yahyaabd/allstats-semantic-search-mini-model-v2

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 allstats-semantic-search-synthetic-dataset-v2 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:
  • allstats-semantic-search-synthetic-dataset-v2

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-semantic-search-mini-model-v2")
# Run inference
sentences = [
    'Tahun berapa Rupiah terdepresiasi 0,23 persen terhadap Dolar Amerika?',
    'Depresiasi Rupiah terhadap Dolar Amerika pada tahun 2016 sebesar 0,5 persen.',
    'Ringkasan Neraca Arus Dana Triwulan Pertama, 2002, (Miliar Rupiah)',
]
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-search-mini-v2-eval and allstat-semantic-search-mini-v2-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	allstats-semantic-search-mini-v2-eval	allstat-semantic-search-mini-v2-test
pearson_cosine	0.9839	0.9831
spearman_cosine	0.8951	0.8922

Training Details

Training Dataset

allstats-semantic-search-synthetic-dataset-v2

• Dataset: allstats-semantic-search-synthetic-dataset-v2 at c76f31a
• Size: 244,856 training samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 3 tokens mean: 12.75 tokens max: 45 tokens	min: 4 tokens mean: 14.81 tokens max: 56 tokens	min: 0.0 mean: 0.54 max: 1.0

• Samples:

  query	doc	label
  Dtaa harg konsymen edesaan (non-makann) 201	Statistik Harga Konsumen Perdesaan Kelompok Nonmakanan (Data 2013)	0.95
  Bagaimna konidsi keuamgan rymah atngga Indonsia 2020-2022?	Statistik Perusahaan Perikanan 2007	0.1
  Tingkat hunian kamar hotel tahun 2023	Tingkat Penghunian Kamar Hotel 2023	0.99

• Loss: CosineSimilarityLoss with these parameters:

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

Evaluation Dataset

allstats-semantic-search-synthetic-dataset-v2

• Dataset: allstats-semantic-search-synthetic-dataset-v2 at c76f31a
• Size: 52,469 evaluation samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 3 tokens mean: 13.04 tokens max: 43 tokens	min: 4 tokens mean: 15.01 tokens max: 54 tokens	min: 0.0 mean: 0.52 max: 1.0

• Samples:

  query	doc	label
  Bulan apa NTP mengalami kenaikan 0,25 persen?	Jumlah Wisatawan Mancanegara Bulan Agustus 2009 Turun 4,49 Persen Dibandingkan Bulan Sebelumnya.	0.0
  Sebutksn keempa komositi tang disebutkn besert persentae mrajin persagangannya.	Marjin Perdagangan Minyak Goreng 3,86 Persen, Terigu 5,92 Persen, Garam 23,74 Persen, Dan Susu Bubuk 13,02 Persen	1.0
  Data kemiskinan per kabupaten/kota tahun 2007	Data dan Informasi Kemiskinan 2007 Buku 2: Kabupaten/Kota	0.87

• 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: 64
• per_device_eval_batch_size: 64
• num_train_epochs: 8
• 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: 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: 8
• 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

Epoch	Step	Training Loss	Validation Loss	allstats-semantic-search-mini-v2-eval_spearman_cosine	allstat-semantic-search-mini-v2-test_spearman_cosine
0.1307	500	0.0963	0.0657	0.6836	-
0.2614	1000	0.0558	0.0428	0.7480	-
0.3921	1500	0.0403	0.0335	0.7665	-
0.5227	2000	0.0324	0.0285	0.7744	-
0.6534	2500	0.0284	0.0255	0.7987	-
0.7841	3000	0.0246	0.0225	0.7883	-
0.9148	3500	0.0217	0.0217	0.7964	-
1.0455	4000	0.0193	0.0187	0.8111	-
1.1762	4500	0.017	0.0174	0.8086	-
1.3068	5000	0.0163	0.0170	0.8157	-
1.4375	5500	0.0157	0.0161	0.8000	-
1.5682	6000	0.015	0.0156	0.8133	-
1.6989	6500	0.0146	0.0146	0.8194	-
1.8296	7000	0.014	0.0140	0.8103	-
1.9603	7500	0.013	0.0132	0.8205	-
2.0910	8000	0.0111	0.0126	0.8353	-
2.2216	8500	0.0102	0.0123	0.8407	-
2.3523	9000	0.0101	0.0118	0.8389	-
2.4830	9500	0.01	0.0115	0.8444	-
2.6137	10000	0.0097	0.0111	0.8456	-
2.7444	10500	0.0097	0.0105	0.8524	-
2.8751	11000	0.0091	0.0102	0.8526	-
3.0058	11500	0.0088	0.0100	0.8561	-
3.1364	12000	0.0069	0.0095	0.8619	-
3.2671	12500	0.0071	0.0094	0.8534	-
3.3978	13000	0.0068	0.0092	0.8648	-
3.5285	13500	0.0069	0.0093	0.8638	-
3.6592	14000	0.0071	0.0091	0.8548	-
3.7899	14500	0.0065	0.0085	0.8711	-
3.9205	15000	0.0064	0.0084	0.8622	-
4.0512	15500	0.0061	0.0080	0.8675	-
4.1819	16000	0.0051	0.0082	0.8673	-
4.3126	16500	0.0052	0.0080	0.8659	-
4.4433	17000	0.0053	0.0078	0.8669	-
4.5740	17500	0.0053	0.0077	0.8690	-
4.7047	18000	0.005	0.0076	0.8758	-
4.8353	18500	0.0048	0.0074	0.8700	-
4.9660	19000	0.0049	0.0072	0.8785	-
5.0967	19500	0.0041	0.0070	0.8795	-
5.2274	20000	0.0039	0.0071	0.8803	-
5.3581	20500	0.0039	0.0071	0.8843	-
5.4888	21000	0.0041	0.0070	0.8818	-
5.6194	21500	0.0039	0.0069	0.8812	-
5.7501	22000	0.0038	0.0068	0.8868	-
5.8808	22500	0.0038	0.0067	0.8831	-
6.0115	23000	0.0037	0.0066	0.8869	-
6.1422	23500	0.003	0.0065	0.8888	-
6.2729	24000	0.0031	0.0064	0.8879	-
6.4036	24500	0.0032	0.0064	0.8881	-
6.5342	25000	0.003	0.0062	0.8919	-
6.6649	25500	0.0031	0.0062	0.8919	-
6.7956	26000	0.0031	0.0061	0.8910	-
6.9263	26500	0.003	0.0061	0.8911	-
7.0570	27000	0.0028	0.0061	0.8925	-
7.1877	27500	0.0025	0.0061	0.8922	-
7.3183	28000	0.0026	0.0060	0.8944	-
7.4490	28500	0.0026	0.0061	0.8953	-
7.5797	29000	0.0026	0.0060	0.8948	-
7.7104	29500	0.0025	0.0060	0.8941	-
7.8411	30000	0.0025	0.0059	0.8950	-
7.9718	30500	0.0025	0.0059	0.8951	-
8.0	30608	-	-	-	0.8922

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.47.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.2.1
• 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",
}