	0.6882	0.6834
cosine_precision	0.9409	0.9415
cosine_recall	0.9515	0.9515
cosine_ap	0.9858	0.9862
cosine_mcc	0.9203	0.9207

Training Details

Training Dataset

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

• Dataset: query-hard-pos-neg-doc-pairs-statictable at 7b28b96
• Size: 25,580 training samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	int
  details	min: 7 tokens mean: 20.14 tokens max: 55 tokens	min: 5 tokens mean: 24.9 tokens max: 47 tokens	0: ~70.80% 1: ~29.20%

• Samples:

  query	doc	label
  Status pekerjaan utama penduduk usia 15+ yang bekerja, 2020	Jumlah Penghuni Lapas per Kanwil	0
  status pekerjaan utama penduduk usia 15+ yang bekerja, 2020	Jumlah Penghuni Lapas per Kanwil	0
  STATUS PEKERJAAN UTAMA PENDUDUK USIA 15+ YANG BEKERJA, 2020	Jumlah Penghuni Lapas per Kanwil	0

• Loss: OnlineContrastiveLoss

Evaluation Dataset

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

• Dataset: query-hard-pos-neg-doc-pairs-statictable at 7b28b96
• Size: 5,479 evaluation samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	int
  details	min: 7 tokens mean: 20.78 tokens max: 52 tokens	min: 4 tokens mean: 26.28 tokens max: 43 tokens	0: ~71.50% 1: ~28.50%

• Samples:

  query	doc	label
  Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?	Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017	0
  bagaimana perbandingan pns pria dan wanita di berbagai golongan tahun 2014?	Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017	0
  BAGAIMANA PERBANDINGAN PNS PRIA DAN WANITA DI BERBAGAI GOLONGAN TAHUN 2014?	Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017	0

• Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 1
• 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: 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: 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: 1
• 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

Epoch	Step	Training Loss	Validation Loss	allstats-semantic-mini-v1_test_cosine_ap	allstats-semantic-mini-v1_dev_cosine_ap
-1	-1	-	-	0.8789	-
0	0	-	0.4455	-	0.8789
0.0125	20	0.4484	0.3363	-	0.8893
0.0250	40	0.1921	0.2230	-	0.9052
0.0375	60	0.1779	0.1435	-	0.9440
0.0500	80	0.1047	0.1269	-	0.9511
0.0625	100	0.0669	0.1498	-	0.9445
0.0750	120	0.1662	0.1028	-	0.9630
0.0876	140	0.0774	0.1115	-	0.9589
0.1001	160	0.0947	0.1204	-	0.9500
0.1126	180	0.1285	0.1464	-	0.9456
0.1251	200	0.0793	0.1024	-	0.9600
0.1376	220	0.0792	0.0992	-	0.9607
0.1501	240	0.0696	0.0931	-	0.9642
0.1626	260	0.0692	0.1205	-	0.9538
0.1751	280	0.1015	0.0980	-	0.9629
0.1876	300	0.0628	0.1001	-	0.9634
0.2001	320	0.0335	0.1094	-	0.9650
0.2126	340	0.0668	0.0941	-	0.9673
0.2251	360	0.0662	0.0765	-	0.9748
0.2376	380	0.0251	0.0674	-	0.9784
0.2502	400	0.0771	0.0667	-	0.9805
0.2627	420	0.0363	0.0576	-	0.9785
0.2752	440	0.0762	0.0787	-	0.9726
0.2877	460	0.0475	0.0649	-	0.9773
0.3002	480	0.0086	0.0692	-	0.9760
0.3127	500	0.0242	0.0636	-	0.9771
0.3252	520	0.0342	0.0700	-	0.9758
0.3377	540	0.0568	0.0547	-	0.9792
0.3502	560	0.0286	0.0508	-	0.9808
0.3627	580	0.0426	0.0518	-	0.9823
0.3752	600	0.03	0.0553	-	0.9806
0.3877	620	0.0146	0.0826	-	0.9748
0.4003	640	0.0417	0.0667	-	0.9779
0.4128	660	0.0081	0.0667	-	0.9775
0.4253	680	0.0094	0.0704	-	0.9798
0.4378	700	0.0225	0.0525	-	0.9841
0.4503	720	0.0217	0.0462	-	0.9861
0.4628	740	0.011	0.0466	-	0.9858
0.4753	760	0.0191	0.0495	-	0.9846
0.4878	780	0.0146	0.0478	-	0.9847
0.5003	800	0.0076	0.0424	-	0.9852
0.5128	820	0.035	0.0549	-	0.9821
0.5253	840	0.0321	0.0551	-	0.9796
0.5378	860	0.0241	0.0559	-	0.9781
0.5503	880	0.0335	0.0525	-	0.9792
0.5629	900	0.0125	0.0539	-	0.9799
0.5754	920	0.0154	0.0512	-	0.9823
0.5879	940	0.0133	0.0497	-	0.9824
0.6004	960	0.0072	0.0532	-	0.9821
0.6129	980	0.0192	0.0520	-	0.9809
0.6254	1000	0.0199	0.0503	-	0.9811
0.6379	1020	0.0069	0.0484	-	0.9824
0.6504	1040	0.0065	0.0514	-	0.9806
0.6629	1060	0.0098	0.0479	-	0.9834
0.6754	1080	0.0	0.0480	-	0.9841
0.6879	1100	0.0247	0.0508	-	0.9835
0.7004	1120	0.0137	0.0481	-	0.9842
0.7129	1140	0.0068	0.0512	-	0.9838
0.7255	1160	0.0182	0.0473	-	0.9851
0.7380	1180	0.0129	0.0442	-	0.9859
0.7505	1200	0.0	0.0436	-	0.9860
0.7630	1220	0.0073	0.0439	-	0.9858
0.7755	1240	0.0081	0.0441	-	0.9859
0.7880	1260	0.0305	0.0460	-	0.9857
0.8005	1280	0.0003	0.0486	-	0.9851
0.8130	1300	0.0218	0.0501	-	0.9852
0.8255	1320	0.0187	0.0435	-	0.9844
0.8380	1340	0.0205	0.0437	-	0.9846
0.8505	1360	0.0094	0.0442	-	0.9851
0.8630	1380	0.0083	0.0426	-	0.9856
0.8755	1400	0.0	0.0423	-	0.9858
0.8881	1420	0.0	0.0424	-	0.9859
0.9006	1440	0.0073	0.0428	-	0.9859
0.9131	1460	0.0075	0.0441	-	0.9859
0.9256	1480	0.0177	0.0447	-	0.9858
0.9381	1500	0.0	0.0438	-	0.9858
0.9506	1520	0.0	0.0438	-	0.9858
0.9631	1540	0.0072	0.0440	-	0.9860
0.9756	1560	0.0101	0.0436	-	0.9861
0.9881	1580	0.0277	0.0437	-	0.9862
-1	-1	-	-	0.9858	-

• 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

@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",
}