yahyaabd/allstats-semantic-mpnet

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on the allstats-semantic-dataset-v4 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-dataset-v4

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/allstats-semantic-mpnet")
# Run inference
sentences = [
    'Pernikahan usia anak di Indonesia periode 2013-2015',
    'Jumlah penduduk Indonesia 2013-2015',
    'Indeks Tendensi Bisnis dan Indeks Tendensi Konsumen 2013',
]
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-mpnet-eval and allstats-semantic-mpnet-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	allstats-semantic-mpnet-eval	allstats-semantic-mpnet-test
pearson_cosine	0.9714	0.9723
spearman_cosine	0.8934	0.8932

Training Details

Training Dataset

allstats-semantic-dataset-v4

• Dataset: allstats-semantic-dataset-v4 at 06c3cf8
• Size: 88,250 training samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 4 tokens mean: 11.38 tokens max: 46 tokens	min: 4 tokens mean: 14.48 tokens max: 67 tokens	min: 0.0 mean: 0.51 max: 1.0

• Samples:

  query	doc	label
  Industri teh Indonesia tahun 2021	Statistik Transportasi Laut 2014	0.1
  Tahun berapa data pertumbuhan ekonomi Indonesia tersebut?	Nilai Tukar Petani (NTP) November 2023 sebesar 116,73 atau naik 0,82 persen. Harga Gabah Kering Panen di Tingkat Petani turun 1,94 persen dan Harga Beras Premium di Penggilingan turun 0,91 persen.	0.0
  Kemiskinan di Indonesia Maret	2018 Feb Tenaga Kerja	0.1

• Loss: CosineSimilarityLoss with these parameters:

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

Evaluation Dataset

allstats-semantic-dataset-v4

• Dataset: allstats-semantic-dataset-v4 at 06c3cf8
• Size: 18,910 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: 11.35 tokens max: 33 tokens	min: 4 tokens mean: 14.25 tokens max: 52 tokens	min: 0.0 mean: 0.49 max: 1.0

• Samples:

  query	doc	label
  nAalisis keuangam deas tshun 019	Statistik Migrasi Nusa Tenggara Barat Hasil Survei Penduduk Antar Sensus 2015	0.1
  Data tanaman buah dan sayur Indonesia tahun 2016	Statistik Penduduk Lanjut Usia 2010	0.1
  Pasar beras di Indonesia tahun 2018	Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, April 2021	0.2

