yahyaabd/allstats-search-multilingual-miniLM-v1

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-multilingual-miniLM-v1")
# 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

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	0.8665	0.8772
spearman_cosine	0.8063	0.8096

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: 64
• per_device_eval_batch_size: 64
• warmup_ratio: 0.05
• 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: 64
• per_device_eval_batch_size: 64
• 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: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.05
• 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-eval_spearman_cosine	allstat-search-mini-v1-test_spearman_cosine
0	0	-	2.3102	0.6855	-
0.05	20	1.7642	1.2458	0.7253	-
0.1	40	0.9637	0.6870	0.7751	-
0.15	60	0.4319	0.4890	0.7897	-
0.2	80	0.3251	0.4944	0.7899	-
0.25	100	0.2665	0.3988	0.7954	-
0.3	120	0.1938	0.3795	0.7972	-
0.35	140	0.1495	0.2839	0.8014	-
0.4	160	0.0681	0.3011	0.8021	-
0.45	180	0.1775	0.3116	0.8004	-
0.5	200	0.0829	0.2536	0.8028	-
0.55	220	0.2332	0.2887	0.8015	-
0.6	240	0.1171	0.2862	0.8021	-
0.65	260	0.1059	0.2467	0.8023	-
0.7	280	0.1089	0.2240	0.8033	-
0.75	300	0.0445	0.1772	0.8048	-
0.8	320	0.0633	0.2392	0.8030	-
0.85	340	0.0506	0.2440	0.8027	-
0.9	360	0.1086	0.1926	0.8054	-
0.95	380	0.064	0.2984	0.8025	-
1.0	400	0.0478	0.2764	0.8025	-
1.05	420	0.0508	0.2393	0.8038	-
1.1	440	0.0266	0.2295	0.8039	-
1.15	460	0.0236	0.2477	0.8032	-
1.2	480	0.0142	0.2077	0.8045	-
1.25	500	0.0128	0.1972	0.8047	-
1.3	520	0.0205	0.2116	0.8042	-
1.35	540	0.0447	0.2425	0.8033	-
1.4	560	0.0	0.1999	0.8045	-
1.45	580	0.0284	0.1989	0.8046	-
1.5	600	0.0222	0.1789	0.8049	-
1.55	620	0.0066	0.1957	0.8045	-
1.6	640	0.0187	0.1993	0.8046	-
1.65	660	0.0489	0.1901	0.8051	-
1.7	680	0.0236	0.1556	0.8058	-
1.75	700	0.0186	0.1597	0.8059	-
1.8	720	0.0475	0.1813	0.8053	-
1.85	740	0.0215	0.1689	0.8060	-
1.9	760	0.0066	0.1746	0.8057	-
1.95	780	0.0158	0.1808	0.8054	-
2.0	800	0.0412	0.1799	0.8050	-
2.05	820	0.0	0.1809	0.8049	-
2.1	840	0.0072	0.1519	0.8059	-
2.15	860	0.032	0.1538	0.8060	-
2.2	880	0.0	0.1605	0.8058	-
2.25	900	0.016	0.1812	0.8053	-
2.3	920	0.0216	0.1550	0.8060	-
2.35	940	0.0124	0.1533	0.8062	-
2.4	960	0.0087	0.1499	0.8064	-
2.45	980	0.0	0.1493	0.8064	-
2.5	1000	0.0063	0.1483	0.8063	-
2.55	1020	0.0	0.1505	0.8063	-
2.6	1040	0.0	0.1508	0.8063	-
2.65	1060	0.0	0.1508	0.8063	-
2.7	1080	0.0	0.1508	0.8063	-
2.75	1100	0.0191	0.1546	0.8062	-
2.8	1120	0.0073	0.1566	0.8063	-
2.85	1140	0.0095	0.1529	0.8063	-
2.9	1160	0.0065	0.1512	0.8064	-
2.95	1180	0.0	0.1508	0.8063	-
3.0	1200	0.0	0.1508	0.8063	-
-1	-1	-	-	-	0.8096

• 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",
}