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 bps-statictable-query-title-pairs 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:
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-ir-mpnet-base-v1")
# Run inference
sentences = [
'Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015',
'Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah)',
'Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023',
]
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-base-v1-eval
andallstat-semantic-base-v1-test
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test |
---|---|---|
pearson_cosine | 0.8898 | 0.9039 |
spearman_cosine | 0.7799 | 0.8077 |
Training Details
Training Dataset
bps-statictable-query-title-pairs
- Dataset: bps-statictable-query-title-pairs at c7df38f
- Size: 2,602 training samples
- Columns:
query
,doc
, andlabel
- Approximate statistics based on the first 1000 samples:
query doc label type string string int details - min: 5 tokens
- mean: 18.35 tokens
- max: 35 tokens
- min: 4 tokens
- mean: 25.83 tokens
- max: 58 tokens
- 0: ~66.50%
- 1: ~33.50%
- Samples:
query doc label Pertumbuhan populasi provinsi di Indonesia 1971-2024
Kecepatan Angin dan Kelembaban di Stasiun Pengamatan BMKG, 2000-2010
0
Perbandingan upah nominal dan riil pekerja pertanian di Indonesia (tahun dasar 2012), periode 2017.
Upah Nominal dan Riil Buruh Tani di Indonesia (Rupiah), 2009-2019 (2012=100)
1
Laporan singkat cash flow statement Q4/2005
Nilai Produksi dan Biaya Produksi per Hektar Usaha Tanaman Bawang Merah dan Cabai Merah, 2014
0
- Loss:
ContrastiveLoss
with these parameters:{ "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true }
Evaluation Dataset
bps-statictable-query-title-pairs
- Dataset: bps-statictable-query-title-pairs at c7df38f
- Size: 558 evaluation samples
- Columns:
query
,doc
, andlabel
- Approximate statistics based on the first 558 samples:
query doc label type string string int details - min: 4 tokens
- mean: 18.45 tokens
- max: 35 tokens
- min: 4 tokens
- mean: 26.04 tokens
- max: 58 tokens
- 0: ~70.97%
- 1: ~29.03%
- Samples:
query doc label Data pengeluaran makanan rata-rata warga Sulteng per minggu di tahun 2022, berdasarkan kelompok pendapatan
Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (84 x 84)
0
Konsumsi & belanja makanan per orang di NTB, beda kelompok pengeluaran, 2021
Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Nusa Tenggara Barat, 2018-2023
1
Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?
Penduduk Berumur 15 Tahun Ke Atas Menurut Provinsi dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008 - 2024
0
- Loss:
ContrastiveLoss
with these parameters:{ "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 32per_device_eval_batch_size
: 32num_train_epochs
: 4warmup_ratio
: 0.1fp16
: Trueload_best_model_at_end
: Trueeval_on_start
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Trueuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | Validation Loss | allstats-semantic-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine |
---|---|---|---|---|---|
0 | 0 | - | 0.0099 | 0.7449 | - |
0.1220 | 10 | 0.0091 | 0.0065 | 0.7640 | - |
0.2439 | 20 | 0.0059 | 0.0040 | 0.7743 | - |
0.3659 | 30 | 0.0045 | 0.0036 | 0.7688 | - |
0.4878 | 40 | 0.0045 | 0.0036 | 0.7694 | - |
0.6098 | 50 | 0.0032 | 0.0037 | 0.7758 | - |
0.7317 | 60 | 0.003 | 0.0025 | 0.7753 | - |
0.8537 | 70 | 0.0035 | 0.0029 | 0.7710 | - |
0.9756 | 80 | 0.0028 | 0.0026 | 0.7745 | - |
1.0976 | 90 | 0.0015 | 0.0023 | 0.7754 | - |
1.2195 | 100 | 0.0013 | 0.0021 | 0.7760 | - |
1.3415 | 110 | 0.0013 | 0.0022 | 0.7751 | - |
1.4634 | 120 | 0.002 | 0.0021 | 0.7746 | - |
1.5854 | 130 | 0.0012 | 0.0020 | 0.7750 | - |
1.7073 | 140 | 0.0007 | 0.0019 | 0.7740 | - |
1.8293 | 150 | 0.0008 | 0.0019 | 0.7738 | - |
1.9512 | 160 | 0.0026 | 0.0018 | 0.7772 | - |
2.0732 | 170 | 0.0009 | 0.0019 | 0.7785 | - |
2.1951 | 180 | 0.0005 | 0.0020 | 0.7781 | - |
2.3171 | 190 | 0.0009 | 0.0017 | 0.7777 | - |
2.4390 | 200 | 0.0005 | 0.0017 | 0.7773 | - |
2.5610 | 210 | 0.0004 | 0.0018 | 0.7766 | - |
2.6829 | 220 | 0.0006 | 0.0018 | 0.7762 | - |
2.8049 | 230 | 0.0006 | 0.0019 | 0.7756 | - |
2.9268 | 240 | 0.0016 | 0.0019 | 0.7777 | - |
3.0488 | 250 | 0.0008 | 0.0018 | 0.7796 | - |
3.1707 | 260 | 0.0005 | 0.0017 | 0.7802 | - |
3.2927 | 270 | 0.0006 | 0.0017 | 0.7802 | - |
3.4146 | 280 | 0.0004 | 0.0017 | 0.7805 | - |
3.5366 | 290 | 0.0004 | 0.0017 | 0.7805 | - |
3.6585 | 300 | 0.0003 | 0.0018 | 0.7802 | - |
3.7805 | 310 | 0.0006 | 0.0018 | 0.7800 | - |
3.9024 | 320 | 0.0003 | 0.0018 | 0.7799 | - |
-1 | -1 | - | - | - | 0.8077 |
- 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",
}
ContrastiveLoss
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
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Dataset used to train yahyaabd/allstats-ir-mpnet-base-v1
Evaluation results
- Pearson Cosine on allstats semantic base v1 evalself-reported0.890
- Spearman Cosine on allstats semantic base v1 evalself-reported0.780
- Pearson Cosine on allstat semantic base v1 testself-reported0.904
- Spearman Cosine on allstat semantic base v1 testself-reported0.808