• 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: 32
• per_device_eval_batch_size: 32
• num_train_epochs: 8
• warmup_ratio: 0.1
• fp16: True
• dataloader_num_workers: 4
• load_best_model_at_end: True
• label_smoothing_factor: 0.05
• 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: 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: 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: 4
• 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: 0.05
• 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-mpnet-eval_spearman_cosine	allstats-semantic-mpnet-test_spearman_cosine
0	0	-	0.0979	0.6119	-
0.0906	250	0.0646	0.0427	0.7249	-
0.1813	500	0.039	0.0324	0.7596	-
0.2719	750	0.032	0.0271	0.7860	-
0.3626	1000	0.0276	0.0255	0.7920	-
0.4532	1250	0.0264	0.0230	0.8072	-
0.5439	1500	0.0249	0.0222	0.8197	-
0.6345	1750	0.0226	0.0210	0.8200	-
0.7252	2000	0.0218	0.0209	0.8202	-
0.8158	2250	0.0208	0.0201	0.8346	-
0.9065	2500	0.0209	0.0211	0.8240	-
0.9971	2750	0.0211	0.0190	0.8170	-
1.0877	3000	0.0161	0.0182	0.8332	-
1.1784	3250	0.0158	0.0179	0.8393	-
1.2690	3500	0.0167	0.0189	0.8341	-
1.3597	3750	0.0152	0.0168	0.8371	-
1.4503	4000	0.0151	0.0165	0.8435	-
1.5410	4250	0.0143	0.0156	0.8365	-
1.6316	4500	0.0147	0.0157	0.8467	-
1.7223	4750	0.0138	0.0155	0.8501	-
1.8129	5000	0.0147	0.0154	0.8457	-
1.9036	5250	0.0137	0.0152	0.8498	-
1.9942	5500	0.0144	0.0143	0.8485	-
2.0848	5750	0.0108	0.0139	0.8439	-
2.1755	6000	0.01	0.0146	0.8563	-
2.2661	6250	0.011	0.0141	0.8558	-
2.3568	6500	0.0107	0.0144	0.8497	-
2.4474	6750	0.01	0.0138	0.8577	-
2.5381	7000	0.0097	0.0136	0.8585	-
2.6287	7250	0.0102	0.0135	0.8521	-
2.7194	7500	0.0106	0.0133	0.8537	-
2.8100	7750	0.0098	0.0133	0.8643	-
2.9007	8000	0.0105	0.0138	0.8543	-
2.9913	8250	0.009	0.0129	0.8555	-
3.0819	8500	0.0071	0.0121	0.8692	-
3.1726	8750	0.006	0.0120	0.8709	-
3.2632	9000	0.0078	0.0120	0.8660	-
3.3539	9250	0.0072	0.0122	0.8656	-
3.4445	9500	0.007	0.0123	0.8696	-
3.5352	9750	0.0075	0.0117	0.8707	-
3.6258	10000	0.0081	0.0115	0.8682	-
3.7165	10250	0.0083	0.0116	0.8617	-
3.8071	10500	0.0075	0.0116	0.8665	-
3.8978	10750	0.0077	0.0119	0.8733	-
3.9884	11000	0.008	0.0113	0.8678	-
4.0790	11250	0.0051	0.0110	0.8760	-
4.1697	11500	0.0052	0.0108	0.8729	-
4.2603	11750	0.0056	0.0108	0.8771	-
4.3510	12000	0.0052	0.0109	0.8793	-
4.4416	12250	0.0049	0.0109	0.8766	-
4.5323	12500	0.0055	0.0114	0.8742	-
4.6229	12750	0.0061	0.0108	0.8749	-
4.7136	13000	0.0058	0.0109	0.8833	-
4.8042	13250	0.0049	0.0108	0.8767	-
4.8949	13500	0.0046	0.0108	0.8839	-
4.9855	13750	0.0052	0.0104	0.8790	-
5.0761	14000	0.0041	0.0102	0.8826	-
5.1668	14250	0.004	0.0103	0.8775	-
5.2574	14500	0.0036	0.0102	0.8855	-
5.3481	14750	0.0037	0.0104	0.8841	-
5.4387	15000	0.0036	0.0101	0.8860	-
5.5294	15250	0.0043	0.0104	0.8852	-
5.6200	15500	0.004	0.0100	0.8856	-
5.7107	15750	0.0043	0.0101	0.8842	-
5.8013	16000	0.0043	0.0099	0.8835	-
5.8920	16250	0.0041	0.0099	0.8852	-
5.9826	16500	0.0036	0.0101	0.8866	-
6.0732	16750	0.0031	0.0100	0.8881	-
6.1639	17000	0.0031	0.0098	0.8880	-
6.2545	17250	0.0027	0.0098	0.8886	-
6.3452	17500	0.0032	0.0097	0.8868	-
6.4358	17750	0.0027	0.0097	0.8876	-
6.5265	18000	0.0031	0.0097	0.8893	-
6.6171	18250	0.0032	0.0096	0.8903	-
6.7078	18500	0.003	0.0096	0.8898	-
6.7984	18750	0.0029	0.0098	0.8907	-
6.8891	19000	0.003	0.0096	0.8896	-
6.9797	19250	0.0026	0.0096	0.8913	-
7.0703	19500	0.0024	0.0096	0.8921	-
7.1610	19750	0.0021	0.0097	0.8920	-
7.2516	20000	0.0023	0.0096	0.8910	-
7.3423	20250	0.002	0.0096	0.8920	-
7.4329	20500	0.0022	0.0096	0.8924	-
7.5236	20750	0.002	0.0097	0.8917	-
7.6142	21000	0.0024	0.0096	0.8923	-
7.7049	21250	0.0025	0.0095	0.8928	-
7.7955	21500	0.0022	0.0095	0.8931	-
7.8861	21750	0.0023	0.0095	0.8932	-
7.9768	22000	0.0022	0.0095	0.8934	-
8.0	22064	-	-	-	0.8932

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.48.0
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• 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",
